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Control Policies for Recovery of Interdependent Systems After Disruptions

Hemant Gehlot, Shreyas Sundaram, and Satish V. Ukkusuri Hemant Gehlot and Satish V. Ukkusuri are with the Lyles School of Civil Engineering at Purdue University. Email: {hgehlot,sukkusur}@purdue.edu. Shreyas Sundaram is with the School of Electrical and Computer Engineering at Purdue University. Email: [email protected]. This research was supported by National Science Foundation award CMMI 1638311.
Abstract

We examine a control problem where the states of the components of a system deteriorate after a disruption, if they are not being repaired by an entity. There exist a set of dependencies in the form of precedence constraints between the components, captured by a directed acyclic graph (DAG). The objective of the entity is to maximize the number of components whose states are brought back to the fully repaired state within a given time. We prove that the general problem is NP-hard, and therefore we characterize near-optimal control policies for special instances of the problem. We show that when the deterioration rates are larger than or equal to the repair rates and the precedence constraints are given by a DAG, it is optimal to continue repairing a component until its state reaches the fully recovered state before switching to repair any other component. Under the aforementioned assumptions and when the deterioration and the repair rates are homogeneous across all the components, we prove that the control policy that targets the healthiest component at each time-step while respecting the precedence and time constraints fully repairs at least half the number of components that would be fully repaired by an optimal policy. Finally, we prove that when the repair rates are sufficiently larger than the deterioration rates, the precedence constraints are given by a set of disjoint trees that each contain at most kk nodes, and there is no time constraint, the policy that targets the component with the least value of health minus the deterioration rate at each time-step while respecting the precedence constraints fully repairs at least 1/k1/k times the number of components that would be fully repaired by an optimal policy.

I Introduction

We focus on a problem where multiple components of a system have been damaged after a disruption (e.g., a disaster, cyberphysical attack, etc.) and the states of the components continue to reduce if they are not being repaired, until they reach a value referred to as permanent failure. There is an entity (or a controller) whose objective is to maximize a performance criteria or reward, e.g., maximizing the number of components whose states are brought back to their maximum value (known as the permanent repair state) within a given time. The states of the components do not change once they reach permanent failure or permanent repair. There exists a set of dependencies between the components that are represented by precedence constraints such that if there is a precedence constraint between an ordered pair of components (i,j)(i,j), then component ii needs to be permanently repaired before the entity can target component jj. This problem has relevance in multiple applications, e.g., disaster recovery, protection of cyberphysical systems against attacks, and fire-fighting. For instance, infrastructure components face accelerated deterioration after disasters due to processes such as corrosion and flooded roads. If the components are not repaired in a timely manner, they can deteriorate to such a level that they become unusable and require full replacement, which is usually expensive [1]. Furthermore, there can be various dependencies between infrastructure components; for example, in order to bring back the functionality of water systems (such as irrigation pumps), it might be necessary to first repair some sections of the power network. Similarly, there might be a limited time before components (e.g., computing elements) of a cyberphysical system become fully compromised after an attack [2]. There might be dependencies or hierarchies between the components such that the components at the top of the hierarchy may be required to be repaired first in order to stop the spread of attack to other components [3]. Thus, the protecting agency would have to make optimal recovery decisions if there is a repair budget (e.g., manpower and resources).

This problem can be cast in the general framework of optimal control and scheduling of switched systems [4, 5]. In switched systems, there are multiple subsystems such that only one subsystem can be controlled at a time. In our problem, components correspond to the subsystems of a switched system. Since the entity can only control one component at a time, our problem is analogous to optimal control and scheduling of discrete time linear switched systems [4]. One of the main complexities that arises in solving switched controlled systems is the combinatorially large number of feasible switching sequences [5]. Despite recent advances in the optimal control and scheduling of switched systems[4], finding general theoretical results or frameworks for efficiently solving optimal control and scheduling problems for switched systems remains an active area of research [6].

At a high level, our problem also has analogies to the resource (e.g., time slot) allocation problem at a base-station to time-dependent flows [7], optimal control of robotic systems [8], switched system control problems such as scheduling of thermostatically controlled loads [9] and patient triage scheduling problems [10]. However, these studies do not consider precedence constraints. In addition, some of these studies [7, 8] do not consider permanent failure of flows/components being served/targeted. Job scheduling with linear time deterioration and precedence constraints that minimize the total number of late jobs also have similarities to our problem [11]. However, job scheduling studies typically do not consider the notion of permanent failure, as jobs are completely processed even if their completion times exceed due dates. In addition, jobs are considered to be late if their completion times exceed the corresponding due dates; however, in our setting, a repair agency should start targeting a component before it fails. Scheduling of real-time tasks [12] also has some high level analogies to our work. However, these studies focus on tasks that sporadically become available for processing, in contrast to the components in our problem that are available for repair starting at the same time (i.e., immediately after a disruption), subject to precedence constraints.

Under the setting described before, we focus on characterizing (near-) optimal switching policies for maximizing the number of components that are permanently repaired. The paper [13] characterized optimal policies for this problem assuming homogeneous rates of repair and deterioration, and [14] studied the problem for the case when there are heterogeneous rates of repair and deterioration. However, these studies do not consider precedence constraints among various components and also do not constrain the maximum time that is available to repair the components. In the conference version of this paper [15], precedence constraints were considered among various components but there was no constraint on the time that is available for repair, and no discussion on the complexity of the general problem. This paper significantly expands on the conference paper by addressing those gaps.

Our contributions

First, we prove that when the deterioration rates are larger than or equal to the repair rates, and the precedence constraints are given by a directed acyclic graph (DAG), the optimal switching policy is to permanently repair a component that is targeted before switching to another component, while respecting the precedence and time constraints. Second, we prove that under the aforementioned assumptions and when the deterioration and the repair rates are homogeneous across all the components, the switching policy that targets the healthiest component at each time-step while respecting the precedence and time constraints is 1/21/2-optimal.111For α(0,1]\alpha\in(0,1], a policy is said to be α\alpha-optimal if the reward obtained by that policy is at least α\alpha times the reward obtained by an optimal policy. We also prove that the aforementioned policy is optimal when time is constrained but when there are no precedence constraints. Third, we prove that when the repair rates are sufficiently larger than the deterioration rates, the precedence constraints are given by a set of disjoint trees that each contain at most kk nodes, and there is no time constraint, the policy that targets the component with the least health value minus the deterioration rate at each time-step while respecting the precedence constraints is 1/k1/k-optimal.222The paper [15] characterizes a near-optimal policy with the same factor (1/k)(1/k) under the aforementioned assumptions where the root nodes of the trees are first targeted and other nodes are only targeted when it is not possible to target any root node; the policy that is characterized in this paper does not require that condition. Finally, we prove that the general problem is NP-hard (even if there is no time constraint).

In the next section, we formally define the problem that we focus in this paper. After that, we divide the characterization of sequencing policies into two cases, corresponding to the relative sizes of the rates of deterioration and repair.

II Problem Statement

There are N(2)N(\geq 2) components indexed by the set 𝒱={1,,N}\mathcal{V}=\{1,\ldots,N\}. A component could represent damaged infrastructure in an area, an infected server, etc., depending on the context. We refer to different components as nodes. There is a repair agency (also referred to as an entity), which is responsible for repairing the nodes. We index time-steps by the variable t={0,1,2,}t\in\mathbb{N}=\{0,1,2,\ldots\}, capturing the resolution at which the entity decides to repair nodes. Let T{}T\in\mathbb{N}\cup\{\infty\} be the largest time-step that is available for repairing nodes (when T=T=\infty we say that there is no time constraint). The control action of the entity at time-step tt is represented by ut𝒱u_{t}\in\mathcal{V}, i.e., the entity targets one node to repair at each time-step.333We keep the analysis of the problem where the entity can simultaneously target multiple nodes for future work. Each node j𝒱j\in\mathcal{V} has an associated health value vtj[0,1]v_{t}^{j}\in[0,1] at time-step tt. We denote the initial health value of each node jj by v0j(0,1)v_{0}^{j}\in(0,1). If a node jj is being repaired at a time-step then the health value of that node increases by Δincj(0,1)\Delta_{inc}^{j}\in(0,1). A node jj is considered to be permanently repaired at time-step tt if vtj=1v^{j}_{t}=1 and vt1j<1v^{j}_{t-1}<1. If a node jj is not being repaired at a time-step, then the health value of node jj decreases by Δdecj(0,1)\Delta_{dec}^{j}\in(0,1). A node jj is considered to be permanently failed at time-step tt if vtj=0v^{j}_{t}=0 and vt1j>0v^{j}_{t-1}>0. Note that the health value of a node does not change further once it gets permanently repaired or permanently failed. Thus, for all j{1,,N}j\in\{1,\ldots,N\}, the health value of node jj dynamically changes depending on the control action taken by the entity as follows:

vt+1j={1if vtj=1,0if vtj=0,min(1,vtj+Δincj)if ut=jandvtj(0,1),max(0,vtjΔdecj)if utjandvtj(0,1).v^{j}_{t+1}=\begin{cases}1&\text{if }v^{j}_{t}=1,\\ 0&\text{if }v^{j}_{t}=0,\\ \min(1,v^{j}_{t}+\Delta_{inc}^{j})&\hbox{if }u_{t}=j\hskip 2.84526pt\text{and}\hskip 2.84526ptv^{j}_{t}\in(0,1),\\ \max(0,v^{j}_{t}-\Delta_{dec}^{j})&\hbox{if }u_{t}\neq j\hskip 2.84526pt\text{and}\hskip 2.84526ptv^{j}_{t}\in(0,1).\end{cases}

