Title: Application of hybrid GA-SA heuristics for single-job production-delivery scheduling problem with inventory and due date considerations
Authors: Ke-Jun Zhu; De-Yun Wang
School of Economics and Management, China University of Geosciences, Lumo Road, Wuhan 430074, China
Laboratoire Systèmes et Transports, Université de Technologie de Belfort-Montbéliard, Thierry Mieg Road, Belfort 90010, France
Abstract: This paper studies a production scheduling problem with delivery considerations in which a set of identical jobs are batch processed on a machine and then, finished jobs need to be delivered to a customer by a capacitated vehicle. Particularly, we assume the existence in production stage of an inventory which works as a buffer to balance the abilities of the two logistical stages. The objective is to find a joint schedule such that the sum of setup, production, delivery and inventory cost is minimised. We formulate the problem as a mixed integer programming model and propose four heuristic algorithms, such as genetic algorithm (GA), simulated annealing (SA), hybrid GA-SA (HGASA) and hybrid SA-GA (HSAGA), for solving it. To evaluate the proposed heuristics, we propose a lower bound by Lagrangian relaxation method. Computational experiments show that the proposed HGASA and HSAGA are efficient on randomly generated problem instances, and perform better than the simple heuristics, GA and SA.
Keywords: production scheduling; GAs; genetic algorithms; simulated annealing; Lagrangian relaxation; inventory control; systems engineering; due dates; mixed integer programming; MIP.
Int. J. of Industrial and Systems Engineering, 2012 Vol.12, No.3, pp.259 - 279
Available online: 03 Oct 2012