This study explores the integration of reverse and forward supply chains in a Closed-Loop Supply Chain (CLSC) for electronic waste management, driven by business and government regulatory concerns. It highlights the e...
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This study explores the integration of reverse and forward supply chains in a Closed-Loop Supply Chain (CLSC) for electronic waste management, driven by business and government regulatory concerns. It highlights the economic benefits and efficient management of Electronic Waste (E-waste), particularly focusing on End-of-Life (EoL) products to tackle the global E-waste crisis. This research introduces a novel multi-objective mixed-integer linear programming model adapted for an E-waste CLSC network. This model incorporates hybrid manufacturing facilities and Triple Bottom Line (TBL) objectives to maximize profits and social innovations while minimizing gas emissions to reduce landfill waste. An application involving a computer manufacturing network in Ontario, Canada, utilizing Google Maps for distance calculations, illustrates the design and optimization impact of an electronic CLSC network. This study employs computational experiments and sensitivity analyses, using three solution methods, including weighted-sum, epsilon-constraint, and hybrid approaches within a multiperiod framework to validate the model's robustness. These methods help decision-makers integrate a TBL approach into the CLSC network which reflects economic, environmental, and social factors. By generating Pareto optimal solutions using these methods, decision-makers can evaluate different options through trade-off analysis. The findings show that the epsilon-constraint method offers a greater number of efficient solutions, enabling a better balance of objectives. Finally, this study concludes with managerial insights and recommendations based on the research outcomes. The findings provide insights into product and part flows, facilities utilization, and distribution across the network segments.
In the hybrid rotating seru production system (HRSPS), how to match multi-skilled workers and operation units is an important problem. The operation units include rotating serus and assembly line processes. This paper...
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In the hybrid rotating seru production system (HRSPS), how to match multi-skilled workers and operation units is an important problem. The operation units include rotating serus and assembly line processes. This paper proposes a flexible matching method of multi-skilled workers and operation units considering possible absences of multi-skilled workers in the HRSPS. First, the principles of matching scheme generation and the principles of matching scheme adjustment under absences are proposed. Second, according to the preference information of multi-skilled workers and operation units, the preference degree calculation methods are proposed. Furthermore, a multi-objective programming model for the flexible matching of multi-skilled workers and operation units is constructed, and an improved multi-objective particle swarm optimization algorithm (IMOPSO) is proposed to solve the model better. The numerical experiment results demonstrate that the method proposed in this paper can generate and adjust matching schemes in a relatively short time, and what is more, the obtained matching schemes and matching adjustment schemes have advantages in the satisfaction of multi-skilled workers and operation units. Therefore, the method proposed in this paper is feasible and effective.
The COVID-19 pandemic has increased the demand for life-saving devices known as 'ventilators,' which help critically ill patients breathe. Owing to the high global demand for ventilators and other medical equi...
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The COVID-19 pandemic has increased the demand for life-saving devices known as 'ventilators,' which help critically ill patients breathe. Owing to the high global demand for ventilators and other medical equipment, many Indian nonmedical equipment companies have risen to meet this demand. This unexpected demand for ventilators during the COVID-19 pandemic, similar to that for other EOL electronic medical devices, has become a severe problem for the nation. Consequently, the healthcare industry must efficiently handle EOL ventilators, which can be outsourced to 3PRLPs. 3PRLPs play a vital role in a company's reverse logistics activities. This study emphasises the 3PRLP selection process as a complex decision-making problem and the optimisation of order allocation to qualified 3PRLPs. As a result, this study proposes a two-phase hybrid decision-making problem. First phase combines the two multi-attribute decision-making methods to select 3PRLPs based on their assessed SPS and Second phase, the evaluated SPS was utilised as one of the objectives of a multi-objective linear programming model to allocate orders to the selected 3PRLPs. To solve the proposed model, both classical and modern approaches were used. The results show that the proposed framework can be successfully implemented in the current scenario of the healthcare industry.
In this paper, we consider a multi-objective Stochastic Interval-Valued Linear Fractional Integer programming problem (MOSIVLFIP). We especially deal with a multi-objective stochastic fractional problem involving an i...
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In this paper, we consider a multi-objective Stochastic Interval-Valued Linear Fractional Integer programming problem (MOSIVLFIP). We especially deal with a multi-objective stochastic fractional problem involving an inequality type of constraints, where all quantities on the right side are log-normal random variables, and the objective functions coefficients are fractional intervals. The proposed solving procedure is divided in three steps. In the first one, the probabilistic constraints are converted into deterministic ones by using the chance constrained programming technique. Then, the second step consists of transforming the studied problem objectives on an optimization problem with an interval-valued objective functions. Finally, by introducing the concept of weighted sum method, the equivalent converted problem obtained from the two first steps is transformed into a single objective deterministic fractional problem. The effectiveness of the proposed procedure is illustrated through a numerical example.
To improve the efficiency of gate reassignment and optimize the plan of gate reassignment,the concept of disruption management is introduced,and a multi-objective programming model for airport gate reassignment is ***...
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To improve the efficiency of gate reassignment and optimize the plan of gate reassignment,the concept of disruption management is introduced,and a multi-objective programming model for airport gate reassignment is *** the interests of passengers and the airport,the model minimizes the total flight delay,the total passengers′walking distance and the number of flights reassigned to other gates different from the planned *** to the characteristics of the gate reassignment,the model is *** the multi-objective programming model is hard to reach the optimal solutions simultaneously,a threshold of satisfactory solutions of the model is *** a simulated annealing algorithm is designed for the *** studies show that the model decreases the total flight delay to the satisfactory solutions,and minimizes the total passengers′walking *** least change of planned assignment is also *** results achieve the goals of disruption ***,the model is verified to be effective.
