Project Scheduling Library Research Articles

Reactive Procedure for Robust Project Scheduling under the Activity Disruptions

The baseline schedule may become infeasible when disruptions happen during the execution of project. This will lead to project delays and financial losses. In this paper, we developed a robust reactive optimization model. Our objective is to maximize the robustness of reactive schedule. Moreover, a robust reactive procedure is proposed for baseline schedule under a series of disruptions caused by activity duration. To test the proposed approach, sets of J60 and J90 instances from the Project Scheduling Library (PSPLIB) were used. Meanwhile, we tested the reactive project scheduling with the aim of minimizing makespan and compared the maximal robustness reactive schedule and the minimal makespan reactive schedule. We developed a reactive procedure involving genetic algorithm for reactive scheduling due to its non-deterministic polynomial hard (NP-hard). The results indicated that robust reactive scheduling has the advantage in maintaining the schedule’s ability of tolerating variabilities and reducing the impact of activity disruptions on the makespan. The research results can help project managers develop a robust schedule which can tolerate variabilities in a random environment.

Scheduling project with stochastic durations and time-varying resource requests: A metaheuristic approach

This paper studies a resource constrained project scheduling problem (RCPSP) under dynamic environments. Along with stochastic activity-durations, resource requests and resource availabilities are also prone to vary with time (i.e., that is, dynamic). To account for stochastic activity durations and the resource dynamic, a chance constrained based mathematical model is proposed. To solve the developed model, an efficient genetic algorithm (GA) based memetic algorithm (MA) is proposed. The proposed algorithm has shown excellent performance for solving 2880 30-activity and 3600 120-activity instances for RCPSP with predefined but time-varying resource requests and availabilities (i.e., RCPSP/t setting). The proposed algorithm has also demonstrated an excellent performance for the extended RCPSP/t setting with stochastic activity durations (i.e., RCPSP/td). Due to the unavailability of any real benchmark instances for this extended RCPSP, this work proposes a pragmatic approach to generate simulated benchmark instances. Numerous time-dependent benchmark instances from the project scheduling library (PSPLIB) are solved to validate the proposed MA. Rigorous experimental analysis with a standard set of 2448 newly generated problem instances for 30, 60, 90, and 120 activities indicates that the proposed meta-heuristic can solve large projects with a reasonable computational burden.

Financial Optimization of the Resource-Constrained Project Scheduling Problem with Milestones Payments

This article presents the resource-constrained project scheduling problem with the discounted cash flow maximization criterion from the perspective of a contractor. Cash flows are considered as the contractor’s expenses related to the execution of activities and client’s payments (revenue to the contractor) after the completion of contractual stages. To solve the problem, dedicated techniques to generate solutions and a simulated annealing algorithm are proposed. Finally, the proposed procedures are examined using the test library, Project Scheduling Library (PSPLIB). An experimental analysis identified the efficient moves and techniques for creating solutions, that is backward scheduling with optimization of completion times of project stages and triple justification.

A reinforcement learning based multi-method approach for stochastic resource constrained project scheduling problems

The Resource-Constrained Project Scheduling Problem (RCPSP) has been widely accepted as a challenging research topic due to its NP-hard nature. Because of the dynamic nature of real-world problems, stochastic-RCPSPs (SRCPSPs) are also receiving greater attention among researchers. To solve these extended RCPSPs (i.e., SRCPSPs), this paper proposes an reinforcement learning based meta-heuristic switching approach that utilizes the powers of both multi-operator differential evolution (MODE) and discrete cuckoo search (DCS) algorithms in single algorithmic framework. Reinforcement learning (RL) is introduced as a technique to select either MODE or DCS based on the diversity of population and quality of solutions. To deal with uncertain durations, a chance-constrained based approach with some belief degrees is also considered and solved by this proposed RL based multi-method approach (i.e., DECSwRL-CC). Extensive experimentation with benchmark data from the project scheduling library (PSPLIB) demonstrates the efficacy of this proposed multi-method approach. Numerous state of the art chance constrained approaches are taken from the literature to compare the proposed approach and to validate the efficacy of this multi-method approach. This particular strategy is applicable to the risk-averse decision-makers who want to realize the project schedule with a high degree of certainty.

