Abstract
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.
Highlights
One of the challenges in industrial environments is the handling of a large number of product variants [1] (p. 46)
We carried out preliminary investigations to determine the runtime of an optimization run
We present and study a 2-phase genetic algorithm for the efficient generation of project-specific composite priority rules for short-term production control of the stochastic resource-constrained multi-project scheduling problem
Summary
One of the challenges in industrial environments is the handling of a large number of product variants [1] (p. 46). We take up the challenge of computational time for the generation of CPRs and develop a concept for project-specific objective fulfillment under stochastic influences. We generate project-specific CPRs by combining different individual job attributes with a weighted sum approach. These CPRs are assigned to the project jobs and used for short-term project scheduling. In the context of the paper, we address the following research questions: Is it possible to improve mean value and standard deviation of project individual objectives by applying the generated CPRs?.
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