Studying the Performance of Unified Particle Swarm Optimization on the Single Machine Total Weighted Tardiness Problem

2006 ◽  
pp. 760-769 ◽  
Author(s):  
K. E. Parsopoulos ◽  
M. N. Vrahatis
Keyword(s):  
Particle Swarm Optimization ◽  
Single Machine ◽  
Particle Swarm ◽  
Total Weighted Tardiness ◽  
Swarm Optimization ◽  
Weighted Tardiness ◽  
Unified Particle Swarm Optimization

scholarly journals Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Tarik Cakar ◽  
Rasit Koker
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Single Machine ◽  
Particle Swarm ◽  
Total Weighted Tardiness ◽  
Optimization Approach ◽  
Release Date ◽  
Solution System ◽  
Swarm Optimization ◽  
Weighted Tardiness

A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system.

scholarly journals Energy-Saving Production Scheduling in a Single-Machine Manufacturing System by Improved Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Qingquan Jiang ◽  
Xiaoya Liao ◽  
Rui Zhang ◽  
Qiaozhen Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Single Machine ◽  
Production Scheduling ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Total Weighted Tardiness ◽  
Production Environment ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

A single-machine scheduling problem that minimizes the total weighted tardiness with energy consumption constraints in the actual production environment is studied in this paper. Based on the properties of the problem, an improved particle swarm optimization (PSO) algorithm embedded with a local search strategy (PSO-LS) is designed to solve this problem. To evaluate the algorithm, some computational experiments are carried out using PSO-LS, basic PSO, and a genetic algorithm (GA). Before the comparison experiment, the Taguchi method is used to select appropriate parameter values for these three algorithms since heuristic algorithms rely heavily on their parameters. The experimental results show that the improved PSO-LS algorithm has considerable advantages over the basic PSO and GA, especially for large-scale problems.

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