An event-based reactive scheduling approach for the Resource Constrained Project Scheduling Problem with unreliable resources

The Resource Constrained Project Scheduling Problem (RCPSP) is a combinatorial optimization problem which is non-deterministic polynomial-time (NP)-hard in nature. Due to the diversified applications of RCPSPs, they have been commonly used as scheduling procedures in real-world problems. Since, in practice, project data are prone to changes or disruptions, this paper introduces a mathematical model for a reactive scheduling approach, called the Event Based Reactive Approach (EBRA). This proposed EBRA approach is employed to examine its recovery performance under both a single disruption and a series of independent resource disruptions. Several simulated disruption data are hypothesized to represent real-world disruption scenarios and, without loss of generality, the proposed reactive approach is proved to be efficient in reducing the number of variables and computational complexity and also to be resilient in realistic changes, such as duration inflation and dynamic resource usages. Along with employing an exact method by LINGO software, this paper also proposes an enhanced iterated greedy (EnIG) approach to meta-heuristically solve larger and computationally expensive benchmark instances taken from the Project Scheduling Library (PSPLIB).

A two-stage multi-operator differential evolution algorithm for solving Resource Constrained Project Scheduling problems

The Resource Constrained Project Scheduling problem (RCPSP) is a complex and combinatorial optimization problem mostly relates with project management, construction industries, production planning and manufacturing domains. Although several solution methods have been proposed, no single method has been shown to be the best. Further, optimal solution of this type of problem requires different requirements of the exploration and exploitation at different stages of the optimization process. Considering these requirements, in this paper, a two-stage multi-operator differential evolution (DE) algorithm, called TS-MODE, has been developed to solve RCPSP. TS-MODE starts with the exploration stage, and based on the diversity of population and the quality of solutions, this approach dynamically place more importance on the most-suitable DE, and then repeats the same process during the exploitation phase. A complete evaluation of the components and parameters of the algorithms by a Design of Experiments technique is also presented. A number of single-mode RCPSP data sets from the project scheduling library (PSPLIB) have been considered to test the effectiveness and performance of the proposed TS-MODE against selected recent well-known state-of-the-art algorithms. Those results reveal the efficiency and competitiveness of the proposed TS-MODE approach.

Evolving rollout-justification based heuristics for resource constrained project scheduling problems

Resource constrained project scheduling is critical in logistic and planning operations across a range of industries. An interesting heuristic for solving this problem is the Rollout-Justification (RJ) procedure. This procedure, which has conceptual similarities with dynamic programming, incrementally builds a solution by identifying the next activity to schedule based on the projections made using a guiding priority rule (heuristic) coupled with forward-backward local search. A critical component that affects the performance of RJ procedure is the guiding priority rule (or a set of rules). In this study, instead of using existing rules from literature, we aim to evolve new priority rules using genetic programming, and systematically investigate their use with the RJ procedure. Apart from evolving new rules, we also investigate new ways of integrating/utilizing the rules within RJ procedure. To this end we consider the use of both forward and backward scheduling, independent and cohesive ensemble rule approaches, limited and unlimited number of function evaluations, among others. We use data from the project scheduling library (PSPLib) to train and test the evolved rules and their integration with RJ. A comprehensive set of numerical experiments are performed to benchmark the rules evolved using the proposed approach against a range of existing rules. The results demonstrate the competence and potential of the proposed approach, both in terms of accuracy and complexity.

