Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource-constrained project scheduling problem

Sebt, Mohammad Hassan; Afshar, Mohammad Reza; Alipouri, Yagub

doi:10.1080/0305215x.2016.1197610

Cited by 43 publications

(19 citation statements)

References 39 publications

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“…For comparing our MVNSH, following meta-heuristic strategies were considered here: simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), discrete particle swarm optimization (DPSO), differential evolutionary algorithm (DEA), estimation of distribution algorithm (EOD), ant colony optimization (ACO), reinforcement learning (RL), hybridization of genetic algorithm, and fully informed particle swarm (HGFA) from different authors. Most recent comparison on MRCPSP instances are available from the work of Sebt, Afshar and Alipouri (2017). Table 3 summarizes the results for those relevant meta-heuristic strategies that were obtained after solving at least 5000 schedules.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

A Modified Variable Neighbourhood Search Heuristic for the Multi‐Mode Resource Constrained Project Scheduling Problem with A Case Study

Chakrabortt

Abbasi

Ryan

2019

INCOSE International Symp

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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.

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Section: Comparison With Existing Methodsmentioning

confidence: 99%

A Modified Variable Neighbourhood Search Heuristic for the Multi‐Mode Resource Constrained Project Scheduling Problem with A Case Study

Chakrabortt

Abbasi

Ryan

2019

INCOSE International Symp

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“…Most recent comparison on MRCPSP instances is available from the work of Sebt et al. (). Table summarizes the results for those relevant meta‐heuristic strategies that were obtained after solving at least 5000 schedules.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…(), Schnell and Hartl (), Sebt et al. (5, ), Soliman and Elgendi (), Wauters et al. (), and Zamani ().…”

Section: Introductionunclassified

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

Chakrabortty

Abbasi

Ryan

2019

Int Trans Operational Res

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This 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.

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“…As an another effective hybrid, a hybridization of genetic algorithms and fully informed particle swarm optimization can be mentioned which has been discussed in [36]. In this procedure, a random key has been used as the representation scheme and the serial generation scheme has been used as a decoding mechanism.…”

Section: Hybridsmentioning

confidence: 99%

An innovative four-layer heuristic for scheduling multi-mode projects under multiple resource constrains

Zamani

2019

RAIRO-Oper. Res.

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In this paper, an innovative four-layer heuristic is presented for scheduling multi-mode projects under multiple resource constraints. For this purpose, a biased-random sampling technique, a local search, a decomposition method, and an evolutionary search mechanism, each in a separate layer, are combined, with each layer passing its output to the next layer for improvement. The procedure has been designed based on the fact that what makes the scheduling of multi-mode projects hard to solve is a massive search space of modes compounded with the starting times of activities. That is why the procedure is aimed at balancing exploration versus exploitation in searching a massive search space. On the one hand, it exploits promising areas further and, on the other hand, it searches unexplored areas for expanding its range. Since the first layer provides an initial solution, and each of the other three layers can either improve the result of its previous layer or keep it unchanged, solutions never deteriorate and hence promising areas are exploited. Moreover, unexplored areas are searched effectively because each layer explores solution space differently than its previous layer. Based on whether or not an improvement each layer can make to the result of its previous layer, the effect of the corresponding layer on the performance of the procedure has been measured.Mathematics Subject Classification. 90B50, 90B35, 90B40, 90.08, 68R05.

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Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource-constrained project scheduling problem

Cited by 43 publications

References 39 publications

A Modified Variable Neighbourhood Search Heuristic for the Multi‐Mode Resource Constrained Project Scheduling Problem with A Case Study

A Modified Variable Neighbourhood Search Heuristic for the Multi‐Mode Resource Constrained Project Scheduling Problem with A Case Study

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

An innovative four-layer heuristic for scheduling multi-mode projects under multiple resource constrains

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