There is a set of dependency constraints between different nodes, represented by a set ={(j,k)|j,k𝒱}\mathcal{E}=\{(j,k)|j,k\in\mathcal{V}\} consisting of edges between various nodes. An edge (j,k)(j,k)\in\mathcal{E} starting from node jj and ending at node kk represents a precedence constraint that node jj needs to be permanently repaired (i.e., to full health) before the entity can start targeting node kk; jj is an in-neighbor of kk. The in-neighbor set of a node k𝒱k\in\mathcal{V} is the set 𝒲k={j𝒱|(j,k)}\mathcal{W}_{k}=\{j\in\mathcal{V}|(j,k)\in\mathcal{E}\}. We assume that the precedence constraints form a directed acyclic graph (DAG) denoted by G={𝒱,}G=\{\mathcal{V},\mathcal{E}\}; such graphs are a common form for representing general precedence constraints in other problems such as job scheduling [11].

We now define the feasible control set as follows.

Definition 1.

Let the health values of all the nodes at time-step tt be given by vt={vt1,,vtN}v_{t}=\{v_{t}^{1},\ldots,v_{t}^{N}\}. Then, the feasible control set at time-step tt is the set 𝒰(vt)𝒱\mathcal{U}(v_{t})\subseteq\mathcal{V} containing the nodes whose in-neighbors are all in the permanently repair state at time-step tt, i.e., 𝒰(vt)={k𝒱|vtj=1,j𝒲k}\mathcal{U}(v_{t})=\{k\in\mathcal{V}|v^{j}_{t}=1,\forall j\in\mathcal{W}_{k}\}.

We now present the definition of a control sequence that respects the precedence constraints as follows.

Definition 2.

A control sequence u¯0:T={u¯0,u¯1,,u¯T}\bar{u}_{0:T}=\{\bar{u}_{0},\bar{u}_{1},\ldots,\bar{u}_{T}\} is said to respect the precedence constraints if for all t[0,T]t\in[0,T], u¯t𝒰(vt)\bar{u}_{t}\in\mathcal{U}(v_{t}).

We define the reward function as follows.

Definition 3.

Given a set of initial health values {v0j}\{v_{0}^{j}\} and a control sequence u¯0:T\bar{u}_{0:T} that respects the precedence constraints, the reward J(v0,u¯0:T)J(v_{0},\bar{u}_{0:T}) is defined as the number of nodes that get permanently repaired through that sequence. More formally, J(v0,u¯0:T)=|{j𝒱|t s.t. 0tT and vt+1j=1}|J(v_{0},\bar{u}_{0:T})=|\{j\in\mathcal{V}\hskip 2.84526pt|\hskip 2.84526pt\exists\hskip 2.84526ptt\text{ s.t. }0\leq t\leq T\text{ and }v^{j}_{t+1}=1\}|.

Based on the above definitions, the following problem is the focus of this paper.

Problem 1 (Optimal Control Sequencing over a DAG).


Consider a directed acyclic graph G={𝒱,}G=\{\mathcal{V},\mathcal{E}\} consisting of N(2)N(\geq 2) nodes with initial health values {v0j}\{v_{0}^{j}\}, along with repair and deterioration rates {Δincj}\{\Delta_{inc}^{j}\} and {Δdecj}\{\Delta_{dec}^{j}\}, respectively. Given a time constraint T{}T\in\mathbb{N}\cup\{\infty\}, find a control sequence u0:Tu^{\ast}_{0:T} that respects the precedence constraints and maximizes the reward J(v0,u0:T)J(v_{0},u^{\ast}_{0:T}).

For our purposes, we also define the concept of a jump.

Definition 4.

If the entity starts targeting a node before permanently repairing the node it targeted in the last time-step, then the entity is considered to have jumped at that time-step. Mathematically, if ut1=ju_{t-1}=j, vtj<1v_{t}^{j}<1 and utju_{t}\neq j, then a jump is said to have been made by the entity at time-step tt. A control sequence that does not contain any jumps is called a non-jumping sequence.

In this paper, we will be obtaining sequences that are generated by state feedback policies, i.e., we will be providing a mapping μ:[0,1]N𝒱\mu:[0,1]^{N}\rightarrow\mathcal{V}, such that u¯t=μ(vt)\bar{u}_{t}=\mu(v_{t}) for all t[0,T]t\in[0,T]. Thus, we will use the same terminology defined above (for sequences) for policies (e.g., a policy respecting the precedence constraints, a non-jumping policy, etc.). Given a policy μ\mu that respects the precedence constraints, we denote the reward (from Definition 3) by Jμ(v0)J_{\mu}(v_{0}), with the time-constraint being implicit.

We now define an α\alpha-optimal policy as follows.

Definition 5.

Let μ\mu^{*} be an optimal policy for Problem 1. For α(0,1]\alpha\in(0,1], a policy μ\mu (that respects the precedence and time constraints) is said to be α\alpha-optimal if Jμ(v0)αJμ(v0),v0[0,1]NJ_{\mu}(v_{0})\geq\alpha J_{\mu^{*}}(v_{0}),\forall v_{0}\in[0,1]^{N}.

In the next section, we start the analysis of the problem for ΔdecjΔincj,j{1,,N}\Delta_{dec}^{j}\geq\Delta_{inc}^{j},\quad\forall j\in\{1,\ldots,N\}.

III Control Sequences for ΔdecjΔincj,j{1,,N}\Delta_{{dec}}^{{j}}\geq\Delta_{{inc}}^{{j}},\quad\forall{j}\in\{1,\ldots,N\}

In this section, we start by showing the optimality of non-jumping sequences when ΔdecjΔincj,j{1,,N}\Delta_{dec}^{j}\geq\Delta_{inc}^{j},\quad\forall j\in\{1,\ldots,N\}.

III-A Optimality of non-jumping sequences

To show that non-jumping policies are optimal when ΔdecjΔincj,j{1,,N}\Delta_{dec}^{j}\geq\Delta_{inc}^{j},\forall j\in\{1,\ldots,N\}, we first analyze the properties of sequences containing at most one jump and later generalize to sequences containing an arbitrary number of jumps. We start with the following result.

Lemma 1.

Let there be N(2)N(\geq 2) nodes that have a set of precedence constraints given by a DAG G={𝒱,}G=\{\mathcal{V},\mathcal{E}\}, ΔdecjΔincj,j{1,,N}\Delta_{dec}^{j}\geq\Delta_{inc}^{j},\forall j\in\{1,\ldots,N\} and T{}T\in\mathbb{N}\cup\{\infty\}. Consider a control sequence AA that respects the precedence and time constraints and targets NN nodes as shown in Figure 1. Suppose sequence AA permanently repairs all the nodes and contains exactly one jump, where the entity partially repairs node i1i_{1} before moving to node i2i_{2} at time-step t¯1A\bar{t}_{1}^{A}. Sequence AA then permanently repairs nodes i2,i3,,iki_{2},i_{3},\ldots,i_{k}, before returning to node i1i_{1} and permanently repairing it. Then, there exists a non-jumping control sequence BB as shown in Figure 2 that targets nodes in the order {i2,i3,,ik,i1,ik+1,,iN}\{i_{2},i_{3},\ldots,i_{k},i_{1},i_{k+1},\ldots,i_{N}\} (which respects the precedence and time constraints) and permanently repairs all the nodes. Furthermore, if tjAt_{j}^{A} (resp. tjBt_{j}^{B}) is the number of time-steps taken to permanently repair node iji_{j} in sequence AA (resp. sequence BB), then the following holds true:

tjA\displaystyle t_{j}^{A} tjB+(2j2)t¯1Aj{2,,k},\displaystyle\geq t_{j}^{B}+\left(2^{j-2}\right)\overline{t}_{1}^{A}\hskip 8.53581pt\forall j\in\{2,\ldots,k\}, (1)
t1A\displaystyle t_{1}^{A} t1B+(2k12)t¯1A,\displaystyle\geq t_{1}^{B}+(2^{k-1}-2)\overline{t}_{1}^{A}, (2)
tjA\displaystyle t_{j}^{A} tjB+(2j12jk)t¯1Aj{k+1,,N}.\displaystyle\geq t_{j}^{B}+\left(2^{j-1}-2^{j-k}\right)\overline{t}_{1}^{A}\hskip 8.53581pt\forall j\in\{k+1,\ldots,N\}. (3)
Refer to caption
Figure 1: Sequence AA with a single jump.
Refer to caption
Figure 2: Non-jumping sequence BB.
Proof:

Since node i1i_{1} is partially repaired by the entity before it targets the nodes i2,,iki_{2},\ldots,i_{k} in sequence AA, there cannot be an edge that starts from i1i_{1} and ends at a node in the set {i2,,ik}\{i_{2},\ldots,i_{k}\}. Thus, if sequence AA follows the precedence constraints, the non-jumping sequence BB also respects the precedence constraints. We can now leverage the analysis in [14], which proved that (1)-(3) hold and each node in sequence BB is targeted at an earlier time-step than in sequence AA. Therefore, sequence BB permanently repairs all the nodes that are permanently repaired by sequence AA while respecting the precedence and time constraints. Thus, the result follows. ∎

The above result considered sequences containing exactly one jump. This leads us to the following key result pertaining to the optimal control policy when ΔdecjΔincj,j\Delta_{dec}^{j}\geq\Delta_{inc}^{j},\forall j.

