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An Evolutionary Algorithm for Expensive mixed-integer Black-Box Optimization with Explicit Constraints

ACM Transactions on Evolutionary Learning and Optimization 2025年第1期5卷 1-16页

作者： Hong, Yuan Arnold, Dirk V. Dalhousie University HalifaxNS Canada

Determining the feasibility of a candidate solution to a constrained black-box optimization problem may be similarly expensive compared to the process of determining its quality, or it may be much cheaper. Constraints that allow obtaining degrees of feasibility or constraint violation without incurring significant computational costs are referred to as explicit. We present an evolutionary algorithm for solving mixed-integer black-box optimization problems where objective function evaluations are expensive but constraints are explicit. We do not assume relaxability of the constraints. The method wraps active-set evolution strategies, an algorithm for solving continuous black-box problems with explicit constraints, in a branching mechanism that allows enforcing integrality constraints. In computer experiments we demonstrate that the algorithm solves a set of mixed-integer problems with significantly fewer objective function evaluations than several algorithms that do not exploit the explicitness of the constraints. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： mixed-integer linear programming

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A Proposal of Fair Output Curtailment Method for Photovoltaic Generators and Wind Farms

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IEEJ Transactions on Power and Energy 2025年第1期145卷 21-31页

作者： Sugawara, Daichi Saitoh, Hiroumi Taguchi, Koyo Miyazaki, Yuichi Department of Electrical Engineering Tohoku University 6-6-05 Aramaki Aza-Aoba Aoba-ku Sendai980-8579 Japan Power System Eng. Dept. Tohoku Electric Power Network Co. Inc. 1-7-1 Honcho Aoba-ku Sendai980-8551 Japan

In an electric power system featuring an abundance of renewable energy sources (RES), such as photovoltaic generators (PVs) and wind farms (WFs), the need for curtailing RES output arises to maintain supply-demand balance during periods of anticipated excess generation across the electric power system. Ensuring equitable treatment among RES units regarding curtailment and minimizing overall curtailed energy become pivotal. This paper introduces a method to equitably establish upper output limits for each RES. The effectiveness of these methods is substantiated through numerical simulations. It was found that the key to achieving fairness is to uniformly improve the output control performance of RESs. © 2025 The Institute of Electrical Engineers of Japan.

关键词： mixed-integer linear programming

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A machine learning approach for predicting the best heuristic for a large scaled Capacitated Lotsizing Problem

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OR SPECTRUM 2025年 1-43页

作者： Kaercher, Jens Meyr, Herbert Univ Hohenheim Dept Supply Chain Management D-70593 Stuttgart Germany

For some NP-hard lotsizing problems, many different heuristics exist, but they have different solution qualities and computation times depending on the characteristics of the instance. The computation times of the individual heuristics increase significantly with the problem size, so that testing all available heuristics for large instances requires extensive time. Therefore, it is necessary to develop a method that allows a prediction of the best heuristic for the respective instance without testing all available heuristics. The Capacitated Lotsizing Problem (CLSP) is chosen as the problem to be solved, since it is a fundamental model in the field of lotsizing, well researched and several different heuristics exist for it. The CLSP addresses the problem of determining lotsizes on a production line given limited capacity, product-dependent setup costs, and deterministic, dynamic demand for multiple products. The objective is to minimize setup and inventory holding costs. Two different forecasting methods are presented. One of them is a two-layer neural network called CLSP-Net. It is trained on small CLSP instances, which can be solved very fast with the considered heuristics. Due to the use of a fixed number of wisely chosen features that are relative, relevant, and computationally efficient, and which leverage problem-specific knowledge, CLSP-Net is also capable of predicting the most suitable heuristic for large instances.

关键词： Algorithm selection problem Machine learning Neural network Heuristics Capacitated lotsizing problem mixed-integer linear programming

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Stochastic Optimal Scheduling of Flexible Traction Power Supply System for Heavy Haul Railway Considering the Online Degradation of Energy Storage

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WORLD ELECTRIC VEHICLE JOURNAL 2025年第4期16卷 206-206页

作者： Li, Zhe He, Yanlin Peng, Gaoqiang Yin, Jie CHN Energy Shuohuang Railway Dev Co Ltd Cangzhou 062350 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

The heavy-haul flexible traction power supply system (HFTPSS), integrated with an energy storage system (ESS) and power flow controller (PFC), offers significant potential for improving energy efficiency and reducing costs. However, the state of ESS capacity and the uncertainty of traction power significantly affect HFTPSS operation, creating challenges in fully utilizing flexibility to achieve economic system operation. To address this challenge, a classical scenario generation approach combining long short-term memory (LSTM), Latin hypercube sampling (LHS), and fuzzy c-means (FCM) is proposed to quantitatively characterize traction power uncertainty. Based on the generated scenarios, and considering the energy balance and safe operation constraints of HFTPSS, a stochastic optimal energy dispatch model is developed. The model aims to minimize the operational cost for heavy-haul electrified railways (HERs) while accounting for the impact of online ESS capacity degradation on the energy scheduling process. Finally, the effectiveness of the proposed strategy and model is validated using operational data from a real HER system.

关键词： battery degradation flexible traction power supply system mixed-integer linear programming stochastic optimization

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Location-allocation of a Biorefinery Based on Linguistic mixed integer Nonlinear Mathematical Modeling

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IEEE Access 2025年 13卷 83373-83393页

作者： Fan, Lei Karimi, Negar Feylizadeh, Mohammad Reza Khorshidnia, Mohsen Esmaeeli, Ehsan Li, Yongzheng Jiangsu University of Science and Technology School of Economics and Management Jiangsu Zhenjiang212100 China Isfahan University of Technology Department of Industrial and Systems Engineering Isfahan Iran Islamic Azad University Department of Industrial Engineering Najafabad Branch Najafabad Iran Sharif University of Technology Department of Industrial Engineering Tehran Iran Jiangsu University of Science of Technology School of Naval Architecture and Ocean Engineering China

Significant research is currently being conducted to explore renewable energy sources, such as biomass fuel, in response to recent fluctuations in fossil fuel prices and environmental concerns. However, utilizing biomass energy poses challenges due to the widespread distribution of feedstock and the need to transport multiple low-energy-density materials to conversion facilities. In this investigation, a Linguistics mixed integer Nonlinear programming (LMINLP) approach with Z-number computations was employed to analyze the optimal allocation of locations for a bio-refinery supply chain network. This proposed method significantly impacted the product process, time value for plutocrats, installation investment costs, and transportation network. This research aims to construct an LMINLP model to determine the optimal position of a single installation within a bio-refinery. The primary goal was to make well-informed investment decisions regarding technologies, facilities, capacities, and site selections. A firefly algorithm incorporating a piecewise linearization approach was utilized to solve the model and achieve global optimality. The model’s effectiveness was demonstrated through numerical examples, including those from the ethanol industry. © 2013 IEEE.

关键词： mixed-integer linear programming

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Optimization of an integrated harvest, and process planning model in the agri-food industry

JOURNAL OF ENGINEERING RESEARCH

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JOURNAL OF ENGINEERING RESEARCH 2025年第1期13卷 331-350页

作者： Bilgen, Bilge Taskiner, Tugce Dokuz Eylul Univ Dept Ind Engn TR-35397 Izmir Turkiye Dokuz Eylul Univ Grad Sch Nat & Appl Sci Dept Ind Engn TR-35397 Izmir Turkiye

In this paper, an integrated harvest and production planning problem in the olive oil industry is addressed. The aim of the paper is to develop and optimize a mathematical model that integrates both olive harvest and olive oil production process. The objective is to maximize the total profit while determining quantity of olives harvested from several olive groves, quantity of olives purchased from external farmers, quantity of olive oil produced, and by-product management to handle hazardous effects of olive oil production. The problem is formulated as a mixed integer linear programming model (MILP). Maximization of profit consists of two components;total sales revenue and total cost including harvesting, purchasing, fixed and variable processing costs. Constraints on the system include harvest planning, harvest capacity, production planning, and processing constraints. The proposed MILP model incorporates several distinguishing characteristics of the problem such as ripeness of olives, olive oil quality, organic and conventional farming, and by-product management. A numerical experiment based on a real-world case study was presented to verify the effectiveness of the developed model. The results show that simultaneously considering harvesting and production processes can significantly assist the profitability of the olive oil supply chain. A scenario analysis is conducted by extending the base model to explore olive loss in the olive groves which can occur due to the severe climatic conditions.

关键词： Harvest planning Production planning mixed-integer linear programming Olive oil industry Optimization

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A novel and efficient mathematical optimization model for multi-stage assembly flow shop considering post-processing

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2025年第1期42卷 1-13页

作者： da Silva Santana, Jose Renatho Fuchigami, Helio Yochihiro Fed Univ Goias UFG Fac Sci & Technol Aparecida De Goiania Brazil Fed Univ Sao Carlos UFSCar Dept Prod Engn Rod Washington Luis BR-13565905 Sao Carlos SP Brazil

We addressed the multistage assembly flow shop problem with post-processing and makespan minimization, a production environment commonly encountered in diverse industries such as automotive, dental, medical equipment, and clothing manufacturing. In this context, we presented an innovative mixed-integer linear programming model with a position-based strategy. Our proposed formulation demonstrated remarkable efficiency when compared to the model of the literature. It achieved optimal solutions in 77.16% of instances, with an average optimality gap of 10.59%. This study constitutes a significant contribution to the efficient resolution of a practical and frequently encountered scheduling problem that has received relatively limited attention in existing literature. The findings highlight the crucial role of mathematical optimization models as valuable decision-making tools for scheduling within the addressed production system. [GRAPHICS]

