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An integrated Bi-objective green vehicle routing and partial disassembly line problem for electronic waste: an industrial case study

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2025年第3期38卷 408-433页

作者： Durmaz, Nida Budak, Aysenur Gebze Tech Univ Fac Engn Ind Engn Dept Kocaeli Turkiye

Today, serious environmental problems arise with overconsumption. The most effective way of protecting the environment is recycling. To obtain maximum benefit from the recycling activities depends on the effective and robust design of the disassembly lines. In addition, the distribution plan of products to be recycled is as important as the disassembly process. Therefore, this study includes the integrated optimization of the partial disassembly line balancing problem and the multi-objective green vehicle routing problem for the first time. The integrated problem is formulated as a Multi-Objective Mixed Integer Linear Programming. The model's objectives consist of minimization of total CO2 emission and cost. The Pareto optimal solution set is found by solving this problem with the augmented epsilon-constraint method. The model respectively determines (i) the total number of opened workstations in the disassembly center, (ii) optimal task assignments to workstations in disassembly centers and non-disassembled tasks, (iii) total workstation times and idle times of each workstation, (iv) optimal distribution routes for all vehicles. This study examines a case study in Turkey. To determine the best Pareto optimal solution for homogeneous and heterogeneous fleets, the integrated multi-criteria decision-making method consisting of EWM (Entropy Weight method) and COPRAS (Complex Proportional Assessment) is used.

关键词： Green vehicle routing problem multi-objective optimization augmented epsilon-constraint method partial disassembly line multi-criteria decision-making

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Optimal design of distributed energy systems for industrial parks under gas shortage based on augmented ε-constraint method

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JOURNAL OF CLEANER PRODUCTION 2019年 218卷 782-795页

作者： Xing, Xiaokai Yan, Yamin Zhang, Haoran Long, Yin Wang, Yufei Liang, Yongtu China Univ Petr Beijing Key Lab Urban Oil & Gas Distribut Technol Fuxue Rd 18 Beijing 102249 Peoples R China Univ Tokyo Ctr Spatial Informat Sci 5-1-5 Kashiwanoha Kashiwa Chiba 2778563 Japan Univ Tokyo Dept Environm Syst Grad Sch Frontier Sci 5-1-5 Kashiwanoha Kashiwa Chiba 2778563 Japan China Univ Petr State Key Lab Heavy Oil Proc Fuxue Rd 18 Beijing 102249 Peoples R China

Natural gas distributed energy systems have developed rapidly owing to their high efficiency, low environmental impact, high energy supply reliability, and good economic returns. As the main users of natural gas distributed energy, industrial parks account for 67.7% of the total installed capacity of the industry. Therefore, disrupted gas supply to industrial parks during gas shortage periods results in decreased production and consequently huge economic losses. This study addresses this issue by attempting to develop an optimized design for distributed energy systems and natural gas allocation in multiple industrial parks under gas shortage conditions. A multi-objective mathematical programming (MOMP) model is established to minimize the total construction investment and operation costs of each industrial park considering emission, energy balance and other technical constraints. Using the augmented e-constraint method, optimal configurations of distributed energy systems, operation strategy, and economic and emission performance of each industrial park are determined. The distributed energy system design for three industrial parks in Jinan, China, is taken as an example to verify the model. Compared with the non-cooperation case, the annual total cost of full-cooperation can be reduced by 8.29%, and the annual total carbon emission of full-cooperation can be reduced by 427.55 t. Additionally, sensitivity analyses of energy price and total input natural gas are conducted to provide further insights into the optimization of distributed energy system for each industrial park. The results show that electricity price has the strongest impact on the economic performance of the industrial parks, followed by natural gas price and biomass price, but natural gas price does not affect the gas allocation ratio. Moreover, when the total input natural gas is increased by 10-90%, the total cost can be reduced by 0.69% -7.83%. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Distributed energy system Industrial park Gas shortage MOMP augmented epsilon-constraint method

