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A Lead-Time-Aware Decomposition Approach to Optimize Disruption Response in Supply Chains

IEEE Transactions on Automation Science and Engineering 2025年

作者： Estrada-Garcia, Juan-Alberto Bi, Mingjie Tilbury, Dawn M. Barton, Kira Shen, Siqian University of Michigan Department of Industrial and Operations Engineering Ann ArborMI48109 United States University of Michigan Robotics Department Ann ArborMI48109 United States University of Michigan Robotics Department and the Department of Mechanical Engineering Ann ArborMI48109 United States

Supply chain (SC) risk management is influenced by both spatial and temporal attributes of different entities (suppliers, retailers, and customers). Each entity has given capacity and lead time to process and transport products to downstream entities. In disruptive events, lead times and capacities may vary, which affects the overall performance of SC. There have been many studies on SC disruption mitigation, but often without considering lead time and the magnitude of lateness. In this paper, we formulate a mixed integer programming (MIP) model to optimize SC operations via a routing and scheduling approach, to model the delivery time of products at different entities as they flow throughout the SC network. We minimize a weighted sum of multiple objectives that involve costs related to transportation, shortages, and delivery lateness. We further develop a Benders decomposition algorithm for speeding up the computation of the NP-hard MIP model. We also develop a discrete-event simulation framework to evaluate the performance of solutions to the MIP model under lead time uncertainty. Through extensive numerical studies, we show how the attributes of SC entities affect the performance, so that we can improve the SC design and operations under various uncertainties. © 2004-2012 IEEE.

关键词： mixed-integer linear programming

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The Competitive Pickup and Delivery Orienteering Problem for Balancing Car-Sharing Systems

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TRANSPORTATION SCIENCE 2021年第6期55卷 1232-1259页

作者： Martin, Layla Minner, Stefan Pocas, Diogo Schulz, Andreas S. Eindhoven Univ Technol Operat Planning Accounting & Control NL-5612 AZ Eindhoven Netherlands Tech Univ Munich Logist & Supply Chain Management D-80333 Munich Germany Tech Univ Munich Munich Data Sci Inst MDSI D-85748 Garching Germany Univ Lisbon DLASIGE Fac Ciencias P-1649004 Lisbon Portugal Tech Univ Munich Operat Res D-80333 Munich Germany

Competition between one-way car-sharing operators is currently increasing. Fleet relocation as a means to compensate demand imbalances constitutes a major cost factor in a business with low profit margins. Existing decision support models have so far ignored the aspect of a competitor when the fleet is rebalanced for better availability. We present mixed-integer linear programming formulations for a pickup and delivery orienteering problem under different business models with multiple (competing) operators. Structural solution properties, including existence of equilibria and bounds on losses as a result of competition, of the competitive pickup and delivery problem under the restrictions of unit-demand stations, homogeneous payoffs, and indifferent customers based on results for congestion games are derived. Two algorithms to find a Nash equilibrium for real-life instances are proposed. One can find equilibria in the most general case;the other can only be applied if the game can be represented as a congestion game, that is, under the restrictions of homogeneous payoffs, unit-demand stations, and indifferent customers. In a numerical study, we compare different business models for car-sharing operations, including a merger between operators and outsourcing relocation operations to a common service provider (coopetition). Gross profit improvements achieved by explicitly incorporating competitor decisions are substantial, and the presence of competition decreases gross profits for all operators (compared with a merger). Using a Munich, Germany, case study, we quantify the gross profit gains resulting from considering competition as approximately 35% (over assuming absence of competition) and 12% (over assuming that the competitor is omnipresence) and the losses because of the presence of competition to be approximately 10%.

关键词： car-sharing competition mixed-integer linear programming

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Exact approaches for competitive facility location with discrete attractiveness

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OPTIMIZATION LETTERS 2021年第2期15卷 377-389页

作者： Lin, Yun Hui Tian, Qingyun Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore Nanyang Technol Univ Sch Civil & Environm Engn Singapore Singapore

We study a variant of the competitive facility location problem, in which a company is to locate new facilities in a market where competitor's facilities already exist. We consider the scenario where only a limited number of possible attractiveness levels is available, and the company has to select exactly one level for each open facility. The goal is to decide the facilities' locations and attractiveness levels that maximize the profit. We apply the gravity-based rule to model the behavior of the customers and formulate a multi-ratio linear fractional 0-1 program. Our main contributions are the exact solution approaches for the problem. These approaches allow for easy implementations without the need for designing complicated algorithms and are "friendly" to the users without a solid mathematical background. We conduct computational experiments on the randomly generated datasets to assess their computational performance. The results suggest that the mixed-integer quadratic conic approach outperforms the others in terms of computational time. Besides that, it is also the most straightforward one that only requires the users to be familiar with the general form of a conic quadratic inequality. Therefore, we recommend it as the primary choice for such a problem.

