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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 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.

In this study, to maximize returns and minimize investment risk, an attempt was made to form an optimal portfolio under conditions where the capital market has a price bubble. According to the purpose, the research was of the applied type, in terms of data, quantitative and postevent, and in terms of the type of analysis, it was of the descriptive-correlation type. Sequence, skewness and kurtosis tests were used to identify the months with bubbles from 2015 to 2021 in the Tehran Stock Exchange. After identifying the bubble courses, artificial bee colony meta-heuristic and invasive weed algorithms were used to optimize the portfolio. The purpose of this paper is to address these issues.

The existence of bubbles in the market, especially in the capital market, can prevent the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. However, due to the goal of investors to achieve a portfolio of high returns with the least amount of risk, there is need to pay attention to these markets increases.

The results identify 14 periods of price bubbles during the study period. Additionally, stock portfolios with maximum returns and minimum risk were selected for portfolio optimization. According to the results of using meta-heuristic algorithms to optimize the portfolio, in relation to the obtained returns and risk, no significant difference was observed between the returns and risk of periods with price bubbles in each of the two meta-heuristic algorithms. This study can guide investors in identifying bubble courses and forming an optimal portfolio under these conditions.

One of the limitations of this research is the non-generalizability of the findings to stock exchanges of other countries and other time periods due to the condition of the price bubble, as well as other companies in the stock market due to the restrictions considered for selecting the statistical sample.

This study intends to form an optimal stock portfolio in a situation where the capital market suffers from a price bubble. This study provides an effective and practical solution for investors in the field of stock portfolio optimization.

The migration background can influence the life satisfaction of migrants. The purpose of this paper is to examine the life satisfaction of migrants and particularly the satisfaction regarding their health in comparison to natives.

The life satisfaction of 50 Turkish-speaking migrants living in Vienna was compared with the life satisfaction of 50 native Austrians by the questionnaire of life satisfaction by Fahrenberg et al. (2000).

Turkish-speaking migrants had lower values than natives in all scales of the questionnaire concerning life satisfaction. Turkish-speaking women reported the lowest satisfaction regarding their health state. In the migrant group the satisfaction regarding health decreased with increasing age.

The Turkish version of the questionnaire was translated into Turkish by authors but not formally validated. Furthermore acculturation strategies as well as the mental and physical health state of the participant, which can crucially influence the life satisfaction of migrants, were not surveyed.

Migrants have lower life satisfaction possibly because of their physical and mental health problems. Therefore in countries with a high proportion of migrants the health-care system should be adapted for the needs of migrants, especially for the needs of women and older migrants in order to increase the utilization of the health-care services, primarily the use of the preventive health-care services.

The results of the present study can be helpful to develop strategies for improving the life satisfaction of migrants, especially the satisfaction regarding their health.

To the authors’ knowledge, the present study is the first research project in Vienna conducted to estimate the impact of migration background on life satisfaction.

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.

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.

This paper aims to discuss the sequence-dependent forward setup time (FST) and backward setup time (BST) consideration for the first time in two-sided assembly lines. Sequence-dependent FST and BST values must be considered to compute all of the operational times of each station. Thus, more realistic results can be obtained for real-life situations with this new two-sided assembly line balancing (ALB) problem with setups consideration. The goal is to obtain the most suitable solution with the least number of mated stations and total stations.

The complex structure it possesses has led to the use of certain assumptions in most of the studies in the ALB literature. In many of them, setup times have been neglected or considered superficially. In the real-life assembly process, potential setup configurations may exist between each successive task and between each successive cycle. When two tasks are in the same cycle, the setup time required (forward setup) may be different from the setup time required if the same two tasks are in consecutive cycles (backward setup).

Algorithm steps have been studied in detail on a sample solution. Using the proposed algorithm, the literature test problems are solved and the algorithm efficiency is revealed. The results of the experiments revealed that the proposed approach finds promising results.

The sequence-dependent FST and BST consideration is applied in a two-sided assembly line approach for the first time. A genetic algorithm (GA)-based algorithm with ten different heuristic rules was used in this proposed model.

The purpose of this paper is to develop a feasible sequence-oriented new discrete particle swarm optimization (NDPSO) algorithm with novel particles’ updating mechanism for solving simple assembly line balancing problems (SALBPs).

In the NDPSO, a task-oriented representation is adopted to solve type I and type II SALBPs, and a particle directly represents a feasible task sequence (FTS) as a permutation. Then, the particle (permutation) is updated as a whole using the geometric crossover based on the edit distance with swaps for two permutations. Furthermore, the fragment mutation with adaptive mutation probability is incorporated into the NDPSO to improve exploration ability.

Case study illustrates the effectiveness of the NDPSO. Comparative results between the NDPSO and existing real-encoded PSO (CPSO) and direct discrete PSO (DDPSO) against benchmark instances of type I SALBP and type II SALBP show promising higher performance of the proposed NDPSO.

A novel particles’ updating mechanism for FTS-encoded particle is proposed to solve the SALBPs. The comparative results indicate that updating of FTS as a whole seems superior to existing updating of FTS by fragment with respect to exploration ability for solving SALBPs. The novel particles’ updating mechanism is also applicable to generalized assembly line balancing problems.