Development of a Multi-Objective Scheduling System for Complex Job Shops in a Manufacturing Environment

Ali, Abdalla (2016) Development of a Multi-Objective Scheduling System for Complex Job Shops in a Manufacturing Environment. Doctoral thesis, Northumbria University.

[img]
Preview
Text (Doctoral thesis)
ali.abdalla_phd.pdf - Published Version

Download (2MB) | Preview

Abstract

In many sectors of commercial operation, the scheduling of workflows and the allocation of resources at an optimum time is critical; for effective and efficient operation. The high degree of complexity of a “Job Shop” manufacturing environment, with sequencing of many parallel orders, and allocation of resources within multi-objective operational criteria, has been subject to several research studies. In this thesis, a scheduling system for optimizing multi-objective job shop scheduling problems was developed in order to satisfy different production system requirements. The developed system incorporated three different factors; setup times, alternative machines and release dates, into one model. These three factors were considered after a survey study of multiobjective job shop scheduling problems.

In order to solve the multi-objective job shop scheduling problems, a combination of genetic algorithm and a modified version of a very recent and computationally efficient approach to non-dominated sorting solutions, called “efficient non-dominated sort using the backward pass sequential strategy”, was applied. In the proposed genetic algorithm, an operation based representation was designed in the matrix form, which can preserve features of the parent after the crossover operator without repairing the solution. The proposed efficient non-dominated sort using the backward pass sequential strategy was employed to determine the front, to which each solution belongs. The proposed system was tested and validated with 20 benchmark problems after they have been modified. The experimental results show that the proposed system was effective and efficient to solve multi-objective job shop scheduling problems in terms of solution quality.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Genetic algorithm, alternative machines, release date, setup times, time uncertainty
Subjects: H100 General Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
University Services > Graduate School > Doctor of Philosophy
Depositing User: Paul Burns
Date Deposited: 08 Feb 2017 16:50
Last Modified: 25 Oct 2017 09:17
URI: http://nrl.northumbria.ac.uk/id/eprint/29578

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence