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Drayage routing with heterogeneous fleet, compatibility constraints, and truck load configurations

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2022年 168卷

作者： Bustos-Coral, Daniel Costa, Alysson M. Univ Melbourne Sch Math & Stat Melbourne Vic 3010 Australia

This paper addresses drayage routing problems with heterogeneous fleets, compatibility con-straints, and truck load configurations. In these problems, containers can be of any size and cargo category. In addition, compatibility and load configuration constraints define which and how many containers can be transported by each truck. We propose a state transition logic to model these constraints. Based on this logic, we develop two mixed-integer programming models for this family of problems. The first model is a compact formulation that can be input into a black-box solver. The second model combines partial routes in a space-time network and is solved with a tailored branch-and-cut approach. To analyze the efficiency of the proposed models, we conduct extensive computational tests on instances with different numbers of requests, geographical distributions of locations, time-window lengths, and fleet compositions. Moreover, we discuss how our modeling approach can assist decision-making in three different drayage routing applications.

关键词： Drayage routing Heterogeneous fleet Compatibility constraint Truck load configuration mixed-integer programming

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Classifying and modelling demand response in power systems

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ENERGY 2022年 242卷 122544-122544页

作者： Morales-Espana, German Martinez-Gordon, Rafael Sijm, Jos TNO Energy Transit The Hague Netherlands Univ Groningen Fac Sci & Engn Groningen Netherlands

Demand response (DR) is expected to play a major role in integrating large shares of variable renewable energy (VRE) sources in power systems. For example, DR can increase or decrease consumption depending on the VRE availability, and use generating and network assets more efficiently. Detailed DR models are usually very complex, hence, unsuitable for large-scale energy models, where simplicity and linearity are key elements to keep a reasonable computational performance. In contrast, aggregated DR models are usually too simplistic and therefore conclusions derived from them may be misleading. This paper focuses on classifying and modelling DR in large-scale models. The first part of the paper classifies different DR services, and provides an overview of benefits and challenges. The second part presents mathematical formulations for different types of DR ranging from curtailment and ideal shifting, to shifting including saturation and immediate load recovery. Here, we suggest a collection of linear constraints that are appropriate for large-scale power systems and integrated energy system models, but sufficiently sophisticated to capture the key effects of DR in the energy system. We also propose a mixed integer programming formulation for load shifting that guarantees immediate load recovery, and its linear relaxation better approximates the exact solution compared with previous models.(c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Demand side management Demand response Linear programming mixed-integer programming Integrated energy system models Power systems models

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A model of supply-chain decisions for resource sharing with an application to ventilator allocation to combat COVID-19

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NAVAL RESEARCH LOGISTICS 2020年第5期67卷 303-320页

作者： Mehrotra, Sanjay Rahimian, Hamed Barah, Masoud Luo, Fengqiao Schantz, Karolina Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA

We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top-most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst-case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk-aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp-up consideration can be based on a cost-benefit analysis.

关键词： COVID-19 emergency management mixed-integer programming resource sharing stochastic programming ventilator allocation

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A truck and drones model for last-mile delivery: A mathematical model and heuristic approach

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APPLIED MATHEMATICAL MODELLING 2020年 80卷 290-318页

作者： Moshref-Javadi, Mohammad Hemmati, Ahmad Winkenbach, Matthias MIT 77 Massachusetts Ave Cambridge MA 02139 USA Univ Bergen N-5020 Bergen Norway

We present a mathematical formulation and a heuristic solution approach for the optimal planning of delivery routes in a multi-modal system combining truck and Unmanned Aerial Vehicle (UAV) operations. In this system, truck and UAV operations are synchronized, i.e., one or more UAVs travel on a truck, which serves as a mobile depot. Deliveries can be made by both UAVs and the truck. While the truck follows a multi-stop route, each UAV delivers a single shipment per dispatch. The presented optimization model minimizes the waiting time of customers in the system. The model determines the optimal allocation of customers to truck and UAVs, the optimal route sequence of the truck, and the optimal launch and reconvene locations of the UAVs along the truck route. We formulate the problem as a mixed-integer Linear programming (MILP) model and conduct a bound analysis to gauge the maximum potential of the proposed system to reduce customer waiting time compared to a traditional truck-only delivery system. To be able to solve real-world problem size instances, we propose an efficient Truck and Drone Routing Algorithm (TDRA). The solution quality and computational performance of the mathematical model and the TDRA are compared together and with the truck-only model based on a variety of problem instances. Further, we apply the TDRA to a real-world case study for e-commerce delivery in Sao Paulo, Brazil. Our numerical results suggest significant reductions in customer waiting time to be gained from the proposed multi-modal delivery model. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Last-mile delivery UAV logistics mixed-integer programming Large neighborhood search

