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A mathematical programming model and dispatching rules for the repatriation program scheduling problem

PUBLIC TRANSPORT 2025年第1期17卷 101-120页

作者： Al-Shihabi, Sameh Al-Abdallat, Yousef Saidan, Motasem N. Univ Sharjah Ind Engn & Engn Management Dept POB 27272 Sharjah U Arab Emirates Univ Jordan Sch Engn Ind Engn Dept Amman 11942 Jordan Univ Sharjah Modeling & Optimizat Sustainable Operat & Supply C Sharjah U Arab Emirates Univ Jordan Sch Engn Chem Engn Dept Amman 11942 Jordan

Commercial flights were temporarily suspended due to the Corona Virus Disease (COVID-19) pandemic in March 2020. Subsequently, several countries had to repatriate their citizens from hosting countries. Typically, flights are planned ahead of time in commercial aviation, after which passengers buy their seats;however, in repatriation, the opposite happens. Citizens express their wish to return to their country first, and flights are scheduled accordingly. Due to the suspension of commercial aviation, decision makers had to rely on their flag-carrying airlines to schedule repatriation flights. These flights were constrained by limitations like the airline's fleet capacity, the availability of quarantine locations, and pilots' availability, while the decision makers' objective was to return the most vulnerable citizens first. The present paper examines this problem, which we call the repatriation program scheduling problem (RPSP). The RPSP objective is to minimize the social impacts of keeping a country's citizens abroad by assigning flights to different countries to repatriate stranded citizens. Therefore, we develop a mixed-integer linear program to model the RPSP and solve the RPSP using a commercial solver. We also suggest and test several dispatching rules that decision makers can use to schedule repatriation flights. We then model a case resembling an actual case and study the heuristics' performances. Heuristics are found to lead to reasonable solutions for large quarantine capacities and limited pilot-airplane assignment options;otherwise, decision makers need to rely on exact solutions.

关键词： Scheduling Repatriation mixed-integer linear programming Dispatching rules Flights Quarantine

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Models for two-dimensional bin packing problems with customer order spread

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2024年第1期48卷 8-8页

作者： Martin, Mateus Yanasse, Horacio Hideki Santos, Maristela O. Morabito, Reinaldo Univ Fed Sao Carlos Dept Engn Prod Rodovia Joao Leme St BR-18052780 Sorocaba SP Brazil Univ Fed Sao Paulo Inst Ciencia & Tecnol Ave Cesare Mansueto Giulio Lattes 1201 BR-12247014 Sao Jose Dos Campos SP Brazil Univ Sao Paulo Inst Ciencias Matemat & Comput Ave Trabalhador Sao carlense 400 BR-13566590 Sao Carlos SP Brazil Univ Fed Sao Carlos Dept Engn Prod Via Washington Luiz km 235 BR-13565905 Sao Carlos SP Brazil

In this paper, we address an extension of the classical two-dimensional bin packing (2BPP) that considers the spread of customer orders (2BPP-OS). The 2BPP-OS addresses a set of rectangular items, required from different customer orders, to be cut from a set of rectangular bins. All the items of a customer order are dispatched together to the next stage of production or distribution after its completion. The objective is to minimize the number of bins used and the spread of customer orders over the cutting process. The 2BPP-OS gains relevance in manufacturing environments that seek minimum waste solutions with satisfactory levels of customer service. We propose integer linear programming (ILP) models for variants of the 2BPP-OS that consider non-guillotine, 2-stage, restricted 3-stage, and unrestricted 3-stage patterns. We are not aware of integrated approaches for the 2BPP-OS in the literature despite its relevance in practical settings. Using a general-purpose ILP solver, the results show that the 2BPP-OS takes more computational effort to solve than the 2BPP, as it has to consider several symmetries that are often disregarded by the traditional 2BPP approaches. The solutions obtained by the proposed approaches have similar bin usage and significantly better metrics of customer satisfaction concerning the approaches that neglect the customer order spread.

