An effective genetic algorithm with a critical-path-guided Giffler and Thompson crossover operator for job shop scheduling problem

Abstract

This work presents an effective genetic algorithm with a critical-path-guided Giffler and Thompson crossover operator for job shop scheduling problem with the objective of makespan minimization (GA-CPG-GT). Even though passing important traits from parents to offspring is known to be an important feature of any uniform crossover operator, most of the proposed operators adopt random exchange of genetic materials between parents; this is probably due to the fact that it is tricky to identify the genetic materials that hold the important traits. For that reason, in this work, a new selective exchange of genetic materials is proposed. In the proposed approach, at first, the genetic materials that hold the important traits are identified according to some domain specific information provided by the critical paths of the parents, and then the exchange is made on favor of them. The properties of critical path are usually utilized by the local search methods such as tabu search and simulating annealing; however, in this work, they are utilized in the global search method GA during the crossover operator. The implications of the proposed approach are investigated using the Giffler and Thompson crossover operator, which is a uniform crossover combined with the G&T algorithm. The proposed approach is tested on 55 benchmark instances, with the proposed selective exchange, and without it using the random one, and also compared with other 5 similar works reported in the literature. The computational results validate the enhancements accomplished by the proposed selective exchange, and show the superiority of the proposed algorithm over the compared works in terms of solution quality, and validate its effectiveness.

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

“…The articles determined for each comparison were selected because they are relevant works in the literature, which deal with the JSSP with the same specific instances and, when existing, papers published in the last three years were adopted. The papers selected for comparison of results were as follows: SSS [ 61 ], GA-CPG-GT [ 14 ], DWPA [ 15 ], GWO [ 11 ], HGWO [ 12 ], MA [ 9 ], IPB-GA [ 8 ] and aLSGA [ 6 ].…”

“…In recent years, several meta-heuristics approaches have been proposed to treat the JSSP, such as Greedy Randomized Adaptive Search Procedure (GRASP) [ 3 ], Local Search Genetic Algorithm (LSGA) [ 4 ], Parallel Agent-based Genetic Algorithm (PaGA) [ 5 ], Agent-based Local Search Genetic Algorithm (aLSGA) [ 6 ], Golden Ball Algorithm (GB) [ 7 ], Initial Population Based Genetic Algorithm (IPB-GA) [ 8 ], Memetic Algorithm (MA) [ 9 ], Improved Biogeography-Based Optimization (IBBO) [ 10 ], Grey Wolf Optimization (GWO) [ 11 ], Hybrid Grey Wolf Optimization (HGWO) [ 12 ], Memetic Chicken Swarm Optimization (MeCSO) [ 13 ], Genetic Algorithm with a critical-path-guided Giffler and Thompson crossover operator (GA-CPG-GT) [ 14 ] and the Discrete Wolf Pack Algorithm (DWPA) [ 15 ].…”

Optimization of Location Assignment for Unit-Load AS/RS with a Dual-Shuttle

“…The individuals converge the optimum solution through crossing over and mutation operators [20]. There are several parameters to be determined in GA like population size, crossing over rate and mutation rate [21]. Implementation of the BGA to the location assignment problem of AS/RS composes of following steps.…”

A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

“…Many heuristic approaches have been developed to solve shop scheduling problems, such as particle swarm optimization (PSO), genetic algorithms (GA), simulated annealing (SA), tabu search (TS), artificial immune (AI), differential evolution algorithm (DEA), and ant colony optimization (ACO), among others, as well as their hybrids [ 16 ]. Mohamed Kurdi [ 17 ] proposed an effective genetic algorithm with a critical-path-guided Giffler and Thompson crossover operator (GA-CPG-GT) for JSSP. Zhou et al [ 18 ] presented a hybrid social-spider optimization algorithm with a differential mutation (SSO-DM) operator to solve JSSP.…”