scholarly journals Completion Vehicle Routing Problem (VRP) In Determining Route And Determining The Number Of Vehicles In Minimizing Transportation Costs In PT. XYZ With Using Genetic Algorithm

2018 ◽  
Vol 2 (01) ◽  
Author(s):  
Ahmad Fauzan Abdurrahman
Keyword(s):  
Genetic Algorithm ◽  
Nearest Neighbor ◽  
Time Window ◽  
Initial Conditions ◽  
Empty Space ◽  
Transportation Costs ◽  
Routing Problem ◽  
Optimal Distance ◽  
Distribution Process ◽  
The Cost

In the transportation process is closely related to the route, the route is the path through which a mode / vehicle to arrive at a destination. The route is related to the number of vehicles and the location where it goes. PT XYZ is a company engaged in fast moving consumer goods (FMCG), with the field makes the flow of goods speed will be high until the goods distribution process becomes fast and often. In the process of distribution is done by using 1 fleet in each customer. Currently in the process of distributing goods, the company still ignores the utility of the vehicles used, so the availability of empty space in capacity is still occurring and this makes the cost of transportation is high. Consolidation of multiple customers becomes possible, keeping in mind the time window, capacity and multiple products. This study designs a route by considering the limits to get the route, the number of vehicles, the utility increase of each vehicle and the optimal distance so that it can minimize transportation costs. The use of a genetic algorithm preceded by the nearest neighbor algorithm is used to solve this problem. Later the route will be formed and get the number of vehicles, the increase in vehicle utility and the optimal distance. This resulted in average vehicle utility improvement of 35,317%, vehicle repairs amounted to 34.05%, and distance of 10.075% so as to reduce transportation costs by 26.56% from initial conditions.

scholarly journals COMPLETION VEHICLE ROUTING PROBLEM (VRP) IN DETERMINING ROUTE AND DETERMINING THE NUMBER OF VEHICLES IN MINIMIZING TRANSPORTATION COSTS IN PT XYZ WITH USING GENETIC ALGORITHM

2018 ◽  
Vol 2 (02) ◽  
pp. 24-30
Author(s):  
Ahmad Fauzan Abdurrahman ◽  
Ari Yanuar Ridwan ◽  
Budi Santosa
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Nearest Neighbor ◽  
Time Window ◽  
Initial Conditions ◽  
Empty Space ◽  
Transportation Costs ◽  
Routing Problem ◽  
Optimal Distance

In the transportation process is closely related to the route, the route is the path through which a mode / vehicle to arrive at a destination. The route is related to the number of vehicles and the location where it goes. PT XYZ is a company engaged in fast moving consumer goods (FMCG), with the field makes the flow of goods speed will be high until the goods distribution process becomes fast and often. In the process of distribution is done by using 1 fleet in each customer. Currently in the process of distributing goods, the company still ignores the utility of the vehicles used, so the availability of empty space in capacity is still occurring and this makes the cost of transportation is high. Consolidation of multiple customers becomes possible, keeping in mind the time window, capacity and multiple products. This study designs a route by considering the limits to get the route, the number of vehicles, the utility increase of each vehicle and the optimal distance so that it can minimize transportation costs. The use of a genetic algorithm preceded by the nearest neighbor algorithm is used to solve this problem. Later the route will be formed and get the number of vehicles, the increase in vehicle utility and the optimal distance. This resulted in average vehicle utility improvement of 35,317%, vehicle repairs amounted to 34.05%, and distance of 10.075% so as to reduce transportation costs by 26.56% from initial conditions. Keywords— Transportation, FMCG, Vehicle Routing Problem, Time Window, Heterogeneous Fleet, Utility, Vehicle Number Determination.

