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A grey-forecasting interval-parameter mixed-integer programming approach for integrated electric-environmental management-A case study of Beijing

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ENERGY 2013年 63卷 334-344页

作者： Wang, Xingwei Cai, Yanpeng Chen, Jiajun Dai, Chao Beijing Normal Univ Sch Environm Minist Educ Key Lab Water & Sediment Sci Beijing 100875 Peoples R China Univ Regina Inst Energy Environm & Sustainable Communities Regina SK S4S 7H9 Canada Peking Univ Dept Environm Sci Beijing 100871 Peoples R China

In this study, a GFIPMIP (grey-forecasting interval-parameter mixed-integer programming) approach was developed for supporting IEEM (integrated electric-environmental management) in Beijing. It was an attempt to incorporate an energy-forecasting model within a general modeling framework at the municipal level. The developed GFIPMIP model can not only forecast electric demands, but also reflect dynamic, interactive, and uncertain characteristics of the IEEM system in Beijing. Moreover, it can address issues regarding power supply, and emission reduction of atmospheric pollutants and GHG (greenhouse gas). Optimal solutions were obtained related to power generation patterns and facility capacity expansion schemes under a series of system constraints. Two scenarios were analyzed based on multiple environmental policies. The results were useful for helping decision makers identify desired management strategies to guarantee the city's power supply and mitigate emissions of GHG and atmospheric pollutants. The results also suggested that the developed GFIPMIP model be applicable to similar engineering problems. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Interval-parameter programming mixed-integer programming Grey-forecasting model Integrated electric-environmental management Beijing Uncertainties

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Tightening Methods for Continuous-Time mixed-integer programming Models for Chemical Production Scheduling

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AICHE JOURNAL 2013年第12期59卷 4461-4467页

作者： Merchan, Andres F. Velez, Sara Maravelias, Christos T. Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA

Significance Important advances in modeling chemical production scheduling problems have been made in recent years, yet effective solution methods are still required. We use an algorithm that uses process network and customer demand information to formulate powerful valid inequalities that substantially improve the solution process. In particular, we extend the ideas recently developed for discrete-time formulations to continuous-time models and show that these tightening methods lead to a significant decrease in computational time, up to more than three orders of magnitude for some instances. (c) 2013 American Institute of Chemical Engineers AIChE J, 59: 4461-4467, 2013

关键词： mixed-integer programming demand propagation valid inequalities

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The lifetime maximization problem in wireless sensor networks with a mobile sink: mixed-integer programming formulations and algorithms

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IIE TRANSACTIONS 2013年第10期45卷 1094-1113页

作者： Behdani, Behnam Smith, J. Cole Xia, Ye Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA Univ Florida Comp & Informat Sci & Engn Dept Gainesville FL 32611 USA

This article considers the problem of maximizing the lifetime of a wireless sensor network with a mobile sink. The sink travels at finite speed among a subset of possible sink locations to collect data from a stationary set of sensor nodes. The considered problem chooses a subset of sink locations for the sink to visit, finds a tour for the sink among the selected sink locations, and prescribes an optimal data routing scheme from the sensor nodes to each location visited by the sink. The sink's tour is constrained by the time it spends at each location collecting data. Two variations of this problem are examined based on assumptions regarding delay tolerance. Exact mixed-integer programming formulations to model this problem are provided along with cutting planes, preprocessing techniques, and a Benders decomposition algorithm to improve its solvability. Computational results demonstrate the effectiveness of the proposed methods.

关键词： Wireless sensor networks lifetime maximization sink trajectory mixed-integer programming mobile sink

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Automated deep learning model development based on weight sensitivity and model selection statistics

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CHEMICAL ENGINEERING SCIENCE 2025年 306卷

作者： Yalcin, Damla Deliismail, Ozgun Tuncer, Basak Boy, Onur Can Bayar, Ibrahim Kayar, Gizem Ozpinar, Muratcan Sildir, Hasan Izmir Inst Technol Dept Chem Engn TR-35430 Izmir Turkiye SOCAR Turkey R&D& Innovat Co TR-35800 Izmir Turkiye SOCAR Turkey Refinery & Petrochem Business Unit Refinery & Petrochem Business Unit TR-35800 Izmir Turkiye

Current sustainable production and consumption processes call for technological integration with the realm of computational modeling especially in the form of sophisticated data-driven architectures. Advanced mathematical formulations are essential for deep learning approach to account for revealing patterns under nonlinear and complex interactions to enable better prediction capabilities for subsequent optimization and control tasks. Bayesian Information Criterion and Akaike Information Criterion are introduced as additional constraints to a mixed-integer training problem which employs a parameter sensitivity related objective function, unlike traditional methods which minimize the training error under fixed architecture. The resulting comprehensive optimization formulation is flexible as a simultaneous approach is introduced through algorithmic differentiation to benefit from advanced solvers to handle computational challenges and theoretical issues. Proposed formulation delivers 40% reduction, in architecture with high accuracy. The performance of the approach is compared to fully connected traditional methods on two different case studies from large scale chemical plants.

