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A stochastic hub location and fleet assignment problem for the design of reconfigurable park-and-ride systems

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 184卷

作者： Guillot, Matthieu Rey, David Furno, Angelo El Faouzi, Nour-Eddin Univ Gustave Eiffel ENTPE LICIT ECO7 Champs Sur Marne France Univ Cote dAzur SKEMA Business Sch Sophia Antipolis France

Park -and -ride systems have the potential to improve the efficiency of transportation networks by providing targeted shared mobility services. The design of a park -and -ride system depends on its role in regards to the broader transportation network. Reconfigurable park -and -ride systems aim to provide complementary shared mobility services in the context of varying travel demand scenarios, such as special events, network maintenance operations or non -recurrent perturbations. The design of reconfigurable park -and -ride systems involves the location of access and egress hubs for shared mobility services. We study an extended version of this hub location problem with integrated fleet assignment decisions. We consider stochastic scenarios representative of varying travel demand and traffic conditions on the network and propose a two -stage stochastic integer programming hub location formulation for this problem. First -stage variables represent hub location decision while second -stage variables represent both scenariobased transportation flows and fleet assignment decisions. The latter represent shared mobility service vehicles and they are modeled as integer variables. We develop solution methods to solve this two -stage stochastic integer programming hub location formulation. Exact approaches based on the L-shaped method are proposed with single- and multi -cut configurations. Valid inequalities along with a tight lower bound for the generation of optimality cuts are presented. We also develop a matheuristic to solve larger problem instances. We report numerical results on problem instances based on real data of the city of Lyon, France. We show how stochastic scenarios representative of varying demand and traffic conditions can be generated from such data. Our experiments demonstrate the benefits of this integrated modeling approach for designing efficient reconfigurable park -and -ride systems while considering fleet assignment decisions.

关键词： two-stage stochastic integer programming L-shaped algorithm Decomposition Matheuristic Hub location Fleet assignment Park-and-ride

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stochastic Last-Mile Delivery with Crowd-Shipping and Mobile Depots

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TRANSPORTATION SCIENCE 2022年第3期56卷 612-630页

作者： Mousavi, Kianoush Bodur, Merve Roorda, Matthew J. Univ Toronto Dept Civil & Mineral Engn Toronto ON M5S 1A4 Canada Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada

This paper proposes a two-tier last-mile delivery model that optimally selects mobile depot locations in advance of full information about the availability of crowd shippers and then transfers packages to crowd-shippers for the final shipment to the customers. Uncertainty in crowd-shipper availability is incorporated by modeling the problem as a two-stage stochastic integer program. Enhanced decomposition solution algorithms including branch-and-cut and cut-and-project frameworks are developed. A risk-averse approach is compared against a risk-neutral approach by assessing conditional-value-at-risk. A detailed computational study based on the City of Toronto is conducted. The deterministic version of the model outperforms a capacitated vehicle routing problem on average by 20%. For the stochastic model, decomposition algorithms usually discover near-optimal solutions within two hours for instances up to a size of 30 mobile depot locations, 40 customers, and 120 crowd-shippers. The cut-and-project approach outperforms the branch and-cut approach by up to 85% in the risk-averse setting in certain instances. The stochastic model provides solutions that are 3.35%-6.08% better than the deterministic model, and the improvements are magnified with increased uncertainty in crowd-shipper availability. A risk-averse approach leads the operator to send more mobile depots or postpone customer deliveries to reduce the risk of high penalties for nondelivery.

关键词： last-mile delivery crowd-shipping mobile depot two-stage stochastic integer programming decomposition value of stochastic solution conditional-value-at-risk

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Vehicle Routing Problems with Synchronized Visits and stochastic Travel and Service Times: Applications in Healthcare

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TRANSPORTATION SCIENCE 2020年第4期54卷 1053-1072页

作者： Doulabi, Hossein Hashemi Pesant, Gilles Rousseau, Louis-Martin Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ H3G 1M8 Canada Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ H3C 3J7 Canada Polytech Montreal Dept Comp & Software Engn Montreal PQ H3T 1J4 Canada Polytech Montreal Dept Math & Ind Engn Montreal PQ H3T 1J4 Canada

This paper, for the first time, studies vehicle routing problems with synchronized visits (VRPS) and stochastic travel and service times. In addition to considering a home healthcare scheduling problem, we introduce an operating room scheduling problem with stochastic durations as a novel application of VRPS. We formulate VRPS with stochastic times as a two-stage stochastic integer programming model that, unlike the deterministic models in the VRPS literature, does not have any big-M constraints. This advantage comes at the cost of a large number of second-stage integer variables. We prove that the integrality constraints on second-stage variables can be relaxed, and therefore, we can apply the L-shaped algorithm and its branch-and-cut implementation to solve the problem. We enhance the model by developing valid inequalities and a lower bounding functional. We analyze the subproblems of the L-shaped algorithm and devise a specialized algorithm for them that is significantly faster than standard linear programming algorithms. Computational results show that the branch-and-cut algorithm optimally solves stochastic home healthcare scheduling instances with 15 patients and 10%-30% of synchronized visits. It also finds solutions with an average optimality gap of 3.57% for instances with 20 patients. Furthermore, the branch-and-cut algorithm optimally solves stochastic operating room scheduling problems with 20 surgeries.

