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An energy systems engineering approach to the optimal design of energy systems in commercial buildings

ENERGY POLICY 2010年第8期38卷 4224-4231页

作者： Liu, Pei Pistikopoulos, Efstratios N. Li, Zheng Univ London Imperial Coll Sci Technol & Med Dept Chem Engn CPSE London SW7 2AZ England Tsinghua Univ Dept Thermal Engn Beijing 100084 Peoples R China

Energy consumption in commercial buildings accounts for a significant proportion of worldwide energy consumption. Any increase in the energy efficiency of the energy systems for commercial buildings would lead to significant energy savings and emissions reductions. In this work, we introduce an energy systems engineering framework towards the optimal design of such energy systems with improved energy efficiency and environmental performance. The framework features a superstructure representation of the various energy technology alternatives, a mixed-integer optimization formulation of the energy systems design problem, and a multi-objective design optimization solution strategy, where economic and environmental criteria are simultaneously considered and properly traded off. A case study of a supermarket energy systems design is presented to illustrate the key steps and potential of the proposed energy systems engineering approach. (c) 2010 Elsevier Ltd. All rights reserved.

关键词： Energy systems engineering mixed-integer programming Commercial buildings

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Hybrid simulated annealing and reduced variable neighbourhood search for an aircraft scheduling and parking problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020年第9期58卷 2626-2646页

作者： Zheng, Shuang Yang, Zhen He, Zhengwen Wang, Nengmin Chu, Chengbin Yu, Haiyang Xi An Jiao Tong Univ Sch Management Xian Shaanxi Peoples R China Univ Paris Est ESIEE Paris Noisy Le Grand France China Acad Civil Aviat Sci & Technol Beijing Peoples R China

Aircraft stands and runways at airports are critical airport resources for aircraft scheduling and parking. Making use of limited apron and runway resources to improve airport efficiency is becoming increasingly important. In this paper, we study a realistic Aircraft Scheduling and Parking Problem (ASPP) with the goal of simultaneously determining the takeoff and landing time of each aircraft with consideration for wake vortex effect constraints and parking positions in the limited parking apron at a target airport. The objective of the ASPP is to minimise the total service time for aircraft. We developed a mixed-integer linear programme formulation for the ASPP. A novel improved bottom-left/right strategy is applied to construct solutions and a Hybrid Simulated Annealing and Reduced Variable Neighborhood Search (HSARVNS) is proposed to identify near-optimal solutions. Numerical experiments on randomly generated ASPP instances and on a large set of benchmarks for a reduced version of the ASPP (i.e. the classical Two-Dimensional Strip-Packing Problem (2D-SPP)) demonstrate the effectiveness and efficiency of the proposed approach. For the ASPP, HSARVNS can find optimal solutions for small instances in a fraction of a second and can find high-quality solutions for instances with up to 250 aircraft within a reasonable timeframe. For the 2D-SPP, the HSARVNS can find optimal solutions for 32 of 38 tested benchmarks within 90 s on average.

关键词： Aircraft scheduling mixed-integer programming strip-packing meta-heuristic

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Subspace dynamic-simplex linear interpolation search for mixed-integer black-box optimization problems

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NAVAL RESEARCH LOGISTICS 2017年第4期64卷 305-322页

作者： Wang, Honggang Rutgers State Univ Dept Ind & Syst Engn Piscataway NJ 08854 USA

Design and management of complex systems with both integer and continuous decision variables can be guided using mixed-integer optimization models and analysis. We propose a new mixed-integer black-box optimization (MIBO) method, subspace dynamic-simplex linear interpolation search (SD-SLIS), for decision making problems in which system performance can only be evaluated with a computer black-box model. Through a sequence of gradient-type local searches in subspaces of solution space, SD-SLIS is particularly efficient for such MIBO problems with scaling issues. We discuss the convergence conditions and properties of SD-SLIS algorithms for a class of MIBO problems. Under mild conditions, SD-SLIS is proved to converge to a stationary solution asymptotically. We apply SD-SLIS to six example problems including two MIBO problems associated with petroleum field development projects. The algorithm performance of SD-SLIS is compared with that of a state-of-the-art direct-search method, NOMAD, and that of a full space simplex interpolation search, Full-SLIS. The numerical results suggest that SD-SLIS solves the example problems efficiently and outperforms the compared methods for most of the example cases. (c) 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 305-322, 2017

关键词： numerical optimization computer simulation mixed-integer programming black-box function energy production systems

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A Compressive Receding Horizon Approach for Smart Home Energy Management

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IEEE ACCESS 2021年 9卷 100407-100435页

