Distribution-free and Risk-averse Disassembly Line Balancing Problem

Disassembly lines should be balanced efficiently to increase productivity of the line and to reduce disassembly costs. This problem is called disassembly line balancing problem (DLBP). The objective of the DLBP is usually to find the minimum number of disassembly workstations required. This study introduces parallel DLBP (PDLBP) with single-product and proposes a network model based on the shortest route model (SRM) for solving PDLBP. The proposed model is illustrated via numerical examples. A comprehensive experiment is also conducted to evaluate problem-specific features of disassembly lines. To the best of our knowledge, this is the first study dealing with PDLBP. This paper will present a different point of view regarding DLBP.

Multi-objective partial parallel disassembly line balancing problem using hybrid group neighbourhood search algorithm

With the advancement of science and technology in recent years, the rate of product upgrading by end users has increased, resulting in a high number of End-Of-Life (EOL) products. For environmental protection and economic benefits, disassembly lines are set up to recycle them. A disassembly line balancing problem arises and has attracted widespread attention. In this paper, we design a model to address a class of disassembly line balancing problems in which the main optimization objective is to maximize profits, while keeping carbon emission in a tolerable range. A discrete whale optimization algorithm is proposed to search their high-quality solutions. Its efficiency is verified by comparing its solutions with their analytic solutions obtained from a commercial optimization solver and other swarm intelligence algorithms. Experimental results show that it is significantly faster than CPLEX and other swarm intelligence algorithms in solving large-scale optimization problems while guaranteeing its solutions quality.

Recently, several mathematical programming formulations and solution approaches have been developed for the stochastic disassembly line balancing problem (DLBP). This paper aims at finding optimal solutions for the stochastic DLBP. Two second-order cone programming (SOCP1 and SOCP2) models and five piecewise linear mixed integer programming (PwLP) models are presented. The PwLP formulations involve two specially ordered sets of type 2 (S1 and S2) models and three convex combination (CC1, CC2 and CC3) models. In each modelling category, the latter models strengthen the initial S1 and CC1 models. Our computational analysis of a total 240 instances of ten problems demonstrates that all the seven models can be used to solve practical-sized DLBP problems to optimality using GUROBI. The SOCP2 model and the strengthened S2 and CC2 models lead to lower computation times, compared to SOCP1, S1, CC1 and CC3, respectively. Using the strengthened S2 and CC2 formulations, the CPU times of the CC3 model available in the literature can be reduced by 50 and 40%, respectively. Besides analysing the optimal solutions and the differences of the computation times, we present insights gained from our results.

This paper introduces the integrated disassembly line balancing and routing problem (I-DLB-RP). The I-DLB-RP simultaneously optimises two well-known problems. The former one balances the disassembly lines in the disassembly centres, whereas the latter one constructs a routing plan to distribute the usable components, generated by the disassembly process, from disassembly centre to the remanufacturing centres, i.e. customers. With the increasing importance of the disassembly process for tackling with the burden of waste and the number of disassembled products, the distribution planning of usable components released after the disassembly process becomes essential. This paper considers several scenarios: single-component distribution, multi-component distribution, inventory cost, and multi-period conditions. We propose five linear and non-linear mathematical models. Extensive computational experiments conducted on generated realistic benchmark instances. The analyses quantify the benefits of integrating the two problems.

Due to increasing environmental concerns, manufacturers are forced to take back their products at the end of products' useful functional life. Manufacturers explore various options including disassembly operations to recover components and subassemblies for reuse, remanufacture, and recycle to extend the life of materials in use and cut down the disposal volume. However, disassembly operations are problematic due to high degree of uncertainty associated with the quality and configuration of product returns. In this research we address the disassembly line balancing problem (DLBP) using a Monte-Carlo based reinforcement learning technique. This reinforcement learning approach is tailored fit to the underlying dynamics of a DLBP. The research results indicate that the reinforcement learning based method is able to perform effectively, even on a complex large scale problem, within a reasonable amount of computational time. The proposed method performed on par or better than the benchmark methods for solving DLBP reported in the literature. Unlike other methods which are usually limited deterministic environments, the reinforcement learning based method is able to operate in deterministic as well as stochastic environments.

The recycling of discarded products is an integral part of resource utilization. A disassembly line balancing problem (DLBP) concerns the recycling and remanufacturing process of end-of-life (EOL) products. Disassembly time is affected by many factors, e.g., the precedence relationships among disassembly tasks, and the skills and learning abilities of disassembly workers. In this paper, we use Petri nets to specify a disassembly process and establish a mixed integer programming model to describe DLBP that considers the learning effect of human workers. We then propose a discrete fruit fly optimization algorithm to solve the proposed problem. By comparing its experimental results with other intelligent optimization algorithms’, its efficiency is verified.

