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作者： Hasan, Mohd. Hafiz University of Michigan

学位级别：Ph.D., Doctor of Philosophy

As cities struggle to cope with the ever-increasing demand on their transportation infrastructures, ride-hailing services have emerged as a potential remedy that promises to revolutionize urban mobility by making on-demand transportation available at the touch of a fingertip. The long-term sustainability of these services, however, can only be realized when their rides are aggregated by having individual vehicles serve multiple trips simultaneously to maximize the utilization of available seat capacity, i.e., by "true" ride sharing. While the community has long recognized ride sharing's potential for reducing traffic congestion, energy consumption, parking utilization, and greenhouse gas emissions, numerous unsolved challenges---from providing attractive mechanisms to incentivize modal shifts to building trust among unacquainted passengers---remain to hinder its widespread adoption. A key obstacle to ride sharing's ubiquity is the difficulty of coordinating rides with matching locations and schedules combined with the absence of algorithms capable of matching riders and drivers quickly and effectively. This research addresses this challenge by focusing on finding optimal routing plans for fleets of conventional and autonomous vehicles that maximize ride sharing for commute trips to power future ride-sharing platforms. The need to design routes that match trips to and from the workplace---that in turn, are dispersed spatially and temporally with schedules that may change every day---while respecting time-window, ride-duration, and vehicle-capacity constraints highlights the complexity of the problem. Driven by an original desire to investigate the potential of optimized ride-sharing platforms in relieving the parking pressure induced by the thousands of commuters traveling to the University of Michigan campus in Ann Arbor, Michigan, this research: (1) develops the mathematical framework for modeling the problem of seeking the optimal routing plan that maximizes ride

关键词： Vehicle routing problem Ride sharing Commuting mixed-integer programming Linear optimization Operations research

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Nurse scheduling with quick-response methods: Improving hospital performance, nurse workload, and patient experience

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第1期283卷 390-403页

作者： Schoenfelder, Jan Bretthauer, Kurt M. Wright, P. Daniel Coe, Edwin Univ Augsburg Fac Business Adm & Econ Hlth Care Operat Hlth Informat Management D-86159 Augsburg Germany Indiana Univ Kelley Sch Business Operat & Decis Technol Dept Bloomington IN 47405 USA Villanova Univ Sch Business Management & Operat Dept Villanova PA 19085 USA St Anthony Hosp CHI Franciscan Hlth Gig Harbor WA 98332 USA

Hospitals continue to face the challenge of providing high-quality patient care in an environment of rising healthcare costs. In response, a great deal of attention has been given to advance planning decisions such as nurse staffing, bed mix, scheduling, and patient flow. However, less attention has been given to incorporating quick-response methods in the nurse scheduling process by both anticipating and responding to patient demand fluctuations. Therefore, in this paper, we present a model that incorporates two classes of quick-response decisions in hospitals' nurse scheduling: (i) adjustments to the unit assignments of cross-trained float nurses and (ii) transfers of patients between units and off-unit admissions. Analyzing three hospitals that are subject to different regulations with respect to patient-to-nurse ratios allows us to draw conclusions on how these hotly debated ratios impact hospital performance, nurse workload, and patient experience. We find that quick-response via cross-trained nurses may lead to higher total costs in settings where an upper limit on patient-to-nurse ratios is enforced. This result has significant managerial and political relevance in locations such as California. Another takeaway is that only a small number of patient transfers or off-unit admissions provides close to the full potential benefit, thus minimizing the negative impact on patient satisfaction and quality of care. Moreover, our proposed scheduling approach reduces the number of undesired assigned shifts. Finally, bed and nurse capacity utilization are shown to be important considerations when determining how and whether to use quick-response methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： OR in health services Nurse scheduling Quick-response methods Flexibility mixed-integer programming

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Learning in Combinatorial Optimization: What and How to Explore

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OPERATIONS RESEARCH 2020年第5期68卷 1585-1604页

作者： Modaresi, Sajad Saure, Denis Vielma, Juan Pablo Univ N Carolina Kenan Flagler Business Sch Chapel Hill NC 27599 USA Univ Chile Ind Engn Dept Beauchef 851 Santiago 8370456 Chile MIT Sloan Sch Management Cambridge MA 02142 USA