关键词： Cutting & Packing mixed-integer linear programming Non-guillotine pattern 2-stage and 3-stage patterns Order spread minimization

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Combination Chemotherapy Optimization with Discrete Dosing

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INFORMS JOURNAL ON COMPUTING 2024年第2期36卷 305-704, C2页

作者： Ajayi, Temitayo Hosseinian, Seyedmohammadhossein Schaefer, Andrew J. Fuller, Clifton D. Nat Source Improved Plants Ithaca NY 14850 USA Univ Cincinnati Dept Mech & Mat Engn Cincinnati OH 45221 USA Rice Univ Dept Computat Appl Math & Operat Res Houston TX 77005 USA Univ Texas MD Anderson Canc Ctr Dept Radiat Oncol Houston TX 77030 USA

Chemotherapy drug administration is a complex problem that often requires expensive clinical trials to evaluate potential regimens;one way to alleviate this burden and better inform future trials is to build reliable models for drug administration. This paper presents a mixed-integer program for combination chemotherapy (utilization of multiple drugs) optimization that incorporates various important operational constraints and, besides dose and concentration limits, controls treatment toxicity based on its effect on the count of white blood cells. To address the uncertainty of tumor heterogeneity, we also propose chance constraints that guarantee reaching an operable tumor size with a high probability in a neoadjuvant setting. We present analytical results pertinent to the accuracy of the model in representing biological processes of chemotherapy and establish its potential for clinical applications through a numerical study of breast cancer.

关键词： combination chemotherapy differential equations mixed-integer linear programming

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Hydrogen supply chain for future hydrogen-fuelled railway in the UK: Transport sector focused

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 86卷 1-13页

作者： Xu, Zhan Zhao, Ning Yan, Yan Zhang, Yuqi Zhang, Tongtong Wu, Dawei Hillmansen, Stuart Gao, Shigen Univ Birmingham Birmingham Ctr Rail Res & Educ Birmingham B15 2TT England Southeast Univ Sch Mech Engn Nanjing 210000 Peoples R China Univ Birmingham Mech Engn Birmingham B15 2TT England Univ Birmingham Birmingham Ctr Energy Storage Birmingham B15 2TT England Univ Birmingham Sch Chem Engn Birmingham B15 2TT England Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China

Though being attractive on railway decarbonisation for regional lines, excessive cost caused by immature hydrogen supply chain is one of the significant hurdles for promoting hydrogen traction to rolling stocks. Therefore, we conduct bespoke research on the UK's hydrogen supply chain for railway, concentrating on hydrogen transportation. Firstly, a map for the planned hydrogen production plants and potential hydrogen lines is developed with the location, capacity, and usage. A spatially explicit model for the hydrogen supply chain is then introduced, which optimises the existing grid-based methodology on accuracy and applicability. Compressed hydrogen at three pressures, and liquid hydrogen are considered as the mediums, incorporating by road and rail transport. Furthermore, three scenarios for hydrogen rail penetration are simulated respectively to discuss the levelised cost and the most suitable national transport network. The results show that the developed model with mix-integer linear programming (MILP) can well design the UK's hydrogen distribution for railway traction. Moreover, the hydrogen transport medium and vehicle should adjust to suit for different era where the penetration of hydrogen traction varies. The levelised cost of hydrogen (LCOH) decreases from 6.13 pound/kg to 5.13 pound/kg on average from the conservative scenario to the radical scenario. Applying different transport combinations according to the specific situation can satisfy the demand while reducing cost for multi-supplier and multitargeting hydrogen transport.

关键词： Hydrogen supply chain Railway Hydrogen transport Levelised cost of hydrogen mixed-integer linear programming

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Evaluating economic feasibility of liquid air energy storage systems in US and European markets

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ENERGY 2024年 300卷

作者： Cetegen, Shaylin A. Gundersen, Truls Barton, Paul I. MIT Proc Syst Engn Lab Dept Chem Engn Cambridge MA 02139 USA Norwegian Univ Sci & Technol NTNU Dept Energy & Proc Engn Kolbjorn Hejes Vei 1B NO-7491 Trondheim Norway

Liquid air energy storage is a clean and scalable long-duration energy storage technology capable of delivering multiple gigawatt-hours of storage. The inherent locatability of this technology unlocks nearly universal siting opportunities for grid-scale storage, which were previously unavailable with traditional technologies such as pumped hydro energy storage and compressed air energy storage. While the technical viability of liquid air energy storage has been established, its economic viability has not yet been rigorously assessed across diverse electricity markets. In this work, a mixed-integer linear program was used to conduct a high-level economic analysis of this technology in US and European markets. By simultaneously optimizing the design and operation of liquid air energy storage systems to maximize their net present value, a yes/no indication of their economic viability can be obtained. Using this approach, the economic viability of this technology was established for the West load zone of Texas through identification of a system with an optimal net present value of $10.4 million and a levelized cost of storage of $119/MWh. Sensitivity analyses were also performed to elucidate how improved technical performance and economic incentives might affect the economic feasibility and, consequently, the adoption of this technology more broadly in the future.

