Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm

• Published in: Applied soft computing Vol. 25; pp. 184 - 203
• Main Authors: Hajiaghaei-Keshteli, M., Aminnayeri, M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2014
• Subjects: Integrated production–distribution, Supply chain optimization; Keshtel algorithm; Rail transportation; Single machine scheduling; Taguchi experimental design; Keshtel algorithm; Rail transportation; Integrated production–distribution, Supply chain optimization; Single machine scheduling; Taguchi experimental design
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2014.09.034

•An integrated production and transportation model, which considers rail transportation, is developed.•A heuristic, two metaheuristics and some related procedures are developed.•Keshtel algorithm is developed for the first time for a mathematical model.•Taguchi experimental design method is utilized to improve their performance.•Performance evaluation, comparison, and analysis are employed for the proposed algorithms. Nowadays, scheduling of production cannot be done in isolation from scheduling of transportation since a coordinated solution to the integrated problem may improve the performance of the whole supply chain. In this paper, because of the widely used of rail transportation in supply chain, we develop the integrated scheduling of production and rail transportation. The problem is to determine both production schedule and rail transportation allocation of orders to optimize customer service at minimum total cost. In addition, we utilize some procedures and heuristics to encode the model in order to address it by two capable metaheuristics: Genetic algorithm (GA), and recently developed one, Keshtel algorithm (KA). Latter is firstly used for a mathematical model in supply chain literature. Besides, Taguchi experimental design method is utilized to set and estimate the proper values of the algorithms’ parameters to improve their performance. For the purpose of performance evaluation of the proposed algorithms, various problem sizes are employed and the computational results of the algorithms are compared with each other. Finally, we investigate the impacts of the rise in the problem size on the performance of our algorithms.