A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem

Abstract

The Job Shop Scheduling Problem (JSP) is a combinatorial optimization problem cataloged as type NP-Hard. To solve this problem, several heuristics and metaheuristics have been used. In order to minimize the makespan, we propose a Memetic Algorithm (MA), which combines the exploration of the search space by a Genetic Algorithm (GA), and the exploitation of the solutions using a local search based on the neighborhood structure of Nowicki and Smutnicki. The genetic strategy uses an operation-based representation that allows generating feasible schedules, and a selection probability of the best individuals that are crossed using the JOX operator. The results of the implementation show that the algorithm is competitive with other approaches proposed in the literature.

Hybrid metaheuristic to solve job sequencing problem

“…The method described in the previous section is used to solve instances of the JSSP resolved by other methods in the literature [14], [26], [28], [40]- [42]. With the intention of comparing its results with those obtained by The method proposed in the present investigation, for which 30 tests were carried out for each instance, using a computer with the following characteristics: Intel (R) Core (TM) i5-8265U processor CPU @ 1.60GHz 1.80 GHz, installed RAM 8.00 GB.…”

“…With the intention of comparing its results with those obtained by The method proposed in the present investigation, for which 30 tests were carried out for each instance, using a computer with the following characteristics: Intel (R) Core (TM) i5-8265U processor CPU @ 1.60GHz 1.80 GHz, installed RAM 8.00 GB. The results obtained are shown in 2 tables, Table 1, presents the best results of the proposed method compared with the best results obtained by methods in the literature, while Table 2, shows the averages of each instance obtained by the proposed method, comparing them with those of literature [14], [26], [28], [40]- [42].…”

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 ].…”

“…Among the great diversity of studies, it is possible to verify the superiority of the meta-heuristics of global search behavior with the inclusion of techniques specialized in performing a local search. This type of procedure is addressed in some works, recent or not, such as the approaches in References [ 4 , 6 , 9 , 11 , 12 , 13 , 22 ], among others.…”

“…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 ].…”