Vehicle routing with transportable resources: Using carpooling and walking for on-site services

Coindreau, Marc‐Antoine; Gallay, Olivier; Zufferey, Nicolas

doi:10.1016/j.ejor.2019.06.039

Cited by 24 publications

(18 citation statements)

References 43 publications

(46 reference statements)

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“…The diversification features are able to explore new regions of the solution space, whereas the intensification operator is able to deeply investigate promising regions of the solution space. The combination of intensification/diversification features has been employed successfully in various VRP problems (e.g., Coindreau et al (2019)).…”

Section: Solution Methods For the Master Problemmentioning

confidence: 99%

Adaptive robust electric vehicle routing under energy consumption uncertainty

Jeong¹,

Ghaddar²,

Zufferey³

et al. 2022

Preprint

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Electric vehicles (EVs) have been highly favoured as a future transportation mode in the transportation section in recent years. EVs have many advantages compared to traditional transportation, especially the environmental aspect. However, despite many EVs' benefits, operating EVs has limitations in their usage.One of the significant issues is the uncertainty in their driving range. The driving range of EVs is closely related to their energy consumption, which is highly affected by exogenous and endogenous factors. Since those factors are unpredictable, uncertainty in EVs' energy consumption should be considered for efficient operation. This paper proposes an adaptive robust optimization framework for the electric vehicle routing problem. The objective is to minimize the worst-case energy consumption while guaranteeing that services are delivered at the appointed time windows without battery level deficiency. We postulate that EVs can be recharged en route, and the charging amount can be adjusted depending on the circumstance. The proposed problem is formulated as a two-stage adaptive robust problem. A column-and-constraint generation based heuristic algorithm, which is cooperated with variable neighborhood search and alternating direction algorithm, is proposed to solve the proposed model. The computational results show the economic efficiency and robustness of the proposed model, and that there is a tradeoff between the total required energy and the risk of failing to satisfy all customers' demand.

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Section: Solution Methods For the Master Problemmentioning

confidence: 99%

Adaptive robust electric vehicle routing under energy consumption uncertainty

Jeong¹,

Ghaddar²,

Zufferey³

et al. 2022

Preprint

Self Cite

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show abstract

“…Replacing the BFCT algorithm by these two constant-time algorithms decreases the number of operations to be performed from (n 5 ) to (n 3 ). It is shown in Coindreau et al [9] that, in a related routing context also involving severe synchronization constraints, considering the same type of speed up procedures (to check the feasibility of offspring solutions) allows to decrease the LNS computational effort by up to 95%.…”

Section: Inserting a Job In An Existing Sequence Of Truck-and-drone Stops: Speed Up Proceduresmentioning

confidence: 99%

Parcel delivery cost minimization with time window constraints using trucks and drones

2021

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We propose a model for solving a parcel delivery problem with a fleet of trucks embedded with drones. When appropriate, drones are loaded with a parcel, launched directly from the truck, and sent to a client. Afterward, the drones autonomously return to the truck to be replenished and recharged. Inspired by the case of a large European logistics provider, the proposed modeling framework confronts realistic delivery problems involving time windows, limited drone autonomy, and the eligibility of clients to be served by drones. The considered global cost function includes fixed daily vehicle fares, driver wages, and the fuel and electricity consumption to power trucks and drones. To solve the problems at hand, we propose a mixed-integer linear programming formulation and an adaptive large neighborhood search. Moreover, we introduce an efficient modeling framework to manage the numerous synchronization constraints induced by the simultaneous use of trucks and drones. We analyze the benefits of this new transportation concept for delivery problems involving up to 100 parcels. Results show that truck-and-drone solutions can reduce costs up to 34% compared to traditional truck-only delivery. From a managerial perspective, we show that a certain percentage of client locations must be reachable by drone to make truck-and-drone solutions competitive (i.e., if the fixed costs of the drones are compensated for by the savings on truck routes) and compare the cost structures of truck-and-drone versus truck-only solutions.

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“…Recently, the pickup and the delivery problem has been examined to enable drones to cooperate with two or more forms of supporting transportation to handle small-size/lighter packages for last-mile deliveries. Initially, trucks (heavy goods) and foot couriers (light goods) are coupled with each other (27)(28)(29) to pick up and deliver the product within the customer's desired time window. This benefited the courier service provider by enabling them to handle small parcels loaded with heavy goods and achieve good marginal cost benefits.…”

Section: Literature Reviewmentioning

confidence: 99%

A Solution to Drone Routing Problems using Docking Stations for Pickup and Delivery Services

Pachayappan¹,

Vijayakumar

2021

Transportation Research Record

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The paper proposes a mathematical model and a heuristic solution for planning optimal pickup and delivery routes in drone operations. In the proposed system, the drone is synchronized with the docking station for a charge during pickup and delivery services within a definite set distance radius. The optimization model presented here minimizes the distance traveled by the drone to complete the scheduled services. It also ensures the optimal use of the drone’s battery capacity, ensuring the safe return of the drone without running out of battery during operations. The solution quality and the computational performance of the Mixed-Integer Linear Programming (MILP) model proved satisfactory in addressing a variety of problems. A real-world case study is conducted to enumerate the practical implications which will be of benefit to drone service providers in the field of logistics.

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Vehicle routing with transportable resources: Using carpooling and walking for on-site services

Cited by 24 publications

References 43 publications

Adaptive robust electric vehicle routing under energy consumption uncertainty

Adaptive robust electric vehicle routing under energy consumption uncertainty

Parcel delivery cost minimization with time window constraints using trucks and drones

A Solution to Drone Routing Problems using Docking Stations for Pickup and Delivery Services

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