Quantifying hazard resilience by modeling infrastructure recovery as a resource-constrained project scheduling problem

The electric power system is one of the most important critical infrastructures of a country. Recently, the number of natural and man‐made disasters is increased, which can impose extensive damages and costs to the power system. A resilient power system can withstand against, adapt to and recover from these disasters. Power system resilience is quantified by mathematical tools which are called “resilience metrics”. Currently, a lot of resilience metrics are proposed in the power system literature. In this paper, based on the extensive research in the critical infrastructure resilience literature which specifically concentrates on the “area‐based” resilience metrics, a new area‐based resilience metric is proposed which can measure the power system resilience considering the government policymaker criteria, which are rarely noticed before. The proposed and conventional area‐based resilience metrics are evaluated based on the real data from the 2012 Superstorm Sandy in the USA, which led to significant damage to the power distribution system. The simulation results show that the proposed area‐based resilience metric is very simple, can successfully address actual power system performance curves and is more meaningful and tangible than the conventional area‐based metrics for the government policymaker. The proposed area‐based resilience metric has also a general form and can be used for other critical infrastructures.

This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known to be the most important constraints for project scheduling. In this context, the standard RCPSP is presented. Then, the classifications of the collected papers according to the year of publication and the different meta-heuristic approaches applied are presented. Five weighted articles and their meta-heuristic techniques developed for RCPSP are described in detail and their results are summarized in the corresponding tables. In addition, researchers have developed various conventional meta-heuristic algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, simulated annealing, evolutionary algorithms, and so on. It is stated that genetic algorithms are more popular among researchers than other meta-heuristics. For this reason, the various conventional meta-heuristics and their corresponding articles are also presented to give an overview of the conventional meta-heuristic optimizing techniques. Finally, the challenges of the conventional meta-heuristics are explored, which may be helpful for future studies to apply new suitable techniques to solve the Resource-Constrained Project Scheduling Problem (RCPSP).

To enrich the calculation means of resource-constrained project scheduling problem(RCPSP), a particle swarm optimization (PSO) algorithm is presented. A new permutation of priority-based encoding scheme is designed in the algorithm, and it inherits all the merits of both the permutation-based encoding scheme and the priority-based encoding scheme. The velocity-updating mechanism is formulized based on the vector similarity theory according to the characteristics of RCPSP, and in the position-updating mechanism the particles can move according to the so called vector velocity and dimension velocity. A full factorial computational experiment was set up using the well-known standard instances in PSPLIB, and the results reveal that the algorithm is effective for the RCPSP.

As federal agencies and businesses rely more on cyber infrastructure, they are increasingly vulnerable to cyber attacks that can cause damages disproportionate to the sophistication and cost to launch the attack. In response, regulatory authorities call for focusing attention on enhancing infrastructure resilience. For example, in the USA, President Obama issued an Executive Order and policy directives focusing on improving the resilience and security of cyber infrastructure to a wide range of cyber threats. Despite the national and international importance, resilience metrics to inform management decisions are still in the early stages of development. We apply the resilience matrix framework developed by Linkov et al. (Environ Sci Technol 47:10108–10110, 2013) to develop and organize effective resilience metrics for cyber systems. These metrics link national policy goals to specific system measures, such that resource allocation decisions can be translated into actionable interventions and investments. In this paper, a number of metrics have been identified and assessed using quantitative and qualitative measures found in the literature. We have proposed a generic approach and could integrate actual data, technical judgment, and literature-based measures to assess system resilience across physical, information, cognitive, and social domains.

Structural health monitoring (SHM) technologies are useful for rapid structural condition assessment, especially for hidden locations after major hazardous events. This chapter describes a strategy for enhancing structural resilience by integrating innovative SHM technologies focused on a few structural fuse members within resilient structures. The proposed framework would be helpful in implementing SHM technologies into civil infrastructures by quantifying and comparing the resilience metrics of different SHM options. The chapter presents preliminary research into integrating SHM technologies with resilient civil infrastructure design and establishing a resilience metric for rating candidate SHM technologies. An explicit SHM resilience metric is proposed, which could be used to quantify the benefits of installing replaceable fuse devices and associated monitoring technology. To address resilience issues, such as uninterrupted use even after strong earthquakes, self-centering behavior has been incorporated into self-centering structures through post-tensioning.

The Resource-Constrained Project Scheduling Problem (RCPSP) is a general problem class of scheduling problems. It even contains the well-known job shop scheduling problem as a special case. In these problems, jobs demand different amounts from multiple resources at once. The goal is to minimize completion time while satisfying resource capacity constraints throughout the schedule. The RCPSP has several variations and generalizations, one of which is the multi-mode problem. Multiple operation modes are given for jobs, differing in resource usages and execution times. Another generalization is where resources have time-varying capacities instead of a constant limit. The S-graph framework was developed for batch process scheduling and used successfully in various applications. The advantages of this approach motivate research to extend its capabilities to more general problem classes and apply it in various specialized case studies. The authors have previously presented an extension of the S-graph framework for RCPSP and its multi-mode generalization. In this work, a further extension is proposed for handling time-varying capacities. The method relies on a model transformation technique and recent algorithmic developments of the framework.