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A hybrid GA-AUGMECON method to solve a cubic cell formation problem considering different worker skills

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COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期75卷 31-40页

作者： Bootaki, Behrang Mandavi, Iraj Paydar, Mohammad Mandi Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Babol Noshirvani Univ Technol Dept Ind Engn Babol Sar Iran

Part quality and consequently customer satisfaction besides cost functions are two of the most important issues for any firm. Balancing between these two goals leads to full utilization from manufacturing resources. Formerly, in cubic cell formation problem, where a part on a machine can be processed by various workers, worker assignment was done just by minimizing inter-cell movement criterion;so, the workers assigned into the processing cell are mostly selected rather than outsider workers. But, it is rational for the ties to be broken by skills of different workers in performing a special part on the dedicated machine. In this paper, a bi-objective cubic cell formation is presented with two non-homogeneous objective functions in order to minimize the inter-cell movements and maximize a part quality index. Quality index for each part is represented through a cubic matrix containing integer values of 1-5 (representing very bad, bad, medium, well and very well), which qualifies the process of part on a specific machine by a specific worker. To solve the problem, a hybrid GA-augmented epsilon-constraint method (GA-AUGMEON) is developed to reduce time consuming difficulty of AUGMECON method. To validate the model and the GA-AUGMECON algorithm, some randomly generated examples in small and large size are solved. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Cubic cellular manufacturing Worker flexibility Bi-objective programming Genetic algorithm augmented epsilon-constraint method

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augmented ε-constraint method in multiobjective staff scheduling problem: a case study

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第5-8期70卷 1505-1514页

作者： Sadjadi, Seyed Jafar Heidari, Mehdi Esboei, Amin Alinezhad Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

Staff scheduling is one of the most relevant issues among production planning managers. The problem is to set up an appropriate schedule for various employees to maximize the performance measurement. There are different conflicting criteria with any scheduling problem such as cost minimization, efficiency maximization, etc. The proposed model of this paper develops a new multiobjective decision-making scheduling problem, and the resulted problem is solved using two different techniques of goal programming and augmented epsilon constraint. The implementation of the new proposed model is demonstrated with a real-world case study, and they are analyzed. The preliminary results indicate that the epsilon-constraint method somewhat performs better than goal programming technique.

关键词： Staff scheduling Multiobjective programming Labor efficiency augmented epsilon-constraint method Goal programming

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An extended ε-constraint method for a bi-objective assortment optimization problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第5期31卷 3197-3219页

作者： Eskandari, Amin Ziarati, Koorush Nikseresht, Alireza Shiraz Univ Dept Comp Engn & Informat Technol Shiraz Iran Shiraz Univ Dept Knowledge & Informat Sci Shiraz Iran

The paper deals with assortment optimization when customers purchase products using a nonparametric choice model. Each customer is presented with a preference list and will buy an available highest-ranked offered product on the preference list. The paper presents a novel bi-objective integer programming model for assortment optimization following a ranking-based consumer choice model. The objectives are maximizing the expected revenue and maximizing customer satisfaction. This problem is recognized as an NP-hard problem. After developing the bi-objective model, an improved version of the augmented epsilon-constraint method is proposed to solve the problem and retrieve the Pareto-optimal solutions. In each iteration, this algorithm solves a single-objective linear integer programming sub-problem. Our approach aims to obtain a complete set of Pareto-optimal solutions. Finally, we present computational experiments on synthetic data. Several numerical illustrations are provided at the end to demonstrate the application of the proposed methodology. The computational results provide the decision-makers with valuable information regarding the factors that mainly affect the solutions.