作者： Leitao, Joaquim Fonseca, Carlos M. Gil, Paulo Ribeiro, Bernardete Cardoso, Alberto Univ Coimbra CISUC Ctr Informat & Syst Dept Informat Engn P-3030790 Coimbra Portugal Univ NOVA Lisboa Caparica UNINOVA Ctr Technol & Syst CTS NOVA Sch Sci & Technol Dept Elect & Comp Engn P-2829516 Caparica Portugal

Current increases in the demand for electricity require sustainable energy management measures and have promoted the adoption of clean and renewable sources, particularly at the residential building level. Active demand management is usually carried out through load shifting based on specific techniques, such as optimisation, heuristics, model-based predictive control and machine learning methodologies. This work addresses the problem of residential load scheduling via optimisation techniques. A compressive receding horizon strategy is proposed for week-ahead load shifting, and the selection is driven by traditional receding horizon and day-ahead allocation strategy misalignment, with weekly household appliance usage patterns. The proposed approach is compared with receding horizon and day-ahead scheduling techniques over 30 different weeks for a prototypical smart home with non-controllable demand, which is representative of a four-resident family and includes micro power generation and battery storage. The simulation results confirm the validity of the proposed strategy in the context of household appliance scheduling problems and show competitive electricity costs and resident discomfort performance compared to state-of-the-art approaches. Furthermore, the proposed compressive receding horizon strategy fully exploits weather and photovoltaic generation forecasts to promote self-consumption and grid demand stress reduction while providing environmental gains and financial benefits to the utility service and consumers, particularly in the case of simultaneously scheduling a huge number of households.

关键词： Home appliances Optimal scheduling Smart homes Resource management Uncertainty State of charge programming Home energy and water management systems mixed-integer programming optimal scheduling receding horizon smart homes

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Flexible staffing of physicians with column generation

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2021年第1期33卷 212-252页

作者： Erhard, Melanie Univ Augsburg Chair Hlth Care Operat Hlth Informat Management Fac Business & Econ Univ Str 16 D-86159 Augsburg Germany Klinikum Augsburg UNIKA T Univ Ctr Hlth Sci Neusasser Str 47 D-86156 Augsburg Germany

In Germany, around 40% of the hospitals do not generate an annual surplus. This leads to an increasing pressure on hospitals' management to reorganize and restructure their processes and resources to decrease the upcoming costs and become profitable. Since personnel, especially physicians, generates a major part of the arising costs, assigning staff efficiently provides an opportunity to decrease associated costs. Up to now, experienced physicians create rosters manually which is cost and time intense due to the problem's complexity and especially the fluctuation in demand. To circumvent this difficulty, it is our main aim to create a new mathematical modeling approach to implement additional flexibility in the rostering process to better match supply and demand. Therefore, we formulate the problem as mixed-integer programming model with the objective to minimize occurring labor costs of physicians over the considered planning horizon subject to coverage of demand to make flexibility monetarily evaluable. In our approach, full flexibility in terms of patterns of working days, shift types, and the placement of the break is provided. To solve the problem under consideration, a column generation heuristic is presented. In our experimental study, the performance of the provided solution approach as well as the effect of additional flexibility in the rostering process are evaluated using real life data. Results indicate the significant impact of implementing flexibility in the scheduling process on the salary costs of the number of required physicians and evidence the superior quality of our solution approach.

关键词： Physicians Column generation Flexibility Hospital mixed-integer programming

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Ellipsoidal mixed-integer representability

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MATHEMATICAL programming 2018年第1-2期172卷 351-369页

作者： Del Pia, Alberto Poskin, Jeffrey Univ Wisconsin Wisconsin Inst Discovery Dept Ind & Syst Engn Madison WI USA Univ Wisconsin Dept Math Madison WI 53706 USA

Representability results for mixed-integer linear systems play a fundamental role in optimization since they give geometric characterizations of the feasible sets that can be formulated by mixed-integer linear programming. We consider a natural extension of mixed-integer linear systems obtained by adding just one ellipsoidal inequality. The set of points that can be described, possibly using additional variables, by these systems are called ellipsoidal mixed-integer representable. In this work, we give geometric conditions that characterize ellipsoidal mixed-integer representable sets.