关键词： Energy storage Grid-scale energy storage Liquid air energy storage mixed-integer linear programming Optimization

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Block-based state-expanded network models for multi-activity shift scheduling

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JOURNAL OF SCHEDULING 2024年第4期27卷 341-361页

作者： Roemer, Michael Bielefeld Univ Decis Analyt Grp Univ Str 25 D-33615 Bielefeld Germany

This paper presents new mixed-integer linear programming formulations for multi-activity shift scheduling problems (MASSP). In these formulations, the rules governing shift feasibility are encoded in block-based state-expanded networks in which nodes are associated with states and arcs represent assignments of blocks of work or break periods inducing state transitions. A key advantage of these formulations is that for the anonymous MASSP in which all employees are considered as equal only a single network with integer flow variables is needed as long as the network encodes all shift composition rules. A challenging aspect is that the networks can become very large, yielding huge models that are hard to solve for large problem instances. To address this challenge, this paper proposes two exact modeling techniques that substantially reduce the size of the model instances: First, it introduces a set of aggregate side constraints enforcing that an integer flow solution can be decomposed into paths representing feasible shifts. Second, it proposes to decouple the shift composition from the assignment of concrete activities to blocks of work periods, thereby removing a large amount of symmetry from the original model. In a computational study with two MASSP instance sets from the literature dealing with shift scheduling problems, we demonstrate the effectiveness of these techniques for reducing the both size of the model instances and the solution time: We are able to solve all instances, including more than 70 previously open instances, to optimality-the vast majority of them in less than 30 min on a notebook computer.

关键词： Multi-activity shift scheduling State-expanded networks mixed-integer linear programming

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A Compact Neural Network-Based Conversion Loss Model With Hard Constraints for Energy Management

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第2期60卷 2588-2600页

作者： Liu, Xuan Liu, Xiong Huang, Zhifeng Zhao, Tianyang Jinan Univ Energy & Elect Res Ctr Guangzhou 510630 Guangdong Peoples R China

The evolving microgrid (MG) technology integrates various converters for different energy sources and loads, leading to increased complexity in optimization problems due to additional decision variables introduced by power conversion processes in modern energy management systems (EMS). Existing conversion loss models are too complex for optimization. This article presents a neural network-based linear surrogate model to accurately and efficiently approximate power conversion losses in EMS. In EMS, the black-box nature of neural networks (NNs) raises concerns about violating optimization constraints. To address this, the proposed NN model is trained using the augmented Lagrangian method (ALM), enforcing additional hard constraints on the network input/output variables. Moreover, the trained NN is reformulated as mixed-integer linear programming (MILP) constraints, allowing the model to be integrated into energy management (EM) problems solvable by MILP solvers. To optimize the model architecture, a novel neural architecture search (NAS) algorithm is introduced, striking a balance between accuracy and complexity. The case study results demonstrate that the proposed model effectively approximate conversion loss with small absolute errors while satisfying the additional hard constraints. Compared to the manually tuned models, the NAS-generated model achieves superior performance with a compact architecture. Moreover, the reformulated MILP model can be solved efficiently using state-of-the-art MILP solvers, facilitating the integration of surrogate models into EM problems. Lastly, NN example optimal power flow (OPF) problem is presented in the case study, showcasing the practical usability of the proposed model in EM problems.

