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Optimization models for the installation planning of offshore wind farms

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期315卷 1182-1196页

作者： Amorosi, Lavinia Fischetti, Martina Paradiso, Rosario Roberti, Roberto Sapienza Univ Rome Dept Stat Sci Rome Italy Univ Seville Inst Matemat Seville Spain Vrije Univ Amsterdam Dept Operat Analyt Amsterdam Netherlands Univ Padua Dept Informat Engn Padua Italy

Challenges posed by global warming motivate the increasing interest in green energy sources, such as wind energy. To make wind energy attractive from an economic viewpoint, the decision -making problems faced in designing, installing, and maintaining wind farms have to be optimized. In this paper, we focus on the problem of optimally planning the installation process of offshore wind farms, as faced by Vattenfall, a leading European energy company. We first formulate the deterministic version of this Installation Planning of Offshore Wind Farms (IPOWF) problem as a mixed integer linear programming (MILP) model. From this model, we then derive other MILPs that can provide lower and upper bounds to the optimal installation cost. To cope with weather uncertainty, we also provide a light -robust MILP. These models are tested on real -life instances corresponding to two wind farms that Vattenfall has recently built in Denmark and Germany. Computational results show that tight primal and dual solutions can be computed for the deterministic case with the proposed models and that the light -robust model yields solutions that are robust to weather uncertainty.

关键词： Wind farms Wind energy Installation mixed integer linear programming

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Residential consumer enrollment in demand response: An agent based approach

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APPLIED ENERGY 2024年 374卷

作者： Sridhar, Araavind Honkapuro, Samuli Ruiz, Fredy Stoklasa, Jan Annala, Salla Wolff, Annika LUT Univ Lappeenranta Finland Politecn Milan Milan Italy

Residential consumers play an important role in the sustainable transition of the energy system by leveraging their household loads for demand response (DR). This paper aims to analyze the enrollment rates of residential consumers within DR through an agent-based model (ABM). Both economic and noneconomic (social/behavioral) parameters that influence the consumer enrollment in DR are considered. An energy management model, a home energy management system (HEMS), is used to identify the potential economic savings of consumers enrolling in DR. Consumers are randomly assigned to different neighborhoods and have different social relationships (e.g., friends, neighbors), which, in turn, influences their decision-making in the ABM. The results of this paper highlight the indirect relationship of expected annual savings and direct relationship of the share of consumers having electric vehicles (EV), photovoltaics (PV), and battery energy storage systems (BESSs) on the DR enrollment rates. Based on the enrollment rates, the maximum energy savings were obtained in April and the minimum during the last quarter of the year. Monte Carlo analysis is employed to handle the randomness associated with different variable selections, which provides a +/- 10% variation of consumer enrollment rate in DR. The results of this study have practical implications for energy flexibility in the residential sector.

关键词： Demand response Home energy management system Agent-based simulation Monte Carlo analysis mixed integer linear programming

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Iterative MILP algorithm to find alternate solutions in linear programming models

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OPTIMIZATION AND ENGINEERING 2024年第4期25卷 2401-2424页

作者： Kakkad, Dev A. Grossmann, Ignacio E. Springub, Bianca Galanopoulos, Christos de Assis, Leonardo Salsano Tran, Nga Wassick, John M. Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Aurubis AG Hamburg 20539 Germany

We address in this paper linear programming (LP) models in which it is desired to find a finite set of alternate optima. An LP may have multiple alternate solutions with the same objective value or with increasing objective values. For many real life applications, it can be interesting to have a pool of solutions to compare what operations should be executed and what is the cost/benefit of doing it. To obtain a specified number of these alternate solutions in the increasing order of objective values, we propose an iterative MILP algorithm in which we successively add integer cuts on inactive constraints. We demonstrate the application and effectiveness of this algorithm on a 2 dimensional LP and on small and large supply chain problems. The proposed iterative MILP algorithm provides an effective approach for finding a specified number of alternate optima in LP models, which provides a useful tool in a variety of applications as for instance in supply chain optimization problems.