Theorem 1.

Let there be N(2)N(\geq 2) nodes that have a set of precedence constraints given by a DAG G={𝒱,}G=\{\mathcal{V},\mathcal{E}\}, ΔdecjΔincj,j{1,,N}\Delta_{dec}^{j}\geq\Delta_{inc}^{j},\forall j\in\{1,\ldots,N\} and T{}T\in\mathbb{N}\cup\{\infty\}. Suppose there is a sequence AA with one or more jumps that respects the precedence and time constraints, and permanently repairs x(N)x(\leq N) nodes. Then, there exists a non-jumping sequence that respects the precedence and time constraints, and permanently repairs xx nodes. Thus, non-jumping sequences are optimal when ΔdecjΔincj,j{1,,N}\Delta_{dec}^{j}\geq\Delta_{inc}^{j},\forall j\in\{1,\ldots,N\}.

The proof of the above result follows immediately by applying Lemma 1 on sequence AA to obtain a sequence that has one less jump than AA, and repeating this procedure on the obtained sequences to get a non-jumping sequence.

Since non-jumping sequences are optimal by the above result, we will only focus on non-jumping sequences when ΔdecjΔincj,j{1,,N}\Delta_{dec}^{j}\geq\Delta_{inc}^{j},\forall j\in\{1,\ldots,N\}. We now present the following NP-hardness result, whose proof is provided in the appendix.

Theorem 2.

Problem 1 is NP-hard.

Since Problem 1 is NP-hard, it is unlikely that the optimal solution can be efficiently computed for all instances of the problem [16]. Thus, we will characterize optimal or near-optimal policies for special instances of the problem.

III-B Near-optimal policy

In this section, we characterize a near-optimal policy for the case when ΔdecjΔincj,j{1,,N}\Delta_{dec}^{j}\geq\Delta_{inc}^{j},\forall j\in\{1,\ldots,N\}. We will make the following assumption in this section.

Assumption 1.

For all j{1,,N}j\in\{1,\ldots,N\}, suppose Δincj=Δinc\Delta_{inc}^{j}=\Delta_{inc}, Δdecj=Δdec\Delta_{dec}^{j}=\Delta_{dec}, and ΔdecΔinc\Delta_{dec}\geq\Delta_{inc}. Also, suppose there exists a positive integer nn such that Δdec=nΔinc\Delta_{dec}=n\Delta_{inc}, and for each node j{1,,N}j\in\{1,\ldots,N\}, there exists a positive integer mjm_{j} such that 1v0j=mjΔinc1-v^{j}_{0}=m_{j}\Delta_{inc}.

Note that the conditions Δdec=nΔinc\Delta_{dec}=n\Delta_{inc} and 1v0j=mjΔinc,j{1,,N}1-v^{j}_{0}=m_{j}\Delta_{inc},\hskip 5.69054pt\forall j\in\{1,\ldots,N\}, in the assumption ensure that no node gets permanently repaired partway through a time-step.

We now start with the following result.

Lemma 2.

Let there be N(2)N(\geq 2) nodes with precedence constraints given by a DAG G={𝒱,}G=\{\mathcal{V},\mathcal{E}\}. Suppose Assumption 1 holds and T{}T\in\mathbb{N}\cup\{\infty\}. Consider a non-jumping sequence AA that respects the precedence and time constraints, and permanently repairs x(N)x(\leq N) nodes. Suppose there is a node hh from the set of NN nodes that has larger initial health value than the first node of sequence AA and the precedence constraints allow node hh to be permanently repaired before the first node of sequence AA. Consider a non-jumping sequence BB where node hh is first targeted and then the remaining targeted nodes of sequence AA are targeted after node hh. Then, at least x1x-1 nodes are permanently repaired in sequence BB while respecting the precedence and time constraints.

Proof:

Let xx be the number of nodes that are permanently repaired in sequence AA. Denote the order of nodes in the sequence AA as {i1,,ix}\{i_{1},\ldots,i_{x}\}. The proof for the case when x=1x=1 is trivial because x1=0x-1=0; we thus focus on the case when x2x\geq 2. Denote the ordering of the first x1x-1 nodes in sequence BB as {i1,,ix1}\{i^{\prime}_{1},\ldots,i^{\prime}_{x-1}\}, where i1=hi^{\prime}_{1}=h. Note that the proof when x=2x=2 is also trivial because node i1=hi^{\prime}_{1}=h in the non-jumping sequence BB is permanently repaired since the time taken to permanently repair node hh in sequence BB is less than the time taken to permanently repair the nodes in sequence AA (because node hh has larger initial health than node i1i_{1}). Therefore, the non-trivial cases are when x3x\geq 3.

We first argue that sequence BB permanently repairs at least x1x-1 nodes, regardless of the time it takes to repair the nodes. We prove the result by contradiction. Suppose sequence BB permanently repairs less than x1x-1 nodes. Let iki^{\prime}_{k} be the first node that permanently fails in sequence BB, where 2kx12\leq k\leq x-1. We first argue that either ik=ik1i^{\prime}_{k}=i_{k-1} or ik=iki^{\prime}_{k}=i_{k}. If node hh does not appear in sequence AA, then the first x1x-1 nodes of sequence BB are ordered as follows {h,i1,,ix2}\{h,i_{1},\ldots,i_{x-2}\} and thus ik=ik1i^{\prime}_{k}=i_{k-1}. On the other hand, if node hh is permanently repaired in sequence AA, let the position of node hh in sequence AA be zz, where z{2,,x}z\in\{2,\ldots,x\}. Then, i1=hi^{\prime}_{1}=h, ij=ij1,j{2,,z}i^{\prime}_{j}=i_{j-1},\quad\forall j\in\{2,\ldots,z\} and ij=ij,j{z+1,,x1}i^{\prime}_{j}=i_{j},\quad\forall j\in\{z+1,\ldots,x-1\}. Thus, if the first node that fails in sequence BB (i.e., node iki^{\prime}_{k}) is such that kzk\leq z, then ik=ik1i^{\prime}_{k}=i_{k-1}; otherwise ik=iki^{\prime}_{k}=i_{k}. We now consider the cases when ik=ik1i^{\prime}_{k}=i_{k-1} and ik=iki^{\prime}_{k}=i_{k} one by one.

Suppose ik=ik1i^{\prime}_{k}=i_{k-1}. If node ik1i_{k-1} fails in sequence BB, then we show that node ik+1i_{k+1} must permanently fail in sequence AA (where 2kx12\leq k\leq x-1). Consider k=2k=2. Let th=1v0hΔinct_{h}=\frac{1-v_{0}^{h}}{\Delta_{inc}} be the number of time-steps taken to permanently repair node hh from its initial health to the full health. If node i1i_{1} permanently fails after permanently repairing node hh in sequence BB, then v0i1Δdecthv_{0}^{i_{1}}\leq\Delta_{dec}t_{h}. That is,

1v0i11Δdecth.1-v_{0}^{i_{1}}\geq 1-\Delta_{dec}t_{h}. (4)

Denote tj=1v0ijΔinct_{j}=\frac{1-v_{0}^{i_{j}}}{\Delta_{inc}} as the number of time-steps taken to permanently repair node iji_{j} from its initial health to full health. Then,

Δdect1=Δdec(1v0i1Δinc)=n(1v0i1)(1v0i1),\Delta_{dec}t_{1}=\Delta_{dec}\left(\frac{1-v_{0}^{i_{1}}}{\Delta_{inc}}\right)=n\left(1-v_{0}^{i_{1}}\right)\geq\left(1-v_{0}^{i_{1}}\right), (5)

as n1n\geq 1 and v0i1<1v_{0}^{i_{1}}<1. Therefore,

Δdect11Δdecth>1Δdect1,\Delta_{dec}t_{1}\geq 1-\Delta_{dec}t_{h}>1-\Delta_{dec}t_{1}, (6)

where the first inequality from the left comes by conditions (4)-(5) and the second inequality comes from the fact that th<t1t_{h}<t_{1} (as node hh has larger initial health than node i1i_{1}). Note that in sequence AA, it takes t1t_{1} time-steps to repair node i1i_{1}, and nt1+t2nt_{1}+t_{2} time-steps to repair node i2i_{2} (it takes nt1nt_{1} time-steps to repair the health that is lost by node i2i_{2} due to deterioration and it takes t2t_{2} time-steps to repair the difference in the initial health of i2i_{2} and full health). By (6), the total reduction in the health of node i3i_{3} after (1+n)t1+t2(1+n)t_{1}+t_{2} time-steps in sequence AA satisfies

Δdec((1+n)t1+t2)\displaystyle\Delta_{dec}\left((1+n)t_{1}+t_{2}\right) =Δdec(nt1+t2)+Δdect1\displaystyle=\Delta_{dec}\left(nt_{1}+t_{2}\right)+\Delta_{dec}t_{1}
>Δdec(nt1+t2)+1Δdect1\displaystyle>\Delta_{dec}\left(nt_{1}+t_{2}\right)+1-\Delta_{dec}t_{1}
>1,\displaystyle>1,

as n1n\geq 1 and t1,t2>0t_{1},t_{2}>0. Thus, node i3i_{3} permanently fails in sequence AA by the time it is reached, contradicting the fact that AA permanently repairs x(3)x(\geq 3) nodes.

