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A Decision Support System for Solving Multi-Objective Redundancy Allocation Problems

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2014年第8期30卷 1249-1262页

作者： Khalili-Damghani, Kaveh Abtahi, Amir-Reza Tavana, Madjid Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran Univ Econ Sci Fac Econ Insinuat Management Dept Knowledge Engn & Decis Sci Tehran Iran La Salle Univ Lindback Distinguished Chair Informat Syst & Deci Philadelphia PA 19141 USA

The Redundancy Allocation Problem (RAP) is a reliability optimization problem in designing series-parallel systems. The reliability optimization process is intended to select multiple components with appropriate levels of redundancy by maximizing the system reliability under some predefined constraints. Several methods have been proposed to solve the RAPs. However, most of these methods often treat RAP as a single objective problem of maximizing the system reliability (or minimizing the system design cost). We propose a Decision Support System for solving Multi-Objective RAPs. Initially, we use the Technique for Order Performance by Similarity to Ideal Solution method to reduce the multiple objective dimensions of the problem. We then propose an efficient epsilon-constraint method to generate non-dominated solutions on the Pareto front. Finally, we use a Data Envelopment Analysis model to prune the non-dominated solutions. A benchmark case is presented to assess the performance of the proposed system, demonstrate the applicability of the proposed framework, and exhibit the efficacy of the procedures and algorithms. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： redundancy allocation problem multiple objective decision support system reliability epsilon-constraint method TOPSIS data envelopment analysis

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A hybrid heuristic solution based on simulated annealing algorithm for energy efficient single machine scheduling problem with sequence dependent setup times

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2021年第1期36卷 407-420页

作者： Bektur, Gulcin Iskenderun Tech Univ Ind Engn TR-31200 Antakya Turkey

Increased carbon dioxide emissions and energy consumption increase interest in energy efficient scheduling problems. In this study, speed scaling method is discussed in order to provide energy saving in multiobjective energy efficient single machine scheduling problem. In the literature with the speed scaling method, the energy consumption rate increases when the machine is operated at high speed, but the job is completed in a shorter time. When the machine is operate at low speed, the energy consumption rate decreases but the completion time of the jobs are prolonged. In the study, the objective functions are minimization of the total amount of energy consuption and the number of tardy jobs. The problem is defined by the scheduling problem in a manufacturing firm that produces plastic parts. Jobs have a sequence dependent setup times, and the setup time of a job depends on the job completed before it. A mathematical model is proposed for the problem. The epsilon-constraint method is used for the solution of the problem. All Pareto efficient solutions for small size problems are obtained by using the proposed heuristic method. However, as a result of the problem not being solved in polynomial time, a multi-objective heuristic algorithm is proposed for solving large-scale problems. The success of the proposed algorithm is shown by comparing with Non- dominated Sorting Genetic Algorithm II.

关键词： Energy efficient single machine scheduling epsilon-constraint method multi-objective heuristic algorithm sequence dependent setup times multi-objective mathematical model

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Developing a hazardous waste management system with consideration of health, safety, and environment

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COMPUTERS & ELECTRICAL ENGINEERING 2020年 82卷

作者： Farrokhi-Asl, Hamed Makui, Ahmad Ghousi, Rouzbeh Rabbani, Masoud Iran Univ Sci & Technol Sch Ind Engn Tehran Iran Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

This paper presents a novel mathematical model for hazardous waste collection problem. The study aims to introduce a model in order to improve health, safety, environment (HSE) issues for hazardous waste collection problem in addition to considering operational cost. In addition, technologies which will be applied at each facility are investigated. To extend previous networks in this field of study, the vehicles' depot are considered,. In order to tackle the problem, epsilon-constraint and non-dominated sorting genetic algorithm II (NSGA-II) methods are applied. According to the results, we can state that NSGA-II mean value of comparison metrics are equivalent to the results of e-constraint with confidence level of 95%. Moreover, the sum of the distance's mean from the ideal point for e-constraint method is 4% less than NSGA-II which can be concluded as an acceptable result for NSGA-II algorithm. All in all, The presented model can help managers and decision makers to have a more comprehensive and thorough understanding the HSE issues with respect to the operational cost in the field of waste collection network design. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Waste collection problem HSE Multi objective optimization epsilon-constraint method Metaheuristic algorithms

