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New multi-objective optimization model for tourism systems with fuzzy data and new approach developed epsilon constraint method

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OPSEARCH 2023年第3期60卷 1360-1385页

作者： Shojatalab, Gholamreza Nasseri, Seyed Hadi Mahdavi, Iraj Univ Mazandaran Fac Math Sci Dept Math Babolsar Iran Mazandaran Univ Sci & Technol Dept Ind Engn Babolsar Iran

Tourism is one of the fastest-growing industries in the world and the most significant service sector industry. A fundamental question that can be prompted is how to minimize the total tour cost and, at the same time, design the tour to make the tourists get maximum satisfaction. This paper proposes a new multi-objective optimization model for tourism systems with fuzzy data and new approach developed epsilon constraint method. We have considered problem modelling in the form of multi-objective optimization in which we can optimize the conflicting goals at the same time. Here we have considered a provider of tour packages that intends to minimize its total costs and, at the same time, maximize the satisfaction of tourists with their packages. In our proposed model, some of the model parameters are fuzzy numbers, so an appropriate algorithm is proposed to solve the multi-objective optimization model. The results can offer constructive suggestions on how to design tours on the part of tourism enterprises and choose a proper tour. One of the advantages of the method used in this article is the simultaneous use of deterministic (certain) variables and fuzzy variables, which is presented as a multi-objective optimization model. Also, a new constraint is added to our model according to the relationship between cost and satisfaction level. Also, to deal with this specific multi-objective optimization model, a new algorithm with new approach Developed epsilon constraint method is proposed. The proposed algorithm is used in a case study, and in this example, it performed better than the other algorithms.

关键词： Multi-objective optimization Tourism system Fuzzy number Solving algorithm epsilon constraint method Pareto optimality

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A Multi-Objective Mathematical Model for Level of Repair Analysis with Lead Times and Multi-Transportation Modes

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2022年第1期21卷 423-440页

作者： Bicakci, Ismail Ic, Yusuf Tansel Karasakal, Esra Dengiz, Berna Baskent Univ Dept Ind Engn TR-06790 Ankara Turkey Middle East Tech Univ Dept Ind Engn TR-06800 Ankara Turkey

In the event of failure of the product, level of repair analysis (LORA) is used to determine (1) whether the defective component should be discarded or repaired and (2) where this repair is made. In the literature, these repair operations are made with the aim of minimizing the total life cycle cost of the product. In this paper, we develop a multi-objective decision model that minimizes both the repair time (affected by lead times) and the repair costs. Our proposed model also considers the movement of the defective components to be performed by multiple transportation modes such as highway, railway, and airway. We use the epsilon constraint method to generate the Pareto frontier and analyze the trade-off between total repair costs and total repair time. We demonstrate the approach on an example problem.

关键词： Level of repair analysis multi-objective optimization epsilon constraint method lead time Pareto frontier

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Life cycle option selection of disassembly parts for material-based CO2 saving rate and recovery cost: Analysis of different market value and labor cost for reused parts in German and Japanese cases

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2019年 213卷 229-242页

作者： Hasegawa, Shota Kinoshita, Yuki Yamada, Tetsuo Bracke, Stefan Univ Electrocommun Dept Informat Tokyo Japan Univ Wuppertal Chair Reliabil Engn & Risk Analyt Wuppertal Germany

Global warming is the major global environmental issue. To achieve less greenhouse gas emissions with circular economy for manufacturing assembly products, the key is to recovery including reuse and recycling End-of-life (EOL) products. Since reuse and recycling can avoid manufacturing of virgin materials, life cycle options such as reuse, recycling and disposal for each part entailing manual disassembly which is in high cost, the disassembly parts selection is carried out to decision for reuse and recycling the EOL assembly product environmentally and economically. Additionally, in the reuse of an assembly product, the revenue by reusing are different among each country owing to the differences of the second-hand market prices and labor cost by disassembly among the countries. This paper proposes a life cycle option selection of disassembly parts for material-based CO2 saving rate and recovery cost with different market value. Each part is selected from reuse, recycling and disposal to harmonize the material based CO2 saving rate and the cost. First, the procedures for designing the disassembly parts selection is presented, and life cycle option selection using 0-1 integer programming with the epsilon constraint method is formulated. Next, a numerical experiment of the results of the disassembly parts selection in the case of a computer is conducted. Finally, owing to the differences of sales revenue by parts reuse in the second-hand market prices, cases of Japan and Germany are examined for establishing multi-value circulation.

