Modified differential evolution and heuristic algorithms for dump tippler machine allocation in a typical sugar mill in Thailand

Abstract

This paper focuses on a computational tool for scheduling and sequencing sugarcane vehicles for dump tippler machines at the mill yard of a sugar mill, which can be represented as an NP-hard problem for parallel capacitated machines with machine restrictions, job grouping, and sequencing independent setup time. This research aims to determine the optimal sequencing of jobs, i.e. minimizing the makespan by considering machine restrictions, capacitated machines, group size, number of jobs, and the constraints of the sugar mill. In the considered problem machines alternatively operate, that distinguishes it from a general parallel machine problem. The mixed integer linear programing model is developed for solving the small-scale problem instances. Large-scale instances are handled by four heuristics, and four differential evolution (DE) metaheuristics. In order to improve the computational results, solution quality and computation time were considered. In addition, modified DE algorithms were used in encoding operation (initial solution), mutation and local search operation. The computational results revealed that the modified DE algorithms had higher relative improvement on the makespan. Furthermore, this decision-making support tool was implemented as a prototype in the sector of cane and sugar industry in Thailand and extended to other similar industries.