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A location-inventory-routing problem to design a circular closed-loop supply chain network with carbon tax policy for achieving circular economy: An augmented epsilon-constraint approach

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2023年 257卷

作者： Govindan, Kannan Salehian, Farhad Kian, Hadi Hosseini, Seyed Teimoor Mina, Hassan Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai Peoples R China Univ Southern Denmark Danish Inst Adv Study Ctr Sustainable Supply Chain Engn Dept Technol & Innovat Odense Denmark Yonsei Univ Yonsei Frontier Lab Seoul South Korea Woxsen Univ Sch Business Sadasivpet Telangana India Univ Tehran Coll Engn Sch Ind Engn Tehran Iran K N Toosi Univ Technol Dept Ind Engn Tehran Iran Amin Mil Sci Univ Fac Sci & Tech Traff Police Tehran Iran Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 201306 Peoples R China

The emergence of the circular economy concept has pushed industry owners towards the green supply chain and waste reduction. The closed-loop supply chain originates from the concept of the circular economy and its purpose is to increase efficiency and profitability by reducing waste and energy consumption. Hence, in this research, an integrated bi-objective mixed-integer linear programming model is developed with the aim of optimizing both operational and strategic decisions in a closed-loop supply chain network. The proposed model benefits the location-inventory-routing problem to structure the network, and it applies a carbon tax policy and vehicle scheduling problem to reduce emissions and vehicle waiting time, respectively. Considering problems such as split-delivery, storage possibility, shortage, supplier selection, order allocation, heterogenous vehicles, and uncertain demand has led to the development of a comprehensive model. A stochastic scenario-based approach is used to deal with demand uncertainty, and an augmented epsilon-constraint method is employed to solve the proposed bi-objective model. The applicability of the proposed model and the effectiveness of the bi-objective solution approach for achieving circular economy are examined through its implementation in a cable and wire industry in Iran.

关键词： Circular closed-loop supply chain Carbon tax policy Location-inventory-routing problem Vehicle scheduling problem epsilon-constraint method

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DMOEA-εC: Decomposition-Based Multiobjective Evolutionary Algorithm With the ε-constraint Framework

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2017年第5期21卷 714-730页

作者： Chen, Jie Li, Juan Xin, Bin Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China

Decomposition is an efficient and prevailing strategy for solving multiobjective optimization problems (MOPs). Its success has been witnessed by the multiobjective evolutionary algorithm MOEA/D and its variants. In decomposition-based methods, an MOP is decomposed into a number of scalar subproblems by using various scalarizing functions. Most decomposition schemes adopt the weighting method to construct scalarizing functions. In this paper, another classical generation method in the field of mathematical programming, that is the epsilon-constraint method, is adopted for the multiobjective optimization. It selects one of the objectives as the main objective and converts other objectives into constraints. We incorporate the epsilon-constraint method into the decomposition strategy and propose a new decomposition-based multiobjective evolutionary algorithm with the epsilon-constraint framework (DMOEA-epsilon C). It decomposes an MOP into a series of scalar constrained optimization subproblems by assigning each subproblem with an upper bound vector. These subproblems are optimized simultaneously by using information from neighboring subproblems. Besides, a main objective alternation strategy, a solution-to-subproblem matching procedure, and a subproblem-to-solution matching procedure are proposed to strike a balance between convergence and diversity. DMOEA-epsilon C is compared with a number of state-of-theart multiobjective evolutionary algorithms. Experimental studies demonstrate that DMOEA-epsilon C outperforms or performs competitively against these algorithms on the majority of 34 continuous benchmark problems, and it also shows obvious advantages in solving multiobjective 0-1 knapsack problems.

