Open AccessJust-in-time scheduling in identical parallel machine sequence-dependent group scheduling problem
Author(s) -
Alireza Goli,
Taha Keshavarz
Publication year - 2022
Publication title -
journal of industrial and management optimization
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.325
H-Index - 32
eISSN - 1553-166X
pISSN - 1547-5816
DOI - 10.3934/jimo.2021124
Subject(s) - tardiness , computer science , mathematical optimization , meta heuristic , scheduling (production processes) , job shop scheduling , heuristic , variable neighborhood search , algorithm , metaheuristic , mathematics , artificial intelligence , schedule , operating system
In this research, a parallel machine sequence-dependent group scheduling problem with the goal of minimizing total weighted earliness and tardiness is investigated. First, a mathematical model is developed for the research problem which can be used for solving small-sized instances. Since the problem is shown to be NP-hard, this research focuses on proposing meta-heuristic algorithms for finding near-optimal solutions. In this regard, the main contribution of this research is to apply the Biogeography-based Optimization (BBO) algorithm as a novel meta-heuristic and Variable Neighborhood Search (VNS) algorithm as a best-known one. In order to evaluate the mathematical model and solution methods, several computational experiments are conducted. The computational experiments demonstrate the efficiency of the proposed meta-heuristic algorithms in terms of speed and solution quality. The maximum gap of BBO algorithm is 1.04% and for VNS algorithm, it is 1.35%.