Service load balancing, task scheduling and transportation optimisation in cloud manufacturing by applying queuing system. Issue 6 (3rd July 2019)

• Record Type: Journal Article
• Authors: Jafarnejad Ghomi, Einollah; Masoud Rahmani, Amir; Nasih Qader, Nooruldeen
• Is Part Of: Enterprise information systems. Volume 13:Issue 6(2019)
• Page Start: 865
• Page End: 894
• Publication Date: 2019-07-03
• Subjects: Cloud manufacturing -- service load balancing -- multi-objective optimisation -- PSO algorithm -- goal programming; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2019.1599448
• Languages: English
• ISSNs: 1751-7575

Abstract

ABSTRACT: Recently, there is a great deal of attention in Cloud Manufacturing (CMfg) as a new service-oriented manufacturing paradigm. To integrate the activities and services through a CMfg, both Service Load balancing and Transportation Optimisation (SLTO) are two major issues to ease the success of CMfg. Based on this motivation, this study presents a new queuing network for parallel scheduling of multiple processes and orders from customers to be supplied. Another main contribution of this paper is a new heuristic algorithm based on the process time of the tasks of the orders (LBPT) to solve the proposed problem. To formulate it, a novel multi-objective mathematical model as a Mixed Integer Linear Programming (MILP) is developed. Accordingly, this study employs the multi-choice multi-objective goal programming with a utility function to model the introduced SLTO problem. To better solve the problem, a Particle Swarm Optimisation (PSO) algorithm is developed to tackle this optimisation problem. Finally, a comparative study with different analyses through four scenarios demonstrates that there are some improvements on the sum of process and transportation costs by 6.1%, the sum of process and transportation times by 10.6%, and the service load disparity by 48.6% relative to the benchmark scenario.