Research On The Disassembly Line Balancing Problem Considering The Randomness Of Disassembly Task Failure

With the rapid development of information technology,the speed of product update and iteration has been accelerated,resulting in a large number of end-of-life products.The problems of resource waste and environmental pollution are becoming increasingly serious.End-of-life products are discarded due to life cycle expiration,failure,obsolescence,etc.,but they usually contain a lot of valuable resources.The recycling and reuse of end-of-life products are conducive to mining the residual value of renewable resources.At the same time,it can reduce the harm of end-of-life products to the environment.Disassembly is the first step in the implementation of recycling and remanufacturing end-of-life products.It is a process of separating products into component states through a series of disassembly tasks.How to quickly and reasonably plan the disassembly scheme to maximize the disassembly efficiency and economic benefit of end-of-life products,that is,the disassembly line balancing problem has become a research topic that has attracted much attention.In this paper,the uncertainty of the disassembly environment is considered,so that the disassembly task can only be successfully performed with a certain probability,which affects the balance of the disassembly line.In this paper,the task success rate is set as a random variable that conforms to a uniform distribution,and the single-objective and multi-objective disassembly line balancing problem models are respectively constructed considering the random failure of disassembly tasks.Then,according to the problem characteristics of the above two models,an improved artificial bee colony algorithm and a multi-objective artificial bee colony algorithm are designed respectively for optimization and solution.In the two solution approach,a feasible initial solution generation strategy and a variety of effective food source update strategies are introduced.Finally,the superiority of the proposed algorithms compared with existing methods is proved by solving cases of different scales and complexity,which provides theoretical and technical guidance for the complex and changeable disassembly practice.