关键词： Supply chain network linear programming mixed integer linear programming Alternate optima

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Modular vehicle-based transit system for passenger and freight co-modal transportation

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 160卷

作者： Lin, Jie Zhang, Fangni Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China

The emergence of vehicle modularity technologies provides an opportunity to develop flexible public transport services that allow en -route vehicle capacity adjustments to meet fluctuating demands. Given the largely imbalanced demands for passenger and freight transport in urban areas, the use of modular vehicles in collaborative passenger and freight transport (comodality) is expected to increase overall transport efficiency. This study explores the potential of integrating modular vehicles into a co -modal transit system that serves both passengers and freight. Specifically, we consider a bi-directional transit corridor where modular vehicles are scheduled to meet time -dependent passenger and freight demands. Each modular vehicle comprises identical modules that can be assigned to either passengers or freight individually. Moreover, a modular vehicle can adjust (i) the number of composed modules by docking and undocking modules, and (ii) module allocation between passengers and freight at intermediate stations. To minimize passenger waiting costs and vehicle operating costs, a mixed integer programming formulation is established that optimizes vehicle dispatch times, (un)docking operations, module allocation, and freight allocation. Given the solution complexity, we propose a two -stage algorithm to solve this problem efficiently. In the first stage, we address vehicle dispatch times, and in the second stage, vehicle formation and freight allocation are optimized. Numerical experiments verify that the proposed two -stage algorithm outperforms state-of-the-art solvers and can obtain near -optimal solutions in an acceptable time for large-scale problems. A case study based on a transit line in Hong Kong validates the effectiveness of co -modal modular transit systems, where station -wise (un)docking and co -modality lead to a cost reduction of 8.2% and 11.2%, respectively. Finally, sensitivity analysis examines the impact of key parameters on the performance of co

关键词： Modular vehicle Urban co-modality Bi-directional transit corridor system mixed integer linear programming Two-stage heuristics

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Appraisal of viable optimization techniques for efficient design and control planning of renewable energy-based microgrid systems

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 120卷

作者： Adewuyi, Oludamilare Bode Krishnamurthy, Senthil Cape Peninsula Univ Technol Ctr Substn Automat & Energy Management Syst Dept Elect Elect & Comp Engn ZA-7535 Bellville South Africa

Due to the rapid increase in energy demand and technological improvements, a noteworthy shift has been witnessed in the energy sector through the implementation of the distributed energy systems concept. At the hub of these changes are renewable energy sources, which have risen to be the substitute solution for meeting the proliferating energy demand while mitigating the effects of climate change and contributing to socioeconomic growth. Contrary to the conventional approach, these distributed energy systems are integrated using microgrid systems, and this sophisticated technological solution necessitates the management of information exchange and power scheduling between the dispersed generating sources, customer loads, and the primary grid infrastructure. Thus, there is a need for improvement in the optimal planning and control of microgrid operation regarding computational efficiency, reliability of the obtained solution, and algorithm robustness. In addition to comparing various MG optimization problem formulations and solution approaches using multiple optimization algorithms, this paper reviews various optimization techniques used in renewable energy-based microgrid systems design and control planning. The paper evaluates particle swam optimization and mixed integer linear programming capacity for microgrid planning, considering computational efficiency, solution dependability, and algorithm resilience while providing credible information on the capabilities of parallel computing for robust optimal of high renewable microgrid design considering uncertainties and energy market dynamics.

关键词： Microgrid system planning Capacity optimization Operational scheduling and control Particle swam optimization mixed integer linear programming Parallel computing

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Exact and heuristic methods for a university course scheduling problem

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 248卷

作者： Xiang, Kewei Hu, Xinyao Yu, Mingzheng Wang, Xingyu Nanjing Univ Sch Management & Engn Management Sci & Engn Nanjing 210008 Peoples R China Tsinghua Univ Sch Econ & Management Management Sci & Engn Beijing 100084 Peoples R China Huazhong Univ Sci & Technol Sch Management Management Sci & Engn Wuhan 430074 Peoples R China Chongqing Univ Sch Econ & Business Adm Mkt Chongqing 400044 Peoples R China