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Cash flow management by risk-neutral and risk-averse stochastic approaches

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2020年第1期71卷 55-68页

作者： Righetto, Giovanni Margarido Morabito, Reinaldo Alem, Douglas Univ Fed Sao Carlos Prod Engn Dept Rodovia Washington Luis SP 310Km 235 BR-1356505 Sao Carlos SP Brazil Univ Edinburgh Business Sch Edinburgh Midlothian Scotland

This article presents a dynamic cash flow management problem with uncertain parameters in a finite planning horizon via two-stage stochastic programming (SP). We propose a risk-neutral mixed-integer two-stage SP model and risk-averse versions based on the minimax regret and conditional value-at-risk (CVaR) criteria. The models support decisions in cash management that deals with different grace periods, piecewise linear yields and uncertainty in the exchange rate of external sales. The developed approach is applied to a real-world stationery company in Brazil. Numerical results assess the trade-off between risk and return, showing that the optimisation models generate effective solutions for the company's treasury with reduced risks, which might be appealing for companies from other sectors as well.

关键词： Cash flow management mixed-integer programming stochastic programming minimax with regret conditional value-at-risk stationery industry

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Airline Timetable Development and Fleet Assignment Incorporating Passenger Choice

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TRANSPORTATION SCIENCE 2020年第1期54卷 139-163页

作者： Wei, Keji Vaze, Vikrant Jacquillat, Alexandre Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA MIT Sloan Sch Management Cambridge MA 02142 USA

Flight timetabling can greatly impact an airline's operating profit, yet data-driven or model-based solutions to support it remain limited. Timetabling optimization is significantly complicated by two factors. First, it exhibits strong interdependencies with subsequent fleet assignment decisions of the airlines. Second, flights' departure and arrival times are important determinants of passenger connection opportunities, of the attractiveness of each (nonstop or connecting) itinerary, and, in turn, of passengers' booking decisions. Because of these complicating factors, most existing approaches rely on incremental timetabling. This paper introduces an original integrated optimization approach to comprehensive flight timetabling and fleet assignment under endogenous passenger choice. Passenger choice is captured by a discrete-choice generalized attraction model. The resulting optimization model is formulated as a mixed-integer linear program. This paper also proposes an original multiphase solution approach, which effectively combines several heuristics, to optimize the network-wide timetable of a major airline within a realistic computational budget. Using case study data from Alaska Airlines, computational results suggest that the combination of this paper's model formulation and solution approaches can result in significant profit improvements as compared with the most advanced incremental approaches to flight timetabling. Additional computational experiments based on several extensions also demonstrate the benefits of this modeling and computational framework to support various types of strategic airline decision making in the context of frequency planning, revenue management, and postmerger integration.

关键词： timetable development fleet assignment passenger choice mixed-integer programming

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A Stochastic mixed-integer programming approach to the energy-technology management problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2012年第3期63卷 594-606页

作者： Stoyan, Stephen J. Dessouky, Maged M. Univ So Calif Daniel J Epstein Dept Ind & Syst Engn Los Angeles CA 90089 USA

As the development and population of North America continues to grow, the demand for environmentally friendly or clean energy generation is becoming more of an issue. We present a model that addresses the energy technologies that may continue to be used and new clean energy technologies that should be introduced in energy generation. The approach involves a Stochastic mixed-integer Program (SMIP) that minimizes cost and emission levels associated with energy generation while meeting energy demands of a given region. The results provide encouraging outcomes with respect to cost, emission levels, and energy-technologies that should be utilized for future generation. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Clean energy Two-stage Scenario Stochastic programming mixed-integer programming

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Robust design and planning for a multi-mode multi-product supply network: a dairy industry case study

