Modified Fuzzy C-Means Clustering Approach to Solve the Capacitated Vehicle Routing Problem

Shalaby, Mohamed; Mohammed, Ayman R.; Kassem, Sally

doi:10.1109/acit50332.2020.9300082

Cited by 4 publications

(4 citation statements)

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“…al in [15] to solve a k-person TSP. A modified fuzzy C-means clustering approach was presented in [33] for solving CVRP. The approach is based on cluster first route second method.…”

Section: Literature Reviewmentioning

confidence: 99%

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Solving capacitated vehicle routing problem with demands as fuzzy random variable

2023

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Capacitated vehicle routing problem (CVRP) is a classical combinatorial optimization problem in which a network of customers with specified demands is given. The objective is to find a set of routes which originates as well as terminates at the depot node. These routes are to be traversed in such a way that the demands of all the customers in the network are satisfied and the cost associated with traversal of these routes come out to be a minimum. In real-world situations, the demand of any commodity depends upon various uncontrollable factors, such as, season, delivery time, market conditions and many more. Due to these factors, the demand can always not be told in advance and a precise information about the demand is nearly impossible to achieve. Hence, the demands of the customers always experience impreciseness and randomness in real-life. The decisions made by the customers about the demands may also have some scope of hesitation as well. In order to handle such demands of customers in the network, fuzzy random variables and intuitionistic fuzzy random variables are used in this work. The work bridges the gap between the classical version of CVRP and the real-life situation and hence makes it easier for the logistic management companies to determine the routes that should be followed for minimum operational cost and maximum profit. Mathematical models corresponding to CVRP with fuzzy stochastic demands (CVRPFSD) and CVRP with Intuitionistic fuzzy stochastic demands (CVRPIFSD) have been presented. A two-stage model has been proposed to find out the solution for the same. To explain the working of the methodology defined in this work, two different example of a network with fuzzy and intuitionistic fuzzy demands have been worked out. The proposed solution approach is also tested on modified fuzzy versions of some benchmark instances.

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“…al in [15] to solve a k-person TSP. A modified fuzzy C-means clustering approach was presented in [33] for solving CVRP. The approach is based on cluster first route second method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The approach is based on cluster first route second method. In [33], the first stage of problem solving corresponds to the formation of clusters and in second phase, each cluster is solved independently as a TSP.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving capacitated vehicle routing problem with demands as fuzzy random variable

2023

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“…VRP, creates ideal delivery routes in which each vehicle only takes one route that starts from a centralized depot. The VRP's aim is to discover a set of least-cost vehicle routes in which each client is visited precisely once by one vehicle, each vehicle begins and ends its route at the depot, and the vehicle capacity is not overloaded [2][3][4]. The problem has been viewed and modeled from different perspectives and using several techniques, for example mathematical models [5], agent based VRP [6], and unified modeling language modeling [7].…”

Section: Introductionmentioning

confidence: 99%

A Heterogeneous Vehicle Routing Problem with Soft Time Windows for 3PL Company’s Deliveries: A Case Study

Ismail¹,

Enin²,

Osama³

et al. 2021

JESA

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Route optimization is tactically important for companies that must fulfill the demands of different customers with fleet of vehicles, considering multiple factors like: the cost of the resources (vehicles) involved and the operating costs of the entire process. As a case study, a third-party logistics service provider, ABC Company, is introduced to implement optimization on. Furthermore, ABC Company’s problem is defined as route optimization and load consolidation problems that will be solved as heterogeneous vehicle routing problem with soft time windows (HVRPSTW). In this paper’s case, Vehicles travel from a central depot with a restricted capacity, serving clients just once within a defined time interval and providing a needed demand before returning to the central depot. ABC Company’s problem is mathematically formulated and solved using branch and bound method. The formulation is solved on LINGO. The final output is the route, time, cost, and load of each vehicle.

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“…They compare the K-means, K-medoids and random clustering method and discovered that K-medoids provided better solution combined with branch and bound algorithm. Shalaby et al (2020) handle the clustering phase using a modified demand weighted fuzzy c-means and used traditional optimization software for the routing phase.…”

Section: Introductionmentioning

confidence: 99%

Optimization of capacitated vehicle routing problem using initial route with same size K-means and greedy algorithm for vaccines distribution

Febria

Dewi

Mailoa³

2021

Int. J. Appl. Sci. Eng.

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Vaccines are the solution that is currently underway to tackle COVID-19. In this paper, vaccine distribution for hospitals in Central Java is developed. The problem case in this paper is classified as a Capacitated Vehicle Routing Problem (CVRP). The method proposed is using an initial route that follows the cluster-first route-second method (CFRS). The same size K-means is used for the clustering phase and the Greedy algorithm is used for the routing phase. The result of the initial route is a clustered route for each vehicle with a balanced capacity. Then, each cluster was re-optimized using metaheuristics Guided Local Search from Google OR-tools. Our experiment results have proven that using the initial route has the effect of reducing runtime by 97.37% -99.17% when compared to without the initial route. This is because using initial routes with the same size K-means means breaking the problem into parts, then using the Greedy algorithm can reduce the number of possible routes. However, the total distance increased by 8.22% -16.69% because no cluster member is allowed to move to another cluster.

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Modified Fuzzy C-Means Clustering Approach to Solve the Capacitated Vehicle Routing Problem

Cited by 4 publications

References 54 publications

Solving capacitated vehicle routing problem with demands as fuzzy random variable

Solving capacitated vehicle routing problem with demands as fuzzy random variable

A Heterogeneous Vehicle Routing Problem with Soft Time Windows for 3PL Company’s Deliveries: A Case Study

Optimization of capacitated vehicle routing problem using initial route with same size K-means and greedy algorithm for vaccines distribution

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