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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.

Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.

This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.

The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.

This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.

This study examines how compliance with government and firm's own policy and reverse logistics practices relate with firm environmental performance.

This study draws on insights from stakeholder theory, and follows a two-phase research approach. The first phase utilized an extended literature review that seeks to provide a qualitative and comprehensive understanding of the research problem. The 2001–2023 data was collected from the Web of Science and Scopus databases, complemented with Google Scholar. The second phase involved an empirical study—adopting a quantitative cross-sectional survey design with a self-administered questionnaire to validate the theoretical conceptualizations deriving from the literature review. The empirical data were collected from 203 food and beverages manufacturing firms in Uganda and analysed using the partial least squares structural equation modelling (PLS-SEM) approach.

The study findings suggest that compliance with government policy positively influences firm environmental performance, both directly, and indirectly through fostering reverse logistics practices, and that the relationship between compliance with government policy and reverse logistics practices is contingent upon compliance with the focal firm's own policy.

The study findings will enhance the theoretical and conceptual development of the ideas that underpin stakeholder theory and applications. The Ugandan government will come up with better mechanisms for enforcing compliance with policy regulating the application of reverse logistics practices. In addition, the study advances the use of multi-method approaches in investigating interesting research aspects requiring in-depth examination. However, considering the fact that the empirical study was conducted in a single country context, and focused on firms more or less from the same sub-sector, the findings of the study might not be generalizable globally.

This study provides useful insights to logistics and supply chain managers involved in reverse logistics activities in food and beverages manufacturing firms. These managers can know how to leverage reverse logistics practices to enhance environmental performance of firms amidst environmental policies in the industry where they operate.

This study contributes to the built body of knowledge in operations, logistics and supply chain management literature; understanding about reverse logistics practices as a mechanism through which compliance with government policy influences environmental performance of firms. The interaction between compliance with government policy and compliance with firm policy is essential in explaining the performance effects of reverse logistics practices. In addition, the study advances the use of multi-method approaches in investigating interesting research aspects requiring in-depth examination. Complementing extended literature review with and empirical research to investigate reverse logistics practices influences on firm environmental performance, and incorporating the role of policy in explaining this relationship should make considerable contribution. Besides, the study highlights important areas for future research.

Snap fits are crucial in automotive applications for rapid assembly and disassembly of mating components, eliminating the need for fasteners. This study aims to focus on designing and analyzing serviceable cantilever fit snap connections used in automobile plastic components. Snap fits are classified into permanent and semi-permanent fittings, with permanent fittings having a snap clipping angle between 0° and 5° and semi-permanent fittings having a clipping angle between 15° and 45°. Polypropylene random copolymer is chosen for its exceptional fatigue resistance and elasticity.

The design process includes determining dimensions, computing assembly, disassembly pressures and creating three-dimensional computer-aided design models. Finite element analysis (FEA) is used to evaluate the snap-fit mechanism’s stress, deformation and general functionality in operational scenarios.

The study develops a modified snap-fit mechanism with decreased bending stress and enhanced mating force optimization. The maximum bending stress during assembly is 16.80 MPa, requiring a mating force of 7.58 N, while during disassembly, it is 37.3 MPa, requiring a mating force of 16.85 N. The optimized parameters significantly improve the performance and dependability of the snap-fit mechanism. The results emphasize the need of taking into account both the assembly and disassembly processes in snap-fit design, because the research demonstrates greater forces during disassembly. The approach developed integrates FEA and design for assembly (DFA) concepts to provide a solution for improving the efficiency and reliability of snap-fit connectors in automotive applications.

The research paper’s distinctiveness comes from the fact that it presents a thorough and realistic viewpoint on snap-fit design, emphasizes material selection, incorporates DFA principles and emphasizes the specific requirements of both assembly and disassembly operations. These discoveries may enhance the efficiency, reliability and sustainability of snap-fit connections in plastic automobile parts and beyond. In conclusion, the idea that disassembly needs to be done with a lot more force than installation in a snap-fit design can have a good effect on buzz, squeak and rattle and noise, vibration and harshness characteristics in automobiles.

The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.

This paper aims at developing a multi-objective mathematical model of circular economy that integrates key concept of leasing as a strategy in addition to reuse, refurbishing, primary recycling, secondary recycling and disposal.

This paper proposes multi-objective fuzzy mixed integer linear programming mathematical model considering multi-product, multi-echelon and multi-capacitated concepts of the circular economy. The three objectives of the proposed model, namely, economic, environmental and social are solved simultaneously using constraint approach to obtain balanced trade-off between the objective functions. The model is validated by solving a case study from the literature. The proposed model is made pragmatic for industrial application by considering multi-external suppliers multi-customer zones, multi-disassembly centers, multi-collection centers and multi-refurbishing centers and accounting for purchasing, processing, transportation, set-up costs and capacity constraints at the same time.

The results show that the leasing of the products improves the economic function in addition to the known environmental improvements. The proposed model also shows that the circular economy can generate the jobs for the unskilled people at different locations.

The proposed model can be further improved by considering the non-linearity due to economy of scale at various centers and in transportation. The model can be further extended to make it multi-period model.

The proposed model of circular economy can be used by the organizations as a policy tool to decide the optimum number of collection centers, disassembly centers, refurbishing centers, recycling centers and disposal centers and their optimum locations and allocations. The organizations can also trade-off among economic, environmental and social benefits of their proposed decisions in circular economy.

