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Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2013年第2期41卷 369-382页

作者： Liu, Songsong Papageorgiou, Lazaros G. UCL Dept Chem Engn Ctr Proc Syst Engn London WC1E 7JE England

The performance of a supply chain should usually be measured by multiple criteria. We address production, distribution and capacity planning of global supply chains considering cost, responsiveness and customer service level simultaneously. A multiobjective mixed-integer linear programming (MILP) approach is developed with total cost, total flow time and total lost sales as key objectives. Also, two strategies to expand the formulation plants' capacities are considered in the model. The epsilon-constraint method and lexicographic minimax method are used as solution approaches to tackle the multiobjective problem. Finally, a numerical example is investigated to demonstrate the applicability of the proposed model and solution approaches. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Multiobjective optimisation Global supply chains MILP epsilon-constraint method Lexicographic minimax method Pareto-optimal solutions

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Multi-Objective Reconfiguration of Radial Distribution Systems Using Reliability Indices

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IEEE TRANSACTIONS ON POWER SYSTEMS 2016年第2期31卷 1048-1062页

作者： Paterakis, Nikolaos G. Mazza, Andrea Santos, Sergio F. Erdinc, Ozan Chicco, Gianfranco Bakirtzis, Anastasios G. Catalao, Joao P. S. Univ Beira Interior Covilha Portugal Univ Lisbon IST INESC ID P-1699 Lisbon Portugal Politecn Torino Dept Energy Power & Energy Syst Unit I-10129 Turin Italy Yildiz Tech Univ Dept Elect Engn Istanbul Turkey Aristotle Univ Thessaloniki GR-54006 Thessaloniki Greece

This paper deals with the distribution network reconfiguration problem in a multi-objective scope, aiming to determine the optimal radial configuration by means of minimizing the active power losses and a set of commonly used reliability indices formulated with reference to the number of customers. The indices are developed in a way consistent with a mixed-integer linear programming (MILP) approach. A key contribution of the paper is the efficient implementation of the epsilon-constraint method using lexicographic optimization in order to solve the multi-objective optimization problem. After the Pareto efficient solution set is generated, the resulting configurations are evaluated using a backward/forward sweep load-flow algorithm to verify that the solutions obtained are both non-dominated and feasible. Since the epsilon-constraint method generates the Pareto front but does not incorporate decision maker (DM) preferences, a multi-attribute decision making procedure, namely, the technique for order preference by similarity to ideal solution (TOPSIS) method, is used in order to rank the obtained solutions according to the DM preferences, facilitating the final selection. The applicability of the proposed method is assessed on a classical test system and on a practical distribution system.

关键词： Distribution system reconfiguration epsilon-constraint method loss minimization mixed-integer linear programming multi-objective optimization reliability

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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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Bi-objective cyclic scheduling for single hoist with processing-time-window constraints considering buffer-setting

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2019年第2期233卷 192-203页

作者： Gao, Zhongshun Zhou, Binghai Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

To solve the scheduling problem of a single hoist with processing-time-window constraints considering buffer-setting, we consider bi-objective scheduling which makes trade-offs between the cycle time and the hoist travel distance. First, a bi-objective mixed-integer programming model is constructed for the basic line without considering buffer-setting. In order to improve the throughput of the basic line, a transform method with buffer-setting is put forward. Based on the basic line model, a subsection searching epsilon-constraint method which combines the transform method is introduced to solve the bi-objective mixed-integer programming model. Experimental results both on benchmark instances and on randomly generated instances indicate that the proposed approach is efficient and buffers can improve the throughputs.

关键词： Bi-objective buffer-setting epsilon-constraint method hoist-scheduling problem mixed-integer programming

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Finite element model updating of an 18-story structure using branch-and-bound algorithm with epsilon-constraint

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JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2021年第3期11卷 575-592页

作者： Otsuki, Yu Li, Dan Dey, Santanu S. Kurata, Masahiro Wang, Yang Georgia Inst Technol Sch Civil & Environm Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA Kyoto Univ Disaster Prevent Res Inst Uji Kyoto Japan Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

This paper studies the finite element (FE) model updating of an 18-story experimental structure. FE model updating requires solving optimization problems that are generally non-convex and have unknown number of local optima. For such problems, neither randomized local optimization algorithms nor stochastic search algorithms can guarantee global optimality. To obtain the global optimum and improve the accuracy of FE model updating, this paper proposes the branch-and-bound (B&B) algorithm for solving non-convex optimization problems in FE model updating. The paper focuses on the modal property difference formulation that minimizes the difference between experimental and simulated eigenvalues and eigenvectors. We propose a reformulation of the modal property difference approach using epsilon-constraint, to enable the application of the B&B algorithm in FE model updating. The proposed approach is first investigated in simulation and compared with the interior-point method and the genetic algorithm. The model updating results using the B&B algorithm are next validated by the shaking table test data of an 18-story steel frame structure.

关键词： Finite element model updating Branch-and-bound algorithm Non-convex optimization Global optimization epsilon-constraint method Shaking table test

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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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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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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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Multi-objective dynamic optimization of path-constrained switched systems

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AUTOMATICA 2024年 159卷

作者： Zhang, Chi Fu, Jun Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

A multi-objective dynamic optimization algorithm is proposed for path-constrained switched systems to locate approximated local Pareto solutions with specified tolerances. The algorithm is based on iteratively solving single-objective modified epsilon-constraint dynamic optimization problems to avoid generating weak Pareto solutions. These problems are first reduced to semi-infinite programs embedded with ordinary differential equations by performing the control vector parameterization technique. Then the guaranteed feasibility is attained within a finite number of iterations by enforcing the path constraints at discretized time points with a restriction parameter on the right-hand side. Furthermore, the approximation error of the obtained local Pareto solutions is quantitatively analyzed. The result shows that the Pareto approximation error can be directly controlled via user-specified parameters. Finally, the effectiveness of the algorithm is illustrated via a numerical example. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： Switched systems Semi-infinite programs epsilon-constraint method Multi-objective dynamic optimization

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Multi-objective design optimization of variable ribs composite grid plates

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2021年第1期63卷 407-418页

作者： Ehsani, Amir Dalir, Hamid Purdue Sch Engn & Technol Dept Mech & Energy Engn Indianapolis IN 46202 USA

Grid structures are among the most lightweight elements for stiffening the plates and shells or as self-sufficient structures. There are different known grid patterns, which are composed of variable numbers of parallel ribs. Selecting an optimum pattern and geometries for a grid to achieve the minimum weight and maximum load-bearing capacity is a challenging procedure for designers. In the current study, a variable ribs model (VRM) is proposed to find the optimum architecture of a grid plate. Therefore, using a genetic algorithm process, a multi-objective optimization is implemented to maximize the axial or shear buckling loads at a minimum possible weight of a grid structure. Eleven geometrical parameters including thickness, width, and the number of ribs as well as the orientation of the grid plate are considered the design variables. The multi-objective optimization is carried out employing the epsilon-constraint method. The buckling loads are obtained based on the first-order shear deformation plate theory using the Ritz method.

关键词： Multi-objective optimization Composite grid plate epsilon-constraint method Genetic algorithm Variable ribs model

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