scholarly journals Comparision of the walk techniques for fitness state space analysis in vehicle routing problem

2021 ◽  
Vol 61 (6) ◽  
pp. 672-683
Author(s):  
Anita Agárdi ◽  
László Kovács ◽  
Tamás Bányai
Keyword(s):  
State Space ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Geometrical Property ◽  
Heuristic Algorithms ◽  
Optimization Method ◽  
Test Results ◽  
Routing Problem ◽  
State Space Methods ◽  
Optimization Task

The Vehicle Routing Problem (VRP) is a highly researched discrete optimization task. The first article dealing with this problem was published by Dantzig and Ramster in 1959 under the name Truck Dispatching Problem. Since then, several versions of VRP have been developed. The task is NP difficult, it can be solved only in the foreseeable future, relying on different heuristic algorithms. The geometrical property of the state space influences the efficiency of the optimization method. In this paper, we present an analysis of the following state space methods: adaptive, reverse adaptive and uphill-downhill walk. In our paper, the efficiency of four operators are analysed on a complex Vehicle Routing Problem. These operators are the 2-opt, Partially Matched Crossover, Cycle Crossover and Order Crossover. Based on the test results, the 2-opt and Partially Matched Crossover are superior to the other two methods.

The Traveling Salesman Problem, the Vehicle Routing Problem, and Their Impact on Combinatorial Optimization

2010 ◽  
Vol 1 (2) ◽  
pp. 82-92 ◽  
Author(s):  
Gilbert Laporte
Keyword(s):  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Traveling Salesman Problem ◽  
Vehicle Routing Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
The Traveling Salesman Problem

The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two of the most popular problems in the field of combinatorial optimization. Due to the study of these two problems, there has been a significant growth in families of exact and heuristic algorithms being used today. The purpose of this paper is to show how their study has fostered developments of the most popular algorithms now applied to the solution of combinatorial optimization problems. These include exact algorithms, classical heuristics and metaheuristics.

A Hybrid Algorithm for the Multi-Objective Time Dependent Vehicle Routing Problem

2014 ◽  
Vol 505-506 ◽  
pp. 1071-1075
Author(s):  
Yi Sun ◽  
Yue Chen ◽  
Chang Chun Pan ◽  
Gen Ke Yang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Time Windows ◽  
Time Dependent ◽  
Ant Colony System ◽  
Optimal Solutions ◽  
Test Results ◽  
Routing Problem ◽  
Traffic Conditions

This paper presents a real road network case based on the time dependent vehicle routing problem with time windows (TDVRPTW), which involves optimally routing a fleet of vehicles with fixed capacity when traffic conditions are time dependent and services at customers are only available in their own time tables. A hybrid algorithm based on the Genetic Algorithm (GA) and the Multi Ant Colony System (MACS) is introduced in order to find optimal solutions that minimize two hierarchical objectives: the number of tours and the total travel cost. The test results show that the integrated algorithm outperforms both of its traditional ones in terms of the convergence speed towards optimal solutions.

Logistics Distribution Vehicle Routing Problem with Time Windows

2012 ◽  
Vol 468-471 ◽  
pp. 2047-2051 ◽  
Author(s):  
Ai Ling Chen
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Optimization Method ◽  
Hybrid Optimization ◽  
Local Optimum ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Routing Optimization ◽  
Hybrid Optimization Method

Vehicle routing optimization problem is one of the major research topics in logistics distribution field. Suitable vehicle routing selection is vital to reduce the logistics cost. The paper presents a hybrid optimization method to solve the vehicle routing problem with time windows. In the hybrid optimization method, discrete particle swarm optimization algorithm is used to assign the customers on routes and simulated annealing (SA) algorithm to avoid becoming trapped in local optimum. The simulation results have shown that the proposed method is feasible and effective for the vehicle routing problem with time windows.