We study dynamic decision making under uncertainty when, at each period, a decision maker implements a solution to a combinatorial optimization problem. The objective coefficient vectors of said problem, which are unobserved before implementation, vary from period to period. These vectors, however, are known to be random draws from an initially unknown distribution with known range. By implementing different solutions, the decision maker extracts information about the underlying distribution but at the same time experiences the cost associated with said solutions. We show that resolving the implied exploration versus exploitation tradeoff efficiently is related to solving a lower-bound problem (LBP), which simultaneously answers the questions of what to explore and how to do so. We establish a fundamental limit on the asymptotic performance of any admissible policy that is proportional to the optimal objective value of the LBP problem. We show that such a lower bound might be asymptotically attained by policies that adaptively reconstruct and solve the LBP at an exponentially decreasing frequency. Because the LBP is likely intractable in practice, we propose policies that instead reconstruct and solve a proxy for the LBP, which we call the optimality cover problem (OCP). We provide strong evidence of the practical tractability of the OCP, which implies that the proposed policies can be implemented in real time. We test the performance of the proposed policies through extensive numerical experiments, and we show that they significantly outperform relevant benchmarks in the long-term and are competitive in the short-term.

关键词： combinatorial optimization multiarmed bandit mixed-integer programming

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Sustainable supply chain network design: A case of the glass manufacturer in Asia

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2022年 248卷

作者： Naeeni, Hannan Sadjady Sabbaghi, Navid Longwood Univ Coll Business & Econ 201 High St Farmville VA 23909 USA St Marys Coll Calif Sch Econ Business Adm 1928 St Marys Rd Moraga CA 94575 USA

Traditionally, profit maximization was the major objective for many businesses. However, due to growing stakeholders' pressure and awareness about the social and environmental impacts of supply chain activities, many companies are now focused on optimizing their profits, while reducing negative impacts of their activities on planet and society. While sustainable development requires concurrent and balanced consideration of social, environmental and economic dimensions, unbalanced approaches have been adopted both in real world practices, and in academic literature. We contribute to the stream of research in the literature that tries to address this gap. We propose a multi-objective mixed-integer programming (MOMIP) model for sustainable supply chain network design (SSCND), encompassing economic, environmental, and social objectives. Both strategic and tactical network design decision variables are considered, including location, size, and technology type of facilities, along with determining the flow of materials and transportation modes. The Normalized Normal Constraint Method is applied to generate evenly distributed Pareto frontiers. The case of a glass manufacturing company in Asia is used to demonstrate applicability of the model and solution procedure to real-world problems. Non-dominated solutions are analyzed, and a more sustainable and balanced network design alternative is proposed, which outperforms the current network configuration in terms of all three dimensions of sustainability. Managerial implications and future research directions are discussed.

关键词： Supply chain network design Sustainability Multi-objective optimization mixed-integer programming Normalized normal constraint method

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An assessment of European electricity arbitrage using storage systems

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ENERGY 2022年 242卷 122916-122916页

作者： Nunez, Fernando Canca, David Arcos-Vargas, Angel Univ Seville Sch Engn Dept Ind Engn & Management Sci Seville Spain

Electricity arbitrage involves the storage of energy at times when prices are low, and offering it on the markets when prices are high. The development of renewable and energy storage technologies may provide a promising business opportunity for electricity arbitrage. In this regard, this study analyses the current viability of the electricity arbitrage business (via Li-Ion batteries) using a sample of European countries in the year 2019;countries where electricity prices (day-ahead market) and financial conditions show a certain degree of heterogeneity. We basically follow a sequence of three analyses in our study. Firstly, a Linear mixed-Integrated programming model has been developed to optimize the arbitrage strategy for each country in the sample. Secondly, using the cash-flows from the optimization model, we calculate two financial indicators (Net Present Value (NPV) and Internal Rate of Return) in order to select the optimal inverter size for each country. Tax and discount rates specific to each country have been used with the calculation of this second rate following the methodology proposed by most of the national agencies. Thirdly, a mixed linear regression model is proposed in order to investigate the importance of observed and unobserved heterogeneity (at country level) in explaining the business profitability. The findings show that, in the near future, the most attractive European countries for the electricity arbitrage business should be the United Kingdom and Ireland, with current NPV close to-400,000 V, while Spain and Portugal might show the worst performances, their current NPV are close to-800,000 V. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Arbitrage business with storage European wholesale electricity market International comparison Discount rate calculation mixed-integer programming mixed regression models

