A Hybrid Algorithm Based on Monte-Carlo Simulation for the Vehicle Routing Problem with Route Length Restrictions

2011 ◽  
pp. 122-135
Author(s):  
Angel A. Juan ◽  
Javier Faulin ◽  
Tolga Bektas ◽  
Scott E. Grasman
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Vehicle Routing ◽  
Customer Service ◽  
Vehicle Routing Problem ◽  
Divide And Conquer ◽  
Additional Restriction ◽  
Routing Problem ◽  
Random Behavior ◽  
Route Length

This chapter describes an approach based on Monte Carlo Simulation (MCS) to solve the Capacitated Vehicle Routing Problem (CVRP) with route length restrictions and customer service times. The additional restriction introduces further challenges to the classical CVRP. The basic idea behind our approach is to combine direct MCS with an efficient heuristic, namely the Clarke and Wright Savings (CWS) algorithm, and a decomposition technique. The CWS heuristic provides a constructive methodology which is improved in two ways: (i) a special random behavior is introduced in the methodology using a geometric distribution; and (ii) a divide-and-conquer technique is used to decompose the original problem in smaller sub-problems that are easier to deal with. The method is tested using a set of well-known benchmarks. The chapter discusses the advantages and disadvantages of the proposed procedure in relation to other approaches for solving the same problem.

AN EFFICIENTLY NOVEL MODEL FOR VEHICLE ROUTING PROBLEMS WITH STOCHASTIC DEMANDS

2009 ◽  
Vol 26 (02) ◽  
pp. 185-197 ◽  
Author(s):  
SELÇUK KÜRŞAT İŞLEYEN ◽  
ÖMER FARUK BAYKOÇ
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Test Problems ◽  
Routing Problem ◽  
Stochastic Demands ◽  
Expected Length ◽  
Service Policy ◽  
Novel Model

In this paper, the Vehicle Routing Problem with Stochastic Demands (VRPSD) is considered where customer demands are normally distributed. We propose a new model for computing the expected length of a tour. Monte Carlo simulation is used to demonstrate the accuracy of the model on randomly generated test problems. It is assumed that the service policy is non-divisible, meaning that the entire demand at each customer must be served in a single visit by a unique vehicle.

Combining Monte Carlo simulation with heuristics to solve a rich and real-life multi-depot vehicle routing problem

2016 ◽  
Author(s):  
Gabriel Alemany ◽  
Jesica de Armas ◽  
Angel A. Juan ◽  
Alvaro Garcia-Sanchez ◽  
Roberto Garcia-Meizoso ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Real Life ◽  
Routing Problem

Ant Colony Optimisation Model for Vehicle Routing Problem With Simultaneous Pickup and Delivery

2016 ◽  
Author(s):  
Robin Scanlon ◽  
Qing Wang ◽  
Jie Wang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Complex Problem ◽  
Ant Colony ◽  
Benchmark Problems ◽  
Pickup And Delivery ◽  
Ant Colony Optimisation ◽  
Routing Problem ◽  
Route Length ◽  
Simultaneous Pickup And Delivery

Reverse logistics is an area that has come under increased scrutiny in recent years as legislators and companies try to increase the amount of goods that businesses reuse and recycle. The vehicle routing problem with simultaneous pickup and delivery arises when firms want to reduce handling costs by dealing with deliveries and returns in one operation. This is a complex problem for planners who aim to minimise the vehicle route length as the vehicle load rises and falls during a tour of facilities. This paper investigates the use of Ant Colony Optimisation to find solutions to this problem. An algorithm combining elements of three different studies is proposed. The algorithm finds results within 0.2% of the best known results and performs well for half of the benchmark problems, but needs further work to reach the same level on the other half. It is found that the proposed changes can have up to a 3.1% improvement in results when compared to previous methods run on this algorithm.

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 Vehicle Routing Problem with Deadline and Stochastic Service Times: Case of the Ice Cream Industry in Santiago City of Chile

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2750
Author(s):  
Sebastián Dávila ◽  
Miguel Alfaro ◽  
Guillermo Fuertes ◽  
Manuel Vargas ◽  
Mauricio Camargo
Keyword(s):  
Monte Carlo ◽  
Tabu Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Search Method ◽  
Superior Performance ◽  
Real Problem ◽  
Routing Problem ◽  
Chaotic Search ◽  
Number Of Customers

The research evaluates the vehicular routing problem for distributing refrigerated products. The mathematical model corresponds to the vehicle routing problem with hard time windows and a stochastic service time (VRPTW-ST) model applied in Santiago de Chile. For model optimization, we used tabu search, chaotic search and general algebraic modeling. The model’s objective function is to minimize the total distance traveled and the number of vehicles using stochastic waiting restrictions at the customers’ facilities. The experiments were implemented in ten scenarios by modifying the number of customers. Experiments were established with several customers that can be solved using the general algebraic modeling technique in order to validate the tabu search and the chaotic search methods. The study considered two algorithms modified with Monte Carlo (tabu search and chaotic search). Additionally, two modified algorithms, TSv2 and CSv2, were proposed to reduce execution time. These algorithms were modified by delaying the Monte Carlo procedure until the first set of sub-optimal routes were found. The results validate the metaheuristic chaotic search to solve the VRPTW-ST. The chaotic search method obtained a superior performance than the tabu search method when solving a real problem in a large city. Finally, the experiments demonstrated a direct relationship between the percentage of customers with stochastic waiting time and the model resolution time.

scholarly journals A Memetic Algorithm for the Cumulative Capacitated Vehicle Routing Problem Including Priority Indexes

2020 ◽  
Vol 10 (11) ◽  
pp. 3943
Author(s):  
Samuel Nucamendi-Guillén ◽  
Diego Flores-Díaz ◽  
Elias Olivares-Benitez ◽  
Abraham Mendoza
Keyword(s):  
Vehicle Routing ◽  
Customer Service ◽  
Vehicle Routing Problem ◽  
Memetic Algorithm ◽  
Mixed Integer ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Total Latency ◽  
Pareto Fronts ◽  
Capacitated Vehicle

This paper studies the Cumulative Capacitated Vehicle Routing Problem, including Priority Indexes, a variant of the classical Capacitated Vehicle Routing Problem, which serves the customers according to a certain level of preference. This problem can be effectively implemented in commercial and public environments where customer service is essential, for instance, in the delivery of humanitarian aid or in waste collection systems. For this problem, we aim to minimize two objectives simultaneously, the total latency and the total tardiness of the system. A Mixed Integer formulation is developed and solved using the AUGMECON2 approach to obtain true efficient Pareto fronts. However, as expected, the use of commercial software was able to solve only small instances, up to 15 customers. Therefore, two versions of a Memetic Algorithm with Random Keys (MA-RK) were developed to solve the problem. The computational results show that both algorithms provided good solutions, although the second version obtained denser and higher quality Pareto fronts. Later, both algorithms were used to solve larger instances (20–100 customers). The results were mixed in terms of quality but, in general, the MA-RK v2 consistently outperforms the first version. The models and algorithms proposed in this research provide useful insights for the decision-making process and can be applied to solve a wide variety of business situations where economic, customer service, environmental, and social concerns are involved.

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