Hybrid Differential Evolution and Greedy Algorithm (DEGR) for solving Multi-Skill Resource-Constrained Project Scheduling Problem

Myszkowski, Paweł B.; Olech, Łukasz P.; Laszczyk, Maciej; Skowroński, M.

doi:10.1016/j.asoc.2017.10.014

Cited by 76 publications

(37 citation statements)

References 34 publications

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“…In that case, both objectives would be equally important in the optimization process. We proposed three baseline weight configurations: duration optimization (DO, ω τ = 1) [7], [6], balanced optimization (BO, ω τ = 0.5) and cost optimization (CO, ω τ = 0) [8]. As CO is rather a trivial task that can be solved by greedy-based approach, BO can be analyzed as cost/duration middle ground.…”

Section: A Ms-rcpsp Definitionmentioning

confidence: 99%

“…More formal definition of MS-RCPSP as optimization problem has been presented in II-A -and is based on work [6] [9]. The MS-RCPSP has been defined in cooperation with international corporation (Volvo Group IT).…”

Section: Formulation Of Ms-rcpspmentioning

confidence: 99%

“…The MS-RCPSP has been defined in cooperation with international corporation (Volvo Group IT). Next section describes MS-RCPSP more formally -is based on [8][9] [6].…”

Section: Formulation Of Ms-rcpspmentioning

confidence: 99%

“…Thus, several types of (meta)heuristic methods can be effectively applied. There can be found applications of (MS-)RCPSP in literature based on: heuristics [3] [12], tabu search [11], evolutionary algorithms (EA) with specialized operators [10], (hybridized) ant colony optimization [9], teaching-learningbased optimization algorithm [13], differential evolution [14], hybridized differential evolution [6] and many others.…”

Section: Introductionmentioning

confidence: 99%

“…E.g. in hybridized differential evolution [6] metaheuristic DE operates on search space that prioritizes resource and task sequencing is solved by greedy-based method. Such approach is effective, but greedy usage may cause that method to get stuck in local optima.…”

Section: Introductionmentioning

confidence: 99%

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Co-Evolutionary Algorithm solving Multi-Skill Resource-Constrained Project Scheduling Problem

Myszkowski¹,

Laszczyk²,

Kalinowski³

2017

Annals of Computer Science and Information Systems

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Abstract-This paper presents methods solving MS-RCPSP as a main task-resource-time assignment optimization problem. In the paper there are presented four variants of Evolutionary Algorithm applied to MS-RCPSP problem: concerning prioritization the tasks (or resources), combined task-resources prioritizing approach and co-evolution based approach that effectively solves problem dividing it to two subproblems. All approaches are examined using benchmark MS-RCPSP iMOPSE dataset and results show that the problem decomposition is effective. All experiments are described, statistically verified and summarized. Conclusions and promising areas of future work are presented.

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Section: A Ms-rcpsp Definitionmentioning

confidence: 99%

Section: Formulation Of Ms-rcpspmentioning

confidence: 99%

“…The MS-RCPSP has been defined in cooperation with international corporation (Volvo Group IT). Next section describes MS-RCPSP more formally -is based on [8][9] [6].…”

Section: Formulation Of Ms-rcpspmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Co-Evolutionary Algorithm solving Multi-Skill Resource-Constrained Project Scheduling Problem

Myszkowski¹,

Laszczyk²,

Kalinowski³

2017

Annals of Computer Science and Information Systems

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hMod: A software framework for assembling highly detailed heuristics algorithms

et al. 2019

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Summary Software design and component reuse for heuristic algorithms have gained in relevance; however, further innovation is needed. In this context, hMod is presented as a software framework suited for implementing heuristic algorithms, with a focus on intensive reuse of highly cohesive operator and data components within algorithmic structures, making it possible to dynamically (re)configure and manage such a structure. Rather than a fast‐prototyping tool, hMod supports heuristic implementation in the long term, whereby complexity can escalate from simple operators to major hyperheuristic architectures. In its core resides a novel object‐oriented representation of algorithms through a pattern‐like implementation, namely, algorithm assembling (AA). Additionally, it incorporates component integration features, such as dependency injection mechanisms. hMod has been mentioned in previous research, in which hyperheuristic methods were implemented and evaluated from an optimization perspective. In this work, a description of the framework is presented from the software design perspective, including the AA model, its architecture, and a detailed presentation of the main features of the framework. Previous hMod applications have demonstrated that it supports not only the software design requirements of heuristic algorithms but performance standards as well. Available sources of the framework can be found in http://gitlab.com/eurra/hmod.

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Optimization Models and Solution Techniques

Hartmann

2021

Application of Mathematics and Optimization in Construction Project Management

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Hybrid Differential Evolution and Greedy Algorithm (DEGR) for solving Multi-Skill Resource-Constrained Project Scheduling Problem

Cited by 76 publications

References 34 publications

Co-Evolutionary Algorithm solving Multi-Skill Resource-Constrained Project Scheduling Problem

Co-Evolutionary Algorithm solving Multi-Skill Resource-Constrained Project Scheduling Problem

hMod: A software framework for assembling highly detailed heuristics algorithms

Optimization Models and Solution Techniques

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