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Disturbing increase in the use of virgin resources to produce new products has threatened the environment. Many countries have reacted to this situation through regulations which aim to eliminate negative impact of products on the environment shaping the concept of environmentally conscious manufacturing and product recovery (ECMPRO). The first crucial and the most time-consuming step of product recovery is disassembly. The best productivity rate is achieved via a disassembly line in an automated disassembly process. In this chapter, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that is concerned with the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence-dependent time increments among disassembly tasks. Due to the high complexity of the SDDLBP, there is currently no known way to optimally solve even moderately sized instances of the problem. Therefore, an efficient methodology based on the simulated annealing (SA) is proposed to solve the SDDLBP. Case scenarios are considered and comparisons with ant colony optimization (ACO), particle swarm optimization (PSO), river formation dynamics (RFD), and tabu search (TS) approaches are provided to demonstrate the superior functionality of the proposed algorithm.

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.

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 formulated. Three new metrics are then developed from each of the general metrics to demonstrate the process of using these general formulae as prototypes.

The three new metrics along with several of the original metrics are selectively applied to a simple, notional case study product to be sequenced on an assembly line and then on a disassembly line. Using these analytical results, the inherent differences between assembly and disassembly, even for a seemingly trivial product, are illustrated.

The research adds several new assembly/disassembly metrics, a case study, unifies the evaluation formulae that assembly and disassembly hold in common as well as structuring prototype formulae for flexibility in generating new evaluation criteria for both, and quantifies (using the case study) how assembly and disassembly – while certainly possessing similarities – also demonstrate measurable differences that can be expected to affect product design, planning, production, and end-of-life processing.

The purpose of this paper is to introduce sequence‐dependent disassembly line balancing problem (SDDLBP) to the literature and propose an efficient metaheuristic solution methodology to this NP‐complete problem.

This manuscript utilizes a well‐proven metaheuristics solution methodology, namely, ant colony optimization, to address the problem.

Since SDDLBP is NP‐complete, finding an optimal balance becomes computationally prohibitive due to exponential growth of the solution space with the increase in the number of parts. The proposed methodology is very fast, generates (near) optimal solutions, preserves precedence requirements and is easy to implement.

Since development of cost effective and profitable disassembly systems is an important issue in end‐of‐life product treatment, every step towards improving disassembly line balancing brings us closer to cost savings and compelling practicality.

This paper introduces a new problem (SDDLBP) and an efficient solution to the literature.

Although disassembly balancing lines has been studied for over two decades, there is a gap in the robotic disassembly. Moreover, combination of problem with heterogeneous employee assignment is also lacking. The hazard related with the tasks performed on disassembly lines on workers can be reduced by the use of robots or collaborative robots (cobots) instead of workers. This situation causes an increase in costs. The purpose of the study is to propose a novel version of the problem and to solve this bi-objective (minimizing cost and minimizing hazard simultaneously) problem.

The epsilon constraint method was used to solve the bi-objective model. Entropy-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization methods for Enrichment Evaluation (PROMETHEE) methods were used to support the decision-maker. In addition, a new criterion called automation rate was proposed. The effects of factors were investigated with full factor experiment design.

The effects of all factors were found statistically significant on the solution time. The combined effect of the number of tasks and number of workers was also found to be statistically significant.

In this study, for the first time in the literature, a disassembly line balancing and employee assignment model was proposed in the presence of heterogeneous workers, robots and cobots to simultaneously minimize the hazard to the worker and cost.

The purpose of this paper is to efficiently solve disassembly line balancing problem (DLBP) and the sequence-dependent disassembly line balancing problem (SDDLBP) which are both known to be NP-complete.

This manuscript utilizes a well-proven metaheuristics solution methodology, namely, variable neighborhood search (VNS), to address the problem.

DLBPs are analyzed using the numerical instances from the literature to show the efficiency of the proposed approach. The proposed algorithm showed superior performance compared to other techniques provided in the literature in terms of robustness to reach better solutions.

Since disassembly is the most critical step in end-of-life product treatment, every step toward improving disassembly line balancing brings us closer to cost savings and compelling practicality.

