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3rd International Conference on Smart Energy Systems and Technologies (SEST)

作者： Rasouli, Vahid Gomes, Alvaro Antunes, Carlos Henggeler Univ Coimbra INESC Coimbra DEEC Coimbra Portugal

ISBN: (纸本)9781728147017

This paper presents a methodology for the characterization of demand-side flexibility of a group of small consumers. The day-ahead time-differentiated price tariffs are considered as the base energy cost to be sent to the consumers by the energy supplier with whom they have a contract. The aggregator, as a third-party agent responsible for the aggregation of demand-side flexibility, defines an hourly reward to motivate consumers to participate in the provision of flexibility by reshaping their consumption. A mixed integer linear programming model is developed including the physically based behavior of demand-side resources consisting of shiftable loads, interruptible loads, thermostatically controlled loads, local generation, battery storage systems and electric vehicles. Each consumer is equipped with a home energy management system that performs the management of end-use loads according to consumer's preferences and restrictions, energy prices and any rewards that may exist. In this study, the potential of consumers to provide flexibility and their response to different reward signals are investigated considering different characteristics of the available energy resources, including load profiles and comfort preferences. Different reward values are defined to assess their impact on the amount of flexibility at the consumer and aggregator levels. Numerical results show the potential of different types of consumers to participate in demand response programs differ as their responsiveness to the rewards is not identical.

关键词： aggregation demand response characterization of demand-side flexibility mixed integer linear programming

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A Hierarchical Centralized Community Energy Management System Using a Model Predictive Controller

A Hierarchical Centralized Community Energy Management Syste...

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International Conference on Smart Grids and Energy Systems (SGES)

作者： Elkazaz, Mahmoud Sumner, Mark Thomas, David Univ Nottingham Dept Elect & Elect Engn Nottingham England

ISBN: (纸本)9781728185507

This paper presents a Hierarchical Centralized Community Energy Management Systems (CCEMS) which facilitates the energy trading between prosumers in a community by coordinating the operation of distributed Home Battery Storage Systems (HBSSs) and shiftable home appliances in a centralized way using a Model Predictive Controller (MPC). The CCEMS achieves a further reduction in the annual household energy costs for each house, compared to being operated individually (not being a part of the community). The results obtained are based on real historic data of several prosumers in a real community system. The results show that the proposed hierarchical CCEMS guarantees a further reduction in the annual household energy costs for each house (up to 15.89%) when being operated as a part of a community, compared to being operated individually.

关键词： Centralized Energy Management system mixed integer linear programming Local Community Model Predictive Controller

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Exact and heuristic solution approaches for the airport gate assignment problem

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2021年 103卷 102422-102422页

作者： Karsu, Ozlem Azizoglu, Meral Alanli, Kerem Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkey Middle East Tech Univ Dept Ind Engn TR-06800 Ankara Turkey

In this study, we consider an airport gate assignment problem that assigns a set of aircraft to a set of gates. The aircraft that cannot be assigned to any gate are directed to an apron. We aim to make aircraft gate assignments so as to minimize the number of aircraft assigned to apron and among the apron usage minimizing solutions, we aim to minimize total walking distance travelled by all passengers. The problem is formulated as a mixed-integer nonlinear programming model and then it is linearized. A branch and bound algorithm, beam search and filtered beam search algorithms that employ powerful lower and upper bounding mechanisms are developed. The results of the computational experiment have shown the satisfactory performance of the algorithms. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Airport gate assignment problem mixed integer linear programming Branch-and-bound algorithm Beam search algorithm Filtered beam search algorithm

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An exact routing optimization model for bio-waste collection in the Brussels Capital Region

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 183卷 115392-115392页

作者： Lavigne, Carolien Belien, Jeroen Dewil, Reginald Katholieke Univ Leuven Res Ctr Econ & Corp Sustainabil Fac Econ & Business Brussels Campus Brussels Belgium Katholieke Univ Leuven Res Ctr Operat Management Fac Econ & Business Brussels Campus Brussels Belgium Katholieke Univ Leuven Res Ctr Ind Management Traff & Infrastruct Grp T Leuven Campus Leuven Belgium