关键词： epsilon-constraint method decomposition main-objective alternation strategy multiobjective optimization solution-to-subproblem matching procedure subproblem-tosolution matching procedure

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Scheduling of virtual manufacturing cells with outsourcing allowed

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2014年第12期27卷 1079-1089页

作者： Arkat, Jamal Ghahve, Homa Univ Kurdistan Dept Ind Engn Sanandaj Iran

This paper presents a bi-objective mixed integer programming model for operations scheduling in virtual manufacturing cells where outsourcing is allowed, and set-up times are considered to be sequence dependent. Two objective functions of the model are the minimisation of the maximum completion time (or makespan) and the minimisation of the total cost of inter and intra-plant transportation. Two multi-objective solution algorithms are then developed to solve the proposed model. The first algorithm is an epsilon-constraint method which can find the exact set of efficient solutions for small-size problems. Since the investigated problem is non-polynomial (NP) hard, exact algorithms cannot be used for large-scale real-world cases and, therefore, a bi-objective genetic algorithm (GA) is developed as the second algorithm. A numerical example is given to evaluate the effectiveness of the proposed model and solution algorithms. The results reveal the superiority of the proposed model over the base models and demonstrate that in comparison with the epsilon-constraint method, the proposed GA can obtain efficient solutions in much less computational time.

关键词： virtual manufacturing cell scheduling outsourcing multi-objective genetic algorithm epsilon-constraint method

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Optimal Operation for Economic and Exergetic Objectives of a Multiple Energy Carrier System Considering Demand Response Program

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ENERGIES 2019年第20期12卷

作者： Huang, Yu Li, Shuqin Ding, Peng Zhang, Yan Yang, Kai Zhang, Weiting North China Elect Power Univ Dept Automat Baoding 071003 Peoples R China

An MECS (multiple energy carrier system) could meet diverse energy needs owing to the integration of different energy carriers, while the distinction of quality of different energy resources should be taken into account during the operation stage, in addition the economic principle. Hence, in this paper, the concept of exergy is adopted to evaluate each energy carrier, and an economic-exergetic optimal scheduling model is formulated into a mixed integer linear programming (MILP) problem with the implementation of a real-time pricing (RTP)-based demand response (DR) program. Moreover, a multi-objective (MO) operation strategy is applied to this scheduling model, which is divided into two parts. First, the epsilon-constraint method is employed to cope with the MILP problem to obtain the Pareto front by using the state-of-the-art CPLEX solver under the General Algebraic Modeling System (GAMS) environment. Then, a preferred solution selection strategy is introduced to make a trade-off between the economic and exergetic objectives. A test system is investigated on a typical summer day, and the optimal dispatch results are compared to validate the effectiveness of the proposed model and MO operation strategy with and without DR. It is concluded that the MECS operator could more rationally allocate different energy carriers and decrease energy cost and exergy input simultaneously with the consideration of the DR scheme.

关键词： multiple energy carrier system epsilon-constraint method exergy optimal operation demand response

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Rough-fuzzy quadratic minimum spanning tree problem

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EXPERT SYSTEMS 2019年第2期36卷

作者： Majumder, Saibal Kar, Samarjit Pal, Tandra Natl Inst Technol Durgapur Dept Comp Sci & Engn Durgapur India Natl Inst Technol Durgapur Dept Math Durgapur 713209 W Bengal India