Now consider the case when k=3k=3. If node i2i_{2} permanently fails in sequence BB, then v0i2Δdec((1+n)th+t1)v_{0}^{i_{2}}\leq\Delta_{dec}((1+n)t_{h}+t_{1}). Following the same arguments as before, we get

Δdect2>1Δdec((2+n)t1).\Delta_{dec}t_{2}>1-\Delta_{dec}\left((2+n)t_{1}\right). (7)

As before, the total number of time-steps required to permanently repair nodes i1,i2i_{1},i_{2}, and i3i_{3} in sequence AA is equal to (1+n)2t1+(1+n)t2+t3(1+n)^{2}t_{1}+(1+n)t_{2}+t_{3}. Thus, the total reduction in the health of node i4i_{4} by the time it is reached in sequence AA satisfies

Δdec((1+n)2t1+(1+n)t2+t3)>1,\Delta_{dec}\left((1+n)^{2}t_{1}+(1+n)t_{2}+t_{3}\right)>1,

by (7), n1n\geq 1 and t1,t2,t3>0t_{1},t_{2},t_{3}>0. Thus, i4i_{4} permanently fails by the time it is reached in sequence AA, leading to a contradiction.

We can repeat the above arguments to show that if node ik1i_{k-1} permanently fails in sequence BB, where k>3k>3, then

Δdectk1>1Δdec((2+n)(1+n)k3t1+(1+n)k4t2++(1+n)0tk2).\Delta_{dec}t_{k-1}>1-\Delta_{dec}((2+n)(1+n)^{k-3}t_{1}+\\ (1+n)^{k-4}t_{2}+\ldots+(1+n)^{0}t_{k-2}). (8)

Therefore, the total reduction in the health of node ik+1i_{k+1} after (1+n)k1t1+(1+n)k2t2++(1+n)0tk(1+n)^{k-1}t_{1}+(1+n)^{k-2}t_{2}+\ldots+(1+n)^{0}t_{k} time-steps in sequence AA is

Δdec((1+n)k1t1++(1+n)1tk1+(1+n)0tk)=\displaystyle\Delta_{dec}\left((1+n)^{k-1}t_{1}+\ldots+(1+n)^{1}t_{k-1}+(1+n)^{0}t_{k}\right)=
Δdec((1+n)k1t1++ntk1+(1+n)0tk)+Δdectk1.\displaystyle\Delta_{dec}\left((1+n)^{k-1}t_{1}+\ldots+nt_{k-1}+(1+n)^{0}t_{k}\right)+\Delta_{dec}t_{k-1}. (9)

Thus, for k>3k>3, we get

Δdec((1+n)k1t1++(1+n)1tk1+(1+n)0tk)>1\displaystyle\Delta_{dec}\left((1+n)^{k-1}t_{1}+\ldots+(1+n)^{1}t_{k-1}+(1+n)^{0}t_{k}\right)>1

by (8), (9), n1n\geq 1 and tj>0jt_{j}>0\hskip 2.84526pt\forall j. Since, the total reduction in the health value of node ik+1i_{k+1} before the entity starts targeting it is larger than one, it is not possible to permanently repair node ik+1i_{k+1} in sequence AA, leading to a contradiction.

We now consider the case when ik=iki^{\prime}_{k}=i_{k} (along with x3x\geq 3). We prove that if node ik=iki^{\prime}_{k}=i_{k} permanently fails in sequence BB, then it is not possible to permanently repair node ik+1=ik+1i^{\prime}_{k+1}=i_{k+1} in sequence AA. Recall that the case ik=iki^{\prime}_{k}=i_{k} happens when node hh is permanently repaired in sequence AA and k>zk>z. Since z2z\geq 2, we have k>2k>2. Consider k=3k=3. Then, z=2z=2 (i.e., i2=hi_{2}=h) because z<kz<k and z2z\geq 2. If node i3=i3i^{\prime}_{3}=i_{3} permanently fails in sequence BB, then

v0i3Δdec((1+n)th+t1)<Δdec((2+n)t1),v_{0}^{i_{3}}\leq\Delta_{dec}((1+n)t_{h}+t_{1})<\Delta_{dec}\left((2+n)t_{1}\right),

or equivalently,

Δdect3>1Δdec((2+n)t1).\Delta_{dec}t_{3}>1-\Delta_{dec}\left((2+n)t_{1}\right). (10)

Therefore, the total reduction in the health of node i4i_{4} after (1+n)2t1+(1+n)t2+t3(1+n)^{2}t_{1}+(1+n)t_{2}+t_{3} time-steps in AA satisfies

Δdec((1+n)2t1+(1+n)t2+t3)\displaystyle\Delta_{dec}\left((1+n)^{2}t_{1}+(1+n)t_{2}+t_{3}\right)
=Δdec((1+n)2t1+(1+n)t2)+Δdect3>1,\displaystyle=\Delta_{dec}\left((1+n)^{2}t_{1}+(1+n)t_{2}\right)+\Delta_{dec}t_{3}>1,

by (10), n1n\geq 1 and t1,t2>0t_{1},t_{2}>0. Thus, node i4i_{4} permanently fails in sequence AA, contradicting the fact that AA permanently repairs xx nodes.

Similarly, it can be shown that if node ik=iki^{\prime}_{k}=i_{k} permanently fails in sequence BB, where k>3k>3, then

Δdectk>1Δdec((2+n)(1+n)k3t1+(1+n)k4t2++(1+n)k1ztz1+(1+n)k2ztz+1++(1+n)0tk1).\Delta_{dec}t_{k}>1-\Delta_{dec}((2+n)(1+n)^{k-3}t_{1}+\\ (1+n)^{k-4}t_{2}+\ldots+(1+n)^{k-1-z}t_{z-1}+\\ (1+n)^{k-2-z}t_{z+1}+\ldots+(1+n)^{0}t_{k-1}). (11)

Therefore, the total reduction in the health of node ik+1i_{k+1} (where k>3k>3) after (1+n)k1t1+(1+n)k2t2++(1+n)0tk(1+n)^{k-1}t_{1}+(1+n)^{k-2}t_{2}+\ldots+(1+n)^{0}t_{k} time-steps in sequence AA is

Δdec((1+n)k1t1++(1+n)1tk1+(1+n)0tk)\displaystyle\Delta_{dec}\left((1+n)^{k-1}t_{1}+\ldots+(1+n)^{1}t_{k-1}+(1+n)^{0}t_{k}\right)
=Δdec((1+n)k1t1++(1+n)1tk1)+Δdectk>1,\displaystyle=\Delta_{dec}\left((1+n)^{k-1}t_{1}+\ldots+(1+n)^{1}t_{k-1}\right)+\Delta_{dec}t_{k}>1,

by (11), n1n\geq 1 and tj>0jt_{j}>0\hskip 2.84526pt\forall j. Therefore, it would not be possible to permanently repair xx nodes in sequence AA, leading to a contradiction.