This paper presents a routing optimization model for a wide range of waste collection problems which allows for multiple depots with homogeneous, capacitated vehicles, intermediate stops at multiple processing facilities, and multiple pick-ups per waste collection location. By minimizing collection routing and vehicle investment costs, the model estimates the total transportation costs for a given network design and waste volumes to be collected at different demand points. The number of feasible routes is severely reduced by restricting the number of subsequent pick-up location visits. The model's use is illustrated through assessing four future, realistic bio-waste collection scenarios of the Brussels Capital Region (BCR). The scenarios differ with respect to assumptions on future collection rates, joint versus separate collection of food and green waste, and number and locations of processing facilities. The results show that the highest cost reduction can be achieved through joint collection of food and green waste combined with three composting locations. Moreover, we found that introducing multiple processing facilities significantly increases the complexity of the waste collection problem.

关键词： Waste collection mixed integer linear programming optimization Multiple depots Intermediate facilities

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Fair Resource Allocation in Macroscopic Evacuation Planning Using Mathematical programming: Modeling and Optimization

Fair Resource Allocation in Macroscopic Evacuation Planning ...

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作者： Bin Obaid, Hamoud University of Oklahoma

学位级别：博士

Evacuation is essential in the case of natural and manmade disasters such as hurricanes, nuclear disasters, fire accidents, and terrorism epidemics. Random evacuation plans can increase risks and incur more losses. Hence, numerous simulation and mathematical programming models have been developed over the past few decades to help transportation planners make decisions to reduce costs and protect lives. However, the dynamic transportation process is inherently complex. Thus, modeling this process can be challenging and computationally demanding. The objective of this dissertation is to build a balanced model that reflects the realism of the dynamic transportation process and still be computationally tractable to be implemented in reality by the decision-makers. On the other hand, the users of the transportation network require reasonable travel time within the network to reach their destinations. This dissertation introduces a novel framework in the fields of fairness in network optimization and evacuation to provide better insight into the evacuation process and assist with decision making. The user of the transportation network is a critical element in this research. Thus, fairness and efficiency are the two primary objectives addressed in the work by considering the limited capacity of roads of the transportation network. Specifically, an approximation approach to the max-min fairness (MMF) problem is presented that provides lower computational time and high-quality output compared to the original algorithm. In addition, a new algorithm is developed to find the MMF resource allocation output in nonconvex structure problems. MMF is the fairness policy used in this research since it considers fairness and efficiency and gives priority to fairness. In addition, a new dynamic evacuation modeling approach is introduced that is capable of reporting more information about the evacuees compared to the conventional evacuation models such as their travel time, evacuation ti

关键词： mixed integer linear programming evacuation planning dynamic traffic assignment max-min fairness

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Scheduling heterogeneous multi-load AGVs with battery constraints

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COMPUTERS & OPERATIONS RESEARCH 2021年 136卷 105517-105517页

作者： Dang, Quang-Vinh Singh, Nitish Adan, Ivo Martagan, Tugce van de Sande, Dirk Eindhoven Univ Technol Dept Ind Engn & Innovat Sci POB 513 NL-5600 MB Eindhoven Netherlands

Recent trends towards larger and more complex systems necessitate the use of heterogeneous and flexible automated guided vehicles (AGVs) to fulfill the transport demand within a factory. To operate the fleet of AGVs efficiently, it is also important to consider their limited battery capacity. In this context, we tackle the problem of scheduling transport requests on multi-load and multi-ability AGVs with battery management. Each AGV can carry more than one load at a time and have specific capabilities such as lift loads, tow loads, or handle loads with a mounted robot arm. Each request consists of a pickup and a delivery task associated with an origin, a destination, a soft time window, and a priority. Each transport request may also require different AGV capabilities, and the AGV batteries can be recharged partially under consideration of a critical battery threshold. The decisions involve assigning transport and charging requests to AGVs, sequencing these requests, and determining the arrival times and charging duration. A mixed-integer linear programming model is formulated. A hybrid adaptive large neighborhood search with an integrated local search method is proposed to find a feasible schedule with the aim to minimize the tardiness costs of requests and travel costs of AGVs. We illustrate the efficacy of the hybrid algorithm with an industry case study using real-world data. The computational results reveal a 20%-50% cost reduction in current practice by using our hybrid algorithm, and around 50% cost reduction with respect to a single-load AGV scheduling approach proposed in the literature.

