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Design of energy-Efficient timetables in two-way railway rapid transit lines

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2017年第Aug.期102卷 142-161页

作者： Canca, David Zarzo, Alejandro Univ Seville Dept Org Ind Gest & Empresas 1 Ind Engn & Management Sci Seville Spain Univ Politecn Madrid ETS Ingenieros Ind Dept Matemat Area Ind Madrid Spain Univ Granada Inst Carlos 1 Granada Spain

A methodology to design energy-efficient timetables in Rapid Railway Transit Networks is presented. Using an empirical description of the train energy consumption as a function of running times, the timetable design problem is modelled as a mixed integer non-linear optimization problem (MINLP) for a complete two-way line. In doing so, all the services in both directions along a certain planning horizon are considered while attending a known passengers' demand. The MINLP formulation, which depends on train loads, is fully linearised supposing train loads are fixed. A sequential mixed integer linear solving procedure is then used to solve the timetabling optimization problem with unknown train loads. The proposed methodology emphasizes the need of considering all the services running during the planning horizon when designing energy-efficient timetables, as consequence of the relationship among train speeds, frequency and fleet size of each line. Moreover, the convenience of considering the energy consumption as part of a broad objective function that includes other relevant costs is pointed out. Otherwise, passengers and operators could face up to an increase in the whole cost and a decrease in the quality of service. A real data scenario, based on the C-2 Line of the Madrid Metropolitan Railways, is used to illustrate the proposed methodology and to discuss the differences between the energy efficient solutions and those obtained when considering operation and acquisition costs. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Railway rapid transit Timetabling Energy consumption mixed integer non-linear programming Sequential MILP procedure

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A location-routing problem for cross-docking networks: A biogeography-based optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 102卷 132-146页

作者： Goodarzi, Asefeh Hasani Zegordi, Seyed Hessameddin Tarbiat Modares Univ Fac Ind & Syst Engn POB 14115-143 Tehran Iran

This paper considers a location-routing problem in a distribution network with a set of part suppliers, cross-docking centers and assembly plants known as customers. We develop a mixed integer nonlinear programming formulation for the problem in which the location for establishing the cross-docks is determined while simultaneously a fleet of vehicles are applied to transport goods from suppliers to the assembly plants via two transportation strategies: direct shipment and shipment through cross dock (indirect shipment). In the second strategy, it is possible to have routes between suppliers. Not considering two problems of location and distribution planning simultaneously would result in increasing the costs of supplying parts since the transportation strategy has a huge effect on location of cross docks. In the other words, if some loads can be directly shipped, then this kind of loads should not be taken into account in determining cross-docks location. Thus, a location - routing problem is presented for cross-docking system in this paper. The goal is to determine the location of cross-docks, allocating suppliers to them and routing decisions, so that the location cost and total shipping cost in the network are minimized, considering variable cost of servicing parts passed through cross-docks. The proposed model is NP-hard based on literature. Thus, a metaheuristic algorithm named Biogeography-based optimization (BBO) is utilized to solve the problem. In order to evaluate its efficiency, BBO results are compared with those of PSO, which is a well-known algorithm in the literature. Solving numerical examples for small size problem instances illustrates that the solving approach performs with a negligible gap relative to GAMS, while it performs much better than PSO in most cases in terms of total cost of the network and computational time. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Cross-docking Location-Routing Problem (LRP) Direct shipment Distribution network mixed integer non-linear programming

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MINLP formulations for solving strip packing problems in LCD mother glass production

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COMPUTERS & CHEMICAL ENGINEERING 2013年 48卷 312-324页

作者： Park, Kyung Tae Kim, Hansol Lee, Soobin Lee, Ho-Kyung Ryu, Jun-hyung Lee, In-Beum POSTECH Dept Chem Engn Pohang South Korea LG Chem Ltd Taejon South Korea Dongguk Univ Dept Nucl & Energy Syst Engn Gyeongju South Korea