We will now show that sequence BB permanently repairs x1x-1 nodes in less time than the time taken to permanently repair xx nodes in sequence AA. Suppose node hh is not permanently repaired in sequence AA. Consider x=3x=3. Then, the time taken to permanently repair the first two nodes, i.e., nodes hh and i1i_{1}, in sequence BB is equal to (1+n)th+t1(1+n)t_{h}+t_{1} and the time taken to permanently repair three nodes in sequence AA is equal to (1+n)2t1+(1+n)t2+t3(1+n)^{2}t_{1}+(1+n)t_{2}+t_{3}. Note that (1+n)th+t1<(2+n)t1<(1+n)2t1+(1+n)t2+t3(1+n)t_{h}+t_{1}<(2+n)t_{1}<(1+n)^{2}t_{1}+(1+n)t_{2}+t_{3} because th<t1t_{h}<t_{1}, n1n\geq 1 and t1,t2,t3>0t_{1},t_{2},t_{3}>0. Consider the case when x>3x>3. Then, the time taken to permanently repair x1x-1 nodes in sequence BB is equal to

(1+n)x2th+(1+n)x3t1++(1+n)0tx2,(1+n)^{x-2}t_{h}+(1+n)^{x-3}t_{1}+\ldots+(1+n)^{0}t_{x-2}, (12)

and the time taken to permanently repair xx nodes in sequence AA is equal to

(1+n)x1t1+(1+n)x2t2++(1+n)0tx.(1+n)^{x-1}t_{1}+(1+n)^{x-2}t_{2}+\ldots+(1+n)^{0}t_{x}. (13)

Then, (1+n)x2th+(1+n)x3t1+(1+n)x4t2++(1+n)0tx2<((2+n)(1+n)x3)t1+(1+n)x4t2++(1+n)0tx2<(1+n)x1t1+(1+n)x2t2++(1+n)0tx(1+n)^{x-2}t_{h}+(1+n)^{x-3}t_{1}+(1+n)^{x-4}t_{2}+\ldots+(1+n)^{0}t_{x-2}<\left((2+n)(1+n)^{x-3}\right)t_{1}+(1+n)^{x-4}t_{2}+\ldots+(1+n)^{0}t_{x-2}<(1+n)^{x-1}t_{1}+(1+n)^{x-2}t_{2}+\ldots+(1+n)^{0}t_{x} as th<t1t_{h}<t_{1}, n1n\geq 1 and tj>0,j{1,,x}t_{j}>0,\forall j\in\{1,\ldots,x\}.

Now consider the case when node hh is permanently repaired in sequence AA. When zx1z\geq x-1 (and x3x\geq 3), the time taken to permanently repair x1x-1 nodes in sequence BB and the time taken to permanently repair xx nodes in sequence AA are given by expressions (12) and (13), respectively, and therefore the proof proceeds in the same way as when node hh is not permanently repaired in sequence AA. Thus, the proof for x=3x=3 follows in exactly the same way as before because z2z\geq 2. We now focus on the case when zx2z\leq x-2 and x>3x>3. Then, the total time taken to permanently repair x1x-1 nodes in sequence BB is equal to

(1+n)x2th+(1+n)x3t1++(1+n)x1ztz1+(1+n)x2ztz+1++(1+n)0tx1,(1+n)^{x-2}t_{h}+(1+n)^{x-3}t_{1}+\ldots+(1+n)^{x-1-z}t_{z-1}+\\ (1+n)^{x-2-z}t_{z+1}+\ldots+(1+n)^{0}t_{x-1}, (14)

and the time taken to permanently repair xx nodes in sequence AA is given by the expression (13). Note that the value of the expression (14) is less than the value of the expression (13) because th<t1t_{h}<t_{1}, n1n\geq 1 and tj>0,j{1,,x}t_{j}>0,\forall j\in\{1,\ldots,x\}. Therefore, if sequence AA permanently repairs xx nodes by time-step TT, then sequence BB must permanently repair at least x1x-1 nodes by time-step TT. Thus, the result follows. ∎

Using the above result, we now propose a near-optimal policy for a subclass of Problem 1.

Theorem 3.

Let there be N(2)N(\geq 2) nodes with precedence constraints given by a DAG G={𝒱,}G=\{\mathcal{V},\mathcal{E}\}. Suppose Assumption 1 holds and T{}T\in\mathbb{N}\cup\{\infty\}. Then, the policy that targets the healthiest node at each time-step while respecting the precedence and time constraints is 1/21/2-optimal.

Proof:

Let AA be the optimal (non-jumping) sequence. Then, we go to the first time-step T(T)T^{\prime}(\leq T) in sequence AA at which the healthiest available node (i.e., the healthiest node that can be targeted at time-step TT^{\prime} without violating the precedence constraints) is not targeted. Denote the portion of sequence AA from time-step 0 to time-step T1T^{\prime}-1 as AA^{\prime} and the portion of sequence from time-step TT^{\prime} onwards as A′′A^{\prime\prime}. We modify the sequence A′′A^{\prime\prime} by Lemma 2 to generate sequence AA^{*} such that the healthiest available node (while respecting the precedence and time constraints) is targeted at time-step TT^{\prime} in AA^{*}. Let yy be the number of nodes that are permanently repaired by sequence A′′A^{\prime\prime}. Then, at least y1y-1 nodes are permanently repaired in sequence AA^{*} (while respecting the precedence and time constraints) by Lemma 2. Then, a sequence BB is generated by concatenating AA^{\prime} with AA^{*}. After this, we go to the next time-step in sequence BB at which the healthiest node (while respecting the precedence and time constraints) is not targeted and repeat this procedure. We iteratively repeat this procedure, so that we finally get a sequence in which the healthiest node (while respecting the precedence and time constraints) is targeted at each time-step. The number of nodes that are permanently repaired in the final sequence will be at least half of the number of nodes that are permanently repaired in sequence AA because 1) at each iteration of this procedure we move one node across the given sequence and the number of nodes that are permanently repaired in the modified sequence reduces at most by one, and 2) in the last iteration of this procedure where there is only one node, there is no reduction in the number of permanently repaired nodes because if the last node in the given sequence can be permanently repaired then a healthier node that replaces it can also be permanently repaired. ∎

We now show that the factor of 1/21/2 in the above result is sharp, in that there exist problem instances where targeting the healthiest node at each time-step repairs only half as many nodes as an optimal policy.

Example 1.

Consider a graph consisting of three nodes as shown in Figure 3. Let the initial health values of the nodes 1, 2 and 3 be 0.6, 0.3 and 0.8, respectively. The deterioration and repair rates are homogeneous across all the nodes and both are equal to 0.1. Suppose T=T=\infty. The healthiest node that can be targeted in the first time-step is node 1 (as we need to permanently repair node 2 before targeting node 3). If node 1 is first permanently repaired then node 2 fails by the time the entity reaches it and we cannot permanently repair any more nodes. However, if node 2 is first permanently repaired then node 3 can also be permanently repaired. Note that although the policy that targets the healthiest node at each time-step (while respecting the precedence and time constraints) is not optimal in this example, it is indeed 1/21/2-optimal as proved in Theorem 3.

Refer to caption
Figure 3: Graph for illustrating optimal policies in the presence of precedence constraints.
Remark 1.

In this paper, we consider all the nodes to be equally important. However, one could also consider weights on the nodes to signify their relative importance, and the objective of Problem 1 can be changed to maximize the total weight of the nodes that are permanently repaired. Then, it is easy to argue that the policy characterized in Theorem 3 would be wmin2wmax\frac{w_{min}}{2w_{max}}-optimal, where wmaxw_{max} and wminw_{min} are the maximum and minimum weights of nodes, respectively.

Theorem 3 shows that the feedback policy that targets the healthiest node at each time-step while respecting the precedence and time constraints is 1/21/2-optimal. In the next section, we show that this policy is, in fact, optimal for special instances of the problem.

III-C Optimal sequencing

The paper [14] showed that the policy that targets the healthiest node at each time-step is optimal when all the conditions of Theorem 3 are satisfied but when there are no precedence and time constraints. We now prove that the aforementioned policy is also optimal when there is a time constraint but there are no precedence constraints.

Theorem 4.

Let there be N(2)N(\geq 2) nodes such that there are no precedence constraints (i.e., =\mathcal{E}=\emptyset). Suppose Assumption 1 holds and T{}T\in\mathbb{N}\cup\{\infty\}. Then, the policy that targets the healthiest node at each time-step while respecting the time constraint is optimal.

Proof:

Consider any optimal (non-jumping) sequence A={i1,,ix}A=\{i_{1},\ldots,i_{x}\} that permanently repairs x(N)x(\leq N) nodes. Let T(T+1)T^{\prime}(\leq T+1) be the total number of time-steps taken by the sequence AA to permanently repair all xx nodes.444Note that the total number of time-steps is one larger than the largest time-step index TT because the first time-step in a sequence is time-step 0. Then,

T=1E1Δinc+1E2Δinc++1ExΔinc,T^{\prime}=\frac{1-E_{1}}{\Delta_{inc}}+\frac{1-E_{2}}{\Delta_{inc}}+\ldots+\frac{1-E_{x}}{\Delta_{inc}}, (15)

where EjE_{j} is the health of node iji_{j} when it is reached in the sequence, i.e., E1=v0i1E_{1}=v_{0}^{i_{1}} and Ek=v0iknj=2k(1Ej1)E_{k}=v_{0}^{i_{k}}-n\sum_{j=2}^{k}\left(1-E_{j-1}\right) for k{2,,x}k\in\{2,\ldots,x\}. Alternatively, Ek=v0i1n(1+n)k2+v0i2n(1+n)k3++v0ik1n+v0ikn(1+n)k2n(1+n)k3nE_{k}=v_{0}^{i_{1}}n\left(1+n\right)^{k-2}+v^{i_{2}}_{0}n(1+n)^{k-3}+\ldots+v_{0}^{i_{k-1}}n+v^{i_{k}}_{0}-n\left(1+n\right)^{k-2}-n(1+n)^{k-3}-\ldots-n for all k{2,,x}k\in\{2,\ldots,x\}. Note that E1E_{1} is the largest when node i1i_{1} has the largest initial health; E2E_{2} is the largest when node i1i_{1} has the largest initial health and node i2i_{2} has the second largest initial health (as the coefficients of v0i1v_{0}^{i_{1}} and v0i2v_{0}^{i_{2}} are nn and 1, respectively); E3E_{3} is the largest when node i1i_{1} has the largest initial health, node i2i_{2} has the second largest initial health and node i3i_{3} has the third largest initial health (as the coefficients of v0i1v_{0}^{i_{1}}, v0i2v_{0}^{i_{2}} and v0i3v_{0}^{i_{3}} are n(1+n)n(1+n), nn and 1, respectively); and so on. Thus, the non-jumping sequence BB that targets the nodes in decreasing order of their initial health is optimal when time is constrained because it permanently repairs xx nodes in less time in comparison to the time taken by sequence AA to permanently repair xx nodes (as the value of TT^{\prime} in (15) for sequence BB is less than the corresponding value for sequence AA). Therefore, the policy that targets the healthiest node at each time-step while respecting the time constraint is optimal by Theorem 1. ∎

We also show that the policy of targeting the healthiest node at each time-step (while respecting the precedence and time constraints) is optimal in certain classes of DAGs. We start with the following definition.

