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A design and analysis of computer experiments based mixed integer linear programming approach for optimizing a system of electric vehicle charging stations

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Chawal, Ukesh Rosenberger, Jay Chen, Victoria C. P. Lee, Wei J. Wijemanne, Mewan Punugu, Raghavendra K. Kulvanitchaiyanunt, Asama Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76019 USA Boeing Co Boeing Res & Technol Integrated Vehicle Syst 2750 Regent Blvd Dallas TX 75261 USA Univ Texas Arlington Dept Elect Engn Arlington TX 76019 USA

This paper formulates a mixed integer linear programming (MILP) model to optimize a system of electric vehicle (EV) charging stations. Our methodology introduces a two-stage framework that integrates the first-stage system design problem with a second-stage control problem of the EV charging stations and develops a design and analysis of computer experiments (DACE) based system design optimization solution method. Our DACE approach generates a metamodel to predict revenue from the control problem using multivariate adaptive regression splines (MARS), fit over a binned Latin hypercube (LH) experimental design. Comparing the DACE based approach to using a commercial solver on the MILP, it yields near optimal solutions, provides interpretable profit functions, and significantly reduces computational time for practical application.

关键词： Electric Vehicle Charging Stations Latin Hypercube Sampling mixed integer linear programming Multivariate Adaptive Regression -Splines Design and Analysis of Computer-Experiments

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mixed integer linear programming model for a multi-depot arc routing problem with different arc types and flexible assignment of end depot

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Transportation Research Procedia 2025年 82卷 1109-1119页

作者： Ameni Kraiem Jean-François Audy Amina Lamghari Department of Management Université du Québec à Trois-Rivières Trois-Rivières QC G8Z 4M3 Canada Interuniversity Research Center on Enterprise Networks Logistics and Transportation (CIRRELT) Montréal QC H3T 1J4 Canada Group for Research in Decision Analysis (GERAD) Montréal QC H3T 1J4 Canada

This article addresses a street sweeping problem that extends the class of multi-depot arc routing problem by integrating a flexible assignment of end depot for each working shift. A unique team of specialized vehicles starts each shift from a depot, visits the required arcs and returns at the end of each shift to a depot. The service in the next shift should start from the end depot of the previous one. An additional new constraint imposes that the highway arcs must be serviced during a night shift while all others arcs (boulevard, street, etc.) can be swept during both day and night. The aim of this arc routing problem is to find optimal shifts that satisfy these two practical aspects, along with other constraints such as maximum shift duration. The problem is motivated by a real-world application that has not been previously studied in the literature. A mixed integer linear programming model is formulated with the objective of minimizing the total travel time and tested on newly generated instances based on Cordeau's multi-depot vehicle routing problem instances. The results show a generation of total travel time savings up to 12 % compared to the single depot arc routing problem.

关键词： Multi-depot arc routing problem flexible end depot mixed integer linear programming street sweeping

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mixed integer linear programming model and an effective algorithm for the bi-objective double-floor corridor allocation problem

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COMPUTERS & OPERATIONS RESEARCH 2021年 132卷

作者： Guan, Chao Zhang, Zeqiang Gong, Juhua Liu, Silu Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Mainten Chengdu 610031 Peoples R China

The bi-objective double-floor corridor allocation problem (bDFCAP) investigated here explores the effective placement of given departments in a double-floor space to minimise the overall flow cost and the corridor length objectives. Within each floor, departments are arranged in two parallel rows on opposite sides along a central corridor without overlapping. In this study, the bDFCAP is formulated as a mixed integer linear programming model, which has improved the performance over the previous one. Thereafter, a genetic algorithm with a variable neighbourhood search technique is designed and employed to solve the bDFCAP in a more effective manner. This technique is utilized to improve the local search capability by adaptively transforming between a deep-searching strategy and broad-searching strategy, and the superior performance of the proposed method is proven through comparisons with two other algorithms in current literature. Besides, the state-of-the-art lower bounds of several benchmark instances are updated.

关键词： Facility layout design Multi-objective optimisation Genetic algorithm mixed integer linear programming Variable neighbourhood search

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A multi-objective optimization model based on mixed integer linear programming for sizing a hybrid PV-hydrogen storage system

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第26期48卷 9748-9761页

作者： Mohammed, Awsan Ghaithan, Ahmed M. Al-Hanbali, Ahmad Attia, Ahmed M. King Fahd Univ Petr & Minerals Construct Engn & Management Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Ind & Syst Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia

