scholarly journals Evolutionary Algorithm for Minmax Regret Flow-Shop Problem

2015 ◽  
Vol 6 (3) ◽  
pp. 3-9 ◽  
Author(s):  
Michał Ćwik ◽  
Jerzy Józefczyk
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problem ◽  
Flow Shop ◽  
A Priori ◽  
Solution Algorithm ◽  
Not Given ◽  
Heuristic Solution ◽  
Processing Times ◽  
Minmax Regret ◽  
Flow Shop Problem

Abstract The uncertain flow-shop is considered. It is assumed that processing times are not given a priori, but they belong to intervals of known bounds. The absolute regret (regret) is used to evaluate a solution (a schedule) which gives the minmax regret binary optimization problem. The evolutionary heuristic solution algorithm is experimentally compared with a simple middle interval heuristic algorithm for three machines instances. The conducted simulations confirmed the several percent advantage of the evolutionary approach.

scholarly journals Minimizing the Makespan for a Two-Stage Three-Machine Assembly Flow Shop Problem with the Sum-of-Processing-Time Based Learning Effect

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Win-Chin Lin
Keyword(s):  
Learning Effect ◽  
Flow Shop ◽  
Optimal Solutions ◽  
Work Experiences ◽  
Two Stage ◽  
Processing Times ◽  
Production Environments ◽  
Flow Shop Problem ◽  
Iterated Greedy ◽  
Machine Assembly

Two-stage production process and its applications appear in many production environments. Job processing times are usually assumed to be constant throughout the process. In fact, the learning effect accrued from repetitive work experiences, which leads to the reduction of actual job processing times, indeed exists in many production environments. However, the issue of learning effect is rarely addressed in solving a two-stage assembly scheduling problem. Motivated by this observation, the author studies a two-stage three-machine assembly flow shop problem with a learning effect based on sum of the processing times of already processed jobs to minimize the makespan criterion. Because this problem is proved to be NP-hard, a branch-and-bound method embedded with some developed dominance propositions and a lower bound is employed to search for optimal solutions. A cloud theory-based simulated annealing (CSA) algorithm and an iterated greedy (IG) algorithm with four different local search methods are used to find near-optimal solutions for small and large number of jobs. The performances of adopted algorithms are subsequently compared through computational experiments and nonparametric statistical analyses, including the Kruskal–Wallis test and a multiple comparison procedure.

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