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Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms

IEEE TRANSACTIONS ON SMART GRID 2018年第3期9卷 2276-2283页

作者： Kampouropoulos, Konstantinos Andrade, Fabio Sala, Enric Garcia Espinosa, Antonio Romeral, Luis Fundacio CTM Ctr Tecnol Manresa 08243 Spain Univ Puerto Rico Mayaguez PR 00681 USA Univ Politecn Cataluna MCIA Res Ctr Terrassa 08222 Spain

This paper presents a novel method for the energy optimization of multi-carrier energy systems. The presented method combines an adaptive neuro-fuzzy inference system, to model and forecast the power demand of a plant, and a genetic algorithm to optimize its energy flow taking into account the dynamics of the system and the equipment's thermal inertias. The objective of the optimization algorithm is to satisfy the total power demand of the plant and to minimize a set of optimization criteria, formulated as energy usage, monetary cost, and environmental cost. The presented method has been validated under real conditions in the car manufacturing plant of SEAT in Spain in the framework of an FP7 European research project.

关键词： Energy optimization multicarrier systems dynamic optimization mixed-integer programming multiobjective problem energy hub energy prediction optimal control manufacturing plants

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Robust Strategic Route Planning in Logistics

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TRANSPORTATION SCIENCE 2018年第1期52卷 38-58页

作者： Richter, Alexander T. Stiller, Sebastian Tech Univ Carolo Wilhelmina Braunschweig Inst Math Optimierung D-38106 Braunschweig Germany

Logistics is a cost sensitive industry with large and fast growing routing networks. In this paper we devise a computational, robust optimization method for the strategic routing decisions of a logistics' customer, i. e., a company that uses the services of different freight forwarders to meet its transportation demands between several sources, sinks, and hubs. The costs of such transports are determined by tariff systems that typically show economies of scale and reward the consolidation of goods that complement each other in properties relevant for transport, such as weight and volume. In the strategic planning phase, routes and hubs have to be chosen roughly one year ahead, in particular, before the actual demand is known. Our method anticipates the fluctuation of demands by minimizing the worst-case cost over a restricted scenario set. The combination of a realistic cost function, a robust modeling of uncertainty, and large-scale networks leads to highly intractable models. We show that the corresponding adversary problem is NP-hard. To nevertheless find solutions for real instances with very good worst-case cost we derive a carefully relaxed and simplified mixed-integer linear program that solves well for large instances because of its powerful linear programming relaxation. We test the method for real-world instances. The results show that robust optimization can significantly reduce worst-case cost. Furthermore, we derive from our method two heuristic techniques to solve even larger networks and report on the corresponding computational results. Neglecting the typical uncertainty about demand values can cause significant cost in logistic routing problems. This paper provides for a practical method to avoid such costs.

关键词： logistics robust optimization strategic route planning modeling mixed-integer programming

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Expanding scope and computational challenges in process scheduling

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COMPUTERS & CHEMICAL ENGINEERING 2018年 114卷 14-42页

作者： Castro, Pedro M. Grossmann, Ignacio E. Zhang, Qi Univ Lisbon Fac Ciencias Ctr Matemat Aplicacoes Fundamentals Invest Operat P-1749016 Lisbon Portugal Carnegie Mellon Univ Ctr Adv Proc Decis Making Dept Chem Engn Pittsburgh PA 15213 USA BASF SE D-67063 Ludwigshafen Germany

In this paper, we present a brief overview of enterprise-wide optimization and challenges in multiscale temporal modeling and integration of different models for the levels of planning, scheduling and control. Next, we review Generalized Disjunctive programming (GDP), as a new modeling paradigm for scheduling problems that are illustrated with the STN and RTN models. We then address scheduling problems that expand the scope of the area: simultaneous scheduling and heat integration, pipeline scheduling, crude oil and refined products blending, and demand side management. We illustrate the advantage of the GDP modeling framework, describe effective strategies for global optimization, and describe multistage affinely adjustable robust optimization for uncertain interruptible load. We address integration of planning and scheduling, for which several approaches are reviewed, including use of traveling salesman constraints for multiperiod refinery planning, and multisite planning and scheduling of multiproduct batch plants. We report computational results to highlight the challenges. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Planning Scheduling Demand side management mixed-integer programming Generalized disjunctive programming