关键词： mixed-integer programming Quadratic programming Representability Ellipsoidal constraints

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Planning inland vessel operations in large seaports using a two-phase approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 106卷 41-57页

作者： Li, Shijie Negenborn, Rudy R. Lodewijks, Gabriel Delft Univ Technol Dept Maritime & Transport Technol Mekelweg 2 NL-2628 CD Delft Netherlands Wuhan Univ Technol Sch Logist Engn 1178 Heping Rd Wuhan Hunan Peoples R China

Inland vessels are often used to transport containers between large seaports and the hinterland. Each time a vessel arrives in such a port, it typically visits several terminals to load and unload containers. In the Port of Rotterdam, the largest port in Europe, there are 77,000 inland vessels that have moored in the port in 2014 for transporting cargo. With the significant growth of containerized cargo transportation over the last decade, large seaports are under pressure to ensure high handling efficiency. Due to this development and the limited capacity at terminals, the inland vessels usually spend longer time in the port that originally planned. This leads to low utilization of terminal resources and congestion in the port. This paper proposes a novel two-phase planning approach that could improve this, taking into account several practical constraints. Specifically, we take into account the restricted opening times of terminals, the priority of sea-going vessels, and the different terminal capacities and sizes. In addition, we also consider the option for inland vessels to carry out additional inter-terminal transport tasks. Our approach is based on the integration of mixed-integer programming (MIP) and constraint programming (CP) to generate rotation plans for inland vessels. In the first phase, a single vessel optimization problem is solved using MIP. In the second phase, a multiple vessel coordination problem is formulated using CP;three large neighborhood search (LNS)-based heuristics are proposed to solve the problem. Simulation experiments show that the proposed INS-based heuristic outperforms the performance obtained with a state-of-the-art commercial CP solvers both regarding the solution quality and the computation time. Moreover, the simulation results indicate significant improvements with shorter departure times, sojourn times and waiting times. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Vessel rotation planning mixed-integer programming Constraint programming

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Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

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NAVAL RESEARCH LOGISTICS 2021年第1期68卷 89-111页

作者： Shehadeh, Karmel S. Cohn, Amy E. M. Jiang, Ruiwei Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed-integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two-stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable)appointment order(AO)policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near-optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor-swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.

关键词： appointment scheduling mixed-integer programming Monte Carlo optimization OR in health services stochastic arrival stochastic programming

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Fast training of Support Vector Machines with Gaussian kernel

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DISCRETE OPTIMIZATION 2016年第PartA期22卷 183-194页

作者： Fischetti, Matteo Univ Padua DEI Via Gradenigo 6-A I-35100 Padua Italy

Support Vector Machines (SVM's) are ubiquitous and attracted a huge interest in the last years. Their training involves the definition of a suitable optimization model with two main features: (1) its optimal solution estimates the a posteriori optimal SVM parameters in a reliable way and (2) it can be solved efficiently. Hinge-loss models, among others, have been used with remarkable success together with cross validation-the latter being instrumental to the success of the overall training, though it can become very time consuming. In this paper we propose a different model for SVM training, that seems particularly suited when the Gaussian kernel is adopted (as it is often the case). Our approach is to model the overall training problem as a whole, thus avoiding the need of cross validation. Though our basic model is an NP-hard mixed-integer Linear Program, some variants can be solved very efficiently by simple sorting algorithms. Computational results on test cases from the literature are presented, showing that our training method can lead to a classification accuracy comparable (or even slightly better) than the classical hinge loss model, with a speedup of 2-3 orders of magnitude. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Support vector machine Classification mixed-integer programming

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Quantum behaved particle swarm optimization of inbound process in an automated warehouse

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2023年第10期74卷 2199-2214页

作者： Yuan, Yingying Zhen, Lu Wu, Jingwen Wang, Xiaofan Shanghai Univ Shanghai Peoples R China

The inbound process is of great importance in enhancing the efficiency of automated warehouse operations. This study investigates an optimization problem on the inbound warehouse process by coordinating multiple resources in a type of automated warehouse system, i.e., Shuttle-Based Storage and Retrieval System (SBS/RS). A mixed-integer programming model is formulated to determine the assignment decisions of the pallets towards three types of the resources in the SBS/RS (i.e., forklifts, lifts and shuttles), the sequencing & timing decisions of these three types of resources for transporting the pallets. Then, a novel solution method, called Adaptive Quantum behaved Particle Swarm Optimization (AQPSO) algorithm, is designed to solve the proposed model. The introduction of the quantum mechanism prevents the algorithm from falling into a local minimum. The integration of the adaptive adjustment strategy improves the efficiency of the algorithm by dynamically adjusting the search scale. The efficiency of the proposed algorithm is verified by comparative experiments that use the CPLEX solver and the basic particle swarm optimization algorithm as rivals. The experimental results indicate that the proposed algorithm have an advantage in the solution quality and the computing time. A series of sensitivity analyses are also conducted to bring out some managerial insights. For example, it is beneficial to reduce energy consumption by adjusting the relative velocity and power of the three types of equipment, and setting the best ratios of shuttles to forklifts.

关键词： Automated warehouses inbound process mixed-integer programming particle swarm optimization

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