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

scholarly journals A Heuristics-Based Parthenogenetic Algorithm for the VRP with Potential Demands and Time Windows

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Chenghua Shi ◽  
Tonglei Li ◽  
Yu Bai ◽  
Fei Zhao
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Computation Time ◽  
Routing Problem ◽  
Split Delivery ◽  
Soft Time Windows ◽  
Comparison Results ◽  
Potential Demand ◽  
The Cost

We present the vehicle routing problem with potential demands and time windows (VRP-PDTW), which is a variation of the classical VRP. A homogenous fleet of vehicles originated in a central depot serves customers with soft time windows and deliveries from/to their locations, and split delivery is considered. Also, besides the initial demand in the order contract, the potential demand caused by conformity consuming behavior is also integrated and modeled in our problem. The objective of minimizing the cost traveled by the vehicles and penalized cost due to violating time windows is then constructed. We propose a heuristics-based parthenogenetic algorithm (HPGA) for successfully solving optimal solutions to the problem, in which heuristics is introduced to generate the initial solution. Computational experiments are reported for instances and the proposed algorithm is compared with genetic algorithm (GA) and heuristics-based genetic algorithm (HGA) from the literature. The comparison results show that our algorithm is quite competitive by considering the quality of solutions and computation time.

scholarly journals Clustering Control of Multi-Objective Problems with Application to E-commerce

2018 ◽  
Vol 1 (1) ◽  
pp. 41
Author(s):  
Liang Chen ◽  
Xingwei Wang ◽  
Jinwen Shi
Keyword(s):  
Ant Colony Algorithm ◽  
Time Window ◽  
Ant Colony ◽  
Distribution Area ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Multi Objective ◽  
The Cost ◽  
Vehicle Capacity ◽  
Cost Of Transportation

In the existing logistics distribution methods, the demand of customers is not considered. The goal of these methods is to maximize the vehicle capacity, which leads to the total distance of vehicles to be too long, the need for large numbers of vehicles and high transportation costs. To address these problems, a method of multi-objective clustering of logistics distribution route based on hybrid ant colony algorithm is proposed in this paper. Before choosing the distribution route, the customers are assigned to the unknown types according to a lot of customers attributes so as to reduce the scale of the solution. The discrete point location model is applied to logistics distribution area to reduce the cost of transportation. A mathematical model of multi-objective logistics distribution routing problem is built with consideration of constraints of the capacity, transportation distance, and time window, and a hybrid ant colony algorithm is used to solve the problem. Experimental results show that, the optimized route is more desirable, which can save the cost of transportation, reduce the time loss in the process of circulation, and effectively improve the quality of logistics distribution service.

A New Multi-Criteria Solving Procedure for Multi-Depot FSM-VRP with Time Window

2017 ◽  
Vol 4 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Lahcene Guezouli ◽  
Samir Abdelhamid
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Vehicle Routing Problem ◽  
Processing Time ◽  
Optimization Problems ◽  
Time Window ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Acceptable Quality ◽  
Capacitated Vehicle

One of the most important combinatorial optimization problems is the transport problem, which has been associated with many variants such as the HVRP and dynamic problem. The authors propose in this study a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that the authors call the Multi-Depot HVRPTW by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles..., and the authors solve this problem by proposing a new scheme based on a genetic algorithm heuristics that they will specify later. Computational experiments with the benchmark test instances confirm that their approach produces acceptable quality solutions compared with previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that the method of genetic algorithm heuristics is effective in solving the MDHVRPTW problem and hence has a great potential.

scholarly journals Container Swap Trailer Transportation Routing Problem Based on Genetic Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hua-wei Ma ◽  
Lei Tao ◽  
Xiao-xuan Hu
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Programming Model ◽  
Routing Problem ◽  
Adjustment Policy ◽  
Organization Mode ◽  
Adaptive Adjustment ◽  
Transportation Routing ◽  
Time Window Constraints ◽  
Self Adaptive

In swap trailer transportation routing problems, trucks and trailers conduct swap operations at special positions called trailer points. The parallelization of stevedoring and transportation can be achieved by means of these trailer points. This logistics organization mode can be more effective than the others. In this paper, an integer programming model with capacity and time-window constraints was established. A repairing strategy is embedded in the genetic algorithm (GA) to solve the model. The repairing strategy is executed after the crossover and mutation operation to eliminate the illegal routes. Furthermore, a parameter self-adaptive adjustment policy is designed to improve the convergence. Then numerical experiments are implemented based on the generated datasets; the performance and robustness of the algorithm parameter self-adaptive adjustment policy are discussed. Finally, the results show that the improved algorithm performs better than elementary GA.

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