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Temporal Decomposition for Improved Unit Commitment in Power System Production Cost Modeling

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IEEE TRANSACTIONS ON POWER SYSTEMS 2018年第5期33卷 5276-5287页

作者： Kim, Kibaek Botterud, Audun Qiu, Feng Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Univ Chicago Computat Inst Chicago IL 60637 USA Argonne Natl Lab Div Energy Syst Lemont IL 60439 USA

Long-term planning in power systems requires simulations of unit commitment (UC) for long time periods up to 20 years. Such simulations are conducted with production cost models (PCMs), which involve solving large-scale mixed-integer programming (MIP) problems with a large number of variables and constraints, because of the long planning horizon. We have developed new optimization modeling and solution techniques based on a decomposition scheme to reduce the solution time and improve the accuracy in PCMs. We propose a temporal decomposition that solves the UC problem by systematically decoupling the long-horizon MIP problem into several subhorizon models. The decomposition is obtained by the Lagrangian relaxation of the time-coupling constraints such as ramping constraints and minimum uptime/downtime constraints. The key challenge is to solve several sub-MIP problems while effectively searching for dual variables to accelerate the convergence of the algorithm. We implement the temporal decomposition in an open-source parallel decomposition framework, which can solve the multiple subproblems in parallel on high-performance computing clusters. We also implement the branch-and-bound method on top of the decomposition in order to find a primal optimal solution. Numerical results of the decomposition method are reported for the IEEE 118-bus and PEGASE 1354-bus test systems with up to an 168-hour time horizon.

关键词： Production cost model mixed-integer programming decomposition method parallel computing

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Branch-and-cut for linear programs with overlapping SOS1 constraints

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MATHEMATICAL programming COMPUTATION 2018年第1期10卷 33-68页

作者： Fischer, Tobias Pfetsch, Marc E. Tech Univ Darmstadt Grad Sch Computat Engn Dolivostr 15 D-64293 Darmstadt Germany Tech Univ Darmstadt Dept Math Res Grp Optimizat Dolivostr 15 D-64293 Darmstadt Germany

SOS1 constraints require that at most one of a given set of variables is nonzero. In this article, we investigate a branch-and-cut algorithm to solve linear programs with SOS1 constraints. We focus on the case in which the SOS1 constraints overlap. The corresponding conflict graph can algorithmically be exploited, for instance, for improved branching rules, preprocessing, primal heuristics, and cutting planes. In an extensive computational study, we evaluate the components of our implementation on instances for three different applications. We also demonstrate the effectiveness of this approach by comparing it to the solution of a mixedinteger programming formulation, if the variables appearing in SOS1 constraints ar bounded.

关键词： Complementarity constraints Special ordered sets mixed-integer programming Branch-and-cut SOS1 branching Bipartite branching

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Efficiently solving linear bilevel programming problems using off-the-shelf optimization software

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OPTIMIZATION AND ENGINEERING 2018年第1期19卷 187-211页

作者： Pineda, S. Bylling, H. Morales, J. M. Univ Malaga Dept Elect Engn Malaga Spain Univ Copenhagen Dept Math Sci Copenhagen Denmark Univ Malaga Dept Appl Math Malaga Spain

Many optimization models in engineering are formulated as bilevel problems. Bilevel optimization problems are mathematical programs where a subset of variables is constrained to be an optimal solution of another mathematical program. Due to the lack of optimization software that can directly handle and solve bilevel problems, most existing solution methods reformulate the bilevel problem as a mathematical program with complementarity conditions (MPCC) by replacing the lower-level problem with its necessary and sufficient optimality conditions. MPCCs are single-level non-convex optimization problems that do not satisfy the standard constraint qualifications and therefore, nonlinear solvers may fail to provide even local optimal solutions. In this paper we propose a method that first solves iteratively a set of regularized MPCCs using an off-the-shelf nonlinear solver to find a local optimal solution. Local optimal information is then used to reduce the computational burden of solving the Fortuny-Amat reformulation of the MPCC to global optimality using off-the-shelf mixed-integer solvers. This method is tested using a wide range of randomly generated examples. The results show that our method outperforms existing general-purpose methods in terms of computational burden and global optimality.

