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Threshold robustness in discrete facility location problems: a bi-objective approach

OPTIMIZATION LETTERS 2015年第7期9卷 1297-1314页

作者： Carrizosa, Emilio Ushakov, Anton Vasilyev, Igor Univ Seville Inst Matemat E-41012 Seville Spain Russian Acad Sci Siberian Branch Matrosov Inst Syst Dynam & Control Theory Irkutsk 664033 Russia

The two best studied facility location problems are the -median problem and the uncapacitated facility location problem (Daskin, Network and discrete location: models, algorithms, and applications. Wiley, New York, 1995;Mirchandani and Francis, Discrete location theory. Wiley, New York, 1990). Both seek the location of the facilities minimizing the total cost, assuming no uncertainty in costs exists, and thus all parameters are known. In most real-world location problems the demand is not certain, because it is a long-term planning decision, and thus, together with the minimization of costs, optimizing some robustness measure is sound. In this paper we address bi-objective versions of such location problems, in which the total cost, as well as the robustness associated with the demand, are optimized. A dominating set is constructed for these bi-objective nonlinear integer problems via the -constraint method. Computational results on test instances are presented, showing the feasibility of our approach to approximate the Pareto-optimal set.

关键词： Discrete facility location Robustness Bi-objective combinatorial optimization p-Median problem UFLP epsilon-constraint method delta-Dominating set

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Multi-Objective Optimization of Pseudo-Dynamic Operation of Naphtha Pyrolysis by a Surrogate Model

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CHEMICAL ENGINEERING & TECHNOLOGY 2015年第5期38卷 900-906页

作者： Jin, Yangkun Li, Jinlong Du, Wenli Qian, Feng E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China E China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

A simple pseudo-dynamic surrogate model is developed in the framework of the state space model with the feed-forward neural network to replace the complex free radical pyrolysis model. The surrogate model is then applied to investigate the multi-objective optimization of two key performance objectives with distinct contradiction: the mean yields of key products and the day mean profits. The epsilon-constraint method is employed to solve the multi-objective optimization problem, which provides a broad range of operation conditions depicting tradeoffs of both key objectives. The Pareto-optimal frontier is successfully obtained and five selected cases on the frontier are discussed, suggesting that flexible operations can be performed based on industrial demands.

关键词： epsilon-constraint method Multi-objective optimization Naphtha pyrolysis process Surrogate model

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Design of SCADA water resource management control center by a bi-objective redundancy allocation problem and particle swarm optimization

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2015年 133卷 11-21页

作者： Dolatshahi-Zand, Ali Khalili-Damghani, Kaveh Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran

SCADA(1) is an essential system to control critical facilities in big cities. SCADA is utilized in several sectors such as water resource management, power plants, electricity distribution centers, traffic control centers, and gas deputy. The failure of SCADA results in crisis. Hence, the design of SCADA system in order to serve a high reliability considering limited budget and other constraints is essential. In this paper, a bi-objective redundancy allocation problem (RAP) is proposed to design Tehran's SCADA water resource management control center. Reliability maximization and cost minimization are concurrently considered. Since the proposed RAP is a non-linear multi-objective mathematical programming so the exact methods cannot efficiently handle it. A multi-objective particle swarm optimization (MOPSO) algorithm is designed to solve it. Several features such as dynamic parameter tuning, efficient constraint handling and Pareto gridding are inserted in proposed MOPSO. The results of proposed MOPSO are compared with an efficient epsilon-constraint method. Several non-dominated designs of SCADA system are generated using both methods. Comparison metrics based on accuracy and diversity of Pareto front are calculated for both methods. The proposed MOPSO algorithm reports better performance. Finally, in order to choose the practical design, the TOPSIS algorithm is used to prune the Pareto front. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： SCADA Multi-objective redundancy allocation problem Meta-heuristics Multi-objective particle swarm optimization epsilon-constraint method Adaptive grid

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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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Optimal Behavior of a Hybrid Power Producer in Day-Ahead and Intraday Markets: A Bi-Objective CVaR-Based Approach

