
The Three-Objective Optimization Model of Flexible Workshop Scheduling Problem for Minimizing Work Completion Time, Work Delay Time, and Energy Consumption
Author(s) -
Neda Karim Ahangar,
Majid Khalili,
Hamed Tayebi
Publication year - 2021
Publication title -
tehnički glasnik
Language(s) - English
Resource type - Journals
eISSN - 1848-5588
pISSN - 1846-6168
DOI - 10.31803/tg-20200815184439
Subject(s) - computer science , mathematical optimization , energy consumption , scheduling (production processes) , schedule , software , time constraint , matlab , set (abstract data type) , industrial engineering , engineering , mathematics , electrical engineering , law , political science , programming language , operating system
In recent years, the optimal design of the workshop schedule has received much attention with the increased competition in the business environment. As a strategic issue, designing a workshop schedule affects other decisions in the production chain. The purpose of this thesis is to design a three-objective mathematical model, with the objectives of minimizing work completion time, work delay time and energy consumption, considering the importance of businesses attention to reduce energy consumption in recent years. The developed model has been solved using exact solution methods of Weighted Sum (WS) and Epsilon Constraint (Ɛ) in small dimensions using GAMS software. These problems were also solved in large-scale problems with NSGA-II and SFLA meta-heuristic algorithms using MATLAB software in single-objective and multi-objective mode due to the NP-Hard nature of this group of large and real dimensional problems. The standard BRdata set of problems were used to investigate the algorithms performance in solving these problems so that it is possible to compare the algorithms performance of this research with the results of the algorithms used by other researchers. The obtained results show the relatively appropriate performance of these algorithms in solving these problems and also the much better and more optimal performance of the NSGA-II algorithm compared to the performance of the SFLA algorithm.