A quadratic minimum spanning tree problem determines a minimum spanning tree of a network whose edges are associated with linear and quadratic weights. Linear weights represent the edge costs whereas the quadratic weights are the interaction costs between a pair of edges of the graph. In this study, a bi-objective rough-fuzzy quadratic minimum spanning tree problem has been proposed for a connected graph, where the linear and the quadratic weights are represented as rough-fuzzy variables. The proposed model is formulated by using rough-fuzzy chance-constrained programming technique. Subsequently, three related theorems are also proposed for the crisp transformation of the proposed model. The crisp equivalent models are solved with a classical multi-objective solution technique, the epsilon-constraint method and two multi-objective evolutionary algorithms: (a) nondominated sorting genetic algorithm II (NSGA-II) and (b) multi-objective cross-generational elitist selection, heterogeneous recombination, and cataclysmic mutation (MOCHC) algorithm. A numerical example is provided to illustrate the proposed model when solved with different methodologies. A sensitivity analysis of the example is also performed at different confidence levels. The performance of NSGA-II and MOCHC are analysed on five randomly generated instances of the proposed model. Finally, a numerical illustration of an application of the proposed model is also presented in this study.

关键词： chance-constrained programming epsilon-constraint method MOCHC NSGA-II quadratic minimum spanning tree rough-fuzzy variable

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ε-constraint and Fuzzy Logic-Based Optimization of Hazardous Material Transportation via Lane Reservation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2013年第2期14卷 847-857页

作者： Zhou, Zhen Chu, Feng Che, Ada Zhou, MengChu Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China Univ Evry Val dEssonne Comp Integrat Biol & Complex Syst Lab F-91020 Evry France Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 200092 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

With economic development, a great amount of hazardous material is shipped in the transport network every day. Hazardous material transportation is well known for its high potential risk. An accident can cause very serious economic damage and will have a negative impact on public health and the environment over the long term. Transporting hazardous materials on special lanes can reduce the risk. However, a lane reservation strategy may worsen traffic conditions for other vehicles. This paper investigates a hazardous material transportation problem with lane reservation. The problem lies in how to choose lanes to be reserved in the network and select the path for each hazardous material shipment from the reserved lanes. The goal is to obtain the best compromise between the impact on normal traffic and the transportation risk. A multiobjective integer programming model is presented for the new problem. Then, an algorithm is developed based on the epsilon-constraint method and a fuzzy-logic-based approach. Pareto optimal solutions are obtained by the former, and a preferred solution is selected by the fuzzy-logic-based approach. Computational results demonstrate the efficiency of the proposed algorithm using an instance based on a real network topology and randomly generated instances.

关键词： Hazardous material transportation lane reservation multiobjective integer programming optimization risk epsilon-constraint method

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Strategic Bidding in Transmission Constrained Electricity Markets Using Artificial Bee Colony Algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2012年第16期40卷 1768-1788页

作者： Jain, A. K. Srivastava, S. C. Singh, S. N. Srivastava, L. Indian Inst Technol Kanpur Dept Elect Engn Kanpur 802016 Uttar Pradesh India Madhav Inst Sci & Technol Dept Elect Engn Gwalior India

This article presents a methodology for a supplier's optimal bidding strategy considering network constraints and rivals' bidding behaviors. This has been formulated as a bi-level multi objective optimization problem, in which the lower-level problem represents the market clearing process by the system operator for minimization of customers' payments. The upper-level problem is a multi-objective formulation that simultaneously considers profit maximization and risk minimization. Pareto-optimal solutions of the bi-level multi-objective optimization problem have been obtained using the epsilon-constraint method. A fuzzy decision-making process has been used to derive the most efficient solution among the Pareto-optimal solutions. Upper-level objective functions are highly non-linear;therefore, the artificial bee colony algorithm, a relatively new heuristic approach, has been applied to obtain the best solution to the bi-level multi objective optimization problem for a day-ahead hourly market. The effectiveness of the proposed method has been established on a 2-bus system and the IEEE-30 bus test system. The results, obtained using the artificial bee colony algorithm, have been compared with those obtained using a genetic algorithm based approach.

关键词： artificial bee colony algorithm bi-level multi objective optimization problem congestion management epsilon-constraint method rivals' bidding behavior optimal bidding strategy

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