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Evaluation of an Additional Generator on the Economic Effect Based on a Load Sharing Optimization of Medium-Speed/High-Speed Diesel Generators in a Microgrid

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ENERGIES 2022年第3期15卷 1007页

作者： Yamano, Shuhei Akisawa, Atsushi Tokyo Univ Agr & Technol Grad Sch Bioapplicat & Syst Engn 2-24-16 Naka Cho Koganei Tokyo 1848588 Japan Tokyo Univ Agr & Technol Inst Engn Fuchu Tokyo 1848588 Japan

This study investigates the additional installation of a high-speed diesel generator to independent microgrids (MG) such as remote islands where relatively efficient medium-speed diesel generators (DGs) are mainly used. While small-sized and lightweight, high-speed DGs are not widely spread for continuous usage because their efficiency is lower than that of the medium-speed DGs. The objective of this study is to evaluate the performance of the new method of load sharing optimization to improve the economy by interconnecting a high-speed DG to an independent MG where the medium-speed DGs are operated. The study investigates the effect of operation cost reduction by installing the new load sharing method of operation following the load sharing pattern derived by the mixed-integer programming. As a result of this study, there was some effect to installing a high-speed DG operated by the conventional method. In addition, by adding the new method of load sharing with optimization, the economic effect became even higher.

关键词： microgrid load sharing high-speed DG medium-speed DG mixed-integer programming

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Polyhedral potential field constructions for obstacle avoidance in a receding horizon formulation

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IFAC-PapersOnLine 2022年第16期55卷 254-259页

作者： Theodor-Gabriel Nicu Florin Stoican Ionela Prodan Univ. Grenoble Alpes Grenoble INP LCIS F-26000 Valence France Institute of Engineering and Management Univ. Grenoble Alpes Research Center for Logic Optimization and Security (LOS) Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest

We revisit and enhance previous work pertaining to the use of polyhedral potential fields for motion planning in a receding horizon framework. We adapt the standard vertex-based mixed-integer (MI) representations of piecewise affine functions and propose three equivalent formulations which balance the complexity of the geometric representation with the size of the associated MI representation. In all cases, we arrive at formulations whose polyhedral support comes (either directly or as a refinement of it) from an associated hyperplane arrangement induced by the hyperplanes bounding the polyhedral obstacles.

关键词： Polyhedral potential field Sum function mixed-integer programming Motion planning Hyperplane arrangement

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A combined optimization-simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities

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JOURNAL OF AIR TRANSPORT MANAGEMENT 2023年 106卷 102258页

作者： Schultz, Michael Soolaki, Majid Salari, Mostafa Bakhshian, Elnaz Bundeswehr Univ Munich Inst Flight Syst D-85577 Neubiberg Germany Univ Westminster Sch Architecture & Cities London England Univ North Dakota Coll Engn & Mines Dept Civil Engn Grand Forks ND USA Univ Coll Dublin Sch Civil Engn Dublin Ireland

The timely handling of passengers is critical to efficient airport and airline operations. The pandemic requirements mandate adapted process designs and handling procedures to maintain and improve operational performance. Passenger activities in the confined aircraft cabin must be evaluated for potential virus transmission, and boarding procedures should be designed to minimize the negative impact on passengers and operations. In our approach, we generate an optimized seat allocation that considers passengers' physical activities when they store their hand luggage items in the overhead compartment. We proposed a mixed-integer programming formulation including the concept of shedding rates to determine and minimize the risk of virus transmission by solving the NP-hard seat assignment problem. We are improving the already efficient outside-in boarding, where passengers in the window seat board first and passengers in the aisle seat board last, taking into account COVID-19 regulations and the limited capacity of overhead compartments. To demonstrate and evaluate the improvements achieved in aircraft boarding, a stochastic agent-based model is used in which three operational scenarios with seat occupancy of 50%, 66%, and 80% are implemented. With our optimization approach, the average boarding time and the transmission risk are significantly reduced already for the general case, i.e., when no specific boarding order is specified (random boarding). If the already efficient outside-in boarding is used as a reference, the boarding time can be reduced by more than 30% by applying our approach, while keeping the transmission risk at the lowest level.