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2021年第2期12卷 931-943页

作者： Khaloie, Hooman Mollahassani-Pour, Mojgan Anvari-Moghaddam, Amjad Univ Sistan & Baluchestan Fac Elect & Comp Engn Zahedan *** Iran Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

Coordinated operation of various energy sources has drawn the attention of many power producers worldwide. In this paper, a Concentrating Solar Power Plant (CSPP) along with a wind power station, a Compressed Air Energy Storage (CAES) unit, and a Demand Response Provider (DRP) constitute the considered Hybrid Power Producer (HPP). In this regard, this paper deals with the optimal participation of the mentioned HPP in the Day-Ahead (DA), and intraday electricity markets by benefiting from the joint configuration of all accessible resources. To attain risk-averse strategies in the suggested model, Conditional Value-at-Risk (CVaR) based on the epsilon-constraint technique is employed, while its efficiency is validated compared to the previously applied method to such problems. On the whole, the main contributions of this work lie in: 1) proposing a novel model for optimal behavior of a CSPP-based HPP in DA, and intraday markets using a three-stage decision-making architecture, and 2) developing a bi-objective optimization framework to improve the functioning of the risk-constrained algorithm. Simulation results reveal that taking advantage of the CSPP in the intraday market, and coordinated operation of all resources not only enhance the profitability of the system but also lessen the associated risk compared to the previous models.

关键词： Compressed Air Energy Storage (CAES) Concentrating Solar Power Plant (CSPP) Demand Response Provider (DRP) wind farm stochastic programming epsilon-constraint method

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Multi-objective mitigation budget allocation problem and solution approaches: The case of Istanbul

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COMPUTERS & INDUSTRIAL ENGINEERING 2015年 81卷 118-129页

作者： Ustun, Abdullah Korkut Anagun, Ahmet Sermet Aksaray Univ Dept Ind Engn TR-68100 Aksaray Turkey Eskisehir Osmangazi Univ Dept Ind Engn TR-26480 Eskisehir Turkey

Istanbul is the financial, commercial, industrial, cultural and educational center of Turkey. However, Istanbul is also an earthquake-prone city that has experienced many earthquakes throughout its history;and it is threatened with a high probability of a devastating earthquake in the next 30 years. In this study, we propose three financial allocation strategies and their multi-objective models related to building reinforcement, with the purpose of reducing the risk of disaster in Istanbul. The study aims to answer three important questions: (1) How should the mitigation budget be distributed among Istanbul's districts? (2) Which building types should be mitigated, and how? (3) Can Pareto-efficient solutions be sorted? Pareto-efficient solutions for all of the proposed models were obtained and analyzed. The proposed superefficiency model has the ability to rank the Pareto-optimal solutions. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Disaster management Multi-objective decision making Weighted sum method epsilon-constraint method Data envelopment analysis

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A multi-objective train scheduling model and solution

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TRANSPORTATION RESEARCH PART B-methodOLOGICAL 2004年第10期38卷 927-952页

作者： Ghoseiri, K Szidarovszky, F Asgharpour, MJ Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

This paper develops a multi-objective optimization model for the passenger train-scheduling problem on a railroad network which includes single and multiple tracks, as well as multiple platforms with different train capacities. In this study, lowering the fuel consumption cost is the measure of satisfaction of the railway company and shortening the total passenger-time is being regarded as the passenger satisfaction criterion. The solution of the problem consists of two steps. First the Pareto frontier is determined using the e-constraint method, and second, based on the obtained Pareto frontier detailed multi-objective optimization is performed using the distance-based method with three types of distances. Numerical examples are given to illustrate the model and solution methodology. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： multi-objective programming train scheduling pareto frontier epsilon-constraint method distance-based methods

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Modelling the effects of machine breakdowns in the generalized cell formation problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2008年第7-8期39卷 838-850页

作者： Ameli, Mohammad Saeed Jabal Arkat, Jamal Barzinpour, Farnaz Univ Kurdistan Sanandaj Kurdistan Iran Iran Univ Sci & Technol Tehran *** Iran

Machines are key elements in manufacturing systems and their breakdowns can dramatically affect system performance measures. This paper proposes a new multi-objective pure integer linear programming approach for the cell formation problem with alternative process routings and machine reliability consideration. The model minimizes total cost and maximizes system reliability simultaneously. Traditional reliability evaluation approaches attempt to model the reliability of the manufacturing system as a function of its elements. These approaches have some negative aspects;therefore, instead of modeling the system reliability as an explicit objective function, we use an approach to model the effects of the machine unreliability in terms of cost and time-based effects. Using the epsilon-constraint method as an optimization tool for multi-objective programming, a numerical example is solved to demonstrate the capability of the proposed model in evaluating various effects of the reliability consideration.