关键词： Artificial neural networks Analytical models Computer architecture Training Data models Optimization Neural networks Power conversion losses linear surrogate models neural network augmented Lagrangian method mixed-integer linear programming

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Approximate Kernel Learning Uncertainty Set for Robust Combinatorial Optimization

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INFORMS JOURNAL ON COMPUTING 2024年第3期36卷 705-937, C2页

作者： Loger, Benoit Dolgui, Alexandre Lehuede, Fabien Massonnet, Guillaume IMT Atlantique LS2N F-44300 Nantes France

Support vector clustering (SVC) has been proposed in the literature as a datadriven approach to build uncertainty sets in robust optimization. Unfortunately, the resulting SVC-based uncertainty sets induces a large number of additional variables and constraints in the robust counterpart of mathematical formulations. We propose a two-phase method to approximate the resulting uncertainty sets and overcome these tractability issues. This method is controlled by a parameter defining a trade-off between the quality of the approximation and the complexity of the robust models formulated. We evaluate the approximation method on three distinct, well-known optimization problems. Experimental results show that the approximated uncertainty set leads to solutions that are comparable to those obtained with the classic SVC-based uncertainty set with a significant reduction of the computation time.

关键词： data-driven robust optimization machine learning mixed-integer linear programming

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Synchronized Deliveries with a Bike and a Self-Driving Robot

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TRANSPORTATION SCIENCE 2024年第1期58卷 1页

作者： Zhao, Yanlu Cattaruzza, Diego Kang, Ningxuan Roberti, Roberto Univ Durham Business Sch Durham DH1 3LB England Univ Lille CNRS Cent Lille INRIAUMR 9189Ctr Rech Informat Signal & Automat F-59000 Lille France JD Com Dept Intelligent Supply Chain Y Beijing 101111 Peoples R China Univ Padua Dept Informat Engn I-35131 Padua Italy

Online e-commerce giants are continuously investigating innovative ways to improve their practices in last-mile deliveries. Inspired by the current practices at *** (the largest online retailer by revenue in China), we investigate a delivery problem that we call the traveling salesman problem with bike and robot (TSPBR), where a cargo bike is aided by a self-driving robot to deliver parcels to customers in urban areas. We present two mixedinteger linear programming models and describe a set of valid inequalities to strengthen their linear relaxation. We show that these models can yield optimal solutions of TSPBR instances with up to 60 nodes. To efficiently find heuristic solutions, we also present a genetic algorithm based on a dynamic programming recursion that efficiently explores large neighborhoods. We computationally assess this genetic algorithm on instances provided by *** and show that high-quality solutions can be found in a few minutes of computing time. Finally, we provide some managerial insights to assess the impact of deploying the bike-and-robot tandem to deliver parcels in the TSPBR setting.

关键词： self-driving robots bike delivery synchronization mixed-integer linear programming genetic algorithm last-mile delivery

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Optimized Planning of DU/CU Placement and Flow Routing in 5G Packet Xhaul Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第1期21卷 232-248页

作者： Klinkowski, Miroslaw Natl Inst Telecommun Cent Chamber Telecommun Metrol PL-04894 Warsaw Poland

Packet-switched Xhaul networks (PXNs) have been proposed as a cost-effective and scalable solution for provisioning of connectivity between densely located radio antenna (remote) units (RU) and distributed (DU) / central (CU) processing units in 5G Radio Access Networks (RANs). A PXN enables statistical multiplexing of different data flows, such as fronthaul, midhaul, and backhaul flows, and their routing over a commonly shared packet transport network, thus increasing network flexibility and decreasing bandwidth requirements. The decisions concerning the placement of virtualized DU and CU entities at selected processing nodes and the selection of paths for routing of data flows between these nodes have a direct impact on flow latencies. In addition, buffering of packets in switches introduces dynamic, flow-dependent latencies, which as well have to be accounted for in latency-sensitive 5G RANs. In this work, we address a network planning problem that concerns the joint latency-aware placement of DUs/CUs and routing of data flows (LADCPR) for a set of RUs in a 5G RAN connected using a PXN. We model the LADCPR problem as a mixed-integer linear programming (MILP) optimization problem and propose two reformulations of the model that facilitate its solving. Since the MILPs have limited scalability, we develop two heuristic algorithms, based on problem decomposition and an iterative search approach. Numerical experiments performed in different network scenarios show that the algorithms are capable of generating good-quality solutions to the LADCPR problem. Also, they can optimize larger network instances - consisting of tens of switches, a number of routing paths, and comprising some hundreds of demands - within relatively low algorithm run times.

关键词： Routing Optimization 5G mobile communication Resource management Bandwidth Minimization Heuristic algorithms 5G Xhaul packet-switched fronthaul resource allocation routing network optimization mixed-integer linear programming heuristics

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