Mother glass production for liquid crystal displays involves the cutting of small rectangles from a large rectangle (strip) to minimize the length of the strip used. This is called the strip packing problem. The main concept for formulating this problem is to generate cutting patterns that can be used to produce items from the strip. This problem is a non-convex mixed integer nonlinear programming (MINLP) problem due to the bilinear terms in the demand and objective function. To obtain global optimal solutions, this problem should be transformed into several linear forms. Numerical examples based on the strip packing problem in LCD mother glass production are provided. Different objective functions for two problems are presented and compared. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： mixed integer non-linear programming Trim loss problems LCD mother glass production optimization

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Multi-step simultaneous changes Constructive Heuristic Algorithm for Transmission Network Expansion Planning

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ELECTRIC POWER SYSTEMS RESEARCH 2009年第4期79卷 586-594页

作者： Bustamante-Cedeno, Enrique Arora, Sant Univ Minnesota Dept Mech Engn Ind & Syst Engn Div Minneapolis MN 55455 USA

In this paper, a Constructive Heuristic Algorithm (CHA) is presented to solve the Transmission Network Expansion Planning Problem (TNEP), a complex non-convex mixed integer non-linear programming (MINLP) problem with multiple local minima. In the proposed algorithm, the non-linearities are resolved through the following feature: when discrete decision variables are given, the model becomes linear in the continuous variables. A CHA is developed which improves the current solution by implementing multiple step simultaneous changes over a number of saturated transmission lines, in contrast to the approach traditionally followed. which implements one change at a time. Solutions to test problems are computed. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Transmission Network Expansion Planning Constructive Heuristic Algorithm mixed integer non-linear programming non-convex optimization Power deregulation

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A supply-chain optimization model of the allocation of containerized imports from Asia to the United States

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2011年第5期47卷 609-622页

作者： Jula, Payman Leachman, Robert C. Simon Fraser Univ Fac Business Adm Burnaby BC V5A 1S6 Canada Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA

This article proposes a mixed integer non-linear programming model for the optimizing supply chains of importers of waterborne containerized goods from Asia to the USA. This model determines the least-cost strategy for an importer, in terms of ports and landside transportation modes to be used, where costs considered include costs for transportation and handling, pipeline inventory, and safety-stock. We introduce a heuristic algorithm to quickly solve the mathematical model to near optimality. We assess the proposed heuristic compared to use of a commercial solver, and provide general recommendations for efficient supply-chain strategies as a function of the value of imported goods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Supply-chain optimization Intermodal freight transport Transportation planning Containerized imports Logistics mixed integer non-linear programming

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Convexification of different classes of non-convex MINLP problems

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COMPUTERS & CHEMICAL ENGINEERING 1999年第3期23卷 439-448页

作者： Pörn, R Harjunkoski, I Westerlund, T Abo Akad Univ Dept Math FIN-20500 Turku Finland Abo Akad Univ Proc Design Lab FIN-20500 Turku Finland

In the present paper, convexification strategies for certain kinds of discrete and integer non-convex optimization problems are introduced and discussed. We show how to solve problems with both posynomial and negative binomial terms in the constraints. The convexification technique may in some cases be generalized to include continuous variables. Posynomial functions are non-convex and for such functions no straightforward methods for finding the optimal solution exist. Such functions appear frequently in different kinds of chemical engineering problems. The different transformation techniques are illustrated in the form of short examples. The techniques are finally applied to a large, bilinear, trim loss problem regularly encountered at paper-converting mills. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： binomials convexification discrete and integer variables mixed integer non-linear programming optimization posynomials

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Comparison of Sen Transformer and UPFC for congestion management in hybrid electricity markets

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期47卷 295-304页

作者： Kumar, A. Sekhar, Charan Natl Inst Technol Dept Elect Engn Kurukshetra Haryana India

Congestion management (CM) is one of the most important and challenging tasks of the Independent System Operator (ISO) in the deregulated environment. Recently, Sen Transformer (ST) has emerged as an important power flow control device which has the capability of power flow control over a wide range like UPFC. This device with a conventional transformer and tap changers has the capability of bidirectional control of active and reactive power and can play a very important role in future markets for mitigating the congestion problems. In this paper, the capability of ST has been utilized to manage transmission line congestion for hybrid based electricity market model. The main contribution of the paper is: (i) to develop an optimal rescheduling of generators strategy for real time congestion management and impact of ST for congestion management, (ii) the comparison of ST with unified power flow controller (UPFC) for congestion management, (iii) the secure bilateral transactions determination in a hybrid market model and congestion management with both power flow controllers in combined pool and bilateral market model. The optimal location of ST and UPFC has been obtained solving mixed integer non-linear programming model of congestion management. The proposed model has been applied for results on IEEE 24-bus RTS test system. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Bid function Congestion management Hybrid electricity market Sen Transformer Unified power flow controller mixed integer non-linear programming