关键词： Alternative process routings Cell formation epsilon-constraint method Multi-objective programming Production sequence Reliability

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Multimode time-cost-robustness trade-off project scheduling problem under uncertainty

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2022年第5期43卷 1173-1202页

作者： Li, Xue He, Zhengwen Wang, Nengmin Vanhoucke, Mario Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China Univ Ghent Fac Econ & Business Adm B-9000 Ghent Belgium

The time/cost trade-off problem is a well-known project scheduling problem that has been extensively studied. In recent years, many researchers have begun to focus on project scheduling problems under uncertainty to cope with uncertain factors, such as resource idleness, high inventory, and missing deadlines. To reduce the disturbance from uncertain factors, the aim of robust scheduling is to generate schedules with time buffers or resource buffers, which are capped by project makespan and project cost. This paper addresses a time-cost-robustness trade-off project scheduling problem with multiple activity execution modes under uncertainty. A multiobjective optimization model with three objectives (makespan minimization, cost minimization, and robustness maximization) is constructed and three propositions are proposed. An epsilon-constraint method-based genetic algorithm along with three improvement measures is designed to solve this NP-hard problem and to develop Pareto schedule sets, and a large-scale computational experiment on a randomly generated dataset is performed to validate the effectiveness of the proposed algorithm and the improvement measures. The final sensitivity analysis of three key parameters shows their distinctive influences on the three objectives, according to which several suggestions are given to project managers on the effective measures to improve the three objectives.

关键词： Project scheduling problem Multiobjective optimization model Trade-off epsilon-constraint method Genetic algorithm

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Solving a bi-objective unrelated parallel batch processing machines scheduling problem: A comparison study

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COMPUTERS & OPERATIONS RESEARCH 2017年 88卷 71-90页

作者： Shahidi-Zadeh, B. Tavakkoli-Moghaddam, R. Taheri-Moghadam, A. Rastgar, I. Islamic Azad Univ South Tehran Branch Sch Ind Engn Tehran Iran Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Univ Tehran Ctr Excellence Intelligence Based Expt Mech Coll Engn Tehran Iran Arts & Metiers Paris Tech LCFC Metz France Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran

Nowadays in competitive markets, production organizations are looking to increase their efficiency and optimize manufacturing operations. In addition, batch processor machines (BPMs) are faster and cheaper to carry out operations;thus the performance of manufacturing systems is increased. This paper studies a production scheduling problem on unrelated parallel BPMs with considering the release time and ready time for jobs as well as batch capacity constraints. In unrelated parallel BPMs, modern machines are used in a production line side by side with older machines that have different purchasing costs;so this factor is introduced as a novel objective to calculate the optimum cost for purchasing various machines due to the budget. Thus, a new bi-objective mathematical model is presented to minimize the makespan (i.e., C-max), tardiness/earliness penalties and the purchasing cost of machines simultaneously. The presented model is first coded. and solved by the epsilon-constraint method. Because of the complexity of the NP-hard problem, exact methods are not able to optimally solve large-sized problems in a reasonable time. Therefore, we propose a multi-objective harmony search (MOHS) algorithm. the results are compared with the multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective ant colony optimization algorithm (MOACO). To tune their parameters, the Taguchi method is used. The results are compared by five metrics that show the effectiveness of the proposed MOHS algorithm compared with the MOPSO, NSGA-II and MOACO. At last, the sensitivity of the model is analyzed on new parameters and impacts of each parameter are illustrated on bi-objective functions. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Production scheduling Unrelated parallel batch processors epsilon-constraint method Multi-objective harmony search Sensitivity analysis

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