The University Course Scheduling Problem (UCSP) is a complex combinatorial optimization problem. Most universities still use hand-operated scheduling, which usually takes a long time and results in numerous irrationalities. The imperative of conceiving a algorithm to navigate this predicament is undeniable. This paper introduces an advanced mixed integer linear programming paradigm, incorporating constraints encompassing both the temporal coherence of courses and the equitable dispersion of these courses throughout the academic week. To tackle this issue, we design a novel two-stage meta-heuristic algorithm;the first stage harnesses the genetic algorithm to cluster courses into sets by exploiting graph coloring techniques, while the subsequent stage leverages an enhanced tabu search heuristic to assign these clusters into distinct temporal intervals. The effectiveness and efficiency of the proposed heuristic has been validated by extensive computational studies based on authentic data gleaned from Huazhong University of Science and Technology's School of Management and the results are compared with CPLEX and common heuristic. Moreover, our research examines the algorithm's convergence properties and the effects of fast neighborhood evaluation strategy. Sensitivity analysis is performed to probe the algorithm's responsiveness to the new constraints.

关键词： Course scheduling mixed integer linear programming Genetic algorithm Tabu search Graph coloring

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Rethinking Motion Estimation: An Outlier Removal Strategy in SORT for Multi-Object Tracking With Camera Moving

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IEEE ACCESS 2024年 12卷 142819-142837页

作者： Min, Zijian Hassan, Gundu Mohamed Jo, Geun-Sik Inha Univ Dept Elect & Comp Engn Artificial Intelligence Lab Incheon 22212 South Korea Augmented Knowledge Corp Incheon 22212 South Korea

Multi-Object Tracking (MOT) involves the simultaneous tracking of multiple targets in a scene, demanding accurate discrimination of foreground and background, as well as precise identification of feature distinctions among diverse objects. Simple Online and Real-Time Tracking (SORT) is a widely adopted method in MOT, leveraging dual-phase Kalman Filter (KF) for object state estimation and ensuring consistent tracker association throughout a video sequence. Recent advancements in SORT-like algorithms aim to address nonlinear object motion and reduce reliance on detection for association in SORT. Despite these improvements, existing SORT-like methods often overlook camera motion, resulting in suboptimal motion prediction under dynamic camera conditions. In this paper, we introduce a novel SORT-like approach, termed Outlier Removal-based SORT (OR-SORT), which introduces a novel triple-phase Kalman Filter, encompassing prediction, re-prediction, and update phases. This framework dissects the object motion state transition model into distinct components-linear velocity self-motion and camera motion. Additionally, our method employs outlier removal based on mixed integer linear programming (MILP) to enhance camera motion estimation accuracy. Experimental evaluations on the MOTChallenge datasets, including the scenarios with both moving cameras and high object densities, demonstrate our method's superior performance, particularly in scenarios with moving cameras. Our approach achieves a state-of-the-art MOTA of 80.7% and IDF1 of 79.6% on MOT17, and 77.9% and IDF1 of 76.4% on MOT20.

关键词： Computer vision multi-object tracking SORT-like tracking SORT-like tracking Kalman filter Kalman filter camera motion camera motion outlier removal outlier removal mixed integer linear programming mixed integer linear programming mixed integer linear programming

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A Practical Urban Distribution Network Planning Method With Geographic Information System

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第6期39卷 7038-7049页

作者： Wang, Zhen Shen, Xinwei Sun, Hongbin Wu, Qiuwei Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Taiyuan Univ Technol Taiyuan 030002 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Urban distribution network planning (UDNP) faces new requirements and restrictions due to rapid urbanization. This paper proposes a framework for UDNP, in which the geographic information system (GIS) and a hybrid optimization model are combined to co-optimize investment and operation decisions. Firstly, we extract the street layout graph from the GIS data and produce the shortest path between distribution transformers/power substations. Then the distance matrix could be formed and used in the following UDNP optimization. The proposed hybrid optimization model utilizes 1) Clark and Wright's Savings (CWS) algorithm to produce a close-loop network topology, and 2) mixed integer linear programming (MILP) to produce the corresponding open-loop operation strategy. Case studies on an urban area in China demonstrate the effectiveness of the proposed method. The CWS algorithm improved the computation efficiency by over 1000 times compared with MILP, while introducing the shortest path for UDNP leads to a 30%+ decrease in investment compared with that of using Euclidean or Manhattan distance.

