Distance-Constrained Vehicle Routing Problems: A Case Study Using Artificial Bee Colony Algorithm

Karaoğlan, Aslan Deniz; Atalay, Ismail; Küçükkoç, İbrahim

doi:10.1007/978-3-030-37062-6_8

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…Chen and Zhou (2018) in their study revealed when the number of iterations for BA was set too high, the solution speed will be slow and it was easy to fall into the local optimum, thus unable to specify the constraints of the problem. Karaoglan et al (2020) used BA with the distanceconstraints for vehicle routing problem. They used BA to minimize the cost of transportation operations from 19 towns by the routes with a maximum length of 550 km.…”

Section: Bees Algorithm In Timber Transportationmentioning

confidence: 99%

“…Although previous research discussed above focused on road network problem in urban areas with BA, but the concepts of solutions they tried to elucidate were similar with forest transportation problem in Malaysian forest. For example, the extraction of log from stump site to landing can be represented with the concepts of serving the communities to reach their targeted location within a minimum travel time and finding the shortest routes (Long et al 2014;Iqbal et al 2015;Ng et al 2017;Alzaqebah et al 2018;Chen and Zhou 2018;George and Binu 2018;Santosh and Suresh 2019;Beed et al 2020;Karaoglan et al 2020). By shortening the traveling time, the cost for timber extraction can be reduced, thus the operational efficiency can be increased.…”

Section: Potential Uses Of Bees Algorithm In Malaysianmentioning

confidence: 99%

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Bees algorithm for Forest transportation planning optimization in Malaysia

Jamaluddin

Norizah

Hasmadi

et al. 2021

Forest Science and Technology

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Algorithm is widely used in various areas due to its ability to solve classes of problems. Due to multiple objectives to be met and varied algorithm application in this digital era, addressing the problem-solving optimization in a more efficient and effective way has become more reasonable. Forest transportation planning is one of the most expensive activities in timber harvesting and can be optimized through algorithm application. Forest transportation planning is a vital component of timber harvesting activities. Inappropriate planning may raise the overall costs of harvesting activities. This paper aims to give an overview of several algorithm application in optimizing the forest transportation planning problem and give an insightful information regarding the relationships between algorithm and the integration of transportation system characteristics and variables. Examples of algorithm that are finding their way to the forest transportation planning problem include Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm, Simulated Annealing (SA) algorithm and Tabu Search (TS) algorithm. Although no literature was found regarding forest transportation planning problem optimization with regards to Bees Algorithm (BA), rules set for several transportation problem evidenced from literature search seems to be applicable to forestry. Generally, in this paper, the BA has been given focus for forest transportation planning problem optimization as a potential algorithm to overcome the challenges of environmental degradation and efficiency of timber extraction used, as well as its accuracy and less processing time for problem-solving.

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Section: Bees Algorithm In Timber Transportationmentioning

confidence: 99%

Section: Potential Uses Of Bees Algorithm In Malaysianmentioning

confidence: 99%

Bees algorithm for Forest transportation planning optimization in Malaysia

Jamaluddin

Norizah

Hasmadi

et al. 2021

Forest Science and Technology

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“…Tabu search was applied by Arockia et al, 2021;Kurniawati et al, 2022;Caballero-Morales et al, 2018;Dam et al, 2017;Obaid, 2018. Furthermore, some scholars use artificial bee colony for solving CVRP (Ding et al, 2018;Karaoglan et al, 2020;Wahyuningsih et al, 2020;Trachanatzi et al 2020;Katiyar et al;.…”

Section: Introductionmentioning

confidence: 99%

Solving Capacitated Vehicle Routing Problem Using Football Game Algorithm

Alif¹,

Garside

Amallynda³

et al. 2022

JITI

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The Capacitated Vehicle Routing Problem (CVRP) plays an important role in the logistics transportation sector. Determining the proper route will reduce the company's operational costs. In CVRP, a number of vehicles have a capacity limit that can serve all customers. This research completes a real case study on a bottled drinking water company where the company still uses the subjective method of the driver to determine the transportation route. Based on the conditions in the company, the selection of the best route will consider vehicle capacity and demand to determine the shortest route. The execution of this case study uses the Football Game Algorithm (FGA) which was first initiated by Fadakar Ebrahimi which proved promising and had the strongest performance in all cases. FGA is expected to be able to determine the shortest distribution route from the existing cases to reduce the distribution costs incurred. This study takes data from 4 days of delivery that served 78 customers. The average daily transportation cost savings result is 42%. This amount indicates that the FGA algorithm is effective for completing a real case study in CVRP.

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“…Mission planning problems of the UAV can be represented as one of vehicle routing problems (VRPs). The VRP has various variations such as distance constraint ( Karaoglan, Atalay & Kucukkoc, 2020 ), multiple trip availability ( Paradiso et al, 2020 ), and asymmetricity of costs ( Ban & Nguyen, 2021 ) among many others. Thus, the VRP can represent some of the mission planning problems for the UAV, which is the complex combinatorial optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning

Jung¹,

Oh²

2022

PeerJ Computer Science

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Large-scale and complex mission environments require unmanned aerial vehicles (UAVs) to deal with various types of missions while considering their operational and dynamic constraints. This article proposes a deep learning-based heterogeneous mission planning algorithm for a single UAV. We first formulate a heterogeneous mission planning problem as a vehicle routing problem (VRP). Then, we solve this by using an attention-based deep reinforcement learning approach. Attention-based neural networks are utilized as they have powerful computational efficiency in processing the sequence data for the VRP. For the input to the attention-based neural networks, the unified feature representation on heterogeneous missions is introduced, which encodes different types of missions into the same-sized vectors. In addition, a masking strategy is introduced to be able to consider the resource constraint (e.g., flight time) of the UAV. Simulation results show that the proposed approach has significantly faster computation time than that of other baseline algorithms while maintaining a relatively good performance.

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Distance-Constrained Vehicle Routing Problems: A Case Study Using Artificial Bee Colony Algorithm

Cited by 4 publications

References 29 publications

Bees algorithm for Forest transportation planning optimization in Malaysia

Bees algorithm for Forest transportation planning optimization in Malaysia

Solving Capacitated Vehicle Routing Problem Using Football Game Algorithm

Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning

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