Resource-Constrained Project Scheduling Problem (RCPSP) is a well-known NP hard problem and more intelligent optimization algorithms are developed to solve it. In this paper, genetic algorithm(GA) is employed to deal with RCPSP. A priority value encoding scheme is designed to in the algorithm. The numerical results indicate that our methods is slightly better as far as solution quality is concerned and requires smaller solution time than the GA where an activity list encoding with schedule mode is used.

In this paper, a permutation chromosome encoding scheme is proposed for obtaining solution to resource constrained project scheduling problem. The proposed chromosome coding method is applied to Genetic algorithm procedure and implemented through object oriented programming. The method is applied to a numerical example with two different stopping criteria. The results obtained are encouraging as they are in agreement with published solutions in literature. The method lends itself easily to programming and can be used to obtain results to large scale problems encountered in practice.http://dx.doi.org/10.4314/njt.v34i1.16

Solving the Resource Constrained Project Scheduling Problem using the parallel Tabu Search designed for the CUDA platform

IP-Based Energetic Reasoning for the Resource Constrained Project Scheduling Problem

This paper provides a fast near‐optimum solution to the Multi‐Mode Resource Constrained Project Scheduling Problem (MRCPSP), for projects with activities that have known deterministic renewable and non‐renewable resource requirements. Multi‐modes allows any activity to execute one of many alternative combinations of resource usages and durations, while each activity needs to pick the most feasible one. This type of setting is more prevalent for real‐life project schedules, which necessitates systematic methodologies and advanced solution approaches in harmony with real‐world implementations. This study proposes a Modified Variable Neighbourhood Search Heuristic (MVNSH) algorithm as an advanced optimization technique for scheduling projects. To validate our approach, exhaustive computational tests were carried out by using relevant benchmark instances. The MVNSH algorithm has been shown to be faster and more accurate than many existing heuristic methods. This research also contributes to developing a practical decision support system for resolving real‐life constraints in multi‐mode resource‐constrained projects.

In this paper we address the Preemptive Resource Constrained Project Scheduling Problem (PRCPSP). PRCPSP requires a partially ordered set of activities to be scheduled using limited renewable resources such that any activity can be interrupted and later resumed without penalty. The objective is to minimize the project duration. This paper proposes an effective branch-and-price algorithm for solving PRCPSP based upon minimal Interval Order Enumeration involving column generation as well as constraint propagation. Experiments conducted on various types of instances have given very satisfactory results. Our algorithm is able to solve to optimality the entire set of J30, BL and Pack instances while satisfying the preemptive requirement. Furthermore, this algorithm provides improved best-known lower bounds for some of the J60, J90 and J120 instances in the non-preemptive case (RCPSP).

In this paper, a renowned metaheuristic algorithm named chemical reaction optimization (CRO) is applied to solve the resource constrained project scheduling problem (RCPSP). This work employed chemical reaction optimization to schedule project tasks to minimize makespan concerning resource and precedence constraints. Chemical reaction optimization is a population-based metaheuristic algorithm. CRO is applied to RCPSP by redesigning its basic operators and taking solutions from the search space using priority-based selection to achieve a better result. The proposed algorithm based on CRO is then tested on large benchmark instances and compared with other metaheuristic algorithms. The experimental results have shown that our proposed method provides better results than other states of art algorithms in terms of both the qualities of result and execution time.

Resource constrained project scheduling problems (RCPSPs) are complex optimization problems that aim to minimize project completion time after considering limited resources and precedence-related activities with known durations. However, due to the dynamic nature of real-world applications, activity durations are vulnerable to change. In addition, resource demands along with resource availability at any stage of a project can also vary due to disruptions, which compel practitioners to re-think existing scheduling methodologies. Consequently, to mitigate the conjoint effect of those uncertainties and/or disruptions, this paper proposes a real-time reactive scheduling approach. To deal with uncertain activity durations, a chance constrained based approach is followed, which is later solved by an advanced meta-heuristic based approaches called IGFBIS and IGFBID. After solving an exhaustive list of stochastic RCPSP instances, this paper proves the efficacy of the proposed approaches, which is proven to be more useful to mitigate uncertainty or disruption while executing real-life projects.

The resource constrained project scheduling problem is considered as a complex scheduling problem. In order to solve this NP-hard problem, an efficient differential evolution DE algorithm is proposed in this paper. In the algorithm, improved mutation and crossover operators are introduced with an aim to maintain feasibility for generated individuals and hence being able to converge quickly to the optimal solutions. The algorithm is tested on a set of well-known project scheduling problem library PSPLIB, with instances of 30, 60, 90 and 120 activities. The proposed DE is shown to have superior performance in terms of lower average deviations from the optimal solutions compared to some of the state-of-the-art algorithms.