关键词： Scheduling Multi-load automated guided vehicles Adaptive large neighborhood search mixed integer linear programming

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COMTOP: Protein Residue-Residue Contact Prediction through mixed integer linear Optimization

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MEMBRANES 2021年第7期11卷 503-503页

作者： Reza, Md Selim Zhang, Huiling Hossain, Md Tofazzal Jin, Langxi Feng, Shengzhong Wei, Yanjie Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China Chinese Acad Sci Ctr High Performance Comp Joint Engn Res Ctr Hlth Big Data Intelligent Anal Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Harbin Univ Sci & Technol Sch Comp Sci & Technol Dept Comp Sci & Technol 52 Xuefu Rd Harbin 150080 Peoples R China

Protein contact prediction helps reconstruct the tertiary structure that greatly determines a protein's function;therefore, contact prediction from the sequence is an important problem. Recently there has been exciting progress on this problem, but many of the existing methods are still low quality of prediction accuracy. In this paper, we present a new mixed integer linear programming (MILP)-based consensus method: a Consensus scheme based On a mixed integer linear opTimization method for prOtein contact Prediction (COMTOP). The MILP-based consensus method combines the strengths of seven selected protein contact prediction methods, including CCMpred, EVfold, DeepCov, NNcon, PconsC4, plmDCA, and PSICOV, by optimizing the number of correctly predicted contacts and achieving a better prediction accuracy. The proposed hybrid protein residue-residue contact prediction scheme was tested in four independent test sets. For 239 highly non-redundant proteins, the method showed a prediction accuracy of 59.68%, 70.79%, 78.86%, 89.04%, 94.51%, and 97.35% for top-5L, top-3L, top-2L, top-L, top-L/2, and top-L/5 contacts, respectively. When tested on the CASP13 and CASP14 test sets, the proposed method obtained accuracies of 75.91% and 77.49% for top-L/5 predictions, respectively. COMTOP was further tested on 57 non-redundant alpha-helical transmembrane proteins and achieved prediction accuracies of 64.34% and 73.91% for top-L/2 and top-L/5 predictions, respectively. For all test datasets, the improvement of COMTOP in accuracy over the seven individual methods increased with the increasing number of predicted contacts. For example, COMTOP performed much better for large number of contact predictions (such as top-5L and top-3L) than for small number of contact predictions such as top-L/2 and top-L/5. The results and analysis demonstrate that COMTOP can significantly improve the performance of the individual methods;therefore, COMTOP is more robust against different types of test se

关键词： protein residue-residue contact contact prediction mixed integer linear programming machine learning protein sequence

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Convalescent plasma bank facility location-allocation problem for COVID-19

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2021年 156卷 102517-102517页

作者： Manupati, Vijaya Kumar Schoenherr, Tobias Wagner, Stephan M. Soni, Bhanushree Panigrahi, Suraj Ramkumar, M. Natl Inst Technol Warangal Dept Mech Engn Warangal 506004 Telangana India Michigan State Univ Broad Coll Business Dept Supply Chain Management 632 Bogue St E Lansing MI 48824 USA Swiss Fed Inst Technol Zurich Chair Logist Management Dept Management Technol & Econ Weinbergstr 56-58 CH-8092 Zurich Switzerland Jawaharlal Inst Postgrad Med Educ & Res Dept Prevent & Social Med Gorimedu 605006 Puducherry India Indian Inst Management Raipur Operat & Quantitat Methods Grp Atal Nagar Raipur 493661 Madhya Pradesh India

