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.     

Disassembly line design with multi-manned workstations: a novel heuristic optimisation approach

As the first and the most time consuming step of product recovery, disassembly is described as the systematic separation of constituent parts from end-of-life products through a series of operations. In this context, designing and balancing disassembly lines are critical in terms of the efficiency of product recovery. Recent research on disassembly line balancing (DLB) has focused on classical stations where only one worker is allocated. However, such a line results in larger space requirement and longer disassembly lead time. In this paper, disassembly line balancing problem (DLBP) with multi-manned stations is introduced to the relevant literature as a solution to overcome these disadvantages. A mixed integer linear programming (MILP) model and two novel framework heuristic algorithms are developed to minimise the number of workers and workstations. MILP model has been applied to a dishwasher disassembly system. The application results indicate the superiority of establishing multi-manned stations over classical disassembly system design with single-worker stations with shorter disassembly lead time (80.9%) and line length (60.2%). Moreover, the proposed heuristics have been compared on newly generated test problems (instances) for DLBP. The results validate that the heuristics provide acceptable solutions in a reasonable amount of time even for large-sized problems.     

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.     

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.     

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.     

Robotic U-shaped assembly line balancing using particle swarm optimization

Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure is proposed. The consecutive heuristic is used to allocate the tasks to the workstation and to assign a best fit robot to that workstation. The proposed algorithm is evaluated using a wide variety of data sets. The results indicate that robotic U-shaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.     

Extending assembly line balancing problem by incorporating learning effects

In the ramp-up phase, or time to volume of new products, pronounced learning effects are observed. They are present especially on assembly lines producing mass-goods because of a high number of repetitions of the tasks. Shortening the ramp-up phase and reaching the steady-state production as soon as possible generates main advantages for firms that introduce new products. Moreover, a careful planning of the ramp-up stage is getting even more important in view of shorter product life cycles and a growing importance of the ‘time to payback’ financial indicators. Former studies on incorporation of learning effects into assembly line balancing have limited applicability, because they rely on unrealistic assumptions. We model learning effects, based on general and realistic assumptions, as an extension of the Simple Assembly Line Balancing Problem. We propose exact and heuristic solution procedures and perform extensive computational tests. We found that for instances similar to the problems, which arise in firms, the duration of the learning stage can be reduced by up to 10% if our specialised methods are applied.     

Reconfiguration of assembly lines under the influence of high product variety in the automotive industry–a decision support system

In this paper we consider the mixed model assembly line reconfiguration problem in the context of auto production which is characterised by a make-to-order production process and a huge product variety. Starting from a given line balancing solution the goal is to minimise production costs in the short term for a largely known production program by reassigning and shifting tasks between workstations. We present a mathematical optimisation model that aims at minimising the costs incurred by overload situations, regular workers and reconfiguration measures. Due to the model's complexity, lack of data and acceptance issues it is hardly possible to fully automate the solution process in an industrial environment. Therefore, we present a decision support approach that consists of visualisation components, new numerical indicators and an integrated heuristic optimisation procedure to semi-automate the reconfiguration process. In particular, reconfiguration costs can be taken into account and no complete precedence graph is required. Finally, we show on the basis of two industrial case studies that our approach can be successfully applied in a practical environment where it was capable of drastically reducing the occurrence of overload situations.     

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.     

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.     

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.     

Balancing Multi-Manned Assembly Lines With Walking Workers: Problem Definition,Mathematical Formulation,and an Electromagnetic Field Optimisation Algorithm