In this paper, a multi-objective mixed-integer linear programming model is developed to design a hybrid PV-hydrogen renewable energy system considering two objective func-tions;minimizing total life costs and loss probability of power supply. The decisions of the hybrid system include the number of PV panels, the number of hydrogen tanks, the number of electrolyzers, the number of fuel cells, and quantity of hydrogen stored over time. An exact method embedded in GAMS software is used to solve the developed model. The model is validated using an electrical testing lab in Saudi Arabia with hourly power demand. Different plans are chosen from the obtained optimal Pareto solutions. For example, one of the plans found that a hybrid system with 212 PV panels, 617 hydrogen tanks, 30 electrolyzers, and 21 fuel cells is sufficient to satisfy the electrical testing lab load with an annual cost of $61663, the loss of power supply probability is 10%, and the CO2 saving of 214,882 kg of CO2. The results indicated the feasibility of combining an electro-lyzer, hydrogen tank storage, and fuel cell with a renewable energy system;however, the cost of energy generated is still high because of the high investment cost. Furthermore, the findings revealed that hydrogen technologies are appealing as an energy storage solution for intermittent renewable energy systems and in other applications such as trans-portation, residential, and industrial sectors. In addition, the findings demonstrated the possibility of using renewable energy as a source of energy, namely, in Saudi Arabia. However, weather conditions, geomorphological conditions, and climatic dependence on hydrogen fuel cell technology can harm the energy yields produced by renewable energy systems.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Photovoltaic system Hydrogen storage mixed integer linear programming Multi-objective optimization

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An assembly timing planning method based on knowledge and mixed integer linear programming

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第2期34卷 429-453页

作者： Qian, Jiahui Zhang, Zhijing Shi, Lingling Song, Dan Beijing Inst Technol Sch Mech Engn 5 Zhongguancun South St Beijing 100081 Peoples R China

Assembly timing planning, which aims to solve the assembly action sequence and assembly part sequence with the shortest assembly time as the goal, is a necessary and critical step in intelligent assembly process planning. However, the current focus of assembly process planning is assembly sequence planning, whereas little research has been performed on assembly timing planning. A novel assembly timing planning method based on knowledge and mixed integer linear programming (MILP) is proposed in this paper. First, a knowledge base of the assembly process for timing planning is constructed using ontology. Then, based on the proposed strategy of dividing assembly timing planning into within-group planning and between-group planning, a MILP model of assembly timing planning for automatic assembly system is constructed. In addition, a software that realizes timing planning through human-machine collaboration is developed to verify and visualize the proposed timing planning method. The implementation is as follows: assembly action sentences are formed by searching the ontology keyword library, then timing knowledge for the action sequence and assembly sequence is established, and finally optimal assembly timing results are obtained after the calculation. Compared with the traditional serial assembly process, this method significantly reduces the assembly time, thereby improving the assembly efficiency, and the assembly schedule can be obtained automatically and quickly to guide the assembly process design.

关键词： Assembly timing planning Knowledge mixed integer linear programming Intelligent assembly

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Energy-saving optimization of application server clusters based on mixed integer linear programming

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2023年 171卷 111-129页

作者： Xiong, Zhi Zhao, Min Yuan, Ziyue Xu, Jianlong Cai, Lingru Shantou Univ Dept Comp Sci & Technol Shantou 515063 Peoples R China Shantou Univ Minist Educ Key Lab Intelligent Mfg Technol Shantou 515063 Peoples R China

The issue of how to dynamically optimize the deployment of an application server cluster according to the changing load to reduce energy consumption is an important problem that must be urgently solved. In this paper, we propose an energy-saving optimization strategy for application server clusters, whose optimization content includes the on/off state, CPU frequency, and load size of each server. Compared with existing research, our strategy is not only more accurate in power and load models but also considers the switching cost of servers to avoid server switching jitter. The strategy includes two schemes, which both formulate the cluster energy-saving optimization as a mixed integer linear programming (MILP) problem and then adopt a toolkit to solve the problem. One scheme defines variables for each server, and the resulting programming problem is called the MILP4PH problem. The other scheme defines variables for each server type, resulting in a programming problem called the MILP4GH problem. The experimental results reveal that for clusters with poor homogeneity, the MILP4PH problem has fewer variables and can be solved in real time, while for clusters with good homogeneity, the MILP4GH problem has fewer variables and can be solved in real time.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Application server cluster Energy-saving optimization mixed integer linear programming Switching jitter Homogeneity

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Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization

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Iran Journal of Computer Science 2024年第1期8卷 21-35页

作者： Talebi, Amirreza Haeri Boroujeni, Sayed Pedram Razi, Abolfazl Department of Integrated Systems Engineering The Ohio State University Columbus OH United States School of Computing Clemson University Clemson 29632 SC United States ENSIMAG Grenoble Alpes University Grenoble France

