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American Control Conference (ACC)

作者： Zhang, Yi Su, Rong Zhang, Yicheng Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682661

We present a platoon-based bus dispatching strategy and passenger boarding strategy which utilizes a platoon of vehicles to improve capacity flexibility in response to dynamically changing demands, and controls passenger boarding flows to minimize the networkwise passengers' perceived delay time. The released buses in the same platoon are allowed to separate when approaching the stop station, which makes our strategy more flexible and data-driven. A mixed integer linear programming (MILP) model is firstly developed to formulate the problem with the linear cost, in which both the passengers' actual delay time and the operating bus vacancy are minimized subject to the volume dynamic constraints on both buses and stops. With the computational complexity as a concern, the Genetic Algorithm (GA) is adopted to solve the problem in real time. Comparison between MILP and GA on the computational time and result quality is conducted to show the efficiency of our method. Also, the optimization model with the nonlinear cost considering the passengers' perceived delay time and the operating bus vacancy is directly solved by the GA. Finally, the performance of our method and the traditional bus schedule strategies under two different objectives is discussed in the case study, which indicates the potential of the platoon dispatching in mitigating the passenger's perceived delay.

关键词： public transport systems bus scheduling mixed-logic constraints mixed-integer linear programming genetic algorithm

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Lossless Compression of Deep Neural Networks 17th

Lossless Compression of Deep Neural Networks

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17th International Conference on the Integration of Constraint programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Serra, Thiago Kumar, Abhinav Ramalingam, Srikumar Bucknell Univ Lewisburg PA 17837 USA Univ Utah Salt Lake City UT USA

ISBN: (纸本)9783030589424;9783030589417

Deep neural networks have been successful in many predictive modeling tasks, such as image and language recognition, where large neural networks are often used to obtain good accuracy. Consequently, it is challenging to deploy these networks under limited computational resources, such as in mobile devices. In this work, we introduce an algorithm that removes units and layers of a neural network while not changing the output that is produced, which thus implies a lossless compression. This algorithm, which we denote as LEO (Lossless Expressiveness Optimization), relies on mixed-integer linear programming (MILP) to identify Rectified linear Units (ReLUs) with linear behavior over the input domain. By using l(1) regularization to induce such behavior, we can benefit from training over a larger architecture than we would later use in the environment where the trained neural network is deployed.

关键词： Deep learning mixed-integer linear programming Neural network pruning Neuron stability Rectified linear Unit

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Emerging Heuristic Optimization Algorithms for Expansion Planning and Flexibility Optimization in Sustainable Electrical Power Systems

Emerging Heuristic Optimization Algorithms for Expansion Pla...

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International Conference on Power, Control and Communication Infrastructure (ICPCCI)

作者： Sarda, Jigar Pandya, Kartik Shah, Margi CHARUSAT Univ Anand 388421 Gujarat India

ISBN: (纸本)9789811502262;9789811502255

The expansion planning and flexibility optimization of sustainable electrical power systems are facing higher complexity introduced by massive integration of variable renewable generation, the increasing need of facts and HVDC devices for flexibility in highly interactive energy markets, responsive demand and multi-energy sector coupling. Therefore, the expansion and flexibility management problems involved in investments decision-making and operational planning need consideration of more accurate models such as non-linear models, probabilistic models and a large number of decision variables. As the problem is difficult to tackle using classical optimisation tools, metaheuristic methods are depicted to solve it. The paper is based on three benchmark systems to evaluate the feasibility and effectiveness of optimization algorithms in systems of different characteristics and size. Also, the paper presents the results and statistical comparative evaluation of the performance of different emerging heuristic optimization algorithms.

关键词： mixed-integer linear programming Power flow control Transmission expansion planning

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Optimizing the e-waste management in India: a sustainable mathematical modeling approach to circular economy

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Quality and Quantity 2025年 1-32页

作者： Ali, Wajahat Faculty of Management Studies Marwadi University Gujarat India

The exponential growth of electronic waste (e-waste) in India, driven by rapid technological advancements and consumer demand, poses significant environmental, economic, and social challenges. This study develops a multi-objective mixed-integer linear programming (MILP) model to optimize e-waste management while aligning with circular economy principles. The proposed model minimizes total cost (₹1154.35), maximizes resource recovery (₹1542.82), and reduces environmental impact (₹1378.90). Using a Goal programming (GP) approach, the study balances these conflicting objectives, ensuring a 26.9% cost reduction, 40.4% increase in resource recovery, and 30.3% lower environmental impact than traditional heuristic methods. A benchmark analysis against three traditional methods—Rule-Based Heuristic, linear programming, and Greedy Cost-Minimization—demonstrates the superiority of the proposed approach. While the Greedy Model achieves the lowest cost (₹978.10), it significantly increases environmental impact (₹2451.98) and limits resource recovery (₹835.21). The Rule-Based Heuristic and linear programming models perform better but fail to optimize all three objectives simultaneously. The MILP-GP model integrates economic, environmental, and operational considerations, proving its effectiveness as a practical and scalable decision-support tool for sustainable e-waste management in India. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Circular economy E-waste management Goal programming Mathematical modeling mixed-integer linear programming

