Rule-based meta-heuristics for integrated scheduling of unrelated parallel machines, batches, and heterogeneous delivery trucks

Display Omitted We study an integrated scheduling for unrelated parallel machines, batches, and trucks.We derive a mixed integer programming model (MIP) for the problem.We propose novel meta-heuristics using a single-stage genetic algorithm (GA) framework.The meta-heuristics include machine-assignment rules, batching rules, and truck-assignment rules.We evaluate the performances of the rule-based meta-heuristics using randomly generated examples. In this article, we deal with an integrated scheduling for unrelated parallel machines, batches, and heterogeneous delivery trucks. In a manufacturing plant, jobs ordered by customers are manufactured by one of several unrelated parallel machines. Then, they are grouped and delivered to the respective customers by heterogeneous trucks with different capacities and travel times. The objective of the problem is to simultaneously determine the machine schedule, batching, and truck-delivery schedule to minimize the make span of the entire process. To solve this problem, we derive a mathematical model to obtain the optimal solution, and we propose rule-based meta-heuristics using single-stage GA framework. Through randomly generated instance examples, the performances of the proposed meta-heuristics are compared.