Optimal Route Planning for Truck–Drone Delivery Using Variable Neighborhood Tabu Search Algorithm

Tong, Bao; Wang, Jianwei; Wang, Xue; Zhou, Feihao; Mao, Xinhua; Zheng, Wenjun

doi:10.3390/app12010529

Cited by 29 publications

(17 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The multi-depot VRP-D in emergency logistics is categorized as an NP-hard problem [37], posing challenges for traditional optimization algorithms to provide effective solutions. To address this challenge, this paper proposes a two-phase hybrid heuristic algorithm named K-GSA.…”

Section: Algorithm Designmentioning

confidence: 99%

Multi-depot vehicle routing problem with drones in emergency logistics

Weng,

She,

Fan

et al. 2024

Preprint

View full text Add to dashboard Cite

In order to expand the application of truck-drone cooperative delivery in emergency logistics, this paper investigates the multi-depot vehicle routing problem with drones for emergency logistics scheduling. A mixed-integer programming model with the objective of minimizing the maximum expected rescue time from urban distribution centres to temporary distribution stations is proposed. To address this problem, a new two-stage hybrid heuristic algorithm is proposed. In the first stage, we employ an improved K-means clustering to cluster the temporary distribution stations. In the second stage, a combination of Tabu search (TS), enhanced genetic algorithm (GA), and simulated annealing (SA) is applied to optimize the routes of both trucks and drones. The numerical study involves the validation of the model using real-world cases. Through comparison experiments with the Gurobi solver, we demonstrate the significant advantages of the proposed heuristic algorithm in terms of solution quality and efficiency.

show abstract

Section: Algorithm Designmentioning

confidence: 99%

Multi-depot vehicle routing problem with drones in emergency logistics

Weng,

She,

Fan

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The TSP is widely studied and is perhaps the most common combinatorial optimization problem in experimental studies. It has been used in machine learning [34,35], ant colony optimization [36,37], GA [19,24,38,39], other forms of EA [22,23,40], and other metaheuristics [41][42][43][44][45]. There are variations of the problem, such as the Asymmetric TSP (ATSP), where the cost of using an edge differs depending upon the direction of travel along the edge [46,47].…”

Section: Tspmentioning

confidence: 99%

“…There are variations of the problem, such as the Asymmetric TSP (ATSP), where the cost of using an edge differs depending upon the direction of travel along the edge [46,47]. Some variations include problem domain characteristics, such as in delivery route planning [43] and wireless sensor networks [48].…”

Section: Tspmentioning

confidence: 99%

Cycle Mutation: Evolving Permutations via Cycle Induction

Cicirello

2022

Applied Sciences

View full text Add to dashboard Cite

Evolutionary algorithms solve problems by simulating the evolution of a population of candidate solutions. We focus on evolving permutations for ordering problems such as the traveling salesperson problem (TSP), as well as assignment problems such as the quadratic assignment problem (QAP) and largest common subgraph (LCS). We propose cycle mutation, a new mutation operator whose inspiration is the well-known cycle crossover operator, and the concept of a permutation cycle. We use fitness landscape analysis to explore the problem characteristics for which cycle mutation works best. As a prerequisite, we develop new permutation distance measures: cycle distance, k-cycle distance, and cycle edit distance. The fitness landscape analysis predicts that cycle mutation is better suited for assignment and mapping problems than it is for ordering problems. We experimentally validate these findings showing cycle mutation’s strengths on problems such as QAP and LCS, and its limitations on problems such as the TSP, while also showing that it is less prone to local optima than commonly used alternatives. We integrate cycle mutation into the open source Chips-n-Salsa library, and the new distance metrics into the open source JavaPermutationTools library.

show abstract

“…Tabu search algorithm is used in many fields such as scheduling problems [33,35] and route planning [34].…”

Section: Tabu Search Algorithmmentioning

confidence: 99%

Machine Learning-Based Comparative Study For Heart Disease Prediction

Gullu

Akçayol

Barışçı

2022

Advances in Artificial Intelligence Research

View full text Add to dashboard Cite

Heart disease is one of the most common causes of death globally. In this study, machine learning algorithms and models widely used in the literature to predict heart disease have been extensively compared, and a hybrid feature selection based on genetic algorithm and tabu search methods have been developed. The proposed system consists of three components: (1) preprocess of datasets, (2) feature selection with genetic and tabu search algorithm, and (3) classification module. The models have been tested using different datasets, and detailed comparisons and analysis were presented. The experimental results show that the Random Forest algorithm is more successful than Adaboost, Bagging, Logitboost, and Support Vector machine using Cleveland and Statlog datasets.

show abstract

Optimal Route Planning for Truck–Drone Delivery Using Variable Neighborhood Tabu Search Algorithm

Cited by 29 publications

References 29 publications

Multi-depot vehicle routing problem with drones in emergency logistics

Multi-depot vehicle routing problem with drones in emergency logistics

Cycle Mutation: Evolving Permutations via Cycle Induction

Machine Learning-Based Comparative Study For Heart Disease Prediction

Contact Info

Product

Resources

About