关键词： Bilevel programming Mathematical programming with complementarity conditions Nonlinear programming mixed-integer programming Optimization solvers

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Binary extended formulations of polyhedral mixed-integer sets

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MATHEMATICAL programming 2018年第1期170卷 207-236页

作者： Dash, Sanjeeb Gunluk, Oktay Hildebrand, Robert IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Virginia Tech Grado Dept Ind & Syst Engn Blacksburg VA USA

We analyze different ways of constructing binary extended formulations of polyhedral mixed-integer sets with bounded integer variables and compare their relative strength with respect to split cuts. We show that among all binary extended formulations where each bounded integer variable is represented by a distinct collection of binary variables, what we call "unimodular" extended formulations are the strongest. We also compare the strength of some binary extended formulations from the literature. Finally, we study the behavior of branch-and-bound on such extended formulations and show that branching on the new binary variables leads to significantly smaller enumeration trees in some cases.

关键词： mixed-integer programming Binarization Extended formulations Cutting planes Branch-and-bound

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A preference-based, multi-unit auction for pricing and capacity allocation

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COMPUTERS & OPERATIONS RESEARCH 2018年 91卷 237-246页

作者： Lessan, Javad Karabati, Selcuk Univ Waterloo Dept Civil & Environm Engn Waterloo ON N2L 3G1 Canada Koc Univ Coll Adm Sci & Econ TR-34450 Istanbul Turkey

We study a pricing and allocation problem of a seller of multiple units of a homogeneous item, and present a semi-market mechanism in the form of an iterative ascending-bid auction. The auction elicits buyers' preferences over a set of options offered by the seller, and processes them with a random-priority assignment scheme to address buyers' "fairness" expectations. The auction's termination criterion is derived from a mixed-integer programming formulation of the preference-based capacity allocation problem. We show that the random priority- and preference-based assignment policy is a universally truthful mechanism which can also achieve a Pareto-efficient Nash equilibrium. Computational results demonstrate that the auction mechanism can extract a substantial portion of the centralized system's profit, indicating its effectiveness for a seller who needs to operate under the "fairness" constraint. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multi-unit auctions Pricing and capacity allocation mixed-integer programming

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Online Scheduling: Understanding the Impact of Uncertainty ⁎

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IFAC-PapersOnLine 2019年第1期52卷 727-732页

作者： Dhruv Gupta Christos T. Maravelias University of Wisconsin-Madison Madison WI 53706 USA

We present a framework to study quality of schedules obtained iteratively and online (real-time) in the presence of demand uncertainty Using this framework, we carry out a computational study, and make interesting observations. First, we find that uncertainty plays a less important role as a manufacturing facility is operated close to capacity. Second, the choice of the horizon for the online iterations, is dependent on the mean load, but independent of the accuracy of the forecasts. Finally, feedback, in the form of re-optimization, plays a very important role in mitigating the impact of uncertainty. Thus, through the analysis presented in this work, we gain insights that are applicable to all general rescheduling approaches.

关键词： Uncertainty Rescheduling Operations MPC mixed-integer programming

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Vehicle Routing Problem with Drones for Last Mile Delivery

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Procedia Manufacturing 2019年 39卷 314-324页

作者： Patchara Kitjacharoenchai Seokcheon Lee School of Industrial Engineering 315 N Grant Street West Lafayette IN 47907-2023 USA

E-commerce and retail companies are seeking ways to cut delivery times and costs by exploring opportunities to use drones for making last mile delivery deliveries. In recent years, drone routing and scheduling has become a highly active area of research. This research addresses the delivery concept of a truck-drone combination along with the idea of allowing autonomous drones to fly from delivery trucks, make deliveries, and fly to delivery trucks nearby. The proposed model considers the synchronized truck drone routing model by allowing multiple drones to fly from a truck, serve customers and immediately return to the same truck for the battery swap and package retrieval. The model also takes into account both trucks and drones capacities to ensure that the amount of loads carried by each drone must not exceed its capacity and the total amount of loads in each delivery route must be less than truck’s capacity. The goal is to find the optimal routes of both trucks and drones which minimize the total arrival time of both trucks and drones at the depot after completing the deliveries. The problem can be solved by the formulated mixed integer programming (MIP) for the small size problem. Numerical results in the case study and benchmark problems are presented to show the delivery time improvement over the delivery time from other delivery types.

关键词： Drone delivery Vehicle Routing Problem Optimization Last Mile Delivery Drones mixed-integer programming

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