Seamus M. McGovern

A balancing method and genetic algorithm for disassembly line balancing

Disassembly activities take place in various recovery operations including remanufacturing, recycling and disposal. The disassembly line is the best choice for automated disassembly of returned products. It is therefore important that the disassembly line be designed and balanced so that it works as efficiently as possible. The disassembly line balancing problem seeks a sequence which: is feasible, minimizes workstations, and ensures similar idle times, as well as other end-of-life specific concerns. However finding the optimal balance is computationally intensive with exhaustive search quickly becoming prohibitively large even for relatively small products. In this paper the problem is mathematically defined and proven NP-complete. Additionally, a new formula for quantifying the level of balancing is proposed. A first-ever set of a priori instances to be used in the evaluation of any disassembly line balancing solution technique is then developed. Finally, a genetic algorithm is presented for obtaining optimal or near-optimal solutions for disassembly line balancing problems and examples are presented to illustrate implementation of the methodology.

Combinatorial optimization analysis of the unary NP-complete disassembly line balancing problem

The growing amount of waste created by products reaching the end of their useful lives poses challenges for the environment, governments and manufacturers. Processing alternatives include reuse, remanufacturing, recycling, storage and disposal. With disposal considered the least desirable, the first process required by the remaining alternatives is disassembly. Just as the assembly line is considered the most efficient way to assemble a product, the disassembly line is the most efficient way to disassemble a product. Finding the optimal balance for the multi-objective disassembly line balancing problem is computationally intensive due to exponential growth. With exhaustive search calculations quickly becoming prohibitively large, methodologies from the field of combinatorial optimization hold promise for providing solutions. The disassembly line balancing problem is described here, then defined mathematically and proven to belong to the class of unary NP-complete problems. Known optimal instances of the problem are developed, then disassembly line versions of exhaustive search, genetic algorithm and ant colony optimization metaheuristics, a greedy algorithm, and greedy/hill-climbing and greedy/2-optimal hybrid heuristics are presented and compared along with a novel uninformed general-purpose search heuristic.

Disassembly sequencing problem: a case study of a cell phone

Selection of an optimal disassembly sequence is essential for the efficient processing of a product at the end of its life. Disassembly sequences are listings of disassembly actions (such as the separation of an assembly into two or more subassemblies, or removing one or more connections between components). Disassembly takes place in remanufacturing, recycling, and disposal with a disassembly line being the best choice for automation. In this paper, the disassembly sequencing problem is solved for a cell phone case on a disassembly line, seeking a sequence which is feasible, minimizes the number of workstations (and hence idle times), provides for early removal of high demand/value parts, provides the removal of parts that lead to the access of greatest number of still-installed parts, and early removal of hazardous parts as well as for the grouping of parts for removal having identical part removal directions. Since finding the optimal sequence is computationally intensive due to factorial growth, a heuristic method is used taking into account various disassembly-specific matters. Using the experimentally determined precedence relationships and task times of a real-world cell phone, a MATLAB program is designed and a sequencing solution is generated. Finally, Design for Disassembly (DFD) improvements are recommended with respect to environmentally conscious manufacturing.

2-Opt Heuristic for the Disassembly Line Balancing Problem

Disassembly activities are an important part of product recovery operations. The disassembly line is the best choice for automated disassembly of returned products. However, finding the optimal balance for a disassembly line is computationally intensive with exhaustive search quickly becoming prohibitively large. In this paper, a greedy algorithm is presented for obtaining optimal or near-optimal solutions to the disassembly line-balancing problem. The greedy algorithm is a first-fit decreasing algorithm further enhanced to preserve precedence relationships. The algorithm seeks to minimize the number of workstations while addressing hazardous and high demand components. A two optimal algorithm is then developed to balance the part removal sequence and attempt to further reduce the total number of workstations. Examples are considered to illustrate the methodology. The conclusions drawn from the study include the consistent generation of optimal or near-optimal solutions, the ability to preserve precedence, the speed of the algorithms and their practicality due to the ease of implementation.

Greedy algorithm for disassembly line scheduling

Remanufacturing, recycling, and disposal recovery operations require the performance of disassembly activities. The disassembly line is the best choice for automated disassembly of returned products, however, finding the optimal balance is computationally intensive with exhaustive search quickly becoming prohibitively large. In this paper, a greedy algorithm is presented for obtaining optimal or near-optimal solutions to the disassembly line balancing problem. The greedy algorithm is a first-fit decreasing algorithm further enhanced to preserve precedence relationships. The algorithm seeks to minimize the number of workstations while accounting for hazardous and high demand components. A hill-climbing heuristic is then developed to balance the part removal sequence. Examples are considered to illustrate the methodology. The conclusions drawn from the study include the consistent generation of optimal or near-optimal solutions, the ability to preserve precedence, the speed of the algorithm and its practicality due to the ease of implementation.

