Service load balancing, task scheduling and transportation optimisation in cloud manufacturing by applying queuing system

• Published in: Enterprise information systems 2019-07, Vol.13 (6), p.865-894
• Main Authors: Jafarnejad Ghomi, Einollah, Masoud Rahmani, Amir, Nasih Qader, Nooruldeen
• Format: Article
• Language: English
• Subjects: Cloud manufacturing; goal programming; multi-objective optimisation; PSO algorithm; service load balancing
• ISSN: 1751-7575; 1751-7583
• DOI: 10.1080/17517575.2019.1599448

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.