Research On Sequence-dependent Disassembly Line Balancing Problem

Stricter environmental laws,enhanced public awareness,and extended manufacturer responsibility have forced a growing number of manufacturers to recycle the end-of-life(EOL)products.Economic benefits of recycling EOL products are also the main driving forces for the development of product recovery and reuse.Product disassembly is a vital stage for industrial recycling and remanufacturing which generates the desired parts and/or subassemblies by means of separation of a product into its elements.After disassembly,reusable parts/subassemblies are cleaned,refurbished,tested and directed to the part/subassembly inventory for remanufacturing process.The recyclable materials are sold to raw-material suppliers,while the residuals are sent to landfills.The high productivity can be achieved by disassembly lines which then are the best choices for automated disassembly processes.The multi-objective disassembly line balancing problem seeks to find a disassembly sequence which provides a feasible disassembly sequence,minimizes the number of opened workstations,balances the lines(ensures similar idle times at each workstation),as well as addressing other specific disassembly concerns.Designing and balancing efficient disassembly lines has gained increasing attention of researchers due to its vital role in product recovery process.Since many products are characterized with the complex structures,some parts with no precedence relationship will interact with each other,and their disassembly time is incremented by additional operating time according to their performed order.These interactions among precedence-free tasks are defined as sequence-dependency.The sequence-dependent disassembly line balancing problem(SDDLBP)is an extension of the basic DLBP which is NP-complete,so the SDDLBP is more complex but has greater relevance in the real-world disassembly systems.Therefore,it is of significant importance in both theory and practice to study the SDDLBP.When optimizing the existing multi-objective SDDLBP,we find out that the tasks with more sequence-dependent time increments will be preferentially disassembled in order to balance the idle times across opened workstations evenly,which means the more convenient operating ways cannot be used to remove parts from the product.As a result,more additional operations are needed,which not only leads to an increase of the total disassembly time,but also results in an unreasonable increase in workload and related resource consumption.Therefore,it is necessary to consider the measure of the total disassembly time to guarantee the most efficient way of performing tasks.Based on the above reason,a multi-objective mathematical model of type I for SDDLBP is established to minimize the number of opened workstations,minimize the total disassembly time,ensure similar idle times at each workstation,handle hazardous components as early as possible,and remove high-demand components on priority.Depending on the classification of layout,this article studied the traditional straight SDDLBP(SSDDLBP),U-shaped SDDLBP(USDDLBP),and two-sided SDDLBP(TSDDLBP)respectively.A novel improved discrete artificial bee colony(IDABC)algorithm is proposed to solve the SSDDLBP.In the proposed algorithm,permutation-based representation is used to represent a feasible solution.In initialization of population phase,a combination strategy is introduced to produce initial solutions with high-quality and diversity.In the employed bee phase,the employed bees exploit new food sources by using reduced variable neighborhood search method to improve local search efficiency.In the onlooker bee phase,the onlookers effectively evaluate the food sources through the multi-stage evaluation mechanism and select a food source for further exploitation by using the same method as the employed bees.In the scout bee phase,the scouts explore new food sources around the best food source found so far to increase the possibility of searching for new better solutions.Compared with existing meta-heuristic algorithms reported in the literature,the comparative results show the highly effective performance of the IDABC algorithm.For the USDDLBP,a multi-objective USDDLBP optimization model is proposed on the basis of the existing model.Then an adaptive evolutionary dynamic neighborhood search algorithm is proposed to solve the problem.In the proposed algorithm,the initial population is generated by combining a heuristic algorithm and one point right operator,and the individual evolution is selected by the tournament selection strategy.In local search process,a neighborhood-structured adaptive selection strategy is designed,and a global learning mechanism based on crossing operator is proposed to accelerate the algorithm to jump from the local optimum.The performance of the proposed algorithm is evaluated by different scale instances,and the superiority of the U-shaped disassembly line is reflected by comparison with the straight-line layout.To effectively represent the feasible disassembly sequence of TSDDLBP,a sequenced-based,combined coding scheme was designed,which can both solve the precedence relation and operational direction constraints.A collaborative genetic algorithm(CGA)is proposed to tackle the TSDDLBP.There are two separate populations without any interaction in their respective evolution process.However,before the next evolution,the two populations will work together to improve the quality of their offspring by introduction and association.The comparisons with other meta-heuristics in terms of solution quality and computation time are provided to demonstrate the higher efficiency of the CGA in solving TSDDLBP.