scholarly journals A hybrid FJA-ALNS algorithm for solving the multi-compartment vehicle routing problem with a heterogeneous fleet of vehicles for the fuel delivery problem

2021 ◽  
Vol 10 (4) ◽  
pp. 497-510 ◽  
Author(s):  
Wasana Chowmali ◽  
Seekharin Sukto
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Optimal Solution ◽  
Computational Time ◽  
Second Phase ◽  
Routing Problem ◽  
Delivery Problem ◽  
Heterogeneous Fleet ◽  
Fuel Delivery

This paper proposes a new hybrid algorithm to solve the multi-compartment vehicle routing problem (MCVRP) with a heterogeneous fleet of vehicles for the fuel delivery problem of a previous study of twenty petrol stations in northeastern Thailand. The proposed heuristic is called the Fisher and Jaikumar Algorithm with Adaptive Large Neighborhood Search (FJA-ALNS algorithm). The objective of this case is to minimize the total distance, while using a minimum number of multi-compartment vehicles. In the first phase, we used the FJA to solve the MCVRP for the fuel delivery problem. The results from solving the FJA were utilized to be the initial solutions in the second phase. In the second phase, a hybrid algorithm, namely the FJA-ALNS algorithm, has been developed to improve the initial solutions of the individual FJA. The results from the FJA-ALNS algorithm are compared with the exact method (LINGO software), individual FJA and individual ALNS. For small-sized problems (N=5), the results of the proposed FJA-ALNS and all methods provided no different results from the global optimal solution, but the proposed FJA-ALNS algorithm required less computational time. For larger-sized problems, LINGO software could not find the optimal solution within the limited period of computational time, while the FJA-ALNS algorithm provided better results with much less computational time. In solving the four numerical examples using the FJA-ALNS algorithm, the result shows that the proposed FJA-ALNS algorithm is effective for solving the MCVRP in this case. Undoubtedly, future work can apply the proposed FJA-ALNS algorithm to other practical cases and other variants of the VRP in real-world situations.

A hybrid algorithm for the Heterogeneous Fleet Vehicle Routing Problem

2012 ◽  
Vol 221 (2) ◽  
pp. 285-295 ◽  
Author(s):  
Anand Subramanian ◽  
Puca Huachi Vaz Penna ◽  
Eduardo Uchoa ◽  
Luiz Satoru Ochi
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Routing Problem ◽  
Heterogeneous Fleet

scholarly journals A granular tabu search algorithm for a real case study of a vehicle routing problem with a heterogeneous fleet and time windows

2017 ◽  
Vol 10 (4) ◽  
pp. 646 ◽  
Author(s):  
Jose Bernal ◽  
John Willmer Escobar ◽  
Rodrigo Linfati
Keyword(s):  
Tabu Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Computational Experiments ◽  
Real Case ◽  
Second Phase ◽  
Routing Problem ◽  
Heterogeneous Fleet

Purpose: We consider a real case study of a vehicle routing problem with a heterogeneous fleet and time windows (HFVRPTW) for a franchise company bottling Coca-Cola products in Colombia. This study aims to determine the routes to be performed to fulfill the demand of the customers by using a heterogeneous fleet and considering soft time windows. The objective is to minimize the distance traveled by the performed routes.Design/methodology/approach: We propose a two-phase heuristic algorithm. In the proposed approach, after an initial phase (first phase), a granular tabu search is applied during the improvement phase (second phase). Two additional procedures are considered to help that the algorithm could escape from local optimum, given that during a given number of iterations there has been no improvement.Findings: Computational experiments on real instances show that the proposed algorithm is able to obtain high-quality solutions within a short computing time compared to the results found by the software that the company currently uses to plan the daily routes.Originality/value: We propose a novel metaheuristic algorithm for solving a real routing problem by considering heterogeneous fleet and time windows. The efficiency of the proposed approach has been tested on real instances, and the computational experiments shown its applicability and performance for solving NP-Hard Problems related with routing problems with similar characteristics. The proposed algorithm was able to improve some of the current solutions applied by the company by reducing the route length and the number of vehicles.

scholarly journals An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 650 ◽  
Author(s):  
He-Yau Kang ◽  
Amy Lee
Keyword(s):  
Computer Science ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Computational Time ◽  
Routing Problem ◽  
Mip Model ◽  
Science Theory

