Evaluation of a new decision-aid parameter for job shop scheduling under uncertainties

Abstract

Abstract. This paper addresses the groups of permutable operations method. This method is a flexible scheduling approach to hedge against uncertainties and is composed of a proactive/reactive phase. The proactive phase consists of computing a set of solutions (schedule) to a scheduling problem, leaving the choice of executing one of these solutions during the reactive phase according to the current state of the shop floor. During the reactive phase, the remaining decisions have to be made in real-time. The worst-case evaluation of the remaining solutions is a decision-aid parameter used during the reactive phase in order to control the final schedule from exceeding a worst-case performance. While the existing literature only tackles the worst-case evaluation of the groups of permutable operations, this paper deals with its best-case evaluation. For solving this problem, a new lower bound calculating this parameter in polynomial time is proposed. The computational efficiency of this parameter in a reactive algorithm exhibits very good performance. Moreover, the experiments show the robustness of this evaluation allowing this parameter to be used in an unstable job shop environment.

Production Management of Hybrid Flow Shop Based on Genetic Algorithm

“…The production management of HFS has a great practical significance [1,2]. Proper production management can improve the machine utilization, reduce the waiting time of jobs, rationalize the allocation of production resources, and lower job-shop production cost [3,4].…”

Game-Based Hybrid Particle Swarm Optimization of Job-Shop Production Control

“…Yahouni et al [15] constructed a multi-disturbance partition model for the discrete job-shop hierarchy tree; the model accurately defines the priorities of disturbance risk vectors, and differentiated between different types of disturbances. Guo [16] established a mathematical model of the time flow of job-shop operations under ideal and actual conditions, and realized the closed-loop control from discrete job-shop disturbance prediction to production based on the hybrid Bayesian decision tree algorithm.…”