Assembly lines are widely used in industrial environments that produce standardised products in high volumes. Multi-manned assembly line is a special version of them that allows simultaneous operation of more than one worker at the same workstation. These lines are widely used in large-sized product manufacturing since they have many advantages over the simple one. This article has dealt with multi-manned assembly line balancing problem with walking workers for minimising the number of workers and workstations as the first and second objectives, respectively. A linear mixed-integer programming formulation of the problem has been firstly addressed after the problem definition is given. Besides that, a metaheuristic based on electromagnetic field optimisation algorithm has been improved. In addition to the classical electromagnetic field optimisation algorithm, a regeneration strategy has been applied to enhance diversification. A particle swarm optimisation algorithm from assembly line balancing literature has been modified to compare with the proposed algorithm. A group of test instances from many precedence diagrams were generated for evaluating the performances of all solution methods. Deviations from lower bound values of the number of workers/workstations and the number of optimal solutions obtained by these methods are concerned as performance criteria. The results obtained by the proposed programming formulations have been also compared with the solutions obtained by the traditional mathematical model of the multi-manned assembly line. Through the experimental results, the performance of the metaheuristic has been found very satisfactory according to the number of obtained optimal solutions and deviations from lower bound values.     

Integrated procedure of balancing and sequencing for mixed-model assembly lines: a multi-objective evolutionary approach

A mixed-model assembly line is a type of production line which is used to assemble a variety of product models with a certain level of similarity in operational characteristics. This variety causes workload variance among other problems resulting in low efficiency and line stops. To cope with these problems, a hierarchical design procedure for line balancing and model sequencing is proposed. It is structured in terms of an amelioration procedure. On the basis of our evolutionary algorithm, a genetic encoding procedure entitled priority-based multi-chromosome (PMC) is proposed. It features a multi-functional chromosome and provides efficient representation of task assignment to workstations and model sequencing. The lean production perspective recognises the U-shape assembly line system as more advanced and beneficial compared to the traditional straight line system. To assure the effectiveness of the proposed procedure, both straight and U-shape assembly lines are examined under two major performance criteria, i.e., number of workstations (or line efficiency) as static criterion and variance of workload (line and models) as dynamic criterion. The results of simulation experiments suggest that the proposed procedure is an effective management tool of a mixed-model assembly line system.     

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.     

Simulated annealing algorithms for the multi-manned assembly line balancing problem: minimising cycle time

Multi-manned assembly lines are often designed to produce big-sized products, such as automobiles and trucks. In this type of production lines, there are multi-manned workstations where a group of workers simultaneously performs different operations on the same individual product. One of the problems, that managers of such production lines usually encounter, is to produce the optimal number of items using a fixed number of workstations, without adding new ones. In this paper, such a class of problems, namely, the multi-manned assembly line balancing problem is addressed, with the objective of minimising the cycle time. A mixed-integer mathematical programming formulation is proposed for the considered problem. This model has the primary objective of minimising the cycle time for a given number of workstations and the secondary objective of minimising the total number of workers. Since the addressed problem is NP-hard, two meta-heuristic approaches based on the simulated annealing algorithm have been developed: ISA and DSA. ISA solves the problem indirectly while DSA solves it directly. The performance of the two algorithms are tested and compared on a set of test problems taken from the literature. The results show that DSA outperforms ISA in term of solution quality and computational time.     

A heuristic approach for balancing mixed-model assembly line of type I using genetic algorithm

The mixed model assembly line is becoming more important than the traditional single model due to the increased demand for higher productivity. In this paper, a set of procedures for mixed-model assembly line balancing problems (MALBP) is proposed to make it efficiently balance. The proposed procedure based on the meta heuristics genetic algorithm can perform improved and efficient allocation of tasks to workstations for a pre-specified production rate and address some particular features, which are very common in a real world mixed model assembly lines (e.g. use of parallel workstations, zoning constraints, resource limitation). The main focus of this study is to study and modify the existing genetic algorithm framework. Here a heuristic is proposed to reassign the tasks after crossover that violates the constraints. The new method minimises the total number of workstation with higher efficiency and is suitable for both small and large scale problems. The method is then applied to solve a case of a plastic bag manufacturing company where the minimum number of workstations is found performing more efficiently.     