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OPERATIONAL RESEARCH 2020年第3期20卷 1811-1840页

作者： Jouzdani, Javid Fathian, Mohammad Makui, Ahmad Heydari, Mehdi Golpayegan Univ Technol Dept Ind Engn Golpayegan Iran Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

As a salient matter of decision, supply chain design and planning has been a point of attraction for both researchers and practitioners. In real-world problems, the data based on which the decision is made are subject to uncertainty. Robust optimization is a well-known approach developed for modeling the uncertainty in such cases. In this research, a robust supply chain network design (RSCND) problem considering multiple products, multiple transportation modes, monetary value of time and uncertainty in transportation costs, demand and supply is studied. To endorse applicability of the proposed model, a case study of dairy products packaging and distribution network is studied and comprehensive analyses are provided. In addition, through using the proposed linearization technique, the model can be solved within a reasonable amount of time by utilizing conventional exact methods for small- and medium-size problems.

关键词： Supply chain design Robust optimization Distribution Facility location mixed-integer programming

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Scheduling of space to ground quantum key distribution

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EPJ QUANTUM TECHNOLOGY 2020年第1期7卷 1-34页

作者： Polnik, Mateusz Mazzarella, Luca Di Carlo, Marilena Oi, Daniel K. L. Riccardi, Annalisa Arulselvan, Ashwin Univ Strathclyde Dept Mech & Aerosp Engn Glasgow Lanark Scotland Univ Strathclyde SUPA Dept Phys Glasgow Lanark Scotland Univ Strathclyde Strathclyde Business Sch Glasgow Lanark Scotland

Satellite-based platforms are currently the only feasible way of achieving intercontinental range for quantum communication, enabling thus the future global quantum internet. Recent demonstrations by the Chinese spacecraft Micius have spurred an international space race and enormous interest in the development of both scientific and commercial systems. Research efforts so far have concentrated upon in-orbit demonstrations involving a single satellite and one or two ground stations. Ultimately satellite quantum key distribution should enable secure network communication between multiple nodes, which requires efficient scheduling of communication with the set of ground stations. Here we present a study of how satellite quantum key distribution can service many ground stations taking into account realistic constraints such as geography, operational hours, and most importantly, weather conditions. The objective is to maximise the number of keys a set of ground stations located in the United Kingdom could share while simultaneously reflecting the communication needs of each node and its relevance in the network. The problem is formulated as a mixed-integer linear optimisation program and solved to a desired optimality gap using a state of the art solver. The approach is presented using a simulation run throughout six years to investigate the total number of keys that can be sent to ground stations.

关键词： Quantum Key Distribution mixed-integer programming Scheduling Satellite Space Networking

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The Benders Dual Decomposition Method

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OPERATIONS RESEARCH 2020年第3期68卷 878-895页

作者： Rahmaniani, Ragheb Ahmed, Shabbir Crainic, Teodor Gabriel Gendreau, Michel Rei, Walter Optimized Markets Inc Pittsburgh PA 15213 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA Univ Quebec Montreal CIRRELT Montreal PQ H3C 3P8 Canada Univ Quebec Montreal Sch Management Ecole Sci Gest Montreal PQ H3C 3P8 Canada Ecole Polytech Montreal CIRRELT Montreal PQ H3C 3A7 Canada Ecole Polytech Montreal Dept Math & Ind Engn Montreal PQ H3C 3A7 Canada

Many methods that have been proposed to solve large-scale mixed integer linear programing (MILP) problems rely on decomposition techniques. These methods exploit either the primal or the dual structure of the problem, yielding the Benders decomposition or Lagrangian dual decomposition methods. We propose a new and high-performance approach, called Benders dual decomposition (BDD), which combines the complementary advantages of both methods. The development of BDD is based on a specific reformulation of the Benders subproblem, where local copies of the master variables are introduced in the proposed subproblem formulation and then priced out into the objective function. We show that this method (i) generates stronger feasibility and optimality cuts compared with the classical Benders method, (ii) can converge to the optimal integer solution at the root node of the Benders master problem, and (iii) is capable of generating high-quality incumbent solutions at the early iterations of the algorithm. We report encouraging computational results on various benchmark MILP problems.

关键词： Benders decomposition Lagrangian relaxation dual decomposition mixed-integer programming

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