A network-based shortest route model for parallel 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.     

Mixed model disassembly line balancing problem with fuzzy goals

The collection of used products is the driving force of remanufacturing systems and enterprises can gain significant economic, technical and social benefits from recycling. All products are disassembled up to some level in remanufacturing systems. The best way to disassemble returned products is valid by a well-balanced disassembly line. In this paper, a mixed integer programming (MIP) model is proposed for a mixed model disassembly line balancing (MMDLB) problem with multiple conflicting objectives: (1) minimising the cycle time, (2) minimising the number of disassembly workstations and (3) providing balanced workload per workstation. In most real world MMDLB problems, the targeted goals of decision makers are frequently imprecise or fuzzy because some information may be incomplete and/or unavailable over the planning horizon. This study is the first in the literature to offer the binary fuzzy goal programming (BFGP) and the fuzzy multi-objective programming (FMOP) approaches for the MMDLB problem in order to take into account the vague aspirations of decision makers. An illustrative example based on two industrial products is presented to demonstrate the validity of the proposed models and to compare the performances of the BFGP and the FMOP approaches.     

Reverse supply chain optimisation with disassembly line balancing

Due to responding environmental issues, conforming governmental legislations and providing economic benefits, there has been a growing interest in recycling activities through the supply chains. Reverse supply chain (RSC) optimisation problem has a great potential as an efficient tactic to achieve this goal. While disassembly, one of the main activities in RSC, enables reuse and recycling of products and prevents the overuse, disassembly line balancing problem involves determination of a line design in which used products are partially/completely disassembled to obtain available components. The aim of this study is to optimise a RSC, involving customers, collection/disassembly centres and plants, that minimises the transportation costs while balancing the disassembly lines, which minimises the total fixed costs of opened workstations, simultaneously. A non-linear mixed-integer programming model, which simultaneously determines: (i) optimal distribution between the facilities with minimum cost, (ii) the number of disassembly workstations that will be opened with minimum cost, (iii) the cycle time in each disassembly centre and (iv) optimal assignment of tasks to workstations, is developed. A numerical example is given to illustrate the applicability of the proposed model. Different scenarios have been conducted to show the effects of sensitivity analyses on the performance measures of the problem.     

An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem

Disassembly is indispensable to recycle and remanufacture end-of-life products, and a disassembly line-balancing problem (DLBP) is studied frequently. Recent research on disassembly lines has focused on a complete disassembly for optimising the balancing ability of lines. However, a partial disassembly process is widely applied in the current industry practice, which aims at reusing valuable components and maximising the profit (or minimising the cost). In this paper, we consider a profit-oriented partial disassembly line-balancing problem (PPDLBP), and a mathematical model of this problem is established, which is to achieve the maximisation of profit for dismantling a product in DLBP. The PPDLBP is NP-complete since DLBP is proven to be a NP-complete problem, which is usually handled by a metaheuristics. Therefore, a novel efficient approach based on gravitational search algorithm (GSA) is proposed to solve the PPDLBP. GSA is an optimisation technique that is inspired by the Newtonian gravity and the laws of motion. Also, two different scale cases are used to test on the proposed algorithm, and some comparisons with the CPLEX method, particle swarm optimisation, differential evolution and artificial bee colony algorithms are presented to demonstrate the excellence of the proposed approach.     

A multi-objective distribution-free model and method for stochastic disassembly line balancing problem

End-of-life product recycling is a hot research topic in recent years, which can reduce the waste and protect the environment. To disassemble products, the disassembly line balancing is a principal problem that selects tasks and assigns them to a number of workstations under stochastic task processing times. In existing works, stochastic task processing times are usually estimated by probability distributions or fuzzy numbers. However, in real-life applications, only their partial information is accessible. This paper studies a bi-objective stochastic disassembly line balancing problem to minimise the line design cost and the cycle time, with only the knowledge of the mean, standard deviation and upper bound of stochastic task processing times. For the problem, a bi-objective chance-constrained model is developed, which is further approximated into a bi-objective distribution-free one. Based on the problem analysis, two versions of the ?-constraint method are proposed to solve the transformed model. Finally, a fuzzy-logic technique is adapted to propose a preferable solution for decision makers according to their preferences. A case study is presented to illustrate the validity of the proposed models and algorithms. Experimental results on 277 benchmark-based and randomly generated instances show the efficiency of the proposed methods.     

