Bicriterion Optimization for Flow Shop with a Learning Effect Subject to Release Dates

Abstract

This paper investigates a two-machine flow shop problem with release dates in which the job processing times are variable according to a learning effect. The bicriterion is to minimize the weighted sum of makespan and total completion time subject to release dates. We develop a branch-and-bound (B&B) algorithm to solve the problem by using a dominance property, several lower bounds, and an upper bound to speed up the elimination process of the search tree. We further propose a multiobjective memetic algorithm (MOMA), enhanced by an initialization strategy and a global search strategy, to obtain the Pareto front of the problem. Computational experiments are also carried out to examine the effectiveness and the efficiency of the B&B algorithm and the MOMA algorithm.

Optimization for Due-Window Assignment Scheduling with Position-Dependent Weights

“…, n. us, the input for the problem contains a matrix of (n × n) jobposition values. Biskup [22] introduced a job-independent learning effect model in which p A i � θ(i, r) � p i r α , where α ≤ 0 is the learning index (see also Wang et al [23]). Mosheiov and Sidney [24] introduced job-dependent learning effects, i.e., p A i � θ(i, r) � p i r α i , where α i ≤ 0 is the job-dependent learning index of job J i .…”

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

“…Multi-criteria problems are usually solved using metaheuristics or hybrid metaheuristics, each with its special characteristics. The following are some of the approaches proposed by the authors: Deliktaş [114] to minimize makespan and total tardiness, Abedi et al [45] to minimize total weighted tardiness and total energy consumption, and Wang et al [115] to minimize the weighted sum of makespan and total completion time, used a memetic multiobjective algorithm to obtain a set of solutions. First, the authors develop a non-dominated sorting method, and second, the authors appeal to the Pareto front.…”