An ant colony system for responsive dynamic vehicle routing

Schyns, Michaël

doi:10.1016/j.ejor.2015.04.009

Cited by 82 publications

(33 citation statements)

References 33 publications

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“…Both a traditional genetic formulation and a rule based formulation will be considered. It should be noted that the solution could be potentially be generated using another type of evolutionary algorithm such as ant colony or swarm optimization [19] [20] [21].…”

Section: A More Challenging Problem: the Mazementioning

confidence: 99%

RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

Alobaidi¹,

Webb²,

Sandgren³

2017

OJOp

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Genetic or evolutionary search algorithms seek and exploit the structure of a problem by operating on an encoding which represents the problem variables. The algorithms employed are generally designed to handle a wide variety of problems and while exploitation and progress can be rapid at the initial stages, the algorithms ultimate convergence rate can be slow. In order to speed up the solution process as well as produce a more refined solution, a rule based encoding is proposed. The rule based structure injects domain specific knowledge into the optimization process. This allows for an intuitive encoding and mimics the process utilized in many decision support applications such as scheduling. The encoding for the approach is often reduced in size as well. Specific rules for a particular problem class are coded into the formulation. This requires additional programming effort, but is valuable for specific applications which are repeated over time. During the solution process, a record is maintained concerning which rules or sequences of rules were successfully applied. This allows the rules to be continuously updated over time. An approach for implementing rules within the design encoding is demonstrated and several simple game problems are solved using this technique. The results are compared to a solution generated by a traditional encoding.

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Section: A More Challenging Problem: the Mazementioning

confidence: 99%

RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

Alobaidi¹,

Webb²,

Sandgren³

2017

OJOp

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“…The authors optimize the routing based on current information and analyze the value of receiving order information earlier. Schyns address the problem of dynamically routing refueling trucks on an airport. Trucks differ in their capacity, speed, and compatibility to different airplanes.…”

Section: Literature Reviewmentioning

confidence: 99%

Same‐day delivery with heterogeneous fleets of drones and vehicles

2018

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In this paper, we analyze how drones can be combined with regular delivery vehicles to improve same‐day delivery performance. To this end, we present a dynamic vehicle routing problem with heterogeneous fleets. Customers order goods over the course of the day. These goods are delivered either by a drone or by a regular transportation vehicle within a delivery deadline. Drones are faster, but have a limited capacity as well as require charging after use. In the same‐day context, vehicle capacity is not a constraint, but vehicles are slow due to urban traffic. To decide whether an order is delivered by a drone or by a vehicle, we present a policy function approximation based on geographical districting. Our computational study reveals two major implications. First, geographical districting is highly effective increasing the expected number of same‐day deliveries. Second, a combination of drone and vehicle fleets may significantly reduce the required delivery resources.

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“…Reoptimization is mainly achieved via MIP often combined with metaheuristics applied on a rolling horizon [1,5,11,12,19,25,31,43,45]. In some cases, reoptimization approaches are combined with a mild anticipation.…”

Section: Literature Reviewmentioning

confidence: 99%

Anticipation versus reactive reoptimization for dynamic vehicle routing with stochastic requests

Ulmer

2018

Networks

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Due to new business models and technological advances, dynamic vehicle routing is gaining increasing interest. Especially solving dynamic vehicle routing problems with stochastic customer requests becomes increasingly important, for example, in e‐commerce and same‐day delivery. Solving these problems is challenging, because it requires optimization along two dimensions. First, as a reaction to new customer requests, current routing plans need to be reoptimized. Second, potential future requests need to be anticipated in current decision making. Decisions need to be derived in real‐time. The limited time often prohibits extensive optimization in both dimensions and the question arises how to utilize the limited calculation time effectively. In this paper, we analyze the merits of reactive route reoptimization and anticipation for a dynamic vehicle routing problem with stochastic requests. To this end, we compare an existing method from each dimension as well a policy allowing for a tunable combination of the two approaches. We show how the appropriate optimization combination is strongly connected to the degree of dynamism, the percentage of unknown requests. We also show that our combination does not provide significant benefit compared to the respectively best optimization dimension.

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An ant colony system for responsive dynamic vehicle routing

Cited by 82 publications

References 33 publications

RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

Same‐day delivery with heterogeneous fleets of drones and vehicles

Anticipation versus reactive reoptimization for dynamic vehicle routing with stochastic requests

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