Balancing of mixed-model parallel U-shaped assembly lines considering model sequences

As a consequence of increasing interests in customised products, mixed-model lines have become the most significant components of today’s manufacturing systems to meet surging consumer demand. Also, U-shaped assembly lines have been shown as the intelligent way of producing homogeneous products in large quantities by reducing the workforce need thanks to the crossover workstations. As an innovative idea, we address the mixed-model parallel U-shaped assembly line design which combines the flexibility of mixed-model lines with the efficiency of U-shaped lines and parallel lines. The multi-line stations utilised in between two adjacent lines provide extra efficiency with the opportunity of assigning tasks into workstations in different combinations. The new line configuration is defined and characterised in details and its advantages are explained. A heuristic solution approach is proposed for solving the problem. The proposed approach considers the model sequences on the lines and seeks efficient balancing solutions for their different combinations. An explanatory example is also provided to show the sophisticated structure of the studied problem and explain the running mechanism of the proposed approach. The results of the experimental tests and their statistical analysis indicated that the proposed line design requires fewer number of workstations in comparison with independently balanced mixed-model U-lines.     

Sustainable disassembly line balancing model based on triple bottom line

End of life (EOL) phase of a product is receiving more attention due to increase in environmental concerns, and many studies have been conducted for value creation in EOL, focusing on concepts as remanufacturing, reuse and recycling in sustainable production manner. This study especially focuses on one of global problem, e-waste. To minimise the amount of wastes and maximise recovered materials from EOL, disassembly is one of the most important concept, associated with reuse, and balancing disassembly line in an optimal way is essential for organisations. In disassembly line balancing (DLB), not only precedence of tasks, but also risk criteria related to environment and human safety should be considered for sustainability. The aim of this study is to propose a model based on triple bottom line (TBL) dimensions, i.e. human safety, environmental safety and business criteria. To achieve sustainability in DLB, and for risk assessment in sustainable DLB, it had been decided to use a multi-criteria method, i.e. TODIM, acronym in Portuguese of ‘Tomada de Decisão Iterativa Multicritério’. The proposed model included 22 disassembly criteria categorised under TBL dimensions, which are derived from the literature. Implementation of the study was conducted for computer disassembly processes, and as a result of the study approximately 12% an improvement in cycle time was succeeded. In the long run, the integration of sustainability in disassembly operations may contribute to the competitive advantage of the company in terms of differentiation and corporate image by achieving business, environment and human targets simultaneously.     

A simulated annealing algorithm for balancing the assembly line type II problem with sequence-dependent setup times between tasks

This paper addresses the general assembly line balancing problem where the simple version is enriched by considering sequence-dependent setup times between tasks. Recently, Andres et al. (Andres, C., Miralles, C., and Pastor, R., 2008. Balancing and scheduling tasks in assembly lines with sequence-dependent setup times. European Journal of Operational Research, 187, (3), 1212–1223.) proposed the type I general assembly line balancing problem with setups (GALBPS-I) and developed a mathematical model and several algorithms for solving the problem. In a similar vein, we scrutinised the GALBPS type II problem where the challenge is to find the minimum cycle time for a predefined number of work stations. To solve the problem, we develop a mathematical model and a novel simulated annealing (SA) algorithm to solve such an NP-hard problem. We then employed the Taguchi method as an optimisation technique to extensively tune different parameters of our algorithm and make the classical SA algorithm more efficient in terms of running time and solution quality. Computational results reflected the high efficiency of the SA algorithm in both aspects.     

Modelling and solving a cost-oriented resource-constrained multi-model assembly line balancing problem

A line balancing problem considers the assignment of operations to workstations in an assembly line. While assembly lines are usually associated to mass production of standardised goods, their advantages have led to their widespread use whenever a product-oriented production system is applicable and the benefits of the labour division and specialisation are significant, even when some of its characteristics may deviate from classical assembly lines. In this work, we study a line balancing problem found in the textile industry in which the line must be balanced for multiple types of goods taking into account resource requirements. In order to solve the problem, a hybrid method that combines classical methods for line balancing with an Estimation of Distribution Algorithm is proposed. Computational experiments show that the new procedure improves upon the state of the art when compared using a benchmark set derived from the literature, as well as when compared using data from the manufacturer that originated this research work.