Definition 6.

Given a DAG, the nodes that do not have any incoming edges are said to belong to the “first level”; after removing all the nodes in the first level and all the outgoing edges of the nodes in the first level, the nodes that do not have any incoming edges are said to belong to the “second level”, and so on. A DAG in which all the nodes in a level have incoming edges from all the nodes in the previous level is termed as a “complete series graph”.

Figure 4 shows a complete series graph that has three levels.

Proposition 1.

Let there be N(2)N(\geq 2) nodes with precedence constraints given by a complete series graph G={𝒱,}G=\{\mathcal{V},\mathcal{E}\}. Suppose Assumption 1 holds and T{}T\in\mathbb{N}\cup\{\infty\}. Then, the policy that targets the healthiest node at each time-step while respecting the precedence and time constraints is optimal.

Proof:

Due to precedence constraints, we need to permanently repair all the nodes in the first level before targeting the other nodes in a complete series graph. Since the optimal policy is to target the healthiest node at each time-step (while respecting the time constraint) when there are no precedence constraints (by Theorem 4), we follow this policy for the nodes in the first level. After permanently repairing all the nodes in the first level, we need to permanently repair all the nodes in the second level before targeting other nodes. Thus, the policy of targeting the healthiest node at each time-step (while respecting the time constraint) in the second level is followed after permanently repairing all the nodes in the first level. The rest of the proof follows the above argument. ∎

Refer to caption
Figure 4: An example of a complete series graph.

So far, we assumed that ΔdecjΔincj,j{1,,N}\Delta_{dec}^{j}\geq\Delta_{inc}^{j},\forall j\in\{1,\ldots,N\}. In the next section, we analyze other cases.

IV Control Sequences for Δdecj<Δincj,j{1,,N}\Delta_{{dec}}^{j}<\Delta_{{inc}}^{j},\quad\forall{j}\in\{1,\ldots,N\}

We now consider the case when repair rates are sufficiently larger than the deterioration rates, i.e., Δincj>(N1)Δdecj,j{1,,N}\Delta_{inc}^{j}>(N-1)\Delta_{dec}^{j},\forall j\in\{1,\ldots,N\} and Δincj>ljΔdecl,j{1,,N}\Delta_{inc}^{j}>\sum_{l\neq j}\Delta_{dec}^{l},\quad\forall j\in\{1,\ldots,N\}. We will use the concept of a modified health value defined as follows.

Definition 7.

The modified health value of a node at a time-step is the health value of a node at that time-step minus its deterioration rate.

IV-A Near-optimal policy

Consider a directed rooted tree where the total number of nodes is at most equal to k(1)k(\geq 1). In the context of disaster recovery, such a tree can represent damaged infrastructure components (with dependencies) within a particular neighborhood. We will consider such trees for the representation of precedence constraints as follows.

Theorem 5.

Let there be N(2)N(\geq 2) nodes with precedence constraints represented by a graph G={𝒱,}G=\{\mathcal{V},\mathcal{E}\}, which is a set of disjoint rooted trees that each contain at most k(1)k(\geq 1) nodes. Suppose Δincj>(N1)Δdecj,j{1,,N}\Delta_{inc}^{j}>(N-1)\Delta_{dec}^{j},\forall j\in\{1,\ldots,N\}, Δincj>ljΔdecl,j{1,,N}\Delta_{inc}^{j}>\sum_{l\neq j}\Delta_{dec}^{l},\quad\forall j\in\{1,\ldots,N\} and T=T=\infty. Then, the policy that targets the node with the least modified health value at each time-step while respecting the precedence constraints is 1k\frac{1}{k}-optimal.

Proof:

Let \mathcal{R} be the set of root nodes of the trees. Let AA denote the sequence that targets the node with the least modified health value in the set \mathcal{R} at each time-step, and let BB denote the optimal sequence. Let the number of nodes that are permanently repaired by sequences AA and BB be yy and xx, respectively. We will show that yxky\geq\frac{x}{k}. We prove this by contradiction. Suppose xk>y\frac{x}{k}>y. Denote the set of root nodes that are permanently repaired in sequence BB by \mathcal{F}\subseteq\mathcal{R}, and let z||z\triangleq|\mathcal{F}|. Note that zxkz\geq\left\lceil\frac{x}{k}\right\rceil because the root node in a tree should be permanently repaired before targeting other nodes in the tree. Then, zy+1z\geq y+1 because xk>y\frac{x}{k}>y implies xky+1\left\lceil\frac{x}{k}\right\rceil\geq y+1. We now construct a sequence CC by modifying sequence BB such that at every time-step of sequence BB where a node not belonging to the set \mathcal{F} is targeted, we replace that node with a node from the set \mathcal{F}. Then, sequence CC also permanently repairs all the nodes in the set \mathcal{F} because the nodes of set \mathcal{F} are targeted more frequently in sequence CC than in sequence BB. Since the optimal policy is to target the node with the least modified health value at each time-step when there are no precedence and time constraints (by Proposition 1 of [14]), we obtain a contradiction because sequence CC permanently repairs zy+1z\geq y+1 nodes whereas sequence AA (which is the optimal sequence to permanently repair the set of root nodes) permanently repairs yy nodes. Therefore, the assumption that xk>y\frac{x}{k}>y is not true.

Let the sequence that targets the node with the least modified health value (in the set 𝒱\mathcal{V}) at each time-step while respecting the precedence constraints be denoted by DD. We now argue that sequence DD permanently repairs at least as many nodes as sequence AA. Denote the set of root nodes that are permanently repaired in sequence AA by 𝒵\mathcal{Z}\subseteq\mathcal{R} and the number of nodes in the set 𝒵\mathcal{Z} by yy. Then, there exists an ordering of nodes in the set 𝒵\mathcal{Z}, denoted by {i1,i2,,iy}\{i_{1},i_{2},\ldots,i_{y}\}, such that the initial health values of those nodes satisfy

v0ij>(yj)Δdecij,j{1,,y}v_{0}^{i_{j}}>(y-j)\Delta_{dec}^{i_{j}},\quad\forall j\in\{1,\ldots,y\} (16)

because of the following argument. Let 𝒜t𝒵\mathcal{A}_{t}\subseteq\mathcal{Z} denote the set of nodes that have not been targeted at least once by the entity prior to time tt in sequence AA. Note that 𝒵=𝒜0𝒜1𝒜y1\mathcal{Z}=\mathcal{A}_{0}\supseteq\mathcal{A}_{1}\supseteq\ldots\supseteq\mathcal{A}_{y-1}. At time t=0t=0, |𝒜t|=y|\mathcal{A}_{t}|=y, where |𝒜t||\mathcal{A}_{t}| denotes the cardinality of set 𝒜t\mathcal{A}_{t}. At t=1t=1, |𝒜t|=y1|\mathcal{A}_{t}|=y-1 as there are y1y-1 nodes of the set 𝒵\mathcal{Z} that have not been targeted at least once by the entity in sequence AA and thus each node k𝒜1k\in\mathcal{A}_{1} should have initial health value larger than Δdeck\Delta_{dec}^{k} to survive until t=1t=1. At t=2t=2, |𝒜t|y2|\mathcal{A}_{t}|\geq y-2 as there are at least y2y-2 nodes of the set 𝒵\mathcal{Z} that have not been targeted at least once by the entity in sequence AA and thus each node k𝒜2k\in\mathcal{A}_{2} should have initial health value larger than 2Δdeck2\Delta_{dec}^{k}. Repeating this argument for the next y3y-3 time-steps, we can argue that there must be a permutation {i1,,iy}\{i_{1},\ldots,i_{y}\} of nodes that satisfies the conditions (16) in order for yy nodes to be permanently repaired in sequence AA.

