A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly. (October 2016)

• Record Type: Journal Article
• Authors: Jiang, Hui; Yi, Jianjun; Chen, Shaoli; Zhu, Xiaomin
• Is Part Of: Journal of manufacturing systems. Volume 41(2016)
• Page Start: 239
• Page End: 255
• Publication Date: 2016-10
• Subjects: Cloud-based disassembly -- Multi-objective genetic algorithm -- Task scheduling and resource allocation -- Cloud manufacturing; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2016.09.008
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: The cloud-based disassembly system is proposed in this paper. To describe the disassembly service formally, this paper builds a mathematical model that considers the uncertainty nature of the disassembly process and precedence relationships of disassembly tasks. Two objectives including minimizing the expected total makespan and minimizing the expected total cost of the disassembly service are discussed in this paper. A multi-objective genetic algorithm based on non-dominated sorting genetic algorithm II is designed to address the problem. Abstract: Some manufacturers outsource their disassembly tasks to professional factories, each factory of them has specialized in its disassembly ability. Different disassembly facilities are usually combined to execute disassembly tasks. This study proposes the cloud-based disassembly that abstracts ability of the disassembly factory as the disassembly resource, the disassembly resource is then able to be allocated to execute disassembly tasks. Based on this concept, the cloud-based disassembly system is proposed, which provides the disassembly service according to the user requirement. The disassembly service is the execution plan for disassembly tasks, which is the result of scheduling disassembly tasks and allocating disassembly resources. To formally describe the disassembly service, this paper builds a mathematical model that considers the uncertainty nature of the disassembly process and precedence relationships of disassembly tasks. Two objectives including minimizing the expected total makespan and minimizing the expected total cost of the disassembly service are also discussed. The mathematical model is NP-complete, a multi-objective genetic algorithm based on non-dominated sorting genetic algorithm II is designed to address the problem. Computation results show that the proposed algorithm performs well, the algorithm generates a set of Pareto optimal solutions. The user can choose a preferred disassembly service among Pareto optimal solutions.