With convalescent plasma being recognized as an eminent treatment option for COVID-19, this paper addresses the location-allocation problem for convalescent plasma bank facilities. This is a critical topic, since limited supply and overtly increasing cases demand a well-established supply chain. We present a novel plasma supply chain model considering stochastic parameters affecting plasma demand and the unique features of the plasma supply chain. The primary objective is to first determine the optimal location of the plasma banks and to then allocate the plasma collection facilities so as to maintain proper plasma flow within the network. In addition, recognizing the perishable nature of plasma, we integrate a deteriorating rate with the objective that as little plasma as possible is lost. We formulate a robust mixed-integer linear programming (MILP) model by considering two conflicting objective functions, namely the minimization of overall plasma transportation time and total plasma supply chain network cost, with the latter also capturing inventory costs to reduce wastage. We then propose a CPLEX-based optimization approach for solving the MILP functions. The feasibility of our results is validated by a comparison study using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a proposed modified NSGA-III. The application of the proposed model is evaluated by implementing it in a real-world case study within the context of India. The optimized numerical results, together with their sensitivity analysis, provide valuable decision support for policymakers.

关键词： Convalescent plasma Location-allocation problem COVID-19 Plasma supply chain mixed integer linear programming CPLEX optimization NSGA-II NSGA-III

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Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities

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ENERGIES 2021年第24期14卷 8480页

作者： Talluri, Giacomo Lozito, Gabriele Maria Grasso, Francesco Iturrino Garcia, Carlos Luchetta, Antonio Univ Florence Dept Informat Engn Via S Marta 3 I-50139 Florence Italy

In this work, a strategy for scheduling a battery energy storage system (BESS) in a renewable energy community (REC) is proposed. RECs have been defined at EU level by the 2018/2001 Directive;some Member States transposition into national legislation defined RECs as virtual microgrids since they still use the existing low voltage local feeder and share the same low-medium voltage transformer. This work analyzes a REC which assets include PV generators, BESS and non-controllable loads, operating under the Italian legislative framework. A methodology is defined to optimize REC economic revenues and minimize the operation costs during the year. The proposed BESS control strategy is composed by three different modules: (i) a machine learning-based forecast algorithm that provides a 1-day-ahead projection for microgrid loads and PV generation, using historical dataset and weather forecasts;(ii) a mixed integer linear programming (MILP) algorithm that optimizes the BESS scheduling for minimal REC operating costs, taking into account electricity price, variable feed-in tariffs for PV generators, BESS costs and maximization of the self-consumption;(iii) a decision tree algorithm that works at the intra-hour level, with 1 min timestep and with real load and PV generation measurements adjusting the BESS scheduling in real time. Validation of the proposed strategy is performed on data acquired from a real small-scale REC set up with an Italian energy provider. A 10% average revenue increase could be obtained for the prosumer alone when compared to the non-optimized BESS usage scenario;such revenue increase is obtained by reducing the BESS usage by around 30% when compared to the unmanaged baseline scenario.

关键词： renewable energy community mixed integer linear programming BESS scheduling machine learning recurrent neural network load forecast experimental database time series

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Exact methods for the traveling salesman problem with multiple drones

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2021年 130卷 103280-103280页

作者： Cavani, Sara Iori, Manuel Roberti, Roberto Univ Modena & Reggio Emilia Dept Sci & Methods Engn I-42122 Reggio Emilia Italy Vrije Univ Amsterdam Dept Operat Analyt NL-1081 HV Amsterdam Netherlands

Drone delivery is drawing increasing attention in last-mile delivery. Effective solution methods to solve decision-making problems arising in drone delivery allow to run and assess drone delivery systems. In this paper, we focus on delivery systems with a single traditional vehicle and multiple drones working in tandem to fulfill customer requests. We address the Traveling Salesman Problem with Multiple Drones (TSP-MD) and investigate the modeling challenges posed by the presence of multiple drones, which have proven to be hard to handle in the literature. We propose a compact mixed-integer linear programming (MILP) model to formulate the TSP-MD and several families of valid inequalities. Moreover, we illustrate an exact decomposition approach based on the compact MILP and a branch-and-cut algorithm. We show that this exact approach can solve instances with up to 24 customers to proven optimality, improving upon existing exact methods that can solve similar problems with up to ten customers only.

关键词： Traveling salesman Drone Exact method Branch-and-cut mixed integer linear programming

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