关键词： Scheduling assembly flow shop mixed-integer linear programming mathematical optimization multi-stage shop post-processing

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MUMIPLOP: optimal multi-core IoT service placement in fog environments

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COMPUTING 2025年第4期107卷 1-33页

作者： Herrera, Juan Luis Scotece, Domenico Galan-Jimenez, Jaime Berrocal, Javier Di Modica, Giuseppe Foschini, Luca Univ Extremadura Dept Comp Sci & Telemat Engn Badajoz Spain Univ Bologna Dipartamento Informat Sci & Ingn Bologna Italy

The Internet of Things paradigm paves the way toward automating real-world tasks, especially in intensive domains. Nonetheless, the criticality of the intensive tasks to be automated, such as video recognition, speech recognition, or data prediction, subjects them to very strict Quality of Service (QoS) requirements, such as short response times. To achieve these low response times, it is key to optimally place the microservices in a Computing Continuum infrastructure, considering their interactions in the form of workflows, their execution times, and the latencies of the underlying network fabric. While there are some proposals in the current state of the art to optimally place the application's microservices, these proposals do not consider the effects that multi-core processing can have over the microservices' execution times in their model, nor have their model compared with emulated network testbeds. This work proposes the Multi-core Microservice Placement Optimizer, a system that advances the state of the art by considering multi-core processing, making use of both the parallelization characteristics of microservices and the cores available at the computing devices. Our evaluation over a smart city case study, based on a real application and a fog computing testbed, shows that MUMIPLOP's model is more accurate than single-core models, and yields shorter response time than state-of-the-art techniques, enhancing the QoS obtained in the Computing Continuum.

关键词： Microservice placement mixed-integer linear programming Computing continuum Fog computing Smart cities

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New mathematical model for machine scheduling with variable capacity over time

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2025年第2期20卷 215-224页

作者： Esmaelian, Majid Momeni, Abolfazl Mohammadi, Milad Univ Isfahan Dept Management Hezarjerib StAzadi Sq Esfahan *** Iran Vali Easr Univ Rafsanjan Fac Econ & Adm Sci Dept Management Rafsanjan Iran

In this study, machine scheduling with variable capacity over time (SVCap) is investigated. The machine capacity is the maximum number of jobs that a machine can process at a time which can be either fixed or variable over time. In common machine scheduling problems, it is assumed that one machine can process one job at a time. However, in variable machine capacity, multiple jobs can be processed on a machine simultaneously. Unlike the current research, a mathematical formulation is not developed yet for solving this problem. In order to solve the problem, a novel mixed integer linear programming (MILP) is proposed. In addition, the SVCap is regarded as a special type of resource-constrained project scheduling problem (RCPSP). Thus, the discrete-time (DT) formulation is generalized to solve the SVCap. In these formulations, the total tardiness is minimized as the objective function. Proposed models are implemented on an irrigation scheduling problem in which water resources are allocated to each plot of farmland. The computational performances of proposed formulations are evaluated on problem instances with different sizes. Results show that the proposed formulations solved all problem instances. The results demonstrate that the proposed MILP formulation is more efficient than the generalized DT formulation in both solution quality and runtimes.

关键词： Scheduling variable capacity over time total tardiness mixed-integer linear programming

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A nationwide planning model for argon supply chains with coordinated production and distribution

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DIGITAL CHEMICAL ENGINEERING 2025年 14卷

作者： Neiro, Sergio M. S. Madan, Tarun Maravelias, Christos T. Pinto, Jose M. Uberlandia Fed Univ UFU Sch Chem Engn Joao Naves Avila 2121Bloco 5K BR-38408100 Uberlandia Brazil Linde plc Linde Digital Amer Danbury CT 06810 USA Princeton Univ Dept Chem & Biol Engn Princeton NJ USA Princeton Univ Andlinger Ctr Energy & Environm Princeton NJ USA

In this work, we address a nationwide tactical planning for industrial gas supply chains, particularly argon. The proposed approaches follow as extensions of our previous work (Comp. & Chem. Eng., 161 (2022) 107778) in which a regional argon supply chain problem is addressed;in that work, both production and distribution could be represented in detail. Two different types of deliveries from the Air Separating Units (ASU) to customers, which involve single driver deliveries for short distance trips and sleeper team that require multiple days. The nationwide problem requires simplifications to keep the problem mathematically tractable, primarily the representation of production sites with different tier costs and the aggregation of customers in clusters. The regional problem addressed in our previous work is used as a benchmark case study for benchmarking. We then focus on a real-world problem that represents a nationwide argon supply chain. Despite the size of the models, near optimal solutions could be found in reasonable times. Finally, we highlight important features of the proposed approaches.

关键词： mixed-integer linear programming Industrial gases Supply chain optimization

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