关键词： Competitive facility location Gravity model Conic programming Outer approximation mixed-integer linear programming

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An MPC-Aided Resilient Operation of Multi-Microgrids With Dynamic Boundaries

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IEEE TRANSACTIONS ON SMART GRID 2021年第3期12卷 2125-2135页

作者： Zhao, Tianqiao Wang, Jianhui Lu, Xiaonan Southern Methodist Univ Dept Elect & Comp Engn Dallas TX 75275 USA Temple Univ Coll Engn Philadelphia PA 19122 USA

Microgrids (MGs) with dynamic boundaries provide an alternative solution to serve distribution systems without energization from the utility. Adopting the concept of dynamic MGs enables a flexible operation of service restoration toward resilient distribution networks by changing system topology through smart switches and conventional circuit breakers. This work proposes a novel restoration framework including three control phases including initial, restorative and corrective actions for the distribution network (DN) with full utilization of the flexibility of distributed generators in various operational modes. Comparing with existing works that only consider MGs as a constant negative load and ignore the dynamics of different components within MGs, each MG is equipped with a model predictive control (MPC)-based controller that is responsible for optimally scheduling available power to the DN based on its local generation and load demand. Aided by the MPC framework, in this article, the conventional restoration strategy is extended that further includes the operational dynamic behavior of MGs during restoration process guaranteeing a more flexible and reliable operation. Several numerical cases are studied using the IEEE 8500-node test system to verify the effectiveness and scalability of the proposed framework.

关键词： Power system dynamics Voltage control Reactive power Distribution networks Load modeling Reliability Switches Microgrids boundaries model predictive control distribution network reconfiguration load restoration mixed-integer linear programming

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Electricity market clearing algorithms: A case study of the Bulgarian power system

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ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2021年第1期16卷 91-117页

作者： Koltsaklis, Nikolaos E. Dagoumas, Athanasios S. Mladenov, Valeri Univ Piraeus Sch Econ Business & Int Studies Energy & Environm Policy Lab Piraeus 18532 Greece Tech Univ Sofia Dept Theory Elect Engn Sofia Bulgaria

This work presents a generic optimization framework (ANNEX model), including three alternative algorithms for the electricity market-clearing process in order to optimally determine the annual energy mix of a power system. The effectiveness of the ANNEX model has been tested on an illustrative case study of the Bulgarian power system to investigate the impacts of each market design on the system's technical and economic aspects. The results highlight the influences of each market-clearing algorithm on the technology selection in the resulting electricity mix, as well as the effects on the system's marginal price, capturing both the annual dynamics and the short-term challenges of critical days. The developed methodological framework can provide useful insights on the determination of the optimal electricity mixes, highlighting the system's requirements to address the future market operating challenges of modern energy markets, subject to several technical, economic, and regulatory constraints.

关键词： Optimization mixed-integer linear programming economic dispatch block orders unit commitment electricity trading market clearing renewable energy sources system’ s marginal price

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Optimizing Autonomous Transfer Hub Networks: Quantifying the potential impact of self-driving trucks

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EURO JOURNAL ON TRANSPORTATION AND LOGISTICS 2024年 13卷

作者： Lee, Chungjae Dalmeijer, Kevin Van Hentenryck, Pascal Zhang, Peibo Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Emory Univ Goizueta Business Sch Atlanta GA 30332 USA

Autonomous trucks are expected to fundamentally transform the freight transportation industry. In particular, Autonomous Transfer Hub Networks (ATHNs), which combine autonomous trucks on middle miles with human- driven trucks on the first and last miles, are seen as the most likely deployment pathway for this technology. This paper presents a framework to optimize ATHN operations and evaluate the benefits of autonomous trucking. By exploiting the problem structure, this paper introduces a flow-based optimization model for this purpose that can be solved by blackbox solvers in a matter of hours. The resulting framework is easy to apply and enables the data-driven analysis of large-scale systems. The power of this approach is demonstrated on a system that spans all of the United States over a four-week horizon. The case study quantifies the potential impact of autonomous trucking and shows that ATHNs can have significant benefits over traditional transportation networks.

关键词： Autonomous Transfer Hub Networks Autonomous trucking Load planning mixed-integer linear programming Case study

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Comparative Performance Analysis of Speed Trajectory Optimization Algorithms for Metro and High-speed Railways

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IEEE Transactions on Transportation Electrification 2025年

作者： Liu, Xiao Peng, Yang Tian, Zhongbei Lu, Shaofeng Jiang, Lin Chen, Minwu University of Liverpool Department of Electrical Engineering and Electronics Liverpool United Kingdom South China University of Technology Shien-Ming Wu School of Intelligent Engineering Guangzhou China University of Birmingham School of Engineering Birmingham United Kingdom Southwest Jiaotong University School of Electrical Engineering Chengdu China

With the increasing concerns about railway energy efficiency, researchers have developed various approaches to optimize train trajectories for energy savings. However, these methods often rely on different models, and most studies validate their effectiveness on a single type of railway system, making it challenging for readers to compare them effectively. To address this, our paper introduces a novel comparative framework that evaluates three distinct optimization methods: Non-dominated Sorting Genetic Algorithm (NSGA-II), Convex Optimization (CO), and mixed integer linear programming (MILP). We develop a continuous train trajectory optimization model, tailored to each method. Comprehensive asymptotic analyses of the computational complexity for NSGA-II and CO are performed, along with an in-depth examination of MILP's NP-hard problem complexity. Additionally, we analyze the distinct characteristics of metro and high-speed railways to assess the applicability and performance of these methods under varied operational conditions. Our comparative analysis reveals that while all methods effectively achieve significant energy savings, they display distinct profiles in terms of computational demand and operational stability. These differences are crucial for practitioners when selecting the most appropriate method for specific railway research and operational needs. © 2015 IEEE.

