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Open AccessArticle

An Algorithm for Efficient Generation of Customized Priority Rules for Production Control in Project Manufacturing with Stochastic Job Processing Times

Institute of Material Handling and Industrial Engineering, Technische Universität Dresden, 01062 Dresden, Germany
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Received: 15 November 2020 / Revised: 10 December 2020 / Accepted: 11 December 2020 / Published: 13 December 2020
(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)
Project Planning and Control (PPC) problems with stochastic job processing times belong to the problem class of Stochastic Resource-Constrained Multi-Project Scheduling Problems (SRCMPSP). A practical example of this problem class is the industrial domain of customer-specific assembly of complex products. PPC approaches have to compensate stochastic influences and achieve high objective fulfillment. This paper presents an efficient simulation-based optimization approach to generate Combined Priority Rules (CPRs) for determining the next job in short-term production control. The objective is to minimize project-specific objectives such as average and standard deviation of project delay or makespan. For this, we generate project-specific CPRs and evaluate the results with the Pareto dominance concept. However, generating CPRs considering stochastic influences is computationally intensive. To tackle this problem, we developed a 2-phase algorithm by first learning the algorithm with deterministic data and by generating promising starting solutions for the more computationally intensive stochastic phase. Since a good deterministic solution does not always lead to a good stochastic solution, we introduced the parameter Initial Copy Rate (ICR) to generate an initial population of copied and randomized individuals. Evaluating this approach, we conducted various computer-based experiments. Compared to Standard Priority Rules (SPRs) used in practice, the approach shows a higher objective fulfilment. The 2-phase algorithm can reduce the computation effort and increases the efficiency of generating CPRs. View Full-Text
Keywords: simulation-based optimization; stochastic project scheduling; genetic algorithm; discrete event simulation; composite priority rules simulation-based optimization; stochastic project scheduling; genetic algorithm; discrete event simulation; composite priority rules
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MDPI and ACS Style

Kühn, M.; Völker, M.; Schmidt, T. An Algorithm for Efficient Generation of Customized Priority Rules for Production Control in Project Manufacturing with Stochastic Job Processing Times. Algorithms 2020, 13, 337. https://0-doi-org.brum.beds.ac.uk/10.3390/a13120337

AMA Style

Kühn M, Völker M, Schmidt T. An Algorithm for Efficient Generation of Customized Priority Rules for Production Control in Project Manufacturing with Stochastic Job Processing Times. Algorithms. 2020; 13(12):337. https://0-doi-org.brum.beds.ac.uk/10.3390/a13120337

Chicago/Turabian Style

Kühn, Mathias; Völker, Michael; Schmidt, Thorsten. 2020. "An Algorithm for Efficient Generation of Customized Priority Rules for Production Control in Project Manufacturing with Stochastic Job Processing Times" Algorithms 13, no. 12: 337. https://0-doi-org.brum.beds.ac.uk/10.3390/a13120337

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