scholarly journals PERENCANAAN JADWAL DAN RUTE DISTRIBUSI ROKOK UNTUK MENEKAN TOTAL BIAYA TRANSPORTASI

2012 ◽  
Vol 13 (2) ◽  
pp. 151 ◽  
Author(s):  
Thomy Eko Saputro ◽  
Aprilia Prihatina
Keyword(s):  
Vehicle Routing ◽  
Customer Service ◽  
Vehicle Routing Problem ◽  
Optimization Method ◽  
Distribution Area ◽  
Nearest Neighbour ◽  
Routing Problem ◽  
Problem Model ◽  
Periodic Vehicle Routing Problem ◽  
Periodic Vehicle Routing

Thomy Eko Saputro DAN Aprilia PrihatinaJurusan Teknik Industri, Fakultas Teknik, Universitas Muhammadiyah MalangLaman: [email protected] satu hal yang berpengaruh dalam meningkatkan pelayanan konsumen adalah bagaimana mengirimkan produkyang tepat waktu kepada seluruh konsumen. Oleh karena itu pelaku bisnis perlu menerapkan suatu strategi yang tepat agardapat mengefisienkan dan mengefektifkan proses distribusinya. PR 567 sebagai distributor rokok perwakilan Purwodadiberupaya agar pendistribusian berjalan dengan baik karena mengingat proses distribusi dengan jumlah agen yang cukupbanyak seringkali mempersulit distributor untuk menentukan jadwal dan rute yang tepat. Permasalahan pendistribusianini termasuk dalam PVRP (Periodic Vehicle Routing Problem). Penyelesaian dilakukan menggunakan metode clusterfirst-second route dengan penugasan agen ke hari kunjungan menggunakan metode optimasi. Solusi akhir nantinyaakan memberikan jadwal dan rute kendaraan dengan total biaya tranportasi yang paling minimum. Pada penelitian iniwilayah distribusi dibagi menjadi 2 cluster dan dari penyelesaian model PVRP diperoleh frekuensi kunjungan yang tepatuntuk cluster 1 adalah sebanyak sekali dalam seminggu. Sedangkan untuk cluter 2 dikunjungi sebanyak 3 kali dalamseminggu. Hasil penentuan jadwal dan rute dari penelitian inimemberikan total biaya transportasi sebesar Rp725.805per minggu. Dengan kata lain terjadi penghematan sebesar Rp320.189/minggu atau menghemat sebesar 44% per minggudari biaya awal yang harus dikeluarkan.Kata kunci: Vehicle routing, periodic, nearest neighbour, optimasiABSTRACTOne of the main issue in improving the customer service is how to deliver the product on time to customers. Therefore,the stakeholders need to apply an appropriate strategy in order to make distribustion process become more efficient andeffective. Because it is hard to determine appropriate schedule and route when dealing with a lot agents, PR 567 as arepresentative distributor of cigarette in Purwodadi attempts to make its distribution process better. This was done by usingPVRP (Periodic Vehicle Routing Problem) model with cluster first-second route approach and optimization method forassigning vehicle. The result of this research were frequency, schedule, and route with the most minimum transportationcost. In this research, the distribution area was defined into two cluster. The best delivery frequency for cluster one was onceweek, while cluster two was three times a week. The transportation cost was Rp725805/week. In the other hand, the savingcost was Rp320189/week or 44%/week from the initial cost.Key words: Vehicle routing, periodic, nearest neighbour, optimization

scholarly journals METAHEURISTICS FOR THE WASTE COLLECTION VEHICLE ROUTING PROBLEM IN URBAN AREAS

2020 ◽  
pp. 001
Author(s):  
Aleksandar Stanković ◽  
Danijel Marković ◽  
Goran Petrović ◽  
Žarko Čojbašić
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Urban Areas ◽  
Heuristic Algorithms ◽  
Municipal Waste ◽  
Waste Collection ◽  
Swarm Optimization ◽  
Urban Waste ◽  
Routing Problem ◽  
Capacitated Vehicle

This paper presents a methodology for solving the municipal waste collection problem in urban areas. The problem is treated as a distance-constrained capacitated vehicle routing problem for municipal waste collection (DCCVRP-MWC). To solve this problem, four meta-heuristic algorithms were used: Genetic algorithm (GA), Simulated annealing (SA), Particle swarm optimization (PSO) and Ant colony optimization (ACO). Vehicle guidance plays a huge role in large transportation companies, and with this test, we propose one of several algorithms for solving urban waste collection problems.

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