The originality of the proposed mathematical model is consideration of leasing as a strategy to have better control over the supply chain for circularity; considering the training of unskilled people for backward supply chain jobs and accounting for primary recycling and secondary recycling separately for economical computation.

This study aims to examine the impact of the decision makers’ risk preference on logistics routing problem, contributing to logistics behavior analysis and route integration optimization under uncertain environment. Due to the unexpected events and complex environment in modern logistics operations, the logistics process is full of uncertainty. Based on the chance function of satisfying the transportation time and cost requirements, this paper focuses on the fourth party logistics routing integrated optimization problem considering the chance preference of decision makers from the perspective of satisfaction.

This study used the quantitative method to investigate the relationship between route decision making and human behavior. The cumulative prospect theory is used to describe the loss, gain and utility function based on confidence levels. A mathematical model and an improved ant colony algorithm are employed to solve the problems. Numerical examples show the effectiveness of the proposed model and algorithm.

The study’s findings reveal that the dual-population improvement strategy enhances the algorithm’s global search capability and the improved algorithm can solve the risk model quickly, verifying the effectiveness of the improvement method. Moreover, the decision-maker is more sensitive to losses, and the utility obtained when considering decision-makers' risk attitudes is greater than that obtained when the decision-maker exhibits risk neutrality.

In an uncertain environment, the logistics decision maker’s risk preference directly affects decision making. Different parameter combinations in the proposed model could be set for decision-makers with different risk attitudes to fit their needs more accurately. This could help managers design effective transportation plans and improve service levels. In addition, the improved algorithm can solve the proposed problem quickly, stably and effectively, so as to help the decision maker to make the logistics path decision quickly according to the required confidence level.

Considering the uncertainty in logistics and the risk behavior of decision makers, this paper studies integrated routing problem from the perspective of opportunity preference. Based on the chance function of satisfying the transportation time and cost requirements, a fourth party logistics routing integrated optimization problem model considering the chance preference of decision makers is established. According to the characteristics of the problem, an improved dual-population ant colony algorithm is designed to solve the proposed model. Numerical examples show the effectiveness the proposed methods.

This study proposes a garment modularization model based on an interactive genetic algorithm. The suggested model consists of extraction and identification of parts and the determination and implementation of connections. Rules and corresponding mathematical equations have been formulated for the part's extractions from the discarded products and connections for the redesigned products.

Sustainability entices scholars and practitioners while referring to reducing waste to control environmental degradation. One of the ways to safeguard natural resources is to increase the reuse of old or discarded products. The current study focuses on the redesign process to improve the reuse of products.

The intelligent system proposed based on the modularization techniques is expected to simplify and quantify the redesign process. The model can further help in the minimization of wastage and environmental degradation.

Presently, manual decisions are taken by the designers based on their memory, experience and intuition to extract and join the parts.

This study aims to explore how green supply chain management (GSCM) strategies can be effectively implemented for business supply chain operations, relationship management and product design to gain green competitive advantages.

An exploratory in-depth case study was conducted with one of the largest Chinese electronics manufacturers that is considered a leading GSCM adopter in the industry, to understand how the company adopts green supply chain practices across its multiple product lines.

The findings show that businesses can build different green focuses across GSCM elements of green operation, green relationship management and green product design to form diverse hybrid strategic solutions. They include green control, lean, leagile, agile and clean innovation while taking consideration of supply chain type and product lifespan. A taxonomy of four key GSCM strategic combinations is proposed based on the findings. The strategies align with green demand and supply chain characteristics balancing a series of business competitive objectives in terms of reducing pollution and waste, improving green cost efficiency, enhancing green demand innovation and building green service effectiveness.

This study lends insight into the strategic alignment relationships between product supply chain types and approaches to GSCM.

The findings of this study can support industry practitioners in formulating aligned GSCM strategies based on product types to achieve optimal results.

Optimised green supply chain design, operations and relationship management incorporating product attributes can help further minimise negative impacts of business activities on the environment.

This research provides a systematic understanding of how product supply chain types can influence GSCM strategy formulation. It gives a holistic picture of how hybrid choices of strategies with green supply chain operations, relationship management and product design can be formulated based on product and supply chain characteristics.

The concept of Circular Economy (CE) has gained significant traction in addressing the issue of Construction and Demolition (C&D) waste, which is generated because of global urbanisation and urban renewal. Therefore, this research aims to explore the applicability of CE strategies to minimise the C&D Waste Management (WM) issues in Sri Lanka considering the preconstruction stage of the building project.

The research adopted a qualitative approach, using three expert interview rounds with the Delphi technique. In each round, 17, 15 and 12 experts were involved. A manual content analysis method was used to analyse the collected data.

Findings uncovered effective strategies in CE to address the 14 issues within C&D WM and its effects on the project management iron triangle in Sri Lanka. Integrating CE strategies within the construction sector to tackle C&D WM issues can significantly contribute to establishing a more sustainable, robust and resource-conscious built environment. By adopting CE strategies such as design for adaptability of existing buildings and design for disassembly, construction projects can optimise the project's timeline, cost and quality factors.

This will help to minimise the demand for virgin materials and reduce the volume of waste generated. Using recycled materials also helps close the loop of the materials cycle, thereby contributing to the CE. Also, this research contributes uniquely by offering practical, context-driven solutions that align with Sri Lanka’s construction sector.