Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints

Authors:
R. Tavakkoli-Moghaddam;F. Taheri;M. Bazzazi;M. Izadi;F. Sassani
Affiliations:
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran;Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran;Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran;Department of Computer Engineering and IT, Amirkabir University of Technology, Tehran, Iran;Department of Mechanical Engineering, The University of British Columbia, Vancouver, Canada
Venue:
Computers and Operations Research
Year:
2009

Citing 11
Cited 7

Scheduling parallel processors to minimize setup time

Computers and Operations Research
Early/tardy scheduling with sequence dependent setups on uniform parallel machines

Computers and Operations Research
Parallel machine scheduling with earliness and tardiness penalties

Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
A tabu search algorithm for parallel machine total tardiness problem

Computers and Operations Research
A genetic algorithm for minimizing maximum lateness on parallel identical batch processing machines with dynamic job arrivals and incompatible job families

Computers and Operations Research
Exact algorithms for a scheduling problem with unrelated parallel machines and sequence and machine-dependent setup times

Computers and Operations Research
A memetic algorithm for the flexible flow line scheduling problem with processor blocking

Computers and Operations Research
Minimizing earliness and tardiness subject to total completion time in an identical parallel machine system

Computers and Operations Research
Worst-case error bounds for parallel machine scheduling problems with bounded sequence-dependent setup times

Operations Research Letters
List scheduling in a parallel machine environment with precedence constraints and setup times

Operations Research Letters

A novel artificial immune system for solving multiobjective scheduling problems subject to special process constraint

Computers and Industrial Engineering
Scheduling algorithms for a semiconductor probing facility

Computers and Operations Research
Integrating parts design characteristics and scheduling on parallel machines

Expert Systems with Applications: An International Journal
A GRASP approach to transporter scheduling and routing at a shipyard

Computers and Industrial Engineering
Parallel machine scheduling with splitting jobs by a hybrid differential evolution algorithm

Computers and Operations Research
Effect of solution representations on Tabu search in scheduling applications

Computers and Operations Research
Application of graph search and genetic algorithms for the single machine scheduling problem with sequence-dependent setup times and quadratic penalty function of completion times

Computers and Industrial Engineering

Quantified Score

Hi-index	0.02

Visualization

Abstract

This paper presents a novel, two-level mixed-integer programming model of scheduling N jobs on M parallel machines that minimizes bi-objectives, namely the number of tardy jobs and the total completion time of all the jobs. The proposed model considers unrelated parallel machines. The jobs have non-identical due dates and ready times, and there are some precedence relations between them. Furthermore, sequence-dependent setup times, which are included in the proposed model, may be different for each machine depending on their characteristics. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches or optimization tools is extremely difficult. This paper proposes an efficient genetic algorithm (GA) to solve the bi-objective parallel machine scheduling problem. The performance of the presented model and the proposed GA is verified by a number of numerical experiments. The related results show the effectiveness of the proposed model and GA for small and large-sized problems.