关键词： vehicle routing with synchronized visits stochastic travel and service times two-stage stochastic integer programming

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An integrated car-and-ride sharing system for mobilizing heterogeneous travelers with application in underserved communities

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IISE TRANSACTIONS 2020年第2期52卷 151-165页

作者： Yu, Miao Shen, Siqian Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

The fast-growing carsharing and ride-hailing businesses are generating economic benefits and societal impacts in modern society, while both have limitations to satisfy diverse users, e.g., travelers in low-income, underserved communities. In this article, we consider two types of users: Type 1 drivers who rent shared cars and Type 2 passengers who need shared rides. We propose an integrated car-and-ride sharing (CRS) system to enable community-based shared transportation. To compute solutions, we propose a two-phase approach where in Phase I we determine initial car allocation and Type 1 drivers to accept;in Phase II we solve a stochastic mixed-integer program to match the accepted Type 1 drivers with Type 2 users, and optimize their pick-up routes under a random travel time. The goal is to minimize the total travel cost plus expected penalty cost of users' waiting and system overtime. We demonstrate the performance of a CRS system in Washtenaw County, Michigan by testing instances generated based on census data and different demand patterns. We also demonstrate the computational efficacy of our decomposition algorithm benchmarked with the traditional Benders decomposition for solving the stochastic model in Phase II. Our results show high demand fulfillment rates and effective matching and scheduling with low risk of waiting and overtime.

关键词： Carsharing ride-hailing stochastic vehicle routing two-stage stochastic integer programming decomposition algorithm

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Optimizing the Profitability and Quality of Service in Carshare Systems Under Demand Uncertainty

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2018年第2期20卷 162-180页

作者： Lu, Mengshi Chen, Zhihao Shen, Siqian Purdue Univ Krannert Sch Management W Lafayette IN 47907 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

Carsharing has been considered as an effective means to increase mobility and reduce personal vehicle usage and related carbon emissions. In this paper, we consider problems of allocating a carshare fleet to service zones under uncertain one-way and round-trip rental demand. We employ a two-stage stochastic integer programming model, in the first stage of which we allocate shared vehicle fleet and purchase parking lots or permits in reservation-based or free-floating systems. In the second stage, we generate a finite set of samples to represent demand uncertainty and construct a spatial-temporal network for each sample to model vehicle movement and the corresponding rental revenue, operating cost, and penalties from unserved demand. We minimize the expected total costs minus profit and develop branch-and-cut algorithms with mixed-integer, rounding-enhanced Benders cuts, which can significantly improve computation efficiency when implemented in parallel computing. We apply our model to a data set of Zipcar in the Boston-Cambridge, Massachusetts, area to demonstrate the efficacy of our approaches and draw insights on carshare management. Our results show that exogenously given one-way demand can increase carshare profitability under given one-way and round-trip price differences and vehicle relocation cost whereas endogenously generated one-way demand as a result of pricing and strategic customer behavior may decrease carshare profitability. Our model can also be applied in a rolling-horizon framework to deliver optimized vehicle relocation decisions and achieve significant improvement over an intuitive fleet-rebalancing policy.

关键词： carshare fleet management demand uncertainty two-stage stochastic integer programming Benders decomposition mixed-integer rounding

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Joint capacity planning and distribution network optimization of coal supply chains under uncertainty

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AICHE JOURNAL 2018年第4期64卷 1246-1261页

作者： Zhou, Rui-Jie Li, Li-Juan Stanford Univ Dept Elect Engn Stanford CA 94305 USA Rensselaer Polytech Inst Dept Chem & Biol Engn Troy NY 12180 USA

A two-stage stochastic integer programming model is developed to address the joint capacity planning and distribution network optimization of multiechelon coal supply chains (CSCs) under uncertainty. The proposed model not only introduces the uses of compound real options in sequential capacity planning, but also considers uncertainty induced by both risks and ambiguities. Both strategic decisions (i.e., facility locations and initial investment, service assignment across the entire CSC, and option holding status) and scenario-based operational decisions (i.e., facility operations and capacity expansions, outsourcing policy, and transportation and inventory strategies) can be simultaneously determined using the model. By exploiting the nested decomposable structure of the model, we develop a new distributed parallel optimization algorithm based on nonconvex generalized Bender decomposition and Lagrangean relaxation to mitigate the computation resource limitation. One of the main CSCs in China is studied to demonstrate the applicability of the proposed model and the performance of the algorithm. (c) 2017 American Institute of Chemical Engineers AIChE J, 64: 1246-1261, 2018

关键词： coal supply chains compound real options risk and ambiguity two-stage stochastic integer programming distributed parallel optimization

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Tactical Production and Distribution Planning in Urban Logistics under Vehicle Operational Restrictions 22

Tactical Production and Distribution Planning in Urban Logis...