After this, it can be argued that sequence DD permanently repairs at least yy nodes by the same way as in Proposition 1 of [14]. Let the number of nodes that are permanently repaired by sequence DD be yy^{\prime}. Then, yyy^{\prime}\geq y and therefore xyxyk\frac{x}{y^{\prime}}\leq\frac{x}{y}\leq k. Thus, the policy that targets the node with the least modified health value at each time-step while respecting the precedence constraints is 1k\frac{1}{k}-optimal. ∎

We now show that the factor of 1k\frac{1}{k} in Theorem 5 is sharp, in that there exist problem instances where targeting the node with the least modified health value at each time-step repairs only 1k\frac{1}{k} times the nodes as an optimal policy.

Example 2.

Consider the graph in Figure 3, which is a set of disjoint rooted trees that each contain at most two nodes. Let the initial health values of the nodes 1, 2 and 3 be 0.01, 0.02 and 0.8, respectively, and T=T=\infty. The deterioration and repair rates are homogeneous across all the nodes and are equal to 0.1 and 0.25, respectively. The least healthy node that can be targeted in the first time-step is node 1 (since deterioration rates are homogeneous across all the nodes, targeting the node with the least modified health value is equivalent to targeting the node with least health value). However, if node 1 is targeted in the first time-step then node 2 fails by the time the entity reaches it and we cannot target any more nodes. However, if node 2 is first permanently repaired then node 3 can also be permanently repaired. Note that although the policy that targets the least healthy node at each time-step while respecting the precedence constraints is not optimal in this example, it is indeed 1k\frac{1}{k}-optimal as proved in Theorem 5.

We now also provide an example to show that the policy that is characterized in Theorem 5 need not be 1k\frac{1}{k}-optimal when TT\in\mathbb{N}.

Example 3.

Consider a graph consisting of two nodes without any precedence constraints (i.e., the graph is a union of disjoint trees that each contain one node). Let the initial health values of the nodes 1 and 2 be 0.01 and 0.11, respectively. The deterioration and repair rates are homogeneous across all the nodes and are equal to 0.1 and 0.11, respectively, and T=10T=10. If the policy of targeting the node with the least health value at each time-step is followed then no node gets permanently repaired while respecting the time constraint. However, if the non-jumping policy that first targets node 2 is followed, then node 2 can be permanently repaired. Thus, the policy that is characterized in Theorem 5 is not a 1-optimal policy (or optimal policy) in this example.

Therefore, characterizing near-optimal policies for this case under a time constraint is an avenue for future research.

IV-B Optimal sequencing

The paper [14] proved that the policy that targets the node with the least modified health value at each time-step is optimal when there are no precedence and time constraints. Examples 2 and 3 showed that this need not be true when there is a precedence or time constraint. However, the policy of targeting the node with the least modified health value at each time-step is optimal for special cases such as when the precedence constraints are given by a complete series graph (defined in the previous section).

Proposition 2.

Let there be N(2)N(\geq 2) nodes with precedence constraints given by a complete series graph G={𝒱,}G=\{\mathcal{V},\mathcal{E}\}. Suppose Δincj>(N1)Δdecj,j{1,,N}\Delta_{inc}^{j}>(N-1)\Delta_{dec}^{j},\forall j\in\{1,\ldots,N\}, Δincj>ljΔdecl,j{1,,N}\Delta_{inc}^{j}>\sum_{l\neq j}\Delta_{dec}^{l},\quad\forall j\in\{1,\ldots,N\} and T=T=\infty. The optimal policy is to target the node with the least modified health at each time-step while respecting the precedence constraints.

This result can be proved similarly as Proposition 1 by using the fact that the policy of targeting the node with the least modified health at each step is optimal when there are no precedence and time constraints [14].

V Conclusions

We studied the problem of finding (near-) optimal control policies for targeting different components whose states are disrupted, when there exist precedence constraints between the components. We characterized control policies depending on the relationship between the repair and deterioration rates. There are several options for future extensions to this work: introducing stochasticity in the health values, extending the problem to time-dependent deterioration and repair rates, characterizing policies with improved ratios α\alpha, and characterizing near-optimal policies when for all j𝒱j\in\mathcal{V}, Δdecj<Δincj<(N1)Δdecj\Delta_{dec}^{j}<\Delta_{inc}^{j}<(N-1)\Delta_{dec}^{j}.

VI Appendix

In this section, we prove that Problem 1 is NP-hard. This proof is inspired from the paper [17]; however, the job scheduling problem in [17] does not consider deterioration of jobs and there are additional differences between the problem that is considered in [17] and Problem 1 as mentioned in the review of job scheduling studies in Section I. We start by defining the NP-complete Clique problem [16] and an instance of a decision version of Problem 1 referred to as ORdOR_{d}.

Problem 2 (Clique).

Given an undirected graph G=(𝒱,)G^{\prime}=(\mathcal{V^{\prime}},\mathcal{E^{\prime}}) consisting of ss vertices and qq edges, and a positive integer p(s)p(\leq s), does GG^{\prime} have a complete subgraph of size pp, i.e., a set of pp vertices such that each pair of vertices in the set is connected by an edge in \mathcal{E^{\prime}}?

Problem 3 (ORdOR_{d}).

Given a directed acyclic graph G={𝒱,}G=\{\mathcal{V},\mathcal{E}\} consisting of N(2)N(\geq 2) nodes with initial health values {v0j}\{v_{0}^{j}\}, along with repair and deterioration rates {Δincj}\{\Delta_{inc}^{j}\} and {Δdecj}\{\Delta_{dec}^{j}\}, respectively, T=T=\infty, and an integer zz such that 0zN0\leq z\leq N, is there a non-jumping control sequence u0:Tu^{\ast}_{0:T} that respects the precedence constraints and gives a reward J(v0,u0:T)NzJ(v_{0},u^{\ast}_{0:T})\geq N-z?

We now present the proof of Theorem 2.

Proof:

Given an instance of Clique we construct an instance of ORdOR_{d} as follows. We construct a total of N=s+q+1N=s+q+1 nodes that are of three types as follows: a v-node is constructed corresponding to each vertex in GG^{\prime}, an e-node is constructed corresponding to each edge in GG^{\prime}, and a root node rr is constructed. The parameters of these nodes are set as follows. For each v-node jj, set v0j=2(2s+q1)+12+2(2s+q1)+1v_{0}^{j}=\frac{2(2^{s+q}-1)+1}{2+2(2^{s+q}-1)+1} and Δdecj=Δincj=12+2(2s+q1)+1\Delta_{dec}^{j}=\Delta_{inc}^{j}=\frac{1}{2+2(2^{s+q}-1)+1}. For each e-node jj, set v0j=2(2p(p+1)21)+12+2(2p(p+1)21)+1v_{0}^{j}=\frac{2(2^{\frac{p(p+1)}{2}}-1)+1}{2+2(2^{\frac{p(p+1)}{2}}-1)+1} and Δdecj=Δincj=12+2(2p(p+1)21)+1\Delta_{dec}^{j}=\Delta_{inc}^{j}=\frac{1}{2+2(2^{\frac{p(p+1)}{2}}-1)+1}. The root node rr has the same parameters as a v-node. We construct directed edges such that there is an edge starting from a v-node and ending in an e-node if the vertex corresponding to the v-node lies at one of the ends of the edge (in graph GG^{\prime}) corresponding to the e-node. We also construct directed edges starting from the root node and ending in all the other nodes, so that a DAG is formed. Finally, set z=qp(p1)2z=q-\frac{p(p-1)}{2}. Note that the constructed instance of ORdOR_{d} is polynomial in the size of the given instance of Clique.

