Multiobjective flow-shop scheduling

Abstract

Previous research on the scheduling of multimachine systems has generally focused on the optimization of individual performance measures. This article considers the sequencing of jobs through a multimachine flow shop, where the quality of the resulting schedule is evaluated according to the associated levels of two scheduling criteria, schedule makespan (Cmax) and maximum job tardiness (Tmax). We present constructive procedures that quantify the trade‐off between Cmax and Tmax. The significance of this trade‐off is that the optimal solution for any preference function involving only Cmax and Tmax must be contained among the set of efficient schedules that comprise the trade‐off curve. For the special case of two‐machine flow shops, we present an algorithm that identifies the exact set of efficient schedules. Heruistic procedures for approximating the efficient set are also provided for problems involving many jobs or larger flow shops. Computational results are reported for the procedures which indicate that both the number of efficient schedules and the error incurred by heuristically approximating the efficient set are quite small.

A double-module immune algorithm for multi-objective optimization problems

“…., d n i } is obtained and further mutated by PM operator to do local disturbance. The resultant solution e i = {e 1 i , e 2 i , . .…”

“…Over the last decades, multi-objective optimization problems (MOPs) have attracted a great interest of researchers, which are motivated by the real-world engineering problems, such as job shop scheduling [1,2], water distribution network design [3,4], antenna design [5] and power supply management [6]. For example, the objectives of makespan, total workload, and critical workload in job shop scheduling are all required to be minimized, while the network cost and total head loss in pipes are preferred to be optimized in the design of water distribution network.…”

Multicriteria scheduling problem: a hybrid ant colony algorithm integrating the decision-maker’s preferences

“…Table 1 summarizes the best obtained values of the makespan (C*max ), Total flowtime (∑Ci*) and Total tardiness (∑Ti*) of a permutation flowshop scheduling problem characterized by 20 tasks and 10 machines ( 10PF/20/Criterion). Regarding the total tardiness criterion, we considered the Daniels and chambers (1990) technique to generate due date of tasks (jobs) of different problems. The due date (dj) is randomly generated within the following interval: It should be noted that the due date for each task is computed by taking T equal to 0.4 and R equal to 0.6 as done by Allouche (2010).…”

Flow shop scheduling algorithm to optimize warehouse activities

“…Gelders and Sambandam (1978) developed four heuristics to minimize the weighted sum of flow time and of the job tardiness for a problem with m machines. Daniels and Chambers (1990) suggested a heuristics procedure for a flow shop problem on m machines in which the makespan, subjected to the maximum tardiness, is minimized. Ho and Chang (1991) studied a new heuristic technique for a multi-objective flow shop problem: the minimization of the total flow time and machines inactivity time.…”