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Fractional 0-1 programs: links between mixed-integer linear and conic quadratic formulations

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JOURNAL OF GLOBAL OPTIMIZATION 2019年第2期75卷 273-339页

作者： Mehmanchi, Erfan Gomez, Andres Prokopyev, Oleg A. Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA Univ Southern Calif Dept Ind & Syst Engn Los Angeles CA 90089 USA

This paper focuses on methods that improve the performance of solution approaches for multiple-ratio fractional 0-1 programs (FPs) in their general structure. In particular, we explore the links between equivalent mixed-integer linear programming and conic quadratic programming reformulations of FPs. Thereby, we show that integrating the ideas behind these two types of reformulations of FPs allows us to push further the limits of the current state-of-the-art results and tackle larger-size problems. We perform extensive computational experiments to compare the proposed approaches against the current reformulations from the literature.

关键词： Fractional 0-1 programming Conic quadratic programming mixed-integer linear programming Polymatroid cuts Binary-expansion Assortment optimization

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Integrated optimization of smart building energy consumption in microgrids using linearized real-time control strategies

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2025年 43卷

作者： Cheng, Xiaochun Zhang, Yunfu Su, Xiaolin Zhejiang Guangsha Vocat & Tech Univ Construct Architectural Engn Inst Dongyang 322100 Zhejiang Peoples R China Zhejiang Dongheng Construct Engn Co Ltd Dongyang 322100 Zhejiang Peoples R China

This research develops a model to reduce main grid electricity costs and boost local demand and generation within a microgrid, adhering to operational constraints. It uses a mixed-integer nonlinear programming (MINLP) framework to manage heating, ventilation, air conditioning, lighting, photovoltaic generation, and energy storage while ensuring indoor comfort. A rolling horizon strategy was employed to simplify the original model, accompanied by pre-processing in EnergyPlus software utilizing linearization techniques, culminating in a mixed-integer linear programming approximation. linearization yields an optimally solvable model that is appropriate for real-time energy management applications. We performed simulations under decentralized and centralized schemes for a 13-bus microgrid with uncontrollable loads and smart buildings. This study conducted a scalability analysis for the 34-bus microgrid case. The rolling horizon method successfully handled uncertainties in demand and reduced the amount of data needed for forecasting across five different consumption models, which included various combinations of photovoltaic units and energy storage systems. The findings indicated a 16 % decrease in peak power demand and an error margin when comparing linearized results with actual data, showcasing notable enhancements in cost efficiency and stability. The testing provided insights into optimal configurations for each region, validating the model's effectiveness in enhancing microgrid reliability, sustainability, cost-effectiveness, and occupant comfort.

关键词： Microgrid scalability analysis mixed-integer linear programming Photovoltaic and energy storage systems Real-time energy management Rolling horizon strategy

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Siting and Sizing of Charging Infrastructure for Shared Autonomous Electric Fleets 21

Siting and Sizing of Charging Infrastructure for Shared Auto...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Ramin Ahadi Wolfgang Ketter John Collins Nicolo Daina Faculty of Management Economics and Social Sciences University of Cologne Department of Computer Science and Engineering University of Minnesota Department of Civil and Environmental Engineering Imperial College London

ISBN: (纸本)9781713832621

Business models rooted in shared economy, electrification, and automation are transforming urban mobility. Accounting for how these transformations interact is crucial if synergies are to be exploited. In this paper, we focus on how a cost-effective charging infrastructure for e-mobility can support the emergence of shared, autonomous mobility. This study addresses the problem of siting and sizing of charging stations for a fleet of shared autonomous electric vehicles (SAEVs). We develop a hybrid simulation-optimization model to find locations and numbers of chargers needed to serve charging demands. Our agent-based model provides an enhanced representation of SAEV operations allowing for smart charging and vehicle cruising when parking/charging is not available. Also, we model charging station placement as full covering optimization and solve the location-allocation problem simultaneously. Finally, we employ real-world trip data from ShareNow in Berlin to evaluate our approach for realistic demand patterns under different charging strategies and fleet sizes. The results show that charging station locations depend mostly on the spatial distribution of installation costs and charging demands. Moreover, charging strategies and fleet size affect the charging patterns and the required number of chargers as well as fleet performance.

