Two solution representations for solving the generalized multi-depot vehicle routing problem with multiple pickup and delivery requests (GVRP-MDMPDR) is presented in this paper. The representations are used in conjunction with GLNPSO, a... more
Two solution representations for solving the generalized multi-depot vehicle routing problem with multiple pickup and delivery requests (GVRP-MDMPDR) is presented in this paper. The representations are used in conjunction with GLNPSO, a variant of PSO with multiple social learning terms. The computational experiments are carried out using benchmark test instances for pickup and delivery problem with time windows (PDPTW) and the generalized vehicle routing problem for multi-depot with multiple pickup and delivery requests (GVRP-MDMPDR). The preliminary results illustrate that the proposed method is capable of providing good solutions for most of the test problems.
We consider a simulated annealing approach with a network reduction technique to solve a special case of a vehicle routing problem with time windows where routes have limited duration (VRPTW-LD). The objective is twofold: minimizing the... more
We consider a simulated annealing approach with a network reduction technique to solve a special case of a vehicle routing problem with time windows where routes have limited duration (VRPTW-LD). The objective is twofold: minimizing the travel time and the total number of vehicles required. The time-window constraint ensures delivery without delay, thus high level of customer satisfaction. The implementation of this algorithm for solving large-scale VRPTW-LD problems has positively impacted GE Appliances & Lighting's operation in terms of customer's satisfaction by reducing delivery time from three to two days, a 33% improvement, and by reducing the number of required trucks by half in some instances.
The presented work is done for a company that currently operates twenty lines of passenger transport in the metropolitan area of Seville. The planning of these was originally carried out manually, building routes and shifts in an Excel... more
The presented work is done for a company that currently operates twenty lines of passenger transport in the metropolitan area of Seville. The planning of these was originally carried out manually, building routes and shifts in an Excel spreadsheet. In order to automate the process as much as possible. It was designed and implemented by a scheduling algorithm that would be much simple than other algorithms in the literature and that, in addition, would make it possible to allow mixing vehicles and drivers between the lines. The objective was, firstly, to employ the minimum number of drivers; then, it is trying to use the least possible number of vehicles; finally, the study tries to reduce as much as possible the amount of split shifts. In addition, restrictions on the design of routes and shifts were added. At all times the service frequencies remained above the set limit. To allow the possibility of unexpected demand peaks, was established in the capacity of each route some slack.
Vehicle Routing Problem (VRP) is a well known NP-hard optimization problem with a number of real world applications and a variety of different versions. Due to its complexity, large instances of VRP are hard to solve using exact methods.... more
Vehicle Routing Problem (VRP) is a well known NP-hard optimization problem with a number of real world applications and a variety of different versions. Due to its complexity, large instances of VRP are hard to solve using exact methods. Instead, various heuristic and meta-heuristic algorithms were used to find feasible VRP solutions. This work proposes a Differential Evolution for VRP that simultaneously looks for an optimal set of routes and minimizes the number of vehicles needed. The algorithm is used to solve Stochastic VRP with Real Simultaneous Pickup and Delivery based on realworld data obtained from Anbessa City Bus Service Enterprise (ACBSE), Addis Ababa, Ethiopia. Additionally, the algorithm is evaluated on several well known VRP instances. Keywords-vehicle routing problem; pickup and
Araç Rotalama Problemleri (ARP veya Vehicle Routing Problem: VRP) Gezgin Satıcı Problemleri’nin (TSP) özel bir durumudur. Bu problemlerde; belirli bir veya birden fazla bölgeye konumlandırılmıŞ depodan baŞlayarak; araç kapasite kısıtını... more
Araç Rotalama Problemleri (ARP veya Vehicle Routing Problem: VRP) Gezgin Satıcı Problemleri’nin (TSP) özel bir durumudur. Bu problemlerde; belirli bir veya birden fazla bölgeye konumlandırılmıŞ depodan baŞlayarak; araç kapasite kısıtını aŞmayacak Şekilde müŞterilerin taleplerini karŞıladıktan sonra tekrar baŞladığı depoya geri dönen araçların kat ettikleri toplam yolun minimize edilmesi amaçlanmaktadır. Araç Rotalama Problemleri NP-zor (polinom olmayan) problemler sınıfında geçmektedir. Bu sınıftaki problemlerin çözüm süresi, veri büyüdükçe polinomsal olarak arttığından kesin çözüm veren algoritmalar kabul edilebilir zaman içerisinde çözüm üretememektedir. Bu nedenle sezgisel yöntemlere baŞvurularak kısa sürede optimale yakın sonuç bulunmaya çalıŞılır. ARP’de amaç mevcut araçları etkin kullanarak, müŞteri taleplerini en uygun Şekilde ve düŞük maliyetle karŞılamaktır. Bu amaç doğrultusunda bilim insanları yeni çözüm yaklaŞımları ve yazılımlar geliŞtirmekte ve performanslarını test etmektedir.Bu çalıŞmada bir akaryakıt firmasının Ankara’daki 45 istasyonuna yakıt dağıtım rotalaması yapılmak istenmiŞtir. Bu bağlamda ARP’de kesin çözüm veren algoritmalar Python programlama dilinde kodlanmıŞ ve çözücü olarak Gurobi kullanılmıŞtır. Ancak bu ölçekte bir problem için kesin sonuç veren algoritmalar çözüm verememektedir. Bu nedenle Larry Snyder tarafından geliŞtirilen VRP Solver çözücüsü kullanılmıŞtır. VRP Solver; Clarke-Wright kazanım sezgiselini kullanarak ARP için çözüm üretmektedir. ÇalıŞmada VRP Solver yazılımı üzerinde talep, araç sayısı, gidilebilecek maksimum km değerleri değiŞtirilerek sonuçların bu değerlere olan duyarlılığı incelenmiŞtir. Duyarlılık analizi bulunan rota sayısı ve çözüm süresi üzerinden değerlendirilmiŞtir. Sonuç olarak büyük ölçekli Araç Rotalama Problemleri için VRP Solver yazılımının zaman ve ürettiği rotalar itibariyle baŞarılı referans sonuçlar üretebildiği sonucuna ulaŞılmıŞtır.
—This paper addresses the Vehicle Routing Problem (VRP) of a Home Health Care (HCC) service provider that serves patients requesting different types of care. In this problem, HCC services are provided by two types of personnel, nurses and... more
—This paper addresses the Vehicle Routing Problem (VRP) of a Home Health Care (HCC) service provider that serves patients requesting different types of care. In this problem, HCC services are provided by two types of personnel, nurses and health care aides, and the number of each type of personnel is limited. Each patient must be visited exactly once even if her servicing requires both personnel and is associated with a strict time window during which the service must be provided. We first present the 0-1 mixed integer programming formulation of the problem. Since the arising VRP is NP-hard, we then develop a Variable Neighborhood Search (VNS) algorithm to solve it. Next, we randomly generate a set of small-sized instances based on Solomon's benchmark problems for the VRP with Time-Windows and solve them using IBM ILOG CPLEX. To test the effectiveness of the VNS algorithm, we compare these solutions to those achieved by VNS. Our preliminary experiments show that VNS is able to find good results fast, yet the HCC crew constraints may complicate the problem. Keywords—Home care; health care; vehicle routing problem with time windows; variable neighborhood search