This paper explores the critical domain of revenue management (RM) within operations research (OR), focusing on intricate pricing dynamics. Utilizing mixed integer linear programming (MILP) models, the study enhances revenue optimization by considering product prices as decision variables and emphasizing the interplay between demand and supply. Recent advancements in discrete choice models (DCMs), particularly those rooted in random regret minimization (RRM) theory, are investigated as potent alternatives to established random utility maximization (RUM)-based DCMs. Despite the widespread use of DCMs in RM, a significant gap exists between cutting-edge RRM-based models and their practical integration into RM strategies. The study addresses this gap by incorporating an advanced RRM-based DCM into MILP models, addressing pricing challenges in both capacitated and uncapacitated supply scenarios. The developed models demonstrate the feasibility and offer diverse interpretations of consumer choice behavior, drawing inspiration from established RUM-based frameworks. This research contributes to bridging the existing gap in the application of advanced DCMs within practical RM implementations. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Choice-based optimization Decision-making behavior Demand prediction mixed integer linear programming Operations research Pricing

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Neural Network-based Lyapunov Stability Verification for Continuous-time Nonlinear Systems via mixed integer linear programming 9

Neural Network-based Lyapunov Stability Verification for Con...

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9th International Conference on Control and Robotics Engineering (ICCRE)

作者： Wang, Jingya Yu, Yongbin Wang, Xiangxiang Feng, Xiao Han, Xinyi Cai, Jingye Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China

ISBN: (纸本)9798350372694;9798350372700

This paper discusses Lyapunov stability verification methods for continuous-time nonlinear systems. Traditional mathematical methods require a lot of manual calculations, which consume a lot of time and energy. To address the problem of the low efficiency in traditional methods, this paper introduces neural networks into the design of the Lyapunov function to achieve independent verification. First, a neural network is used to represent the Lyapunov function. Then, the Lyapunov stability condition is converted into a mixed integer linear programming (MILP) problem, and the solution to the optimization problem is solved through the MILP solver to verify whether the output of the neural network satisfies the Lyapunov stability condition. In addition, this paper gives the training loss function of the Lyapunov neural network, which mainly consists of optimization problems. Finally, a simulation example is given to illustrate the effectiveness of this method.

关键词： neural networks Lyapunov function continuous-time nonlinear systems mixed integer linear programming

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Efficient mixed integer linear programming Approaches to Dynamic Path Restoration 12

Efficient Mixed Integer Linear Programming Approaches to Dyn...

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12th IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

作者： Rubtsov, Alexander Bauwens, Bruno Shmelkin, Dmitri Rudenko, Elizaveta Lavrov, Alexey HSE Univ Fac Comp Sci Moscow Russia Independent Univ Moscow Moscow Russia HSE Univ Fac Math Moscow Russia

ISBN: (纸本)9798350351866;9798350351859

We consider the problem of single link failure in an elastic optical network, (also known as flex-grid WDM network). The task is to reroute optical connections that go through the broken link using free capacity of other links of the network. Nowadays, dynamic restoration gains popularity, in which the possiblity of rerouting is only inspected after a link failure is detected. Since the problem of recovery is NP-hard, heuristic algorithms are used to either find such routes, or suggest that the routes do not exist. In order to understand the quality of these heuristics, often mixed integer linear programming is used to obtain exact positive and negative answers. We present a detailed such model that checks whether restoration is possible without the use of additional regenerators. This means, that the new light paths need to satisfy a length constraint. As preprossing we apply a trimming procedure that takes advantage of this length constraint, and significantly speeds up the evaluation of these models. Our model is more general, and besides solving the problem of link restoration, also solves the full problem of wavelength and spectrum assignment.

关键词： mixed integer linear programming Routing and Spectrum Allocation Problem Combinatorial Optimization

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Variable Speed Limit and Ramp Metering Control of Highway Networks Using Lax-Hopf Method: A mixed integer linear programming Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第7期23卷 7441-7456页

作者： Vishnoi, Suyash C. Claudel, Christian G. Univ Texas Austin Dept Civil Architectural & Environm Engn Austin TX 78712 USA

This paper presents a novel optimization formulation to solve the problem of variable speed limit control on road networks modeled by the Lighthill-Whitham-Richards (LWR) partial differential equation. It also presents some mathematical rules that allow for a reduction in the size and computational time of the optimization problem. Using the analytical solutions to the LWR model, an optimization problem is formulated for the variable speed limit and ramp metering control of traffic on highway networks using the Lax-Hopf algorithm. The resulting problem, which is non-linear in the decision variables, is transformed into a mixed integer linear Program. An example is presented to show the effectiveness of the approach, including its application to a real-world highway network with multiple ramp connections. The possibility of linear relaxation of integer variables in the problem is also considered. Lastly, the method is compared to a classical Link Transmission Model formulation of the variable speed limit control problem.

关键词： Lax-Hopf method variable speed limits network control mixed integer linear programming

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