scholarly journals Implementation of the Model Capacited Vehicle Routing Problem with Time Windows with a Goal Programming Approach in Determining the Best Route for Goods Distribution

2020 ◽  
Vol 17 (2) ◽  
pp. 231-239
Author(s):  
Wahri Irawan ◽  
Muhammad Manaqib ◽  
Nina Fitriyati
Keyword(s):  
Vehicle Routing ◽  
Goal Programming ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Goal Function ◽  
Programming Approach ◽  
Routing Problem ◽  
Total Capacity ◽  
Vehicle Capacity ◽  
Number Of Customers

This research discusses determination of the best route for the goods distribution from one depot to customers in various locations using the Capacitated Vehicle Routing Problem with Time of Windows (CVRPTW) model with a goal programming approach. The goal function of this model are minimize costs, minimize distribution time, maximize vehicle capacity and maximize the number of customers served. We use case study in CV. Oke Jaya companies which has 25 customers and one freight vehicle with 2000 kg capacities to serve the customers in the Serang, Pandeglang, Rangkasbitung and Cikande. For simulation we use software LINGO. Based on this CVRPTW model with a goal programming approach, there are four routes to distribute goods on the CV. Oke Jaya, which considers the customer’s operating hours, with total cost is Rp 233.000,00, the total distribution time is 17 hours 57 minutes and the total capacity of goods distributed is 6150 kg.

scholarly journals VEHICLE ROUTING PROBLEM WITH TIME WINDOWS USING HYBRID ENCODING GENETIC ALGORITHM

2014 ◽  
Vol 12 (10) ◽  
pp. 3945-3951
Author(s):  
Dr P.K Chenniappan ◽  
Mrs.S.Aruna Devi
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Time Windows ◽  
Minimal Cost ◽  
Total Size ◽  
Routing Problem ◽  
Time Window Constraints ◽  
Vehicle Capacity ◽  
Vehicle Routes

The vehicle routing problem is to determine K vehicle routes, where a route is a tour that begins at the depot, traverses a subset of the customers in a specified sequence and returns to the depot. Each customer must be assigned to exactly one of the K vehicle routes and total size of deliveries for customers assigned to each vehicle must not exceed the vehicle capacity. The routes should be chosen to minimize total travel cost. Thispapergivesasolutiontofindanoptimumrouteforvehicle routingproblem using Hybrid Encoding GeneticAlgorithm (HEGA)technique tested on c++ programming.The objective is to find routes for the vehicles to service all the customers at a minimal cost and time without violating the capacity, travel time constraints and time window constraints

scholarly journals A Bucket Graph–Based Labeling Algorithm with Application to Vehicle Routing

2020 ◽  
Author(s):  
Ruslan Sadykov ◽  
Eduardo Uchoa ◽  
Artur Pessoa
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Resource Constraints ◽  
Time Window ◽  
Time Windows ◽  
Routing Problem ◽  
Constrained Vehicle Routing ◽  
Time Window Constraints ◽  
Vehicle Capacity ◽  
First Time

We consider the shortest path problem with resource constraints arising as a subproblem in state-of-the-art branch-cut-and-price algorithms for vehicle routing problems. We propose a variant of the bidirectional label-correcting algorithm in which the labels are stored and extended according to the so-called bucket graph. This organization of labels helps to significantly decrease the number of dominance checks and the running time of the algorithm. We also show how the forward/backward route symmetry can be exploited and how to eliminate arcs from the bucket graph using reduced costs. The proposed algorithm can be especially beneficial for vehicle routing instances with large vehicle capacity and/or with time window constraints. Computational experiments were performed on instances from the distance-constrained vehicle routing problem, including multidepot and site-dependent variants, on the vehicle routing problem with time windows, and on the “nightmare” instances of the heterogeneous fleet vehicle routing problem. Significant improvements over the best algorithms in the literature were achieved, and many instances could be solved for the first time.

scholarly journals Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

2017 ◽  
pp. 283-302 ◽  
Author(s):  
Hossein Yousefi ◽  
Reza T avakkoli-Moghaddam ◽  
Mahyar Taheri Bavil Oliaei ◽  
Mohammad Mohammadi ◽  
Ali Mozaffari
Keyword(s):  
Vehicle Routing ◽  
Goal Programming ◽  
Vehicle Routing Problem ◽  
Programming Approach ◽  
Routing Problem

Two Novel Heuristics Based on a New Density Measure for Vehicle Routing Problem

2015 ◽  
Vol 6 (1) ◽  
pp. 78-90 ◽  
Author(s):  
Abdesslem Layeb
Keyword(s):  
Density Matrix ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Nearest Neighbor ◽  
Routing Problem ◽  
Stochastic Version ◽  
Third Phase ◽  
Vehicle Capacity ◽  
Number Of Customers ◽  
Optimal Set

The vehicle routing problem (VRP) is a known optimization problem. The objective is to construct an optimal set of routes serving a number of customers where the demand of each customer is less than the vehicle' capacity, and each customer is visited exactly once like in TSP problem. The purpose of this paper is to present new deterministic heuristic and its stochastic version for solving the vehicle routing problem. The proposed algorithms are inspired from the density heuristic of knapsack problem. The proposed heuristic is based on four steps. In the first step a density matrix (demand/distance) is constructed by using given equations. In the second step, a giant tour is constructed by using the density matrix; the customer with highest density is firstly visited, the process is repeated until all customers will be visited. In the third phase, the split procedure is applied to this giant tour in order to get feasible routes subject to vehicles capacities. Finally, each route is improved by the application of the nearest neighbor heuristic. The results of the experiment indicate that the proposed heuristic is better than the nearest neighbor heuristic for VRP. Moreover, the proposed algorithm can easily be used to generate initial solutions for a wide variety of VRP algorithms.

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