A Fuzzy AHP and PROMETHEE method-based heuristic for disassembly line balancing problems

Nowadays, new products have been introduced in the market at an ever increasing pace due to rapid technology advancement. Consequently, products are becoming outdated and discarded faster than ever before. Since the demand for new solutions to economically deal with such outdated products begun to rise, the disassembly line has emerged as a viable solution to this problem. The disassembly line has been considered as a viable choice for automated disassembly of returned products. The problem of sequence generation in disassembly is complex due to its NP-Hard nature and therefore the heuristically solutions are most preferable for these types of problems. In this paper, a heuristic has been proposed to assign the disassembly tasks/parts to the work stations under its precedence constraints. It incorporates Fuzzy analytic hierarchy process (Fuzzy AHP) and PROMETHEE method for the selection of tasks for assignment to the disassembly line. The Fuzzy AHP has been used to find the relative importance of each criteria and PROMETHEE method has been used for prioritising the tasks for assignment. The proposed heuristics has been illustrated with an example and the results have been compared to the heuristic proposed by McGovern and Gupta. The proposed heuristic performs well and has shown improvements in terms of cycle time and idle time of the workstations.     

A comparison of piecewise linear programming formulations for stochastic disassembly line balancing

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.     

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

In recent years, the interests of disassembly line have increased owing to economic reasons and the increase of environmental awareness. Effective line can provide many advantages in terms of economic aspect and it facilitates competition the companies with others. This study contributes to the relevant literature by a branch, bound and remember algorithm for disassembly line balancing problem with AND/OR precedence. The proposed exact solution method employs the memory-based dominance rule to eliminate the reduplicated sub-problems by storing all the searched sub-problems and to utilise cyclic best-first search strategy to obtain high-quality complete solutions fast. In this paper, minimising the number of stations is taken as the performance measure. The proposed methodology is tested on a set of 260 instances and compared with the mathematical model using CPLEX solver and five well-known metaheuristics. Computational results show that the proposed method is capable of obtaining the optimal solutions for all the tested instances with less than 0.1?seconds on average. Additionally, comparative study demonstrates that the proposed method is the state-of-the-art algorithm and outperforms the CPLEX solver and metaheuristics in terms of both solution quality and search speed aspects.     

A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation

The safety hazards existing in the process of disassembling waste products pose potential harms to the physical and mental health of the workers. In this article, these hazards involved in the disassembly operations are evaluated and taken into consideration in a disassembly line balancing problem. A multi-objective mathematical model is constructed to minimise the number of workstations, maximise the smoothing rate and minimise the average maximum hazard involved in the disassembly line. Subsequently, a Pareto firefly algorithm is proposed to solve the problem. The random key encoding method based on the smallest position rule is used to adapt the firefly algorithm to tackle the discrete optimisation problem of the disassembly line balancing. To avoid the search being trapped in a local optimum, a random perturbation strategy based on a swap operation is performed on the non-inferior solutions. The validity of the proposed algorithm is tested by comparing with two other algorithms in the existing literature using a 25-task phone disassembly case. Finally, the proposed algorithm is applied to solve a refrigerator disassembly line problem based on the field investigation and a comparison of the proposed Pareto firefly algorithm with another multi-objective firefly algorithm in the existing literature is performed to further identify the superior performance of the proposed Pareto firefly algorithm, and eight Pareto optimal solutions are obtained for decision makers to make a decision.     

Disassembly line balancing using linear physical programming

Disassembly is the separation of a product into its constituent parts in a systematic way. It has gained importance recently due to its vital importance in product recovery. Cost-effective implementation of disassembly operation has a direct impact on the profitability of product recovery activities (recycling, remanufacturing etc.). Although it is possible to carry out disassembly operations in a disassembly station or in a disassembly cell, the highest productivity is achieved in a disassembly line. The output of a disassembly line can be maximised only if the line is balanced. A linear physical programming-based disassembly line balancing method is proposed in this study. This method was used to balance a mixed-model disassembly line and the effectiveness of the method was illustrated by analysing the results.     

