Project Planning and Control (PPC) problems with stochastic job processing times belong tothe 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 assemblyof complex products. PPC approaches have to compensate stochastic influences and achieve highobjective fulfillment. This paper presents an efficient simulation-based optimization approach togenerate Combined Priority Rules (CPRs) for determining the next job in short-term productioncontrol. The objective is to minimize project-specific objectives such as average and standard deviationof project delay or makespan. For this, we generate project-specific CPRs and evaluate the resultswith the Pareto dominance concept. However, generating CPRs considering stochastic influences iscomputationally intensive. To tackle this problem, we developed a 2-phase algorithm by first learningthe algorithm with deterministic data and by generating promising starting solutions for the morecomputationally intensive stochastic phase. Since a good deterministic solution does not always leadto a good stochastic solution, we introduced the parameter Initial Copy Rate (ICR) to generate aninitial population of copied and randomized individuals. Evaluating this approach, we conductedvarious 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 computationeffort and increases the efficiency of generating CPRs.
|Algorithms / Molecular Diversity Preservation International (MDPI)
|Veröffentlicht - 2020
- simulation-based optimization, stochastic project scheduling, genetic algorithm, discrete event simulation, composite priority rules