Vehicle Routing Problem Considering Reconnaissance and Transportation

Ju, Byungjun; Kim, Minsu; Moon, Ilkyeong

doi:10.3390/su13063188

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…Ju et al, [51], the authors formulated a VRP considering reconnaissance and transportation (VRPCRT) for horde movements during the war as a mixed-integer programming model. Considering that, these models were compared with their approach with the ant colony optimization (ACO) algorithm to test the performance.…”

Section: Solutions Approaches To Vehicle Routing Problemsmentioning

confidence: 99%

Decision-Making Support in Vehicle Routing Problems: A Review of Recent Literature

Syazwani Zainal Abidin,

Nor Izzati Zainal Abidin,

Hanita Daud

2024

ARASET

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The Vehicle Routing Problem (VRP) involves a set of customers with known locations, each having a certain demand for goods or services. There is also a fleet of vehicles available, each with limited capacity and often a fixed starting point. With the aid of mathematical programming tools, this paper offers an overview of the most recent VRP research. This study also examines the algorithms for solving VRP models and categorizes them in terms of their application areas. For these reasons, related publications that appeared in the international journal have been compiled and studied. According to the literature review, multi-criteria decision-making (MCDM) techniques have yet to constitute the most mathematical programming methods used to solve the VRP problem.

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Section: Solutions Approaches To Vehicle Routing Problemsmentioning

confidence: 99%

Decision-Making Support in Vehicle Routing Problems: A Review of Recent Literature

Syazwani Zainal Abidin,

Nor Izzati Zainal Abidin,

Hanita Daud

2024

ARASET

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“…Ju et al [16] developed PDP model for transporting military personnel. The environment was a wartime area.…”

Section: Introductionmentioning

confidence: 99%

Pickup and delivery problem in the collaborative city courier service by using genetic algorithm and nearest distance

Kusuma

Kallista

2022

Bulletin EEI

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One problem in collaborative pickup delivery problem (PDP) was excessive outsourced jobs. It happened in many studies on the collaborative PDP. Besides, the revenue sharing in it was unclear although important. This work aimed to propose a novel collaborative PDP model which minimizes total travel distance while maintains low outsourced jobs. It proposed several contributions. First, it prioritized internal jobs first rather than full collaborative model. Second, it proposed new revenue sharing model. It adopted cluster-first route-second and mixed pickup and delivery. It was developed by combining the genetic algorithm and nearest distance algorithm where the genetic algorithm was used in the clustering process and the nearest distance was used in the routing process. The simulation result shows that the proposed model was better than the comparing models: (1) combined K-means and genetic algorithm model (KMGA) and (2) combined simulated annealing and last-in first-out (SNLIFO) model. When the number of orders was high (300 units), the total travel distance of the proposed model was 37 percent lower than the KMGA model and 30 percent lower than the SNLIFO model. In average, the outsourcing rate of the proposed model was 70 percent lower than the previous models.

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“…Urban transportation is perhaps the fundamental application area of VRP in such a way that the travel time, trips per vehicle, and loading time (constants are time, vehicle capacity, and trip duration) are important criteria of the problem where in [ 36 ] multi-trip time-dependent vehicle routing problem with time windows (MT-TDVRPTW) is proposed and solved with hybrid meta-heuristic algorithm adaptive large neighborhood search (ALNS) and variable neighborhood descent (VND). Transporting military personnel is aVRP in [ 24 ] and named the vehicle routing problem, and it considers reconnaissance and transportation (VRPCRT) for wartime troop movements. Additionally, an ant colony optimization (ACO) algorithm is developed and used to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective problem modeling of the capacitated vehicle routing problem with urgency in a pandemic period

Altınöz

2022

Neural Comput & Applic

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This research is based on the capacitated vehicle routing problem with urgency where each vertex corresponds to a medical facility with a urgency level and the traveling vehicle could be contaminated. This contamination is defined as the infectiousness rate, which is defined for each vertex and each vehicle. At each visited vertex, this rate for the vehicle will be increased. Therefore time-total distance it is desired to react to vertex as fast as possible- and infectiousness rate are main issues in the problem. This problem is solved with multiobjective optimization algorithms in this research. As a multiobjective problem, two objectives are defined for this model: the time and the infectiousness, and will be solved using multiobjective optimization algorithms which are nondominated sorting genetic algorithm (NSGAII), grid-based evolutionary algorithm GrEA, hypervolume estimation algorithm HypE, strength Pareto evolutionary algorithm shift-based density estimation SPEA2-SDE, and reference points-based evolutionary algorithm.

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Vehicle Routing Problem Considering Reconnaissance and Transportation

Cited by 6 publications

References 44 publications

Decision-Making Support in Vehicle Routing Problems: A Review of Recent Literature

Decision-Making Support in Vehicle Routing Problems: A Review of Recent Literature

Pickup and delivery problem in the collaborative city courier service by using genetic algorithm and nearest distance

Multiobjective problem modeling of the capacitated vehicle routing problem with urgency in a pandemic period

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