关键词： Aircraft boarding Passenger seat assignment COVID-19 constraints Hand luggage mixed-integer programming Optimized seat allocation Stochastic agent-based model Simulation

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Bi-criteria Pareto optimization to balance irradiation time and dosimetric objectives in proton arc therapy

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PHYSICS IN MEDICINE AND BIOLOGY 2022年第24期67卷 245017-245017页

作者： Wuyckens, Sophie Zhao, Lewei Saint-Guillain, Michael Janssens, Guillaume Sterpin, Edmond Souris, Kevin Ding, Xuanfeng Lee, John A. UCLouvain Mol Imaging Radiotherapy & Oncol MIRO Brussels Belgium Beaumont Hlth Dept Radiat Oncol Royal Oak MI USA UCLouvain ICTEAM Louvain La Neuve Belgium Ion Beam Applicat SA Louvain La Neuve Belgium KULeuven Dept Oncol Leuven Belgium

Objective. Proton arc therapy (PAT) is a new delivery technique that exploits the continuous rotation of the gantry to distribute the therapeutic dose over many angular windows instead of using a few static fields, as in conventional (intensity-modulated) proton therapy. Although coming along with many potential clinical and dosimetric benefits, PAT has also raised a new optimization challenge. In addition to the dosimetric goals, the beam delivery time (BDT) needs to be considered in the objective function. Considering this bi-objective formulation, the task of finding a good compromise with appropriate weighting factors can turn out to be cumbersome. Approach. We have computed Pareto-optimal plans for three disease sites: a brain, a lung, and a liver, following a method of iteratively choosing weight vectors to approximate the Pareto front with few points. mixed-integer programming (MIP) was selected to state the bi-criteria PAT problem and to find Pareto optimal points with a suited solver. Main results. The trade-offs between plan quality and beam irradiation time (static BDT) are investigated by inspecting three plans from the Pareto front. The latter are carefully picked to demonstrate significant differences in dose distribution and delivery time depending on their location on the frontier. The results were benchmarked against IMPT and SPArc plans showing the strength of degrees of freedom coming along with MIP optimization. Significance. This paper presents for the first time the application of bi-criteria optimization to the PAT problem, which eventually permits the planners to select the best treatment strategy according to the patient conditions and clinical resources available.

关键词： proton arc therapy multi-criteria optimization Pareto front mixed-integer programming scalarization bi-objective optimization

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Production scheduling under demand uncertainty in the presence of feedback: Model comparisons, insights, and paradoxes

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COMPUTERS & CHEMICAL ENGINEERING 2022年 168卷

作者： Avadiappan, Venkatachalam Gupta, Dhruv Maravelias, Christos T. Univ Wisconsin Madison Dept Chem & Biol Engn Madison WI USA Princeton Univ Dept Chem & Biol Engn Princeton NJ 08544 USA Princeton Univ Andlinger Ctr Energy & Environm Princeton NJ 08544 USA

We investigate the importance of accounting for uncertainty a priori in production scheduling in the presence of feedback. First, we examine different optimization models (deterministic, robust, and stochastic programming), used to generate the open-loop schedules and describe the modeling of uncertainty in each case. Second, we present a formal procedure for carrying out closed-loop simulations in order to study and compare the closed-loop performance across the models as attributes such as the demand uncertainty observation horizon, order size max-mean relative difference, and load on the process network are varied. Finally, we analyze the results of the simulations to draw insights on how the above attributes affect the closed-loop performance of the different models across networks and expound on the paradoxes observed.

关键词： Online scheduling Real-time optimization mixed-integer programming

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