Multi‐mode resource‐constrained project scheduling using modified variable neighborhood search heuristic

AbstractThis paper aims at providing a fast near‐optimum solution to the multi‐mode resource‐constrained project scheduling problems (MRCPSPs), for projects with activities that have known deterministic renewable and nonrenewable resource requirements. The MRCPSP is known to be nondeterministic polynomial‐time hard and has been solved using various exact, heuristic, and meta‐heuristic procedures. In this paper, a modified variable neighborhood search heuristic algorithm is used as an advanced optimization technique that suits scheduling problems. For the experimental study, we have considered a standard set of 3929 multi‐mode benchmark instances from the project scheduling library with a range of projects comprising 10–30 activities. Moreover, for a better comparison, this research also considers a standard set of 4320 newly developed multi‐mode instances from MMLIB50, MMLIB100, and MMLIB+ datasets. With the limit of 50,000 schedules on these datasets, our proposed algorithm provides better makespan for 106, 34, and 1601 instances, respectively, which justifies the efficiency of the proposed algorithm, particularly for projects with a larger number of activities. The results reported in this paper can be used as a benchmark for other researchers to compare and improve.

Single mode resource constrained project scheduling with unreliable resources

Over the last few decades, resource constrained project scheduling has been widely studied. In real life, resources may fail or be interrupted due to various reasons. They then require reactive rescheduling to minimize the effect of such disruptions. During the course of a project, a single or a series of independent disruptions may take place where the disruption information is not known a priori. In this paper, we have formulated two discrete time based models to deal with two types of disruption scenarios. We have also proposed a solution approach that can deal with a single, as well as a series of independent disruptions, in a reactive rescheduling manner. To judge the performance of the proposed approach, a number of test instances from the Project Scheduling Library were combined with randomly generated disruption events. The computational experiments were also conducted to analyse the effects of different factors related to the disruption recovery process.

Multi-mode resource constrained project scheduling under resource disruptions

Over the last few decades, research on resource constrained project scheduling has focused on the development of mathematical programming based approaches for the generation of a nominal schedule under a deterministic environment. During the implementation phase, however, the nominal schedule may need to be revised when one or more resources are disrupted for a length of time. In this paper, we formulate two discrete time based models to deal with two different disruption scenarios for multi-mode resource constrained problems. We propose a reactive re-scheduling procedure for a single, as well as a series of disruptions, without having any disruption information in advance. To test the proposed approaches, sets of ten, twenty and thirty-activity multi-mode test instances from Project Scheduling Library (PSLIB) were used after introducing randomly generated disruption events. The experimental studies were also carried out to determine the effect of different factors related to the disruption recovery process.

On the generation of circuits and minimal forbidden sets

We present several complexity results related to generation and counting of all circuits of an independence system. Our motivation to study these problems is their relevance in the solution of resource constrained scheduling problems, where an independence system arises as the subsets of jobs that may be scheduled simultaneously. We are interested in the circuits of this system, the so-called minimal forbidden sets, which are minimal subsets of jobs that must not be scheduled simultaneously. As a consequence of the complexity results for general independence systems, we obtain several complexity results in the context of resource constrained scheduling. On that account, we propose and analyze a simple backtracking algorithm that generates all minimal forbidden sets for such problems. The performance of this algorithm, in comparison to a previously suggested divide-and-conquer approach, is evaluated empirically using instances from the project scheduling library PSPLIB.

Ant colony optimization for resource-constrained project scheduling

An ant colony optimization (ACO) approach for the resource-constrained project scheduling problem (RCPSP) is presented. Several new features that are interesting for ACO in general are proposed and evaluated. In particular, the use of a combination of two pheromone evaluation methods by the ants to find new solutions, a change of the influence of the heuristic on the decisions of the ants during the run of the algorithm, and the option that an elitist ant forgets the best-found solution are studied. We tested the ACO algorithm on a set of large benchmark problems from the Project Scheduling Library. Compared to several other heuristics for the RCPSP, including genetic algorithms, simulated annealing, tabu search, and different sampling methods, our algorithm performed best on average. For nearly one-third of all benchmark problems, which were not known to be solved optimally before, the algorithm was able to find new best solutions.