关键词： mixed-integer programming Deep learning Input selection Weight sensitivity Bayesian Information Criterion Akaike Information Criterion

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Route, speed, and bunkering optimization for LNG-fueled tramp ship with alternative bunkering ports

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OCEAN ENGINEERING 2024年 305卷

作者： He, Ping Jin, Jian Gang Pan, Wei Chen, Jianghang Shanghai Jiao Tong Univ Sch Ocean & Civil Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MOE Key Lab Marine Intelligent Equipment & Syst Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Liaoning Univ Int Business & Econ Inst Big Data Res Dalian 116052 Peoples R China Xian Jiaotong Liverpool Univ Int Business Sch Suzhou 215123 Peoples R China

To reduce emissions, shipping companies are deploying eco-friendly ships, like those powered by LNG or methanol. Unlike traditional fuels, LNG bunkering is available at a limited number of ports. At the same time, tramp or bulk ships have fewer ports of call on their shipping lines, which may necessitate LNGfueled ships sailing to alternative bunkering ports for refueling. The selection of bunkering ports impacts the operating costs of shipping lines due to differences in fuel prices and geographical locations. Moreover, it must factor in ship speed limits to meet loading and unloading schedules. To optimize the shipping route, speeds, and bunkering plan for LNG-fueled tramp ships, we develop an arc-based mixed-integer linear programming model with the objective of minimizing total operation costs. Subsequently, we propose a leg -based formulation based on the predetermined port call orders. Finally, case studies are conducted to demonstrate the effectiveness and efficiency of the models. Experimental results indicate that the bunkering mode with alternative bunkering ports effectively reduces total operating costs without altering the total voyage time, particularly with substantial differences in fuel prices between ports.

关键词： LNG-fueled tramp ship Alternative bunkering ports Optimization of shipping line mixed-integer programming

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Valid Linear programming Bounds for Exact mixed-integer programming

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INFORMS JOURNAL ON COMPUTING 2013年第2期25卷 271-284页

作者： Steffy, Daniel E. Wolter, Kati Oakland Univ Dept Math & Stat Rochester MI 48309 USA Zuse Inst Berlin Dept Optimizat D-14195 Berlin Germany

Fast computation of valid linear programming (LP) bounds serves as an important subroutine for solving mixed-integer programming problems exactly. We introduce a new method for computing valid LP bounds designed for this application. The algorithm corrects approximate LP dual solutions to be exactly feasible, giving a valid bound. Solutions are repaired by performing a projection and a shift to ensure all constraints are satisfied;bound computations are accelerated by reusing structural information through the branch-and-bound tree. We demonstrate this method to be widely applicable and faster than solving a sequence of exact LPs. Several variations of the algorithm are described and computationally evaluated in an exact branch-and-bound algorithm within the mixed-integer programming framework SCIP (Solving Constraint integer programming).

关键词： mixed-integer programming exact computation

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A constraint programming approach for resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery

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COMPUTERS & OPERATIONS RESEARCH 2025年 173卷

作者： Zeng, Chuangfeng Liu, Jianjun Li, Qinsong Guangdong Univ Technol Sch Electromech Engn State Key Lab Precis Elect Mfg Technol & Equipment Guangdong CIM Prov Key Lab Guangzhou Peoples R China

Production and distribution are both crucial components of supply chains. Integrated production and distribution scheduling (IPDS) in the context of flexible assembly flow shop scheduling and batch delivery problems is often overlooked. A realistic problem inspired by the production and distribution processes of a dishwasher factory can be modeled as a resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery (RCFAFSP-BDD). In this problem, order requirements are decomposed into several production tasks processed at different stages of the workshop, and are then delivered in batches via a third-party logistics provider to regional distributors at various locations. Auxiliary resource restrictions, hierarchical coupling constraints, machine eligibility restrictions and sequence-dependent setup times, are incorporated into the problem as operational constraints. To the best of our knowledge, this is the first attempt to solve this problem. This work formulates a mixed-integer linear programming (MIP) model to minimize total costs, including tardiness, inventory, and delivery costs. Given the problem's strong NP-hard nature, the focus is on developing an efficient solution approach using constraint programming (CP). A CP model is proposed and enhanced with multiple redundant constraints. To reduce runtime, two branching strategies are designed for the CP model. Numerical experiments with varying instance scales reveal that the proposed CP model outperforms the MIP model in accuracy and efficiency within the given time limit. The redundant constraints and search strategy can reduce CP model runtime by up to 263.83%. Compared to manual scheduling at the studied factory, the CP model can cut costs by up to 26.59% for real data, offering viable alternatives for factory planners.

