A bi-objective truck scheduling problem in a cross-docking center with probability of breakdown for trucks. (June 2016)

• Record Type: Journal Article
• Authors: Amini, Alireza; Tavakkoli-Moghaddam, Reza
• Is Part Of: Computers & industrial engineering. Volume 96(2016)
• Page Start: 180
• Page End: 191
• Publication Date: 2016-06
• Subjects: Cross docking -- Truck scheduling -- Multi-objective optimization -- Breakdown -- Response surface methodology -- Meta-heuristics; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.03.023
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We consider possibility of breakdown for trucks in cross-docking truck scheduling. We assume that trucks may be broken based on a Poisson distribution function. We mathematically model the problem. We employ a complete enumeration method to obtain optimum results. We employ multi-objective meta-heuristics to solve large-scale problems. Abstract: This paper addresses a truck scheduling problem in a cross-docking center, in which trucks may confront breakdowns during their service times. In fact, the number of breakdowns in one unit of time for each truck follows a Poisson distribution function. On the other hand, customers are promised to receive required items in a pre-determined time; so, a due date is assigned to each outbound truck. Thus, a bi-objective linear mathematical model is developed inspired by models in the body of the respective literature. A complete enumeration method is employed to find optimum solutions subject to the complexity of large-scale problems, and we modify three multi-objective meta-heuristics; namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Simulated Annealing (MOSA) and Multi-Objective Differential Evolutionary (MODE). In addition, a Response Surface Methodology (RSM) as a statistical tool is used to find an appropriate amount of factors associated with the forgoing meta-heuristics. Finally, the performances of the proposed meta-heuristics are measured and compared with each other.