Robust Optimization of a Broad Class of Heterogeneous Vehicle Routing Problems Under Demand Uncertainty

Subramanyam, Anirudh; Repoussis, Panagiotis P.; Gounaris, Chrysanthos E.

doi:10.1287/ijoc.2019.0923

Cited by 31 publications

(15 citation statements)

References 42 publications

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“…Since some instances are still large, we use an ITS metaheuristic that was developed in a prior study. We refer the interested reader to [33] for the implementation details of the ITS. To justify the quality of the ITS, we compare it with the MIP in the following section.…”

Section: Design Of Experimentsmentioning

confidence: 99%

A Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery

Cokyasar¹,

Larson²,

Stinson³

et al. 2022

Preprint

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Electric vehicle routing problems can be particularly complex when recharging must be performed mid-route. In some applications such as the e-commerce parcel delivery truck routing, however, midroute recharging may not be necessary because of constraints on vehicle capacities and maximum allowed time for delivery. In this study we develop a mixed-integer optimization model that exactly solves such a time-constrained capacitated vehicle routing problem, especially of interest to ecommerce parcel delivery vehicles. We compare our solution method with an existing metaheuristic and carry out exhaustive case studies considering four U.S. cities-Austin, TX; Bloomington, IL; Chicago, IL; and Detroit, MI-and two vehicle types: conventional vehicles and battery electric vehicles (BEVs). In these studies we examine the impact of vehicle capacity, maximum allowed travel time, service time (dwelling time to physically deliver the parcel), and BEV range on systemlevel performance metrics including vehicle miles traveled (VMT). We find that the service time followed by the vehicle capacity plays a key role in the performance of our approach. We assume an 80-mile BEV range as a baseline without mid-route recharging. Our results show that BEV range has a minimal impact on performance metrics because the VMT per vehicle averages around 72 miles. In a case study for shared-economy parcel deliveries, we observe that VMT could be reduced by 38.8% in Austin if service providers were to operate their distribution centers jointly.

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Section: Design Of Experimentsmentioning

confidence: 99%

A Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery

Cokyasar¹,

Larson²,

Stinson³

et al. 2022

Preprint

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show abstract

“…Problem Description. HVRPUD is a variant of CVRP considering the capacity of multiple vehicles and the uncertainty of customer demand [17]. It is assumed that there is a distribution center with a known location and a group of customers with a known location.…”

Section: Heterogeneous Vrp With Uncertainmentioning

confidence: 99%

“…Subramanyam et al studied robust variants of an extended model of the classical heterogeneous vehicle routing problem (HVRP). e proposed local search is then incorporated in a modular fashion within two metaheuristic algorithms to determine robust HVRP solutions [17]. To solve the VRPUD, Sungur et al established a robust optimization model in which the uncertain demand parameter set is a convex hull set, an ellipsoid set, and a box set and solved the problem by using the duality principle [18].…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Robust Optimization for Solving the Heterogeneous Vehicle Routing Problem with Customer Demand Uncertainty

Zhang

Feng

et al. 2021

Complexity

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In practice, the parameters of the vehicle routing problem are uncertain, which is called the uncertain vehicle routing problem (UVRP). Therefore, a data-driven robust optimization approach to solve the heterogeneous UVRP is studied. The uncertain parameters of customer demand are introduced, and the uncertain model is established. The uncertain model is transformed into a robust model with adjustable parameters. At the same time, we use a least-squares data-driven method combined with historical data samples to design a function of robust adjustable parameters related to the maximum demand, demand range, and given vehicle capacity to optimize the robust model. We improve the deep Q-learning-based reinforcement learning algorithm for the fleet size and mix vehicle routing problem to solve the robust model. Through test experiments, it is proved that the robust optimization model can effectively reduce the number of customers affected by the uncertainty, greatly improve customer satisfaction, and effectively reduce total cost and demonstrate that the improved algorithm also exhibits good performance.

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“…This section describes an iterated tabu search metaheuristic tailored to solving large-scale REEVRP instances (e.g., with more than a thousand nodes). This approach is inspired by existing local-search-based metaheuristics for the heterogeneous VRP [Penna et al, 2013, Subramanyam et al, 2020, albeit with some important differences that exploit characteristics of the REEVRP. A key feature of this ITS approach is its simplicity because it introduces few user-defined parameters and does not require any instance-specific features to accelerate the search process.…”

Section: Iterated Tabu Search Metaheuristicmentioning

confidence: 99%

Joint Routing of Conventional and Range-Extended Electric Vehicles in a Large Metropolitan Network

Subramanyam,

Cokyasar,

Larson

et al. 2021

Preprint

Self Cite

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Range-extended electric vehicles combine the higher efficiency and environmental benefits of batterypowered electric motors with the longer mileage and autonomy of conventional internal combustion engines. This combination is particularly advantageous for time-constrained delivery routing in dense urban areas, where battery recharging along routes can be too time-consuming to economically justify the use of all-electric vehicles. However, switching from electric to conventional fossil fuel modes also results in higher costs and emissions and lower efficiency. This paper analyzes this heterogeneous vehicle routing problem and describes two solution methods: an exact branch-price-and-cut algorithm and an iterated tabu search metaheuristic. From a methodological perspective, we find that the exact algorithm consistently obtains tight lower bounds that also serve to certify the metaheuristic solutions as nearoptimal. From a policy standpoint, we examine a large-scale real-world case study concerning parcel deliveries in the Chicago metropolitan area and quantify various operational metrics including energy costs and vehicle miles traveled. We find that by deploying roughly 20% of range-extended vehicles with a modest all-electric range of 33 miles, parcel distributors can save energy costs by up to 17% while incurring less than 0.5% increase in vehicle miles traveled. Increasing the range to 60 miles further reduces costs by only 4%, which can alternatively be achieved by decreasing the average service time by 1 minute or increasing driver working time by 1 hour. Our study reveals several key areas of improvement on which vehicle manufacturers, distributors, and policy makers can focus their attention.

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Robust Optimization of a Broad Class of Heterogeneous Vehicle Routing Problems Under Demand Uncertainty

Cited by 31 publications

References 42 publications

A Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery

A Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery

Data‐Driven Robust Optimization for Solving the Heterogeneous Vehicle Routing Problem with Customer Demand Uncertainty

Joint Routing of Conventional and Range-Extended Electric Vehicles in a Large Metropolitan Network

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