This paper is the first adaptation of VNS algorithm for solving DLBP and SDDLBP.

The purpose of this paper is to present an interactive system to enable product design for disassembly and to offer robust and quick design solutions based on designers’ input.

The system utilizes an interactive questionnaire to communicate with the designer. The questionnaire is in the form of binary questions (Yes/No) and design questions that would enable the system to learn the objectives of the design. Solutions are based on a CAD supported design platform. The efficiency of each design is calculated using disassembly time as the metric of measurement using motion-time measurement (MTM). The designer would be able to make an informed decision with respect to component functionality, ease of disassembly and disassembly time. The paper presents a detailed framework and structure of this system.

The value of the system is corroborated by means of a case study of an actual product design. The system is structured to offer multiple solutions to a design problem so as to enable the designer to choose the option that best serves their needs.

This novel interactive system would accept customers’ design preferences as input and offer multiple solutions in order to solve the design problem. Process time is directly calculated using the MTM system of measurement by converting design features into time measurement units. Disassembly time can then be easily converted into disassembly cost by using standard conversion rates. The value to designers is obvious.

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.

Changing the product characteristics and demand quantity resulting from the variability of the modern market leads to re-assigned tasks and changing the cycle time on the production line. Therefore, companies need re-balancing of their assembly line instead of balancing. The purpose of this paper is to propose an efficient algorithm approach for U-type assembly line re-balancing problem using stochastic task times.

In this paper, a genetic algorithm is proposed to solve approach for U-type assembly line re-balancing problem using stochastic task times.

The performance of the genetic algorithm is tested on a wide variety of data sets from literature. The task times are assumed normal distribution. The objective is to minimize total re-balancing cost, which consists of workstation cost, operating cost and task transposition cost. The test results show that proposed genetic algorithm approach for U-type assembly line re-balancing problem performs well in terms of minimizing total re-balancing cost.

Demand variation is considered for stochastic U-type re balancing problem. Demand change also affects cycle time of the line. Hence, the stochastic U-type re-balancing problem under four different cycle times are analyzed to present practical case.

As per the authors’ knowledge, it is the first time that genetic algorithm is applied to stochastic U-type re balancing problem. The large size data set is generated to analyze performance of genetic algorithm. The results of proposed algorithm are compared with ant colony optimization algorithm.

The purpose of this paper is to review the fundamental methodology and its development of intelligent disassembly planning research.

Following a brief introduction, this paper first discusses the fundamental problems associated with disassembly planning and analysis. And then considers the role of intelligent optimization methods in the disassembly planning field. This is followed by a summary and conclusion.

Many advances have been made in computerized intelligent disassembly planning research, which is a natural evolutionary result of both traditional solving methodology and much research effort over past two decades. But as yet, some fundamental limitations are also rooted in this computational model‐based methodology.

The paper provides a fundamental review on the development of computerized intelligent disassembly planning research.

Technology is an important force in the entrepreneurial ecosystem as it has the potential to impact entrepreneurial opportunities and processes. This paper explores the emerging technology of artificial intelligence (AI) and its implications for reverse logistics within the circular economy (CE). It considers key reverse logistics functions and outlines how AI is known to, or has the potential to, impact these functions.

The paper is conceptual and utilizes the literature from entrepreneurship, the CE and reverse logistics to explore the implications of AI for reverse logistics functions.

AI provides significant benefits across all functions and tasks in the reverse logistics process; however, the various reverse logistics functions and tasks rely on different forms of AI (mechanical, analytical, intuitive).

The paper highlights the importance of technology, and in particular AI, as a key force in the digital entrepreneurial ecosystem and discusses the specific implications of AI for entrepreneurial practice. For researchers, the paper outlines avenues for future research within the entrepreneurship and/or CE domains of the study.

This paper is the first to present a structured discussion of AI's implications for reverse logistics functions and tasks. It addresses a call for more research on AI and its opportunities for the CE and emphasizes the importance of emerging technologies, particularly AI, as an external force within the entrepreneurial ecosystem. The paper also outlines avenues for future research on AI in reverse logistics.