关键词： assortment optimization bi-objective optimization customer satisfaction ranked-based choice model augmented epsilon-constraint method

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Bi-objective location problem with balanced allocation of customers and Bernoulli demands: two solution approaches

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SOFT COMPUTING 2019年第13期23卷 4999-5018页

作者： Shiripour, Saber Mahdavi-Amiri, Nezam Univ Garmsar Fac Engn Garmsar Iran Sharif Univ Technol Fac Math Sci Tehran Iran

A bi-objective stochastic capacitated multi-facility location-allocation problem is presented where the customer demands have Bernoulli distributions. The capacity of a facility for accepting customers is limited so that if the number of allocated customers to the facility is more than its capacity, a shortage will occur. The problem is formulated as a bi-objective mathematical programming model. The first objective is to find optimal locations of facilities among potential locations and optimal allocations of stochastic customers to the facilities so that the total sum of fixed costs of establishment of the facilities and the expected values of servicing and shortage costs is minimized. The second objective is to balance the number of allocated customers to the facilities. To solve small problems, the augmented epsilon-constraint method is used. Also, two metaheuristic solution approaches, non-dominated sorting genetic algorithm II (NSGA-II) and controlled elitist non-dominated sorting genetic algorithm II (CNSGA-II), are presented for solving large problems. Several sample problems are generated and with various criteria are tested to show the performance of the proposed model and the solution approaches.

关键词： Capacitated location-allocation problem Bi-objective model Stochastic demands augmented epsilon-constraint method Metaheuristic algorithm

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A multi-objective stochastic model for a reverse logistics supply chain design with environmental considerations

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第7期12卷 8017-8040页

作者： Moslehi, M. Saleh Sahebi, Hadi Teymouri, Ashkan Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

Some electronic devices have a short lifetime, and variety-seeking and consumerism are increasingly growing in today's societies. Moreover, electronic wastes contain precious substances such as gold, silver, copper, and aluminum. The proper disposal and processing of them by recycling offer considerable advantages to the environment, given the hazardous natures of such devices' substances. The proposed reverse logistics with waste electrical and electronic equipment (WEEE) is an important task considered by researchers, the use of which offers economic benefits and reduces the environmental impacts of wastes. The present study models the electrical and electronic equipment (EEE) reverse logistics process as a bi-objective mixed-integer programming model under uncertainties. The mathematical model investigates two objectives: an economic objective and an environmental objective. The first is minimizing cost, while the second is maximizing the environmental score by reverse logistics processes in recovering and recycling. The parameters of demand and WEEE return rate which is obtained from the customer were considered as two uncertain parameters. A scenario-based stochastic programming (SSP) approach is applied to deal with the uncertainties. A case study of an electronic equipment manufacturer in Esfahan, Iran was included. The model was solved by a nominal approach and an SSP approach via the epsilon-constraint (EC) and augmented epsilon-constraint (AEC) methods to obtain optimal Pareto solutions and compare the methods. Finally, the optimal results of the two approaches were evaluated. The results indicated that the SSP approach using the AEC method had better outcomes.

关键词： Reverse logistics Waste electrical and electronic equipment Scenario-based stochastic programming augmented epsilon-constraint method

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Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option

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ENERGY 2017年 118卷 827-839页

作者： Tabar, Vahid Sohrabi Jirdehi, Mehdi Ahmadi Hemmati, Reza Kermanshah Univ Technol Dept Elect Engn Kermanshah Iran

Renewable energy resources are often known as cost-effective and lucrative resources and have been widely developed due to environmental-economic issues. Renewable energy utilization even in small scale (e.g., microgrid networks) has attracted significant attention. Energy management in microgrid can be carried out based on the generating side management or demand side management. In this paper, portable renewable energy resource are modeled and included in microgrid energy management as a demand response option. Utilizing such resources could supply the load when microgrid cannot serve the demand. This paper addresses energy management and scheduling in microgrid including thermal and electrical loads, renewable energy sources (solar and wind), CHP, conventional energy sources (boiler and micro turbine), energy storage systems (thermal and electrical ones), and portable renewable energy resource (PRER). Operational cost of microgrid and air pollution are considered as objective functions. Uncertainties related to the parameters are incorporated to make a stochastic programming. The proposed problem is expressed as a constrained, multi-objective, linear, and mixed-integer programing., augmented epsilon-constraint method is used to solve the problem. Final results and calculations are achieved using GAMS24.1.3/CPLEX12.5.1. Simulation results demonstrate the viability and effectiveness of the proposed method in microgrid energy management. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： augmented epsilon-constraint method Microgrid Multi objective programming Portable renewable energy resource Stochastic programming

