Total distance approximations for routing solutions

Nicola, Daniel; Vetschera, Rudolf; Dragomir, Alina

doi:10.1016/j.cor.2018.10.008

Cited by 31 publications

(28 citation statements)

References 25 publications

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“…Their research shows that a good heuristic can be further improved by guiding the heuristic search process using classification data. The research in [17] is focused on the prediction of travel distance using linear regression, based on several customer-oriented features, like geographic information and demand. They show that the approximation of distance is accurate for the TSP and VRP, especially for Solomon instances [22] with clustered customers.…”

Section: Literaturementioning

confidence: 99%

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Distance Approximation for Dynamic Waste Collection Planning

Akkerman

Mes

Heijnen

2020

Lecture Notes in Computer Science

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Approximating the solution value of transportation problems has become more relevant in recent years, as these approximations can help to decrease the computational effort required for solving those routing problems. In this paper, we apply several regression methods to predict the total distance of the traveling salesman problem (TSP) and vehicle routing problem (VRP). We show that distance can be estimated fairly accurately using simple regression models and only a limited number of features. We use features found in scientific literature and introduce a new class of geographical features. The model is validated on a dynamic waste collection case in the city of Amsterdam, The Netherlands. We introduce a cost function that combines the travel distance and service level, and show that our model can reduce distances up to 17%, while maintaining the same service level, compared to a well-known heuristic approximation. Furthermore, we show the benefits of using approximations for combining offline learning with online or frequent optimization.

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Section: Literaturementioning

confidence: 99%

“…They show that the approximation of distance is accurate for the TSP and VRP, especially for Solomon instances [22] with clustered customers. For a comprehensive literature review on distance-approximation literature, we refer to [17].…”

Section: Literaturementioning

confidence: 99%

Distance Approximation for Dynamic Waste Collection Planning

Akkerman

Mes

Heijnen

2020

Lecture Notes in Computer Science

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“…Basel and Willemain [8] estimated the optimal tour length for 17 TSP instances using the square root of the number of cities and the variability of tour lengths. Nicola et al [9] developed empirically based regression models for estimating the travel distance in the TSP, in the capacitated VRP with time windows, and in the multi-region, multi-depot pickup and delivery problem.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Tour Length for the Close Enough Traveling Salesman Problem

et al. 2021

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We construct empirically based regression models for estimating the tour length in the Close Enough Traveling Salesman Problem (CETSP). In the CETSP, a customer is considered visited when the salesman visits any point in the customer’s service region. We build our models using as many as 14 independent variables on a set of 780 benchmark instances of the CETSP and compare the estimated tour lengths to the results from a Steiner zone heuristic. We validate our results on a new set of 234 instances that are similar to the 780 benchmark instances. We also generate results for a new set of 72 larger instances. Overall, our models fit the data well and do a very good job of estimating the tour length. In addition, we show that our modeling approach can be used to accurately estimate the optimal tour lengths for the CETSP.

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“…New variants of the existing basic TSP developed. Several previous studies that removed baseline TSP can be seen in [1], [2], [3], [4], [5], and [6]. TSP variants were developed not only for solving complex real problems but also for obtaining better results with the addition of certain constraints.…”

Section: Introductionmentioning

confidence: 99%

Study of the Brand and Bound Algorithm Performance on Traveling Salesman Problem Variants

Wahyuningsih¹,

Sari²

2021

Advances in Social Science, Education and Humanities Research

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Traveling salesman problem (TSP) can be applied to distribution problems, namely determining the minimum route from the depot to all customers exactly once and back to the depot. Some constraints can be added to the problem such as time constraints, additional salesmen on the route that is passed, the need for an order of delivery, and passing one-way road. This article will discuss TSP variants developed from basic TSP, namely TSPTW, MTSP, TSPPC, and ATSPTW. The differences in formulations of these variants are described and the branch and bound algorithm is used to solve them. There are three main steps to the branch and bound algorithm namely the initialization stage to obtain the initial solution, the process stage and solution improvement, and the determination of the optimum solution. Identification of the similarities and differences in the application of the branch and bound algorithm steps on these variants are discussed in this article. An example is given of the application of the branch and bound algorithm to the four variants of 8 customers and one depot. The results of the application examples are depicted on a graph in order of the minimum total distance, namely ATSPTW, TSPTW, TSPPC, and MTSP.

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Total distance approximations for routing solutions

Cited by 31 publications

References 25 publications

Distance Approximation for Dynamic Waste Collection Planning

Distance Approximation for Dynamic Waste Collection Planning

Estimating the Tour Length for the Close Enough Traveling Salesman Problem

Study of the Brand and Bound Algorithm Performance on Traveling Salesman Problem Variants

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