An efficient hybrid artificial bee colony algorithm for disassembly line balancing problem with sequence-dependent part removal times

This study deals with a sequence-dependent disassembly line balancing problem by considering the interactions among disassembly tasks, and a multi-objective mathematical model is established. Subsequently, a novel hybrid artificial bee colony algorithm is proposed to solve the problem. A new rule is used to initialize a bee colony population with certain diversity, and a dynamic neighbourhood search method is introduced to guide the employed/onlooker bees to promising regions. To rapidly leave the local optima, a global learning strategy is employed to explore higher quality solutions. In addition, a multi-stage evaluation method is designed for onlookers to effectively select employed bees to follow. The performance of the proposed algorithm is tested on a set of benchmark instances and two case scenarios, and the results are compared with several other metaheuristics in terms of solution quality and computation time. The comparisons demonstrate that the proposed algorithm exhibits superior performance.     

An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times

The purpose of this work is to efficiently design disassembly lines taking into account the uncertainty of task processing times. The main contribution of the paper is the development of a decision tool that allows decision-makers to choose the best disassembly alternative (process), for an End of Life product (EOL), and assign the corresponding disassembly tasks to the workstations of the line under precedence and cycle time constraints. Task times are assumed to be random variables with known normal probability distributions. The case of presence of hazardous parts is studied and cycle time constraints are to be jointly satisfied with at least a certain probability level, or service level, fixed by the decision-maker. An AND/OR graph is used to model the precedence relationships among tasks. The objective is to minimise the line cost composed of the workstation operation costs and additional costs of workstations handling hazardous parts of the EOL product. To deal with task time uncertainties, lower and upper-bounding schemes using second-order cone programming and approximations with convex piecewise linear functions are developed. The applicability of the proposed solution approach is shown by solving to optimality a set of disassembly problem instances (EOL industrial products) from the literature.     

Robust disassembly line balancing with ambiguous task processing times

Disassembly line balancing problem (DLBP), which is to select disassembly process, open workstations and assign selected tasks to opened workstations, plays an important role in the recycling of End Of Life products. In real-world disassembly operations, task processing times are usually stochastic due to various factors. Most related works address the uncertain processing times by assuming that the probability distribution is known and the task processing times are independent of each other. In practice, however, it is difficult to get the complete distributional information and there is always underlying correlation between the uncertain processing times. This paper investigates the DLBP with partial uncertain knowledge, i.e. the mean and covariance matrix of task processing times. A new distributionally robust formulation with a joint chance constraint is proposed. To solve the problem, an approximated mixed integer second-order cone programming (MI-SOCP) model is proposed, and a two-stage parameter-adjusting heuristic is further developed. Numerical experiments are conducted, to evaluate the performance of the proposed method. We also draw some managerial insights and consider an extension problem.     

A new distribution-free model for disassembly line balancing problem with stochastic task processing times

Effective conduct with End of Life (EOL) products is a hot research topic in green and smart manufacturing. For EOL product recycling and remanufacturing, a fundamental problem is to design an efficient disassembly line under consideration of stochastic task processing times. This problem focuses on selecting alternative task processes, determining the number of opened workstations, and assigning operational tasks to the workstations. The goal is to minimise the total cost consisting of workstation operational cost and hazardous component processing cost. Most existing works assume that the probability distribution of task processing times can be estimated, however, it is often not likely to access the complete probability distribution due to various difficulties. Therefore, this study investigates disassembly line design with the assumption that only the mean, standard deviation and an upper bound of task processing times are known. Our main contributions include: (i) a new decomposition color graph is proposed to intuitively describe all possible processes, (ii) a new distribution-free model is proposed, and (iii) some problem properties are established to solve the model. Experimental results show that the distribution-free model can effectively deal with stochastic task processing times without given probability distributions.     