关键词： Charging stations Shared autonomous electric vehicles Agent-based simulation mixed-integer linear programming CHARGING(ACCOUNTTNG) SITING Charger Device Component distribution substation SIZING mixed integer linear programming Fleet Event driven simulation

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Impact of levels of modeling details in energy system sizing optimization: A case-study

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Energy 2025年 332卷

作者： Joseph Brisson Mathieu Vallée Antoine Piguet Pierre Lemaire Siao-Leu Phouratsamay Univ. Grenoble Alpes CEA Liten Campus Ines Le Bourget du Lac 73375 France Univ. Grenoble Alpes CNRS Grenoble INP G-SCOP Grenoble 38000 France

Future local energy systems are evolving towards becoming more distributed and exhibiting greater synergies between energy vectors. Sizing and design optimization models are becoming increasingly complex, incorporating uncertainty, multiple objectives, and more precise system simulations. The concept of the level of modeling detail is examined, highlighting how a case study can support the selection of an appropriate level of detail. The modeling of control strategies should be considered an important aspect of sizing optimization models. While energy systems should integrate smart control strategies, optimization models often assume a perfectly anticipative control horizon. However, in practice, control strategies like Model Predictive Control have only limited knowledge of future conditions. Although operational simulation models already incorporate this level of detail, a case-study indicates that it can impact sizing decisions for future local energy systems.

关键词： Local energy systems Levels of modeling detail Sizing optimization Model predictive control Control strategy mixed-integer linear programming

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A hybrid metaheuristic to solve the multi-trip team orienteering problem with integrated tours and increasing profits for blood transportation

Operations Research, Data Analytics and Logistics

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Operations Research, Data Analytics and Logistics 2025年 45卷

作者： Andrea Pirabán-Ramírez William Javier Guerrero-Rueda Nacima Labadie Doctorado en Logística y Gestión de Cadenas de Suministros Universidad de La Sabana Campus Puente del Común Km. 7 Autopista Norte de Bogotá. Chía Cundinamarca Colombia Computer Science and Digital Society Laboratory (LIST3N) University of Technology of Troyes 12 Rue Marie Curie CS 42060 10004 Troyes CEDEX France School of Engineering Universidad de La Sabana Campus Puente del Común Km. 7 Autopista Norte de Bogotá. Chía Cundinamarca Colombia

The efficient management of the blood supply chain is paramount, given its critical impact on human lives if improperly handled. This research focuses on the location of mobile collection sites over a multiperiod horizon and transferring blood units from these sites to blood centers using a multi-trip shuttle fleet. The goal is to minimize the total cost comprising the transportation, delay, wastage, and shortage costs. The model considers constraints such as the tight time limit between the collection and processing of platelets and cryoprecipitates. Additionally, collection levels at donation sites are modeled assuming linear increasing rates. First, the problem is modeled as a mixed-integer linear program; then, a hybrid iterative local search is designed as a solution approach. The effectiveness and applicability of the proposed method are evaluated through a set of 39 new instances based on the blood collection system of Bogota, Colombia, which demonstrates promising solutions by systematically computing the best-known solutions.

关键词： BBS Bogota blood system BC blood center BCP blood collection problem BKS best-known solution BSC blood supply chain cryo cryoprecipitate CS collection site ILS iterative local search MILP mixed-integer linear programming MT-TOPIT-IP multi-trip team orienteering problem with integrated tours and increasing profits MT-VRPIP multi-trip vehicle routing problem with increasing profits NP-hard non-deterministic polynomial-time hard PLT platelet VND variable neighborhood descent WB whole blood Healthcare logistics Optimization OR in health services Perishable products Variable profits

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A Process Systems Engineering approach towards responsive and sustainable (bio-)pharmaceutical supply chains

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Computers and Chemical Engineering 2025年 201卷

作者： Sarkis, Miriam Sachio, Steven Papathanasiou, Maria M. The Sargent Centre for Process Systems Engineering Imperial College London Greater London London SW72BB United Kingdom Department of Chemical Engineering Imperial College London Greater London London SW72BB United Kingdom

The growth of advanced pharmaceutical products paves the way to a step change in manufacturing network design and planning, with an increasing pressure to meet uncertain demands. In this space, the ability to ramp up manufacturing capacity becomes a crucial metric to consider during planning. This work presents an optimization-based approach for network design and scalability assessment. Firstly, techno-economic and life cycle analyses are used to quantify input optimization parameters for candidate technologies. Secondly, a multi-product design and planning problem is solved to obtain optimized network designs. Thirdly, the scalability of given designs is quantified through a solution space assessment methodology, whereby uncertain demand and adjustable production yield parameters are sampled simultaneously and the size of the feasible solution region is quantified through α-shape methods. This becomes an indicator of the capacity of the network design to withstand demand variations through process manipulation, intensification, and scale-out, thus quantifying a space of operation versus a single optimal point. Comparative results are presented for multi-use (MU) equipment-based manufacturing and a single-use (SU) counterpart, which enables a modular manufacturing approach. Costs and footprints of the SU-based network are lower compared to MU, whilst scalability significantly increases. This highlights the potential of modular and flexible manufacturing in improving resource use and overall supply chain responsiveness. © 2025 The Authors

关键词： mixed-integer linear programming Modular manufacturing Next-generation pharmaceuticals Supply chain optimization

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