关键词： mixed-integer linear programming

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Optimal User and Target Scheduling, User-Target Pairing, and Low-Resolution Phase-Only Beamforming for ISAC Systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Abanto-Leon, Luis F. Maghsudi, Setareh Ruhr University Bochum Bochum44801 Germany

We investigate the joint user and target scheduling, user-target pairing, and low-resolution phase-only beamforming design for integrated sensing and commmunications (ISAC). Scheduling determines which users and targets are served, while pairing specifies which users and targets are grouped into pairs. Additionally, the beamformers are designed using few-bit constant-modulus phase shifts. This resource allocation problem is a nonconvex mixed-integer nonlinear program (MINLP) and challenging to solve. To address it, we propose an exact mixed-integer linear program (MILP) reformulation, which leads to a globally optimal solution. Our results demonstrate the superiority of an optimal joint design compared to heuristic stage-wise approaches, which are highly sensitive to scenario characteristics. © 1967-2012 IEEE.

关键词： mixed-integer linear programming

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An Exact Algorithm for the Hazardous Orienteering Problem

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Networks 2025年第0期

作者： Montemanni, Roberto Smith, Derek H. Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia Italy Computing and Mathematics University of South Wales Pontypridd United Kingdom

The hazardous orienteering problem is the topic of this study. It is a variant of the well-studied orienteering problem, where a vehicle, given a maximum mission time, has to select and visit customers out of a set of requests, aiming at maximizing the total profit associated with the customers selected. In the hazardous version of the problem we consider, the customers are associated with parcels that have to be collected, and some of them might explode during the transportation, after having been picked up. The probability of explosion depends on the characteristics of the parcel and on the time spent by the dangerous parcel itself on the vehicle. If an explosion happens, the collected profit is totally lost. The target becomes then to select the tour that maximizes the expected profit, taking into account the probability of catastrophic events happening. In this article, we propose an exact solving approach based on a mixed integer linear programming model which is dynamically modified by adding new constraints. The computational results substantially improve the state-of-the-art for the problem. © 2025 The Author(s). Networks published by Wiley Periodicals LLC.

关键词： mixed-integer linear programming

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Price-Based Dynamic Optimal Power Flow With Emergency Repair

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IEEE TRANSACTIONS ON SMART GRID 2021年第1期12卷 324-337页

作者： Schmitz, Magdiel Bernardon, Daniel Pinheiro Garcia, Vinicius Jacques Schmitz, William Ismael Wolter, Martin Pfitscher, Luciano Lopes Fed Univ Santa Maria UFSM Dept Elect Engn BR-97105900 Santa Maria RS Brazil Fed Univ Santa Maria UFSM Dept Ind Engn BR-97105900 Santa Maria RS Brazil Otto von Guericke Univ Inst Elect Power Syst D-39106 Magdeburg Germany Fed Univ Santa Catarina UFSC Dept Power Syst BR-88040900 Florianopolis SC Brazil

In a scenario involving damaged lines or equipment, healthy regions of a network with distributed energy resources can operate through islanded microgrids. With this, the hourly demand constraint is flexible and the dynamic optimal power flow (DOPF) follows a price signal, making use of inter-temporal technologies to maximize the power utility's profit;this is known as price-based DOPF (PB-DOPF). Repair crews must also be dispatched to affected locations when restoring the network, which is known as the emergency order (EO) scheduling problem. This article proposes a mathematical model for co-optimization of the interdependent PB-DOPF and EO scheduling problems, in order to minimize the PB-DOPF cost in a horizon. The problem is formulated as a mixed-integer linear programming model with linearized three-phase power flow. An algorithm is implemented to perform the PB-DOPF and repair crew dispatches over a horizon with multiple EOs incidences, integrating a pre-scheduling methodology to handle the computational complexity. The optimization process focuses on systematically improving the solution by releasing the route and keeping track of the results related to the solution progress. Computational tests are carry on IEEE 123-bus and IEEE 8500-node distribution systems, constructing the benchmark cases with the proposed algorithm and its other two variations.

关键词： Maintenance engineering Load modeling Dynamic scheduling Voltage control Economics Mathematical model Distributed energy resources distribution system emergency order scheduling mixed-integer linear programming price-based dynamic optimal power flow

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