Two exact formulations for disassembly line balancing problems with task precedence diagram construction using an AND/OR graph

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.

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

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

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.

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.

ABSTRACTIn the context of Industry 4.0, a production line must be flexible and adaptable to stochastic or real‐world environments. As a result, the assembly line balancing (ALB) problem involves managing uncertainty or stochasticity. Several methods have been proposed, including stochastic mathematical programming models and simulations. However, programming models can only incorporate a few sources of uncertainty that result in impractical or unfeasible solutions to implement due to overlooked complexities, while simulation is only used to test solutions from deterministic approaches or adjust parameters without maintaining their optimum value. The proposed methodology uses a deterministic mathematical model to minimize the cycle time, followed by the simulation to measure the impact of selected sources of uncertainty on the cycle time. Finally, the optimum value of the stochastic parameters is computed using simulation‐based optimization to maintain the average cycle time close to the deterministic one. A real‐life assembly line balancing problem for a motorcycle manufacturing company is solved to test the proposed methodology. The sources of uncertainty are the tasks' stochastic processing times, inter‐arrival time, the number of workers in each station, and the speed of the material handling system. Results show that the average cycle time is above 2.7% from the deterministic value computed by the programming model when the inter‐arrival time is set to 270 60 s; the processing times are allowed to increase or decrease by 3 s; the material handling system's speed is 1.5 m/s; and the number of workers in cells is between 4 and 6, with a speed of 2 m/s. The reader can download the source code and the simulation model to apply the methodology to other instances.

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.

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.

The manufacturing industry has evolved in the past decades, due to the competition of the global economy, where the market demands high quality and customized products, and meeting lowest possible costs are paramount. One of the many processes in manufacturing is the assembly line operation, namely the assembly line balancing (ALB) problem. ALB problem is dependent on optimum resource utilization in order to improve production output, reduces costs, and shortens production lead times. In these recent years, various approaches have been proposed to solve the complexity of assembly line balancing operations, which composed of exact, heuristic, and meta-heuristic approaches. However, little work had been done to solve the type E assembly line balancing problem. This paper proposed an approach using artificial immune system (AIS) algorithm, namely as the artificial immune cell (AIC) approach, for solving type E assembly line balancing problem. An initialization mechanism through the bone marrow model and probabilistic clonal selection mechanism had encouraged efficient exploration and exploitation of the solution space. The computational results over 242 instances of 24 datasets had demonstrated the efficiency of the proposed AIC approach by achieving high-quality solutions (up to 85.94% optimum solutions was obtained). Also, the results were statistically justified by comparing with multi-rule multi-objective simulated annealing (MRMOSA), priority-based genetic approach (PriGA), two-phased genetic approach (2P-GA), and assignment genetic approach (MA-GA).  Â

Purpose The purpose of this paper is to find the most efficient assembly line balancing solution across many heuristic line balancing methods, in assistance with a developed computer program. Design/methodology/approach In this paper, assembly line balancing problem was analyzed using t-shirt and knitted pants data. A computer program using MATLAB software for the solution of assembly line balancing problems has been developed. In this study, following heuristic assembly line balancing methods were applied: Hoffman method; position weight method; COMSOAL method; and Kilbridge and Wester method. A MATLAB program has been developed by taking into account of theoretical solution of all these methods. Later the program is developed further by analyzing solutions made manually and is made to verify the developed program. Findings Pre-studies which were conducted in order to decide which programming language would be the best choice for line balancing methods’ application came out with the result that MATLAB, from between C, C++, C# and Java, would be the best software choice. The main reason for this choice is that MATLAB is a powerful matrix operation software with a powerful user interface designing tool and has the tools to make development program to be used universally in every computer. Originality/value When the researches were investigated, it is clearly seen that, this study is the first research on using computer program for solving assembly line balancing problem.

With the increasing pressure from the market and the surge of “Industry 4.0,” staying competitive and relevant is becoming more and more difficult. The assembly line, which represents a long-term investment of the manufacturing industry, needs to be efficiently utilized. While assembly line balancing (ALB) problem had been studied for decades, oversights on the bottleneck resources could significantly impede its efficiency. In leveraging such information as part of the optimization problem, a contagious artificial immune network (CAIN) approach is proposed to simultaneously address ALB efficiency and bottleneck resources while achieving a truly balanced production line. A computational experiment conducted on benchmark data sets has demonstrated a proof-of-concept, where leveraging knowledge-intensive optimization approach had successfully produced high-quality solutions up to 100% improvement with statistically significant justification. Such findings may play an essential determinant in the manufacturing industry, whether being relevant or left out in the era of increasingly being information-driven.

Assembly sequence planning (ASP) and assembly line balancing (ALB) problems are two essential problems in the assembly optimization. This paper proposes an ant colony algorithm for integrating assembly sequence planning and assembly line balancing, to deal with the two problems on parallel, and resolve the possible conflict between two optimization goals. The assembly sequence planning problem and the assembly line balancing problem are discussed, the process of the proposed ant colony algorithm is investigated. The results can provide a set of solutions for decision department in assembly planning.

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.

Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem

In an assembly system, balancing the line and designing the part feeding setting play a fundamental role in guaranteeing efficiency and effectiveness. Although these two aspects are interrelated, they are usually performed subsequent to each other, with the part feeding decisions only being made once the line has already been balanced. However, the simultaneous solving of the assembly line balancing and part feeding problems can lead to interesting benefits. In this paper, an innovative approach for solving the Joint Assembly Line Balancing and Feeding Problem (JALBFP) is proposed, to achieve an integrated balancing of assembly tasks and parts picking/kitting and a fully synchronised assembly-feeding system. The aim is to minimise the operational system cost, including workforce, space and transportation costs. The Mixed Integer Linear Programming (MILP) model is applied to several instances and two case studies and compared to a hierarchical approach. The proposed model leads to a significant improvement in reducing the number of assembly stations required and the inventory level at the line, where space is notoriously scarce, also avoiding workforce oversizing in the feeding system. The strength of the model relies on its formulation and on its versatility, which makes it applicable to different assembly sectors.