Combinatorial optimization methods for disassembly line balancing

Disassembly takes place in remanufacturing, recycling, and disposal with a line being the best choice for automation. The disassembly line balancing problem seeks a sequence which: minimizes workstations, ensures similar idle times, and is feasible. Finding the optimal balance is computationally intensive due to factorial growth. Combinatorial optimization methods hold promise for providing solutions to the disassembly line balancing problem, which is proven to belong to the class of NP-complete problems. Ant colony optimization, genetic algorithm, and H-K metaheuristics are presented and compared along with a greedy/hill-climbing heuristic hybrid. A numerical study is performed to illustrate the implementation and compare performance. Conclusions drawn include the consistent generation of optimal or near-optimal solutions, the ability to preserve precedence, the speed of the techniques, and their practicality due to ease of implementation.

Uninformed and probabilistic distributed agent combinatorial searches for the unary NP-complete disassembly line balancing problem

Disassembly takes place in remanufacturing, recycling, and disposal, with a line being the best choice for automation. The disassembly line balancing problem seeks a sequence which: is feasible, minimizes workstations, and ensures similar idle times, as well as other end-of-life specific concerns. Finding the optimal balance is computationally intensive due to exponential growth. Combinatorial optimization methods hold promise for providing solutions to the disassembly line balancing problem, which is proven here to belong to the class of unary NP-complete problems. Probabilistic (ant colony optimization) and uninformed (H-K) search methods are presented and compared. Numerical results are obtained using a recent case study to illustrate the search implementations and compare their performance. Conclusions drawn include the consistent generation of near-optimal solutions, the ability to preserve precedence, the speed of the techniques, and their practicality due to ease of implementation.

Unified assembly- and disassembly-line model formulae

Purpose – There is a rich body of literature on sequencing assembly and on sequencing disassembly, but little that either fuses or contrasts the two, which may be valuable for long-range planning in the closed-loop supply chain and simply convenient in terms of consistency in nomenclature and mathematical formulations. The purpose of this paper is to concisely unify and summarize assembly and disassembly formulae – as well as to add new formulations for completeness – and then demonstrate the similarities and differences between assembly and disassembly. Design/methodology/approach – Along with several familiar assembly-line formulae which are adapted here for disassembly, five (two specific and three general) metrics and a comparative performance formula from disassembly-line balancing are proposed for use in assembly- and disassembly-line sequencing and balancing either directly, through generalization, or with some extension. The size of assembly and disassembly search spaces are also quantified and fo...

Metrics and experimental data for assessing unbalanced disassembly lines

Disassembly lines are inherently multicriteria, with balance having the possibility of being one of the lower priorities. In addition, complete disassembly may not be desired, required, or even possible, resulting in only partial disassembly being conducted. The result may be a disassembly sequence that readily satisfies the decision-maker’s primary requirements, but at the expense of an unbalanced line. However, obtaining the decision-maker’s primary requirements may not sufficiently justify exceptionally poor balance. Alternatively, the difference between a balanced disassembly line and one that is unbalanced may be so insignificant that a focus purely on balance may obfuscate the benefits of considering other criteria. Therefore, metrics are needed to evaluate the merits of all considered criteria, including the level of balance (or unbalance). In this paper, a multicriteria benchmark dataset and associated metrics are developed for use in quantitatively evaluating an unbalanced paced disassembly line.

Deterministic hybrid and stochastic combinatorial optimization treatments of an electronic product disassembly line

Disassembly takes place in remanufacturing, recycling, and disposal, with a line being the best choice for automation. The disassembly line balancing problem seeks a sequence that is feasible, minimizes the number of workstations, and ensures similar idle times, as well as other end-of-life specific concerns. Finding the optimal balance is computationally intensive due to exponential growth. Combinatorial optimization methods hold promise for providing solutions to the problem, which is proven here to be NP-hard. Stochastic (genetic algorithm) and deterministic (greedy/hill-climbing hybrid heuristic) methods are presented and compared. Numerical results are obtained using a recent electronic product case study.