关键词： Circular economy Life cycle inventory database with the input-output tables Recyclability evaluation method Life expectancy epsilon constraint method Multi-value circulation

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Network configuration distributed production scheduling problem: A constraint programming approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 188卷

作者： Ziadlou, Ghazal Emami, Saeed Asadi-Gangraj, Ebrahim Babol Noshiravani Univ Technol Dept Ind Engn Babol *** Iran

Nowadays, many massive factories are forced to distribute their products in several manufacturing units. This issue has caused the emergence of a novel category of problems called distributed production scheduling, which is vital in today's growing world. In this paper, the distributed production scheduling problem by considering network configuration with two echelons is addressed. The first and second echelon factories have different job configurations and have a hybrid flow shop and a flexible job shop environment, respectively. For this problem, A bi-objective mixed integer linear programming (MILP) model is presented to minimize the maximum completion time of jobs and transportation costs between the selected factories in two echelons, respectively. Consequently, the epsilon constraint method is used to deal with this bi-objective model. In addition, since distributed scheduling problems are classified as NP-Hard problems, it is very challenging to solve them for largesized instances. For this reason, a constraint programming model (CP) is also proposed. To evaluate the performance of the proposed MILP model and CP model, a total of 180 numerical instances are randomly generated in small, medium, and large sizes. The obtained results demonstrate the significant ability of the constraint programming approach in solving complex distributed scheduling problems even for large-sized instances with 30 jobs, 10 stages/operations for each job, 6 machines for each stage/operation, and 4 factories at each echelon in a reasonable time and proof that the CP model can outperform the MILP model in this problem.

关键词： Distributed scheduling Multi-factory scheduling Network configuration constraint programming epsilon constraint method

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A mutli-objective artificial electric field algorithm with reinforcement learning for milk-run assembly line feeding and scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 190卷

作者： Zhou, Binghai Wen, Mingda Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

In automotive mixed-model assembly lines (MMALs), a large number of different parts need to be supplied to the assembly lines on time, which poses significant logistical challenges for manufacturers. However, consistently supplying parts for MMALs is a very complex issue due to factors such as diverse component requirements and logistical coordination in the supply chain. In this paper, we propose a bi-objective optimization problem to minimize the line -side inventory and energy consumption in a milk-run material distribution system. Meanwhile, the number of Kanban and the capacity of the material bin that affect the scheduling is jointly optimized, so that the material distribution scheduling plan is optimized. Considering the character of the problem, a multiobjective artificial electric field algorithm with SARSA mechanism (MOAEFASA) is developed to solve the problem. The algorithm proposed combines the merits of the artificial electric field algorithm (AEFA) and the framework of the non-dominated sorting genetic algorithm (NSGA-II). In addition, several optimization strategies are used to optimize the performance of the algorithm. Finally, the validity of the mathematical model is verified through the epsilon constraint method and the superiority of the MOAEFASA is illustrated by numerical experiments with four outstanding meta-heuristics.

关键词： Multi-objective artificial electric field algorithm SARSA selection mechanism Milk-run Scheduling epsilon constraint method

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Multi-objective dragonfly algorithm for optimizing a sustainable supply chain under resource sharing conditions

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Golmohammadi, Amir-Mohammad Abedsoltan, Hamidreza Goli, Alireza Ali, Irfan Arak Univ Dept Ind Engn Arak Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Univ Isfahan Fac Engn Dept Ind Engn & Future Studies Esfahan Iran Aligarh Muslim Univ Dept Stat & Operat Res Aligarh 202002 India

This research addresses optimizing production, inventory, location, and routing in a multi-level supply chain. In the studied supply chain, products are first sent to several distribution centers (DC) after production. Next, these products are delivered to several companies. Companies are divided into two independent groups considering sharing logistics resources. In this regard, vehicle routing optimization is applied for each group. For this pur -pose, a mixed-integer linear programming model has been developed. This model simultaneously minimizes total supply chain costs and total negative environmental impacts. A multi-objective dragonfly algorithm (MODA) has been developed to solve this model. Then, the implementation results are compared with the Non-Dominated Sorting Genetic Algorithm and epsilon constraint method (EPC). Comparing the solution methods based on numerous test problems shows that the proposed MODA algorithm can be examined differently. The MODA algorithm has been able to have a significant advantage in providing solutions close to ideal points. Moreover, this algorithm has provided the most effective Pareto solutions in different test problems. However, the NSGA-II algorithm performs better regarding the spread of non-dominated solutions.

关键词： Shared logistics Location and routing Supply chain Multi-objective dragonfly algorithm NSGA-II algorithm epsilon constraint method

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Multi-objective location-distribution optimization in blood supply chain: an application in Turkiye

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BMC PUBLIC HEALTH 2024年第1期24卷 1-15页

作者： Altunoglu, Burcu Sir, Gul Didem Batur Univ Turkish Aeronaut Assoc Dept Ind Engn TR-06790 Ankara Turkiye Gazi Univ Dept Ind Engn TR-06570 Ankara Turkiye

Purpose Blood donors are crucial in maintaining the blood supply chain. This study aims to improve the location and distribution of blood donation centers by focusing on two main objectives: minimizing costs and maximizing quality. Minimizing costs includes setting up and transporting blood efficiently while maximizing quality to ensure that blood products are delivered to hospitals promptly and in the right quantities. methods A multi-objective mathematical model is proposed to address the placement of both fixed and mobile blood donation centers. The first objective focuses on minimizing the costs of setting up centers and transporting blood. The second objective aims to maximize quality by ensuring timely deliveries and meeting hospitals' blood demand. The model utilizes real-world traffic and blood donation data from urban settings to simulate its effectiveness and applicability in practice. The model uses the epsilon constraint method to optimize both objectives simultaneously. Results The model was tested in various scenarios, optimizing cost and quality separately. The algorithm determined the ideal locations for blood donation centers to meet demand by exploring different options. It also accounted for factors that reduce quality, such as delayed deliveries and product returns, and showed that these issues could be minimized. Conclusion This study highlights the need to balance cost and quality when determining the locations of blood donation centers. Using the epsilon constraint method, the model successfully optimized both objectives, offering valuable insights for improving the efficiency and effectiveness of blood donation operations.

