A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly

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. (C) 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.