This paper addresses the problem of profit-oriented disassembly line design and balancing considering partial disassembly, presence of hazardous parts and uncertainty of task processing times. Few papers have studied the stochastic disassembly line balancing problem and existing approaches have focused on heuristic and metaheuristic methods. Most existing work has concentrated on complete disassembly where task times are assumed to be normal random variables and where AND/OR graphs are not considered. The objective of this paper is the design of a serial line that obtains the maximum revenue and then balances the workload under uncertainty. The processing time of a disassembly task is assumed to be a random variable with any known probability distribution. An AND/OR graph is used to model the precedence relationships among tasks. Stochastic programming models and exact-based solution approaches combining the L-shaped algorithm and Monte Carlo sampling techniques are proposed. The relevance and applicability of the proposed models and solution methods are shown by solving efficiently a set of disassembly problem instances from the literature.

Disassembly operations are inevitable elements of product recovery with the disassembly line as the best choice to carry out the same. In the light of different structures of returned products (models) and variations in task completion times, the process of disassembly could not be efficiently mapped on a simple straight line. Another important issue that needs consideration is the task-time variability pertaining to human factor. In order to resolve these complexities a Mixed-Model U-shaped Disassembly Line with Stochastic Task Times has been proposed in this article. A novel approach, Collaborative Ant Colony Optimization (CACO), has been utilized that simultaneously tackles the interrelated problem of line balancing and model sequencing. The distinguishing feature of the proposed approach is that it maintains bilateral colonies of ants which independently identifies the two sequences, but utilizes the information obtained by their collaboration to guide the future path. The approach is tested on benchmark instances that were generated using Design of Experiment techniques and Analysis of Variance is performed to determine the impact of various factors on the objective. The robustness of proposed algorithm is authenticated against Ant Colony Optimization over which it always demonstrated better results thereby proving its superiority on the concerned problem.

In this paper, the disassembly line balancing problem, which involves determining a line design in which used products are completely disassembled to obtain useable components in a cost-effective manner, is studied. Because of the growing demand for a cleaner environment, this problem has become an important issue in reverse manufacturing. In this study, two exact formulations are developed that utilize an AND/OR Graph (AOG) as the main input to ensure the feasibility of the precedence relations among the tasks. It is also shown that traditional task precedence diagrams can be derived from the AOG of a given product structure. This procedure leads to considerably better solutions of the traditional assembly line balancing problems; it may alter the approach taken by previous researchers in this area.

Disassembly is a critical remanufacturing process to obtain reusable components from discarded products. Due to the limitation of disassembly by humans or robots alone, the human-robot collaborative disassembly method is used to obtain components. Three types of components are considered in this paper: dangerous, delicate and normal. Robots disassemble dangerous components, humans disassemble delicate components, and both humans and robots can disassemble normal components. A mathematical model that maximizes disassembly profit is established. An improved gray wolf optimizer algorithm to solve the single-product disassembly line balancing problem is proposed. The algorithm is compared with the migratory bird optimization algorithm and the brain storming optimization algorithm to test its performance. Experimental results show that the proposed algorithm has a faster convergence speed.

A branch, bound, and remember algorithm for the simple disassembly line balancing problem

This paper concerns a disassembly line balancing problem (DLBP) in remanufacturing that aims to allocate a set of tasks to workstations to disassemble a product. We consider two objectives in the same time, i.e., minimising the number of workstations required and minimising the operating costs. A common approach to such problems is to covert the multiple objectives into a single one and solve the resulting problem with either exact or heuristic methods. However, the appropriate weights must be determined a priori, yet the results provide little insight on the trade-off between competing objectives. Moreover, DLBP problems are proven NP-complete and thus the solvable instances by exact methods are limited. To this end, we formulate the problem into a multi-objective dynamic program and prove the monotonicity property of both objective functions. A backward recursive algorithm is developed to efficiently generate all the non-dominated solutions. The numerical results show that our proposal is more efficient than alternative exact algorithms proposed in the literature and can handle much larger problem instances.

Increasing environmental awareness in today's society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life (EOL) products through different options (e.g., recycling, remanufacturing,). Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics (i.e., genetic algorithms (GAs) and simulated annealing (SA)) are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.

In present era of environmentally conscious manufacturing, End-of-life (EOL) product disassembly is increasingly used for the product recovery process. However, the low efficiency in disassembly process put a great challenge in front of manufacturers to achieve the environmental goals set for the product. To achieve the efficient utilization in part recycling processes at the end of its life, disassembly line has important role to play. Thus, much emphasis is given by the research community to reduce the company reliance on the availability of human experts to improve the intelligence in the disassembly process. This research aims to optimize the number of machine required to disassemble the parts in a disassembly line. The complexity while disassembly increases in case of defects in the tasks to be allocated on the workstation. In this paper an evolutionary approach named Psycho-Clonal is proposed to solve the disassembly line-balancing problem in the presence of task failure. The proposed algorithm is inspired by artificial immune systems (AIS), which is further enhanced by the authors by incorporating Maslow’s need hierarchy theory work as a meta-heuristic. The main attributes of the algorithm are the various need levels, immune memory, and affinity maturation. Need levels incorporated in the algorithm help in satisfying the model constraints, thus empowering the algorithm to work for a multi-objective, multi-constraints problem. Immune memory helps in preserving the best solution and affinity maturation helps in exploring and exploiting the search space.Copyright © 2005 by ASME