The vehicle routing problem (VRP) is a challenging combinatorial optimization problem. This research focuses on the problem under which a manufacturer needs to outsource materials from other suppliers and to ship the materials back to the company. Heterogeneous vehicles are available to ship the materials, and each vehicle has a limited loading capacity and a limited travelling distance. The purpose of this research is to study a multiple vehicle routing problem (MVRP) with soft time window and heterogeneous vehicles. Two models, using mixed integer programming (MIP) and genetic algorithm (GA), are developed to solve the problem. The MIP model is first constructed to minimize the total transportation cost, which includes the assignment cost, travelling cost, and the tardiness cost, for the manufacturer. The optimal solution can present multiple vehicle routing and the loading size of each vehicle in each period. The GA is next applied to solve the problem so that a near-optimal solution can be obtained when the problem is too difficult to be solved using the MIP. A case of a food manufacturing company is used to examine the practicality of the proposed MIP model and the GA model. The results show that the MIP model can obtain the optimal solution under a short computational time when the scale of the problem is small. When the problem becomes non-deterministic polynomial hard (NP-hard), the MIP model cannot find the optimal solution. On the other hand, the GA model can obtain a near-optimal solution within a reasonable amount of computational time. This paper is related to several important topics of the Symmetry journal in the areas of mathematics and computer science theory and methods. In the area of mathematics, the theories of linear and non-linear algebraic structures and information technology are adopted. In the area of computer science, theory and methods, and metaheuristics are applied.

scholarly journals Implementation of discrete particle swarm optimization algorithm in the capacitated vehicle routing problem

2020 ◽  
Vol 4 (2) ◽  
pp. 117-128
Author(s):  
Aisyahna Nurul Mauliddina ◽  
Faris Ahmad Saifuddin ◽  
Adesatya Lentera Nagari ◽  
Anak Agung Ngurah Perwira Redi ◽  
Adji Candra Kurniawan ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimal Solution ◽  
Computational Time ◽  
Hard Problem ◽  
Np Hard ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Np Hard Problem ◽  
Capacitated Vehicle

Capacitated Vehicle Routing Problem (CVRP) is known as an NP-hard problem. It is because CVRP problems are very hard for finding optimal solutions, especially in large instances. In general, the NP-hard problem is difficult to solve in the exact method, so the metaheuristic approach is implemented in the CVRP problem to find a near-optimal solution in reasonable computational time. This research uses the DPSO algorithm for solving CVRP with ten instances of benchmark datasets. DPSO implementation uses tuning parameters with the One Factor at Time (OFAT) method to select the best DPSO parameters. The outcome objective function will be compared with several PSO models proposed in previous studies. Statistical test using One Way Reputed Measure ANOVA is needed to compare algorithm performance. First, ANOVA uses for comparing’s results. Then, ANOVA is also used to test DPSO’s performance compared with DPSO-SA, SR-1, and SR-2 algorithm. The computational result shows that the basic DPSO algorithm not competitive enough with other methods for solving CVRP.

scholarly journals Waste Collection Vehicle Routing Problem Model with Multiple Trips, Time Windows, Split Delivery, Heterogeneous Fleet and Intermediate Facility

2020 ◽  
Vol 24 (5) ◽  
pp. 55-64
Author(s):  
Filscha Nurprihatin ◽  
Anggun Lestari
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Computational Time ◽  
Waste Collection ◽  
Routing Problem ◽  
Problem Model ◽  
Split Delivery ◽  
Heterogeneous Fleet ◽  
Intermediate Facilities

Waste Collection Vehicle Routing Problem (WCVRP) is one of the developments of a Vehicle Routing Problem, which can solve the route determination of transporting waste. This study aims to develop a model from WCVRP by adding characteristics such as split delivery, multiple trips, time windows, heterogeneous fleet, and intermediate facilities alongside an objective function to minimize costs and travel distance. Our model determines the route for transporting waste especially in Cakung District, East Jakarta. The additional characteristics are obtained by analyzing the characteristics of waste transportation in the area. The models are tested using dummy data to analyze the required computational time and route suitability. The models contribute to determining the route of transporting waste afterward. The WCVRP model has been successfully developed, conducted the numerical testing, and implemented with the actual characteristics such as split delivery, multiple trips, time windows, heterogeneous fleets, and intermediate facilities. The output has reached the global optimal for both dummy and real data.

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