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22nd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES)

作者： Du, Mu Kong, Nan Hu, Xiangpei Zhao, Lindu Southeast Univ Inst Syst Engn 2 Sipailou Nanjing 210096 Jiangsu Peoples R China Purdue Univ Weldon Sch Biomed Engn W Lafayette IN 47906 USA Dalian Univ Technol Inst Syst Engn 2 Linggong Rd Dalian 116023 Peoples R China

Significant uncertainty associated with Chinese urban logistics, caused by random vehicle operational restrictions due to severe weather (e.g., smog) in addiction to normal traffic variation makes the tactical production and distribution planning decisions quite challenging. In this paper, we propose a two-stage stochastic integer programming model for an optimal production distribution capacity planning problem under the aforementioned uncertainties. We aim to minimize both procurement spending and the expected operational cost under logistic uncertainty. Given the computational burden of solving the resultant stochastic integer program for real-world instances, we develop an improved stochastic branch-and-bound (SBB) algorithm embedding with Tabu search method. We conduct the numerical study to verify the superiority of the proposed algorithm. We also offer managerial insights to practitioners and policy recommendations to municipal governments based on the numerical study results. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Tactical productiondistribution planning two-stage stochastic integer programming stochastic branch-and-bound algorithm urban logistics

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Optimal Day-Ahead Power Procurement With Renewable Energy and Demand Response

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IEEE TRANSACTIONS ON POWER SYSTEMS 2017年第5期32卷 3924-3933页

作者： Kwon, Soongeol Ntaimo, Lewis Gautam, Natarajan Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77843 USA

This study proposes the demand-side power procurement problem to optimally reduce consumer's energy cost. The motivation stems from pressing issues on an increase of energy cost in an industrial section. From an energy consumer's perspective, there exists an opportunity to reduce energy cost by adjusting purchase and consumption of energy in response to time-varying electricity price while utilizing renewable energy, which is called demand response. In this case, energy storage can be used to mitigate fluctuation of intermittent renewable supply and volatile electricity price. Although it is anticipated to serve a significant amount of energy consumption from renewable energy and to avoid peak electricity price, variability and uncertainty in power demand, renewable supply, and electricity price make it challenging to determine an optimal power procurement. The main objective of this study is to suggest a decision-making methodology that enables energy consumers to optimally determine power procurement against time varying and stochastic electricity price and renewable supply. Specifically, this study formulates an optimal day-ahead power procurement as a two-stage stochastic mixed-integer program and proposes a solution approach based on Benders decomposition. The proposed methodology can be successfully applied to energy-intensive industries, such as data centers.

关键词： Benders decomposition day-ahead power procurement demand response energy storage renewable energy two-stage stochastic integer programming

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Tactical Production and Distribution Planning in Urban Logistics under Vehicle Operational Restrictions

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Procedia Computer Science 2018年 126卷 1720-1729页

作者： Mu Du Nan Kong Xiangpei Hu Lindu Zhao Institute of Systems Engineering Southeast University No.2 Sipailou Nanjing 210096 China Weldon School of Biomedical Engineering Purdue University West Lafayette IN 47906 Unite States Institute of Systems Engineering Dalian University of Technology No.2 Linggong Road Dalian 116023 China

关键词： Tactical production distribution planning two-stage stochastic integer programming stochastic branch-and-bound algorithm urban logistics

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Hybrid Evolutionary Optimization of two-stage stochastic integer programming Problems: An Empirical Investigation

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EVOLUTIONARY COMPUTATION 2009年第4期17卷 511-526页

作者： Tometzki, Thomas Engell, Sebastian Tech Univ Dortmund Proc Dynam & Operat Grp Dept Biochem & Chem Engn D-44227 Dortmund Germany

In this contribution, we consider decision problems on a moving horizon with significant uncertainties in parameters. The information and decision structure on moving horizons enables recourse actions which correct the here-and-now decisions whenever the horizon is moved a step forward. This situation is reflected by a mixed-integer recourse model with a finite number of uncertainty scenarios in the form of a two-stage stochastic integer program. A stage decomposition-based hybrid evolutionary algorithm for two-stage stochastic integer programs is proposed that employs an evolutionary algorithm to determine the here-and-now decisions and a standard mathematical programming method to optimize the recourse decisions. An empirical investigation of the scale-up behavior of the algorithms with respect to the number of scenarios exhibits that the new hybrid algorithm generates good feasible solutions more quickly than a state of the art exact algorithm for problem instances with a high number of scenarios.

关键词： Optimization under uncertainty two-stage stochastic integer programming hybrid evolutionary algorithms

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