关键词： Health systems Blood donation Multi-objective mathematical model epsilon constraint method Location problem Distribution problem

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Research on multi-objective network optimisation of power material distribution based on the improved e constraint method

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2022年第3期16卷 225-238页

作者： Wang, Guanghui Li, Yumin Zhengzhou Univ State Grid Qinghai Elect Power Co Power Dispatching Control Ctr Sch Management Room 1312 Bldg 13 Xining 810008 Qinghai Peoples R China Zhengzhou Univ Family Coll Sch Management 100 Sci Ave High Tech Zone Zhengzhou 450066 Henan Peoples R China

Aiming at the multi-objective distribution path optimisation of power materials, a multi-objective distribution planning system based on the improved epsilon constraint algorithm is designed. Then a local power material transportation example in China is selected to simulate and test the system. The results show that the optimal solution routes obtained by the system and the planning system based on the naive epsilon constraint algorithm are completely consistent except for the material demand node j(5), j(7), j(8) . At the same time, the average total transportation time and average total transportation cost of the system within epsilon(3) calculation accuracy are 386.55 min and 283.06 k$ respectively, which are 4.62%, 9.41%, 6.33% and 1.56%, 6.98%, 3.45% less than the corresponding data of the optimal solution of the system based on the naive epsilon constraint algorithm, particle swarm optimisation algorithm and fast RCNN algorithm, respectively. The data show that the algorithm can shorten the time and cost of transportation tasks, and the planning effect is better.

关键词： epsilon constraint method electric power materials multi-objective planning transportation route cost control

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A NEW MODEL FOR PROBABILISTIC MULTI-PERIOD MULTI-OBJECTIVE PROJECT SELECTION PROBLEM 24

A NEW MODEL FOR PROBABILISTIC MULTI-PERIOD MULTI-OBJECTIVE P...

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24th International Conference on Production Research (ICPR)

作者： Khalili-Damghani, K. Poortarigh, M. Pakgohar, A. Islamic Azad Univ Tehran South Branch Dept Ind Engn Tehran Iran Econ Sci Univ Tehran Iran Sheffield Business Sch Dept Finance Accounting & Business Syst Sheffield S Yorkshire England

ISBN: (纸本)9781605955070

The project selection problem is considered as one of the most imperative decisions for investor organizations. Due to non-deterministic nature of some criteria in the real world projects in this paper, a new model for project selection problem is proposed in which some parameters are assumed probabilistic. This model is formulated as a non-linear, multi-objective, multi-period, zero-one programming model. Then the epsilon constraint method and an algorithm are applied to check the Pareto front and to find optimal solutions. A case study is conducted to illustrate the applicability and effectiveness of the approach, with the results presented and analysed. Since the proposed model is more compatible with real world problems, the results are more tangible and trustable compared with deterministic cases. Implications of the proposed approach are discussed and suggestions for further work are outlined.

关键词： Project selection multi objective programming chance constraint epsilon constraint method

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A multi-objective optimization model for shelter location-allocation in response to humanitarian relief logistics

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ASIAN JOURNAL OF SHIPPING AND LOGISTICS 2021年第2期37卷 149-156页

作者： Praneetpholkrang, Panchalee Van Nam Huynh Kanjanawattana, Sarunya Japan Adv Inst Sci & Technol Nomi Ishikawa 9231211 Japan Suranaree Univ Technol Nakhon Ratchasima 30000 Thailand

Decision-making for shelter location-allocation influences the success of disaster response and affects the security of victims. This paper proposes a multi-objective optimization model for determining shelter location-allocation in response to humanitarian relief logistics. Three objective functions are formulated to improve both efficiency and effectiveness. The first objective is to minimize total costs, including fixed costs for opening the shelters, transportation costs, and service costs. The second objective is to minimize the total time for evacuating victims from all affected areas to allocated shelters. The third objective is to minimize the number of shelters required to provide thorough service to victims. The epsilon constraint method (EC) and Goal Programming (GP) are employed for solving the proposed model. The applicability of the proposed model is validated through a case study of flooding in Surat Thani, Thailand. The Pareto efficiency obtained from solving the proposed model is compared with current shelter location-allocation plans determined by the government sector. The comparisons reveal that the results obtained from solving the proposed model outperform current shelter location-allocation plans. Furthermore, the results of this study could provide an advantage to decision-makers considering appropriate strategies for disaster response. (C) 2021 The Author(s). Production and hosting by Elsevier B.V.

关键词： epsilon constraint method Goal Programming Emergency logistics Discrete facility location Multi-criteria Disaster management Flood

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