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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.

The main operations of the powder-coating process are staggered along a closed-loop conveyor. Given the volatile market demands, using a fixed level of staffing may result in significant productivity losses. The present study aims to capture stochastic behavior and optimize operator assignment problems in a practical powder-coating process. By using the proposed methodology, when demand changes, the optimal operator assignment configuration can be provided, ensuring high labor productivity.

The powder-coating process is an important industrial application and is often a labor-intensive system. The present study adopts a practical case to optimize its staffing level. Because of its operational complexity, the problem is solved by a proposed simulation-optimization approach. The results are promising, and the proposed methodology is shown to be an effective approach.

The proposed methodology was tested for various demand levels. The optimized operator assignment configuration always improves on the performance of other staffing levels. Given the same daily throughput, the optimized operator assignment configuration can improve performance by as much as 19%. In scenarios where there is increased demand, the resulting reduction in overtime work improves performance by between 20.33% and 56.72%. In scenarios where there is reduced demand, the optimized staffing level produces improvements between 3.13% and 50%. Compared with the fixed staffing policy of the case company, the flexible staffing policy of the proposed methodology can maintain high labor productivity across demand variations. The results are consistent with the Shojinka philosophy of the Toyota Production System.

This study proposes a solution to the operator assignment decision in a labor-intensive manufacturing system – a powder-coating processing system. Powder coating provides a solid powder coating without any solvent. Because of its excellent application performance and environmental protection, it is widely used in the field of metal coating, especially appliances for offices and homes. Most of the existing literature has solved the problem by making unrealistic assumptions. The present study proposes a simulation-optimization method to solve a practical problem in powder-coating processing. The effectiveness of the proposed methodology is illustrated by a practical application. According to the experimental results, five operators can be saved for the same daily throughput. An average of 35 and 19 min of overtimes can be saved when demand increases by 10% and 20% with one less operator; between 2 and 16 operators can be saved when demand falls by 10%–60%.

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.

The purpose of this paper is to cut down energy consumption and eliminate production waste on mixed-model assembly lines. Therefore, a supermarket integrated dynamic cyclic kitting system with the application of electric vehicles (EVs) is introduced. The system resorts to just-in-time (JIT) and segmented sub-line assignment strategies, with the objectives of minimizing line-side inventory and energy consumption.

Hybrid opposition-based learning and variable neighborhood search (HOVMQPSO), a multi-objective meta-heuristics algorithm based on quantum particle swarm optimization is proposed, which hybridizes opposition-based learning methodology as well as a variable neighborhood search mechanism. Such algorithm extends the search space and is capable of obtaining more high-quality solutions.

Computational experiments demonstrated the outstanding performance of HOVQMPSO in solving the proposed part-feeding problem over the two benchmark algorithms non-dominated sorting genetic algorithm-II and quantum-behaved multi-objective particle swarm optimization. Additionally, using modified real-life assembly data, case studies are carried out, which imply HOVQMPSO of having good stability and great competitiveness in scheduling problems.

The feeding problem is based on static settings in a stable manufacturing system with determined material requirements, without considering the occurrence of uncertain incidents. Current study contributes to assembly line feeding with EV assignment and could be modified to allow cooperation between EVs.

The dynamic cyclic kitting problem with sub-line assignment applying EVs and supermarkets is solved by an innovative HOVMQPSO, providing both novel part-feeding strategy and effective intelligent algorithm for industrial engineering.

The purpose of the study is to fill a gap in the literature on mathematical production planning (joint balancing and sequencing) in the fashion industry. It considers in particular situations of mass customization, made-to-measure or small lot sizes.

The paper develops a mathematical model based on product options and attributes instead of fixed variants. It proposes an easy-to-use genetic algorithm to solve the resulting optimization problem. Functionality and performance of the algorithm are illustrated via a computational study.

An easy-to-implement, yet efficient algorithm to solve the multi-objective implementation of a problem structure that becomes increasingly relevant in the fashion industry is proposed. Implementation of the algorithm revealed that the algorithm is ideally suited to generate significant savings and that these savings are impervious to problem and thus company size.

The solutions from the algorithm (Pareto-efficient frontier) offer decision-makers more flexibility in selecting those solutions they deem most fitting for their situation. The computational study illustrates the significant monetary savings possible by implementing the proposed algorithm to practical situations.

In contrast to existing papers, for the first time, to the best of the author’s knowledge, the focus of the joint balancing and sequencing approach has been applied in the fashion instead of the automotive industry. The applicability of the approach to specific fields of the fashion industry is discussed. An option and attributes-based model, rarely used in general assembly line sequencing per se, is used for more flexibility in representing a diverse set of model types.

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.

The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.

This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.

The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.

Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

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.

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 aim of the present work is to develop a method to find the preventive measures for COVID-19 and their priorities. These preventive measures are prioritized according to the expert opinion.

An integrated method using the Kano model and Fuzzy-AHP is used to achieve the study objectives. First, the preventive measures are identified according to the expert. Next, the Kano model is used to determine the different Kano categories for remedial activities that are identified by the World Health Organization (WHO) and other medical authorities. Finally, Fuzzy-AHP is applied to determine the weights of these activities and assign the priorities according to their impact.

It is observed that the activity Avoid Travelling is the most important classification or category with the highest weight as compared to the other activities and sub-activities. It is also noticed that the category packed food items get the lowest weight and is the least important classification or category. In this work, two different approaches, designed for different purposes, provide similar results and verify each other.

Research contributing to the classification and prioritization of preventive activities using Kano and Fuzzy-AHP methods is not available. In the critical time of COVID-19, when governments are obliged to deal with many infected patients and a high number of deaths, one can focus on different preventive activities according to their classification, weights and ranks.