The ecological fragility of arid farming-pastoral ecotones is pronounced, and the promotion of regional ecological sustainability under the constraints of limited resources has become a crucial issue for these zones. ...
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The ecological fragility of arid farming-pastoral ecotones is pronounced, and the promotion of regional ecological sustainability under the constraints of limited resources has become a crucial issue for these zones. This study presents a novel framework for evaluating ecological sustainability, quantifying its relationship with ecological water consumption, and optimizing water resources allocation for improving ecological sustainability. A fuzzy credibility-constrained stochastic multi-objective programming (FCCSMOP) model is applied to optimize regional water allocation schemes. The proposed approach has advantages in: (1) quantifying the water-driven dynamics of ecological sustainability at the county scale;(2) balancing economic, ecological, and social benefits;and (3) addressing randomness and fuzziness caused by hydrological variability and water managers' preferences. This approach was applied to the northern foot of Yinshan Mountain (NFYM), Inner Mongolia Autonomous Region, China, leading to the following findings: (1) ecological sustainability in NFYM is poor, with 70.31% of the region having an ecological sustainability score below 0.43 in 2019;(2) there is a positive relationship between ecological sustainability and water consumption with the relatively high correlation (R2 is an element of [0.39, 0.87]);(3) the FCCSMOP model can effectively address the multiple conflicting objectives, randomness, and fuzziness when generating optimal water allocation schemes. A comparison between the optimization results (normal year, beta=0.75) and the status quo shows improvements of 5.60%, 19.45%, and 6.35% in ecological sustainability, water use efficiency, and the index of water use structure balance, respectively. The models and methods can also be applied to similar regions suffering water and ecological crisis.
PurposeAlthough 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. Th...
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PurposeAlthough 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) ***/methodology/approachThe 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 *** 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 ***/valueIn 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.
This paper presents triple-objective stochastic energy planning and management of a deltoid structure in which a microgrid, nano-grid, and main grid connect and exchange power simultaneously. In addition, the impact o...
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This paper presents triple-objective stochastic energy planning and management of a deltoid structure in which a microgrid, nano-grid, and main grid connect and exchange power simultaneously. In addition, the impact of hydrogen stations due to the growth of hydrogen vehicles and their crucial role in the power system's future to reduce pollution is also discussed. Moreover, the effect of time-based demand response programs (TOU) according to the elasticity matrix (different operators and price-sensitive flexible loads) for the proposed multilateral grid is investigated under diverse scenarios. Stochastic planning is performed to make results more realistic and authentic. The uncertain parameters for stochastic planning include wind pace, solar radiation, fuel rate, and various demands. The assumed triple objective functions for the proposed planning are the microgrid's profit, the nano-grid's cost, and the total multilateral grid's pollution. The problem is modeled as mixed-integer linear programming (MILP) and solved using the GAMS and LP metric approach. The final results show that by implementing the supposed planning, the microgrid's profit increases by about 22.53 $/day (10.8%), and the nano-grid's cost decreases by about 1.31 $/day (9.8%). On the other hand, the total environmental pollution is reduced significantly and reaches 1.06 kg/day.
This paper presents a novel multi-objective mean-variance mathematical programming approach to the dynamic pricing problem for seasonal products. The basic pricing scheme is a combination of phase-out pricing that gra...
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This paper presents a novel multi-objective mean-variance mathematical programming approach to the dynamic pricing problem for seasonal products. The basic pricing scheme is a combination of phase-out pricing that gradually lowers the price over time and clearance pricing in which the end-of-season inventory is sold altogether at a lower price to a wholesaler in order to make room for the next season's products. The model is then applied to a real-world case of a Jeans retailer in three different risk attitudes. Results show that the retailer should follow an almost fixed non-dynamic pricing strategy in the risk-taking attitudes, and a more flexible dynamic pricing strategy in risk-averse attitudes.
Inverse data envelopment analysis (DEA), which is an effective tool for determining inputs and outputs, is commonly applied in areas such as output prediction, resource allocation, and target setting. However, existin...
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Inverse data envelopment analysis (DEA), which is an effective tool for determining inputs and outputs, is commonly applied in areas such as output prediction, resource allocation, and target setting. However, existing inverse DEA methods typically assume precise and deterministic data, which limits applicability in uncertain production environments, particularly when both random and fuzzy environments are present. This study introduces a novel inverse DEA approach for optimizing inputs and outputs in mixed uncertainty environments. The proposed model allows decision-makers to achieve target efficiency and meet various input/output targets under different production scale assumptions. First, a new optimality principle for multi-objective fuzzy random problems is presented and the necessary theoretical conditions for input/output calculations are derived. Second, an equivalent linear model is introduced to solve the inverse DEA problem with fuzzy random variables, thereby overcoming the challenges associated with nonlinear programming. Notably, the proposed model offers enhanced flexibility as it does not rely on specific fuzzy numbers or predefined assumptions regarding random distributions. Finally, the effectiveness of the model is validated through numerical examples and a case study, demonstrating its practical application in complex decision-making scenarios.
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