We will now prove that the answer to the given instance of Clique is yes if and only if the answer to the constructed instance of ORdOR_{d} is yes. Suppose that the answer to the instance of Clique is yes. Then, we will show that it is possible to create a non-jumping sequence for the constructed instance of ORdOR_{d} in which no more than qp(p1)2q-\frac{p(p-1)}{2} nodes permanently fail while respecting the precedence constraints. The first node in the created sequence is node rr because of the precedence constraints. Note that node rr is permanently repaired in the created sequence because the first node in any non-jumping sequence is always permanently repaired (when T=T=\infty). After permanently repairing node rr, pp v-nodes whose corresponding vertices form a complete graph in the Clique are targeted in the created sequence. Note that it takes two time-steps to permanently repair node rr in the created sequence because 1v0rΔdecr=2\frac{1-v_{0}^{r}}{\Delta_{dec}^{r}}=2. Also, the health value of each v-node jj after two time-steps is equal to v2j=v0j2Δdecj=2(2s+q1)+122+2(2s+q1)+1v_{2}^{j}=v_{0}^{j}-2\Delta_{dec}^{j}=\frac{2(2^{s+q}-1)+1-2}{2+2(2^{s+q}-1)+1}. Thus, the number of time-steps it takes to permanently repair the first v-node jj in the created sequence is equal to 1v2jΔdecj=4\frac{1-v_{2}^{j}}{\Delta_{dec}^{j}}=4. Thus, the second v-node jj starts getting targeted after six time-steps in the created sequence and at that time its health value is equal to v6j=v0j6Δdecj=2(2s+q1)+162+2(2s+q1)+1v_{6}^{j}=v_{0}^{j}-6\Delta_{dec}^{j}=\frac{2(2^{s+q}-1)+1-6}{2+2(2^{s+q}-1)+1}. Thus, the number of time-steps it takes to permanently repair the second v-node jj in the created sequence is equal to 1v6jΔdecj=8\frac{1-v_{6}^{j}}{\Delta_{dec}^{j}}=8. Proceeding in this way, it can be shown that the total number of time-steps taken to permanently repair node rr and p1p-1 v-nodes is equal to 2(2p1)2(2^{p}-1) because the iith node in the created sequence (where 1ip1\leq i\leq p) takes 2i2^{i} time-steps to get permanently repaired. Note that v0jΔdecj=2(2s+q1)+1\frac{v_{0}^{j}}{\Delta_{dec}^{j}}=2\left(2^{s+q}-1\right)+1 for all v-nodes jj, and represents the number of time-steps it takes for jj to permanently fail if it is not targeted within the first v0jΔdecj\frac{v_{0}^{j}}{\Delta_{dec}^{j}} time-steps. Thus, for all v-nodes jj, we define γv=v0jΔdecj=2(2s+q1)+1\gamma_{\textit{v}}=\frac{v_{0}^{j}}{\Delta_{dec}^{j}}=2(2^{s+q}-1)+1. Note that the time-step at which the ppth v-node starts getting targeted in the created sequence is less than γv\gamma_{\textit{v}} because 2(2p1)2s+12<2s+112(2s+q1)+12(2^{p}-1)\leq 2^{s+1}-2<2^{s+1}-1\leq 2(2^{s+q}-1)+1 (as psp\leq s and q0q\geq 0). Thus, all the pp v-nodes are permanently repaired in the created sequence. After permanently repairing pp v-nodes that correspond to the solution of Clique, p(p1)2\frac{p(p-1)}{2} e-nodes that correspond to the edges of the complete subgraph in the solution of Clique are targeted. Note that v0jΔdecj=2(2p(p+1)21)+1\frac{v_{0}^{j}}{\Delta_{dec}^{j}}=2(2^{\frac{p(p+1)}{2}}-1)+1 for all e-nodes jj. Thus, for all e-nodes jj, we define γe=v0jΔdecj=2(2p(p+1)21)+1\gamma_{\textit{e}}=\frac{v_{0}^{j}}{\Delta_{dec}^{j}}=2(2^{\frac{p(p+1)}{2}}-1)+1. Note that there are 1+p+p(p1)21=p(p+1)21+p+\frac{p(p-1)}{2}-1=\frac{p(p+1)}{2} nodes that are targeted before the last e-node among the aforementioned e-nodes in the created sequence. Thus, the last e-node among the aforementioned e-nodes starts getting targeted in the created sequence at time-step 2(2p(p+1)21)2(2^{\frac{p(p+1)}{2}}-1) because the iith node in the created sequence (where 1ip(p+1)21\leq i\leq\frac{p(p+1)}{2}) takes 2i2^{i} time-steps to get permanently repaired. Thus, all the aforementioned e-nodes get permanently repaired because 2(2p(p+1)21)<γe2(2^{\frac{p(p+1)}{2}}-1)<\gamma_{\textit{e}}. Finally, the remaining sps-p v-nodes are targeted in the created sequence. Note that there are 1+p+p(p1)2+sp1=s+p(p1)21+p+\frac{p(p-1)}{2}+s-p-1=s+\frac{p(p-1)}{2} nodes that are targeted before the last v-node in the created sequence. Thus, it takes 2(2s+p(p1)21)2(2^{s+\frac{p(p-1)}{2}}-1) time-steps in order to start targeting the last v-node because the iith node in the created sequence (where 1is+p(p1)21\leq i\leq s+\frac{p(p-1)}{2}) takes 2i2^{i} time-steps to get permanently repaired. Thus, it is possible to permanently repair all the v-nodes because 2(2s+p(p1)21)<γv2(2^{s+\frac{p(p-1)}{2}}-1)<\gamma_{\textit{v}} (as qp(p1)2q\geq\frac{p(p-1)}{2}). Thus, except the remaining qp(p1)2q-\frac{p(p-1)}{2} e-nodes, all the nodes are permanently repaired in the created sequence.

Now we show the opposite direction. Suppose the answer to the constructed instance of ORdOR_{d} is yes. Then, there exists a non-jumping sequence in which at most z=qp(p1)2z=q-\frac{p(p-1)}{2} nodes permanently fail. Note that there are at most N1=s+qN-1=s+q nodes that are permanently repaired before the last node that is targeted in the given sequence. Thus, the largest time-step at which the last targeted node in the given non-jumping sequence starts getting targeted is equal to 2(2s+q1)2(2^{s+q}-1) because the iith node in the given sequence takes 2i2^{i} time-steps to get permanently repaired. Thus, all the v-nodes are permanently repaired in the given sequence because 2(2s+q1)<2(2s+q1)+1=γv2(2^{s+q}-1)<2(2^{s+q}-1)+1=\gamma_{\textit{v}}. Note that the first node that is targeted in the given sequence is node rr because of the precedence constraints. Since the first node in the given sequence is permanently repaired and all v-nodes are also permanently repaired, the set of all the nodes that permanently fail in the given sequence is a subset of all the e-nodes. The number of e-nodes that are permanently repaired in the given sequence is at least equal to p(p1)2\frac{p(p-1)}{2} because at most qp(p1)2q-\frac{p(p-1)}{2} nodes permanently fail in the given sequence and the total number of e-nodes is equal to qq. Therefore, at least pp v-nodes need to be permanently repaired before at least p(p1)2\frac{p(p-1)}{2} e-nodes can be permanently repaired in the given sequence because of the following two reasons. First, a v-node is an in-neighbor of an e-node if the vertex corresponding to the v-node lies at one of the ends of the edge (in graph GG^{\prime}) corresponding to the e-node. Secondly, among all the undirected graphs that have p(p1)2\frac{p(p-1)}{2} edges, a complete graph (containing pp vertices) has the least number of vertices. Note that all the e-nodes that are permanently repaired in the given sequence start getting targeted before time-step γe=2(2p(p+1)21)+1\gamma_{\textit{e}}=2(2^{\frac{p(p+1)}{2}}-1)+1. Also, the maximum number of nodes (when the iith node takes 2i2^{i} time-steps to get permanently repaired) that can be targeted such that the last node starts getting targeted before time-step 2(2p(p+1)21)+12(2^{\frac{p(p+1)}{2}}-1)+1 is equal to p(p+1)2+1\frac{p(p+1)}{2}+1. Thus, node rr, pp v-nodes and p(p1)2\frac{p(p-1)}{2} e-nodes are targeted before time-step 2(2p(p+1)21)+12(2^{\frac{p(p+1)}{2}}-1)+1 in the given sequence such that the vertices corresponding to the pp v-nodes form a complete subgraph of size pp in graph GG^{\prime}.

Since non-jumping sequences are optimal when the precedence constraints are given by a DAG, ΔdecjΔincj,j{1,,N}\Delta_{dec}^{j}\geq\Delta_{inc}^{j},\forall j\in\{1,\ldots,N\} and T{}T\in\mathbb{N}\cup\{\infty\} by Theorem 1, we conclude that Problem 1 is NP-hard. ∎

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[Uncaptioned image] Hemant Gehlot is a PhD candidate in the Lyles School of Civil Engineering at Purdue University, being supervised by Dr. Satish V. Ukkusuri and Dr. Shreyas Sundaram. He received his BTech and MTech degrees from the Indian Institute of Technology Kanpur in 2015. He was a finalist for the Best Student Paper Award at the IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys) 2019. His research interests include optimal control and combinatorial optimization.
[Uncaptioned image] Shreyas Sundaram is an Associate Professor in the School of Electrical and Computer Engineering at Purdue University. He received his MS and PhD degrees in Electrical Engineering from the University of Illinois at Urbana-Champaign in 2005 and 2009, respectively. He was a Postdoctoral Researcher at the University of Pennsylvania from 2009 to 2010, and an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo from 2010 to 2014. He is a recipient of the NSF CAREER award, and an Air Force Research Lab Summer Faculty Fellowship. At Purdue, he received the Hesselberth Award for Teaching Excellence and the Ruth and Joel Spira Outstanding Teacher Award. At Waterloo, he received the Department of Electrical and Computer Engineering Research Award and the Faculty of Engineering Distinguished Performance Award. He received the M. E. Van Valkenburg Graduate Research Award and the Robert T. Chien Memorial Award from the University of Illinois, and he was a finalist for the Best Student Paper Award at the 2007 and 2008 American Control Conferences. His research interests include network science, analysis of large-scale dynamical systems, fault-tolerant and secure control, linear system and estimation theory, game theory, and the application of algebraic graph theory to system analysis.
[Uncaptioned image] Satish V. Ukkusuri is a Professor in the Lyles School of Civil Engineering and Director of the Urban Mobility Networks and Intelligence Lab at Purdue University. His research is in the area of interdisciplinary transportation networks with current interests in data driven mobility solutions, disaster management, resilience of interdependent networks, connected and autonomous traffic systems, dynamic traffic networks and smart logistics. He has published more than 350 peer reviewed journal and conference articles on these topics.