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Different transformations for solving non-convex trim-loss problems by MINLP

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 1998年第3期105卷 594-603页

作者： Harjunkoski, I Westerlund, T Porn, R Skrifvars, H Abo Akad Univ Prot Design Lab FIN-20500 Turku Finland Abo Akad Univ Dept Math FIN-20500 Turku Finland

In the present paper trim-loss problems, often named the cutting stock problem, connected to the paper industry are considered. The problem is to cut out a set of product paper rolls from raw paper rolls such that the cost function, including the trim loss as well as the costs for the over production, is minimized. The problem is non-convex due to certain bilinear constraints. The problem can, however, be transformed into linear or convex form. The resulting transformed problems can, thereafter, be solved as mixed-integer linear programming problems or convex mixed-integer non-linear programming problems. The linear and convex formulations are attractive from a formal point of view, since global optimal solutions to the originally non-convex problem can be obtained. However, as the examples considered will show, the numerical efficiency of the solutions from the different transformed formulations varies considerably. An example based on a trim optimization problem encountered daily at a Finnish paper converting mill is, finally, presented in order to demonstrate differences in the numerical solutions. (C) 1998 Elsevier Science B.V.

关键词： optimization mixed integer non-linear programming integer linear programming trim-loss problems

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Application of global optimization strategies to refinery hydrogen network

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2014年第27期39卷 14503-14511页

作者： Birjandi, Mohammad Reza Sardashti Shahraki, Farhad Birjandi, Moslem Sardashti Nobandegani, Mojtaba Sinaei Univ Sistan & Baluchestan Dept Chem Engn Zahedan Iran Univ Sistan & Baluchestan Dept Mech Engn Zahedan Iran Univ Sistan & Baluchestan Dept Chem Engn Chabahar Iran

In this paper, a new method is presented in optimization of hydrogen network. The mixed integer non-linear programming (MINLP) and non-linear programming (NLP) problems have been solved with two methods, simultaneously. The linearization technique for non-linear programming models which proposed by McCormick (1976) and also a new method proposed by Faria and Bagajewicz (2011) have been used to solve these problems. Application of this new method is presented in global optimization of MINLP/NLP, and hydrogen network problem. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hydrogen network Global optimization Refinery mixed integer non-linear programming non-linear programming

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Empirical decision model learning

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ARTIFICIAL INTELLIGENCE 2017年 244卷 343-367页

作者： Lombardi, Michele Milano, Michela Bartolini, Andrea Univ Bologna DISI I-40126 Bologna Italy Univ Bologna DEI I-40126 Bologna Italy

One of the biggest challenges in the design of real-world decision support systems is coming up with a good combinatorial optimization model. Often enough, accurate predictive models (e.g. simulators) can be devised, but they are too complex or too slow to be employed in combinatorial optimization. In this paper, we propose a methodology called Empirical Model Learning (EML) that relies on Machine Learning for obtaining components of a prescriptive model, using data either extracted from a predictive model or harvested from a real system. In a way, EML can be considered as a technique to merge predictive and prescriptive analytics. All models introduce some form of approximation. Citing G.E.P. Box [1] "Essentially, all models are wrong, but some of them are useful". In EML, models are useful if they provide adequate accuracy, and if they can be effectively exploited by solvers for finding high-quality solutions. We show how to ground EML on a case study of thermal-aware workload dispatching. We use two learning methods, namely Artificial Neural Networks and Decision Trees and we show how to encapsulate the learned model in a number of optimization techniques, namely Local Search, Constraint programming, mixed integer non-linear programming and SAT Modulo Theories. We demonstrate the effectiveness of the EML approach by comparing our results with those obtained using expert-designed models. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization Machine learning Complex systems Local search Constraint programming mixed integer non-linear programming SAT modulo theories Artificial neural networks Decision trees

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