Ergonomics in assembly line balancing based on energy expenditure: a multi-objective model

In many assembly systems, ergonomics can have great impact on productivity and human safety. Traditional assembly systems optimisation approaches consider only time and cost variables, while few studies include also ergonomics aspects. In this study, a new multi-objective model for solving assembly line balancing problem is developed and discussed in order to include also the ergonomics aspect. First, based on main features of assembly workstations, the energy expenditure concept is used in order to estimate the ergonomics level, thanks to a new technique, called Predetermined Motion Energy System, which helps rapidly estimate the energy expenditure values. Then, a multi-objective approach, based on four different objective functions, is introduced in order to define the efficient frontiers of optimal solutions. To complete the study, a simple numerical example for a real case is presented to analyse the behaviour of Pareto frontiers varying several parameters linked to the energy and time value.     

An optimisation support for the design of hybrid production lines including assembly and disassembly tasks

The optimisation problems related to the assignment of tasks to workstations in assembly and disassembly lines have been largely discussed in the literature. They are known, respectively, as Assembly Line Balancing and Disassembly Line Balancing Problems. In this study, both types of task performed on the identical product are integrated in a common hybrid production system. Therefore, the logistic process is simplified and disassembly tasks can supply easier the assembly tasks with the required components. The considered production system has the layout of two parallel lines with common workstations. The product flow is conventional in the assembly line and reverse in the disassembly line. The paper provides a new mathematical model for designing such a hybrid system and an approximate approach based on ant colony optimisation for solving large-scale instances. The solution method is tested in a case study. The obtained results are compared with the solution provided by the design of two independent lines. The analysis of the results highlights the potential benefits of the hybrid production system.     

Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy consumption respectively. Task precedence diagrams are used to model the precedence relations among tasks. Considering uncertainties in disassembly process, the task processing times are assumed to be interval numbers. A mixed-integer mathematical programming model is proposed to minimise the cycle time, peak workstation energy consumption, and total energy consumption. This model has a significant managerial implication in real-life disassembly line systems. Since the studied problem is known as NP-hard, a metaheuristic approach based on an evolutionary simulated annealing algorithm is developed. Computational experiments are conducted and the results demonstrate the proposed algorithm outperforms other multi-objective algorithms on optimisation quality and computational efficiency.     

Two-sided U-type assembly line balancing problem

U-type and two-sided assembly lines are two types of design having advantages over traditional straight assembly lines. In this paper, a new line design hybrid of U-type and two-sided lines is presented. A bi-objective 0-1 integer programming model is developed to solve the line balancing problem of the proposed design. Zoning constraints are also considered for the proposed design. A number of test problems from the literature with up to 65 tasks are solved. Benefits of two-sided U-type lines are discussed.     

Assembly line balancing: general resource-constrained case

The problem of designing and balancing assembly lines has been widely studied in the literature. A recently introduced issue is the efficient use of constrained resources with specific assumptions, in which a task needs a resource type (A) or one of two resources (A ∨ B). This paper presents a more general resource-constrained case, in which each task needs resources that may be simple or multiple, alternative and/or concurrent: for instance, (3A), (A ∧ 4B ∧ 3C), (3A ∨ 2B ∨ C), (A ∧ B) ∨ (2C ∧ D) or (A ∨ B) ∧ (2C ∨ D). We also introduce an upper bound on the number of available resources. Finally, we present a computational experiment using the mathematical models that we develop, showing the instances that can be efficiently solved.     

Stochastic assembly line balancing using beam search

This paper presents a beam search-based method for the stochastic assembly line balancing problem in U-lines. The proposed method minimizes total expected cost comprised of total labour cost and total expected incompletion cost. A beam search is an approximate branch and bound method that operates on a search tree. Even though beam search has been used in various problem domains, this is the first application to the assembly line balancing problem. The performance of the proposed method is measured on various test problems. The results of the computational experiments indicate that the average performance of the proposed method is better than the best-known heuristic in the literature for the traditional straight-line problem. Since the proposed method is the first heuristic for the stochastic U-type problem with the total expected cost criterion, we only report its results on the benchmark problems. Future research directions and the related bibliography are also provided in the paper.