A Multi-compartment Vehicle Routing Problem for Livestock Feed Distribution

Kandiller, Levent; Eliiyi, Deniz Türsel; Taşar, Bahar

doi:10.1007/978-3-319-42902-1_20

Cited by 9 publications

(10 citation statements)

References 7 publications

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“…In this section, mathematical formulations of the problem are presented. The basic model, adapted from [41], is presented in Section 2.1, while multiple-trip and split-delivery extensions are presented in Section 2.2.…”

Section: Formulationmentioning

confidence: 99%

“…Our study derives its roots from a real-life VRP of a local feed distributor. The basic VRP and its multitrip extension was modeled in an earlier study [41]. As the problem is clearly NP-hard, pilot experimentation revealed that only small-size instances could be solved to optimality within acceptable CPU times.…”

Section: The Basic Modelmentioning

confidence: 99%

“…We replace the all-subtour elimination constraint that was used in the model presented in the paper [41] with Constraints 19 and 20, which are subtour product incompatibility. Loading and unloading times are included in the traveling time.…”

Section: The Basic Modelmentioning

confidence: 99%

“…Our initial numerical experiments show that these constraints eliminate the majority of the resulting subtours at a small computational cost. Therefore, we included only these constraints in the model, as opposed to the highly time-consuming one used in [41]. Further subtour constraints for four or more customers are added to the model whenever subtours appear in the optimal solution, which will be explained in Section 4.…”

Section: Construction and Improvement Heuristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vehicle Routing with Compartments Under Product Incompatibility Constraints

Taşar

Eliiyi

Kandiller

2019

PROMET

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This study focuses on a distribution problem involving incompatible products which cannot be stored in a compartment of a vehicle. To satisfy different types of customer demand at minimum logistics cost, the products are stored in different compartments of fleet vehicles, which requires the problem to be modeled as a multiple-compartment vehicle routing problem (MCVRP). While there is an extensive literature on the vehicle routing problem (VRP) and its numerous variants, there are fewer research papers on the MCVRP. Firstly, a novel taxonomic framework for the VRP literature is proposed in this study. Secondly, new mathematical models are proposed for the basic MCVRP, together with its multiple-trip and split-delivery extensions, for obtaining exact solutions for small-size instances. Finally, heuristic algorithms are developed for larger instances of the three problem variants. To test the performance of our heuristics against optimum solutions for larger instances, a lower bounding scheme is also proposed. The results of the computational experiments are reported, indicating validity and a promising performance of an approach.

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Section: Formulationmentioning

confidence: 99%

Section: The Basic Modelmentioning

confidence: 99%

Section: The Basic Modelmentioning

confidence: 99%

Section: Construction and Improvement Heuristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vehicle Routing with Compartments Under Product Incompatibility Constraints

Taşar

Eliiyi

Kandiller

2019

PROMET

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A practical and robust approach for solving the multi‐compartment vehicle routing problem under demand uncertainty using machine learning

Chamurally,

Rieck

2024

Networks

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The multi‐compartment vehicle routing problem is an extension of the vehicle routing problem and consists of designing a set of routes to perform the collection or delivery of different product types from customers with minimal costs. The product types are incompatible with each other and must be transported separately in multiple compartments. In practice, several uncertainties such as uncertainty in demands can arise, where the exact demand of customers is not known at the time of planning. To deal with these uncertainties, decision‐makers have to rely on robust solutions. A solution is considered robust when it can resist perturbations in every possible demand scenario as much as possible. In the day to day business of most logistic companies, historical data about each customer can be stored and used to make intelligent decisions regarding the expected demands. In this article, we propose an adaptive large neighborhood search for solving the robust multi‐compartment vehicle routing problem under demand uncertainty and present a robust solution approach for the problem in practical settings by employing machine learning. We show that by using our approach, the solutions obtained have lower recourse costs and have a lower gap between expected and actual costs, which is a favorable outcome to have in practice.

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