A hybrid heuristic to solve the parallel machines job-shop scheduling problem

2009 ◽  
Vol 40 (2) ◽  
pp. 118-127 ◽  
Author(s):  
Andrea Rossi ◽  
Elena Boschi
Keyword(s):  
Parallel Machines ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Hybrid Heuristic ◽  
Shop Scheduling

scholarly journals Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 219
Author(s):  
Xiang Tian ◽  
Xiyu Liu
Keyword(s):  
Local Search ◽  
Heuristic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Membrane System ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Hybrid Heuristic ◽  
Shop Scheduling ◽  
Hybrid Heuristic Algorithm

In real industrial engineering, job shop scheduling problem (JSSP) is considered to be one of the most difficult and tricky non-deterministic polynomial-time (NP)-hard problems. This study proposes a new hybrid heuristic algorithm for solving JSSP inspired by the tissue-like membrane system. The framework of the proposed algorithm incorporates improved genetic algorithms (GA), modified rumor particle swarm optimization (PSO), and fine-grained local search methods (LSM). To effectively alleviate the premature convergence of GA, the improved GA uses adaptive crossover and mutation probabilities. Taking into account the improvement of the diversity of the population, the rumor PSO is discretized to interactively optimize the population. In addition, a local search operator incorporating critical path recognition is designed to enhance the local search ability of the population. Experiment with 24 benchmark instances show that the proposed algorithm outperforms other latest comparative algorithms, and hybrid optimization strategies that complement each other in performance can better break through the original limitations of the single meta-heuristic method.

scholarly journals Research on Construction and Application for the Model of Multistage Job Shop Scheduling Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jianzhong Xu ◽  
Song Zhang ◽  
Yuzhen Hu
Keyword(s):  
Production Efficiency ◽  
Parallel Machines ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Second Stage ◽  
Sequencing Algorithm ◽  
Two Stages

Based on the practical application of an enterprise, we address the multistage job shop scheduling problem with several parallel machines in the first stage (production), a few parallel machines in the second stage (processing and assembly), and one machine in the following stages (including joint debugging, testing, inspection, and packaging). First, we establish the optimization objective model for the first two stages. Then, based on the design of the sequencing algorithm in the first two stages, a correction algorithm is designed between the first stage and the second stage to solve this problem systematically. Finally, we propose two benchmark approaches to verify the performance of our proposed algorithm. Verification of numerical experiments shows that the model and algorithm constructed in this paper effectively improve the production efficiency of the enterprise.

A hybrid intelligent algorithm for a fuzzy multi-objective job shop scheduling problem with reentrant workflows and parallel machines

2020 ◽  
Vol 39 (5) ◽  
pp. 7769-7785
Author(s):  
Mohammad-Ali Basiri ◽  
Esmaeil Alinezhad ◽  
Reza Tavakkoli-Moghaddam ◽  
Nasser Shahsavari-Poure
Keyword(s):  
Genetic Algorithm ◽  
Parallel Machines ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Intelligent Algorithm ◽  
Hybrid Intelligent Algorithm ◽  
Shop Scheduling ◽  
Multi Objective

This paper presents a multi-objective mathematical model for a flexible job shop scheduling problem (FJSSP) with fuzzy processing times, which is solved by a hybrid intelligent algorithm (HIA). This problem contains a combination of a classical job shop problem with parallel machines (JSPM) to provide flexibility in the production route. Despite the previous studies, the number of parallel machines is not pre-specified in this paper. This constraint with other ones (e.g., sequence-dependent setup times, reentrant workflows, and fuzzy variables) makes the given problem more complex. To solve such a multi-objective JSPM, Pareto-based optimization algorithms based on multi-objective meta-heuristics and multi-criteria decision making (MCDM) methods are utilized. Then, different comparison metrics (e.g., quality, mean ideal distance, and rate of achievement simultaneously) are used. Also, this paper includes two major phases to provide a new model of the FJSSP and introduce a new proposed HIA for solving the presented model, respectively. This algorithm is a hybrid genetic algorithm with the SAW/TOPSIS method, namely HGASAW/HGATOPSIS. The comparative results indicate that HGASAW and HGATOPSIS outperform the non-dominated sorting genetic algorithm (NSGA-II) to tackle the fuzzy multi-objective JSPM.

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