关键词： geographic information system shortest path mixed integer linear programming urban distribution network planning urban distribution network planning shortest path urban distribution network planning

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Optimizing Cutting Log Operations in Softwood Sawmills: A Multi-Objective Approach Tailored for SMEs

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IEEE ACCESS 2024年 12卷 128141-128150页

作者： Ramos-Maldonado, Mario Munoz, Felipe T. Mora, Pablo Venegas-Vasconez, Diego Univ Bio Bio Fac Ingn Dept Ingn Maderas Concepcion 4050231 Chile Univ Bio Bio Fac Ingn Dept Ingn Ind Concepcion 4050231 Chile

The production planning problem in the Pinus radiata sawmill industry revolves around determining how to cut a set of logs of different diameters to obtain pieces with a rectangular base, typically of the same length as the original log. The primary objective is often to maximize the volumetric yield or the ratio between the total volume of the produced pieces and the available volume of the logs. Given the scarcity of timber forests, small and medium-sized (SME) sawmills must optimize their operations to maximize the volumetric yield of logs, usually procured from third parties and may vary in quality and dimensions. In this context, the absence of decision support tools directly contributes to inefficient raw material utilization, consequently impacting the business's profit. This study introduces a multi-objective mixed-integer linear programming model incorporating log availability and product demand as input parameters. The objective functions aim to minimize the total volumetric loss of utilized logs and the surplus quantity associated with products exceeding demand. The model integrates cutting patterns pre-determined for each log diameter as an additional input. The Cutlog (c) software was used to identify all the optimal cutting patterns. The $\varepsilon $ -constraint method, implemented in the CPLEX (c) solver, was employed to solve the model. The model was validated using representative instances designed to emulate the challenges faced by SME sawmills. Real industry data, surveys, and commercial records from SME sawmills in southern Chile were utilized. The results confirm the effectiveness of the proposed model in addressing the multi-objective challenges encountered by these businesses. The model successfully identifies multiple solutions on the Pareto frontier, offering valuable insights for decision-making.

关键词： Optimization methods linear programming Raw materials Industries Forests Pareto optimization mixed integer linear programming Production planning Wood industry Lumber industry Multi-objective optimization coniferous sawmills cutting stock problem small and medium-sized enterprises Pareto frontier mixed integer linear programming production planning

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Capacity planning and scheduling for jobs with uncertainty in resource usage and duration

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JOURNAL OF SUPERCOMPUTING 2024年第15期80卷 22428-22461页

作者： Patra, Sunandita Pathan, Mehtab Mahfouz, Mahmoud Zehtabi, Parisa Ouaja, Wided Magazzeni, Daniele Veloso, Manuela JP Morgan AI Res New York NY 10017 USA JP Morgan CIB Athena Appl Intelligence London England JP Morgan AI Res London England

Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement toward using a cloud computing infrastructure, our organization follows a hybrid approach with both cloud and on-prem servers. The objective of this work is to perform capacity planning, i.e., estimate resource requirements, and job scheduling for on-prem grid computing environments. A key contribution of our approach is handling uncertainty in both resource usage and duration of the jobs, a critical aspect in the finance industry where stochastic market conditions significantly influence job characteristics. For capacity planning and scheduling, we simultaneously balance two conflicting objectives: (a) minimize resource usage and (b) provide high quality of service to the end users by completing jobs by their requested deadlines. We propose approximate approaches using deterministic estimators and pair sampling-based constraint programming. Our best approach (pair sampling-based) achieves up to 41.6% estimated peak reduction in resource usage compared to manual scheduling without compromising on the quality of service.

关键词： Capacity planning Stochastic resource usage Stochastic job durations Scheduling under uncertainty Sample average approximation Constraint programming mixed integer linear programming

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