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A multi-objective distance friction minimization model for performance assessment through data envelopment analysis

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第1期279卷 132-142页

作者： Xiong, Beibei Chen, Haoxun An, Qingxian Wu, Jie Hunan Univ Sch Business Adm Changsha 410082 Hunan Peoples R China Univ Sci & Technol China Sch Management Hefei 230026 Anhui Peoples R China Univ Technol Troyes Charles Delaunay Inst Ind Syst Optimizat Lab UMR CNRS 6281 F-10004 Troyes France Cent S Univ Sch Business Changsha 410083 Hunan Peoples R China

The distance friction minimization (DFM) model proposed by Suzuki, Nijkamp, Rietveld, & Pels (2010) is an important data envelopment analysis (DEA) model for performance improvement. This model has been widely applied to improve the performance of government, finance and transportation sectors and energy-environment management. The DFM model is constructed to set input and output improvements based on the optimal weight vector of the traditional CCR model. However, non-unique optimal weight vectors of the CCR model may be obtained when different linear programming solvers are used. Thus, different values for the two distance friction (DF) objectives may be obtained. Furthermore, the DF values obtained using one optimal weight vector may be dominated by those obtained using another optimal vector. To address this issue, we propose an improved DFM model that considers all the possible optimal weight vectors of the CCR model to determine a set of non-dominated DF values definitively. Our DFM model is a multi-objective, quadratic and nonlinear programming model that is solvable by an augmented epsilon-constraint method that ensures all solutions are Pareto efficient. We demonstrate the applicability of our new model by using it to analyse China's transportation sectors. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Data envelopment analysis Distance friction minimization model augmented epsilon-constraint method

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Bi-objective optimization approach to a multi-layer location-allocation problem with jockeying

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 149卷 106740-106740页

作者： Chaleshtori, Amir Eshaghi Jahani, Hamed Aghaie, Abdollah KN Toosi Univ Technol Tehran Iran RMIT Univ Sch Business IT & Logist Melbourne Vic Australia

Multi-layer congested facility location problems (MLCFLPs) have been receiving increased attention over the past few years. In MLCFLPs, each layer includes several facilities that can provide different service. Reducing the total idle times in each layer's facilities' queues is of the utmost importance - especially when the network includes multiple identical collaborative facilities or when the organizers desire to apply severe dispatching rules in queue monitoring. As a new provision in this field of research, this study addresses a novel MLCFLP that includes a classical queuing system with jockeying, which allows the applicants/customers to receive service from the other layers of the network. Regarding the optimization approach, two objective functions are considered: (1) the minimization of the sum of waiting and traveling times, and (2) the minimization of facilities' maximum idleness probability. To find Pareto optimal solutions, an augmented-constraint method is utilized for solving the problem. Since the MLCFLP is NP-hard, in medium- and large-scale problems the MLCFLP is solved by a non-dominated sorting genetic algorithm (NSGA-II). Considering that the quality of meta-heuristic algorithms is critically related to their initial parameters, the Taguchi approach is used to calibrate the parameters of the NSGA-II. Four standard performance metrics are employed to evaluate the algorithms. We examine the effect of jockeying in a simulated manufacturing system as an example of a real-world problem. Several numerical experiments are introduced to evaluate the applicability of the model along different scales. The results demonstrate that in small-scale problems, the augmented epsilon-constraint method is a satisfactory solution. However, in the medium and larger-scale experiments, the NSGA-II provides optimal solutions with significantly lower computational times.

关键词： Multi-layer location-allocation problem Queuing system Jockeying NSGA-II Taguchi approach augmented epsilon-constraint method

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