关键词： Integrated production and distribution scheduling Flexible assembly flow shop Batch delivery Auxiliary resource Constraint programming mixed-integer programming

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A Mix-integer programming Based Deep Reinforcement Learning Framework for Optimal Dispatch of Energy Storage System in Distribution Networks

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JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY 2025年第2期13卷 597-608页

作者： Hou, Shengren Salazar, Edgar Mauricio Palensky, Peter Chen, Qixin Vergara, Pedro P. Delft Univ Technol Intelligent Elect Power Grids IEPG Grp NL-2628 CD Delft Netherlands Eindhoven Univ Technol Elect Energy Syst EES Grp Eindhoven Netherlands Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Operat & Control Beijing 100084 Peoples R China

The optimal dispatch of energy storage systems (ESSs) in distribution networks poses significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand, and the variability inherent in renewable energy sources. By exploiting the generalization capabilities of deep neural networks (DNNs), the deep reinforcement learning (DRL) algorithms can learn good-quality control models that adapt to the stochastic nature of distribution networks. Nevertheless, the practical deployment of DRL algorithms is often hampered by their limited capacity for satisfying operational constraints in real time, which is a crucial requirement for ensuring the reliability and feasibility of control actions during online operations. This paper introduces an innovative framework, named mixed-integer programming based deep reinforcement learning (MIP-DRL), to overcome these limitations. The proposed MIP-DRL framework can rigorously enforce operational constraints for the optimal dispatch of ESSs during the online execution. This framework involves training a Q-function with DNNs, which is subsequently represented in a mixed-integer programming (MIP) formulation. This unique combination allows for the seamless integration of operational constraints into the decision-making process. The effectiveness of the proposed MIP-DRL framework is validated through numerical simulations, demonstrating its superior capability to enforce all operational constraints and achieve high-quality dispatch decisions and showing its advantage over existing DRL algorithms.

关键词： Distribution networks Training Real-time systems programming Microgrids Voltage control Artificial neural networks Voltage regulation optimal dispatch distribution network mixed-integer programming deep reinforcement learning (DRL) energy management

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A New Multicommodity Network Flow Model and Branch and Cut for Optimal Quantum Boolean Circuit Synthesis

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INFORMS JOURNAL ON COMPUTING 2025年第1期37卷 1-196, C2页

作者： Jung, Jihye Choi, In-Chan Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Korea Univ Div Ind Management Engn Seoul 02841 South Korea

This study introduces a new optimization model and a branch-and-cut approach for synthesizing optimal quantum circuits for reversible Boolean functions, which are pivotal components in quantum algorithms. Although heuristic algorithms have been extensively explored for quantum circuit synthesis, research on exact counterparts remains relatively limited. However, the need to design quantum circuits with guaranteed optimality is increasing, especially for improving computational fidelity on noisy intermediate-scale quantum devices. This study presents mathematical optimization as a viable option for optimal synthesis, with the potential to accommodate practical considerations arising in fast-evolving quantum technologies. We set a demonstrative problem to implement reversible Boolean functions using high-level gates known as multiple control Toffoli gates while minimizing a technology-based proxy called quantum cost-the number of low-level gates used to realize each high-level gate. To address this problem, we propose a discrete optimization model based on a multicommodity network and discuss potential future variations at an abstract level to incorporate technical considerations. A customized branch and cut is then developed upon different aspects of our model, including polyhedron integrality, surrogate constraints, and variable prioritization. Our experiments demonstrate the robustness of the proposed approach in finding cost-optimal circuits for all benchmark instances within a two-hour time frame. Furthermore, we present interesting intuitions from these experiments and compare our computational results with relevant studies, highlighting newly discovered circuits with the lowest quantum costs reported in this paper.

关键词： mixed-integer programming multicommodity network branch and cut quantum computing circuit synthesis

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Resource allocation for Hyphantria cunea invasive management:a novel simulation-based optimization model

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APPLIED MATHEMATICAL MODELLING 2025年 138卷

作者： Zhang, Shuhua Liu, Ming Wang, Peng Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Peoples R China Jiangsu Prov & Chinese Acad Sci Inst Bot Jiangsu Key Lab Res & Utilizat Plant Resources Nanjing 210014 Peoples R China

In this study, we propose an integrated mixed-integer programming (MIP) model for allocating a limited budget to control a destructive invasive insect endangering the forest vegetation, Hyphantria cunea (H. cunea). Our model seeks to minimize the number of infected and dead trees within a preset planning period, factoring in various infestation scenarios, migration behaviors, resource limitations, intervention timing, and monitoring capabilities. Our modeling framework is novel in the sense that it depicts the transmission process of H. cunea as an infectious compartmental model. Our optimization model is also meaningful because it innovatively bridges the spread dynamics of H. cunea and the optimal resource allocation by using the time-varying number of infected trees that can be accepted in testing and surveillance. Our numerical test data and parameter settings have been collected from large-scale field surveys on H. cunea in Jiangsu Province over the past three years. The scenarios-based method offers significant computational advantages in searching for the best alternatives to real-world size problems in a rational time. Furthermore, our test results specify that the proposed model can not only aid in controlling H. cunea, but also can be adopted as a potential tool for managing other invasive species in the future.

关键词： Invasive species management Resources allocation Evolutionary dynamics Scenarios-based optimization mixed-integer programming

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