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

Abstract EN

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.