Vehicle Routing Optimization Using Multiple Local Search Improvements

Fosin, Juraj; Carić, Tonči; Ivanjko, Edouard

doi:10.7305/automatika.2014.01.580

Cited by 7 publications

(8 citation statements)

References 17 publications

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“…If one of these resources is exceeded, a new route is initiated and the process is repeated until all customers have been visited. The solution will be improved in the second phase by means of an improvement heuristic through relocation moves (Fosin et al, 2014). The insertion of customers visited by drones is carried out in the third phase.…”

Section: Initial Solutionmentioning

confidence: 99%

An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones

Sacramento

Pisinger

Røpke

2019

Transportation Research Part C: Emerging Technologies

311

105

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Unmanned Aerial Vehicles, commonly known as drones, have attained considerable interest in recent years due to the potential of revolutionizing transport and logistics. Amazon were among the first to introduce the idea of using drones to deliver goods, followed by several other distribution companies working on similar services.The Traveling Salesman Problem, frequently used for planning last-mile delivery operations, can easily be modified to incorporate drones, resulting in a routing problem involving both the truck and aircraft. Introduced by Murray and Chu (2015), the Flying Sidekick Traveling Salesman Problem considers a drone and truck collaborating. The drone can be launched and recovered at certain visits on the truck route, making it possible for both vehicles to deliver goods to customers in parallel. This generalization considerably decreases the operational cost of the routes, by reducing the total fuel consumption for the truck, as customers on the routes can be serviced by drones without covering additional miles for the trucks, and hence increase productivity.In this paper a mathematical model is formulated, defining a problem similar to the Flying Sidekick Traveling Salesman Problem, but for the capacitated multipletruck case with time limit constraints and minimizing cost as objective function. The corresponding problem is denoted the Vehicle Routing Problem with Drones. Due to the difficulty of solving large instances to optimality, an Adaptive Large Neighborhood Search metaheuristic is proposed. Finally, extensive computational experiments are carried out. The tests investigate, among other things, how beneficial the inclusion of the drone-delivery option is compared to delivering all items using exclusively trucks. Moreover, a detailed sensitivity analysis is performed on several drone-parameters of interest.

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Section: Initial Solutionmentioning

confidence: 99%

An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones

Sacramento

Pisinger

Røpke

2019

Transportation Research Part C: Emerging Technologies

311

105

View full text Add to dashboard Cite

show abstract

“…The study of (Juraj Fosin & et al, 2014). Study the use of the Local Frequency Search (ILS) algorithm to be used to solve vehicle routing problem , Two methods were used to search for the algorithm.…”

Section: Reference Review:-mentioning

confidence: 99%

“…The second represents the binary substitution process between two different paths and the use of two types of strategies as a condition for stopping. The algorithm efficiency was proved by the computational experiments of a set of known issues, On the practical reality in the real world of a company in Ukraine [8].…”

Section: Reference Review:-mentioning

confidence: 99%

Solve the Capacitance Vehicle Routing Problem ( Cvrp) Using the Tabu Search Algorithm (Ts) and the Particle Swarm Optimization With Practical Application.

Bakhit¹,

noori²

2018

IJAR

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Capacitated vehicle routing problem (CVRP), tabu search (TS) ,particle swarm optimization(PSO).In this paper we examine the vehicle routing problem (VRP) which is classified as a problem (NP-HARD). This problem addresses the design of tracks for a group of vehicles that equip a number of customers deployed in different locations. The objective of this problem is to reduce the total cost of delivery, taking into account the limitations of the problem, and also to design the optimal distribution of the tracks within the specified vehicle capacity limits. The study includes the most important methods used to solve the problem, the use of the tabu search algorithm (TS) and particle swarm optimization(pso) and the hybridization of a new algorithm that adopts the characteristics of tabu search with particle swarm optimization ( TS-PSO) in solving problem Issues. The performance and efficiency of the algorithms are compared by solving known issues of the problem. The results show that the tabu search algorithm (TS) is efficient in finding solutions to the problem. The ability to reach the mean deviation about (1.764) from the standard solutions for the set of standard issues. However, the TS-PSO is able to produce results that overcame the results of the tabu search algorithm as well as the particle swarm algorithm, although the hybrid algorithm requires double and large computational times compared to the two individually designed algorithms. The method of the problem has been applied in the General Company for the manufacture of grain distribution process from one of the mills to a group of agents deployed geographically The method was able to reach to draw and design the best possible paths for vehicles available at the least distance cut and the lowest number of vehicles. …………………………………………………………………………………………………….... Introduction:-The field of transport and logistics services has become an important basis for the management and supply of goods and services in distribution systems and for a large number of real-world applications where institutions and commercial centers have begun to adopt procedures and systems that adopt planning in the management of logistics transport and Which will help in the effective changes in the reduction of the total costs. In fact, the transfer process represents a large proportion of the costs usually in addition to the cost of goods and services and the process of

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“…As for local search procedure, it can be classified into two categories which are edge-exchange algorithms and node-exchange algorithms. For edge-exchange algo-rithms, there are k-opt [44,45], Or-opt [46], 2-opt * [39], λ-interchange [47], CROSS-exchange [48], etc. For nodeexchange algorithms, there are relocate [45], exchange [49], GENI-exchange [50], etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-type ant system algorithm for the time dependent vehicle routing problem with time windows

Deng

Zhu

et al. 2018

Journal of Systems Engineering and Electronics

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The time dependent vehicle routing problem with time windows (TDVRPTW) is considered. A multi-type ant system (MTAS) algorithm hybridized with the ant colony system (ACS) and the max-min ant system (MMAS) algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection (NNS) mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm's good performance with all these strategies on classic vehicle routing problem with time windows (VRPTW) benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.

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Vehicle Routing Optimization Using Multiple Local Search Improvements

Cited by 7 publications

References 17 publications

An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones

An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones

Solve the Capacitance Vehicle Routing Problem ( Cvrp) Using the Tabu Search Algorithm (Ts) and the Particle Swarm Optimization With Practical Application.

Multi-type ant system algorithm for the time dependent vehicle routing problem with time windows

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