Метод розв'язання періодичної задачі маршрутизації транспортних засобів

The desire of companies to reduce transportation costs requires the development of efficient methods of route construction. In this paper we consider the Periodic Vehicle Routing Problem with Time Window (PVRPTW), which extends the well-known Vehicle Routing Problem. Routes are designed for a planning horizon of several days. Customers need to be served one or more times during the planning horizon, following one of the proposed visit schedules. For example, a customer may request to be visited twice during a five-day period and that these visits can occur on one of the following day combinations: Monday-Thursday, Tuesday-Friday, or Wednesday-Friday. A fleet of vehicles of varying capacities is available. Each vehicle performs a maximum of one route per day. Any route starts and ends at the warehouse.  For each customer, their demand and time window during each day when the customer expects to be visited are known. The problem is to construct a set of minimum cost routes that provide service to all customers according to their schedules. The solution of the PVRPTW involves assigning a combination of visit days to each customer and obtaining a set of routes for each day of the planning period such that the number of routes for each day does not exceed the number of available vehicles and the capacity of any vehicle is not exceeded. A heuristic algorithm consisting of the stages of construction and improvement of the solution is proposed. In the first stage, a greedy algorithm is used to construct a solution that is acceptable in terms of attendance schedules. The second stage consists in applying an ensemble of local search algorithms, each of which allows to improve the obtained solution. Computational experiments were conducted on data sets of up to 210 clients.Розглянуто підходи до розв'язання періодичної задачі маршрутизації транспортних засобів. Запропоновано евристичний метод для розв'язання періодичної задачі маршрутизації з часовими вікнами, який ґрунтується на концепції локального пошуку. Розроблено програмне забезпечення, що реалізує запропонований метод, та дозволяє будувати маршрути для заданого періоду планування

Optimal Routing for Safe Construction and Demolition Waste Transportation: A CVaR Criterion and Big Data Analytics Approach

Rapid urbanisation worldwide, especially in developing countries and areas, has led to the generation of large amounts of construction and demolition waste (C&DW). The resultant transportation demands pose severe threats to safe transportation and secure city operation. By considering the low-probability–high-consequence nature of C&DW traffic accidents and the effectiveness of route optimisation in transportation risk control, a risk-averse project was implemented. Furthermore, an optimal routing model based on the conditional value at risk (CVaR) criterion is proposed. The model considered various risk-averse attitudes of decision-makers. For practicality and for strongly supporting policy-making, big data technology, including the construction of multistructure databases and in-depth analysis, was applied to achieve the proposed CVaR routing model. Therefore, the present study extended the CVaR method to optimal routing design in the field of safe urban C&DW transportation and integrated the optimal model with big data technology

Sustainable practices in logistics systems: An overview of companies in Brazil

© 2019 by the authors. The main purpose of this article is to present an overview of the applications of sustainable practices in logistic operations performed by Brazilian companies. To reach this objective, the following steps were carried out: (1) a review of the literature on logistics systems and sustainability in logistics activities; (2) the collection of sustainability reports published by companies that perform logistics operations, which are recognized in Brazil; (3) a content analysis of the reports collected and (4) a discussion of the results, cross-checked with the literature and the extrapolation of conclusions. It was possible to identify 22 sustainable practices, and these practices were grouped into five macro areas. The authors of this paper believe that the findings presented here can be useful for professionals and researchers in the implementation of sustainability practices in logistics systems

PVRP-DC-SO instances

These are the 36 instances used in the article "The Periodic Vehicle Routing Problem with Driver Consistency and Service Time Optimization", written by Inmaculada Rodríguez-Martín and Hande Yaman, and published in …………….. These data files have the same format as the classical PVRP instances from the literature. We have kept this format, but in for the PVPR-DC-SO we skip some of the information in the files. The infomation we do not consider is: - The maximum duration of a route (denoted by D bellow) - The vehicles’capacity (denoted by Q bellow) - The service duration for each customer (denoter by d bellow) Taking this into account, the files are read in the following way: The first line contains the following information: type m n t where type = 1 (PVRP), m = number of vehicles, n = number of customers, t = number of days. The next t lines contain, for each day ,the following information: D Q where D = maximum duration of a route (0 means 'unbounded'), Q = maximum load of a vehicle. The next lines contain, for the depot and each customer, the following information: i x y d q f a list where i = customer number (0 corresponds to the depot), x = x coordinate, y = y coordinate, d = service duration, q = demand, f = frequency of visit, a = number of possible visit combinations, list = list of all possible visit combinations. Each visit combination is coded with the decimal equivalent of the corresponding binary bit string. For example, in a 5-day period, the code 10 which is equivalent to the bit string 01010 means that a customer is visited on days 2 and 4. (Days are numbered from left to right.

PVRP-DC instances

Here are the 240 instances used in the article "The Periodic Vehicle Routing Problem with Driver Consistency ", written by Inmaculada Rodríguez-Martín, Juan-José Salazar-González, and Hande Yaman, and published in "European Journal of Operational Research" 2018. The instance name gives information about the number of nodes, the number of time periods, and the number of vehicles. For example, test11-p2-m3-a-dat is an instance wiht 11 nodes, 2 time periods, and 3 vehicles.These data files have the same format as the classical PVRP instances from the literature, though the vehicles' capacity and the customers' demands are not considered in this paper. That is:The first line contains the following information: type m n twheretype = 1 (PVRP),m = number of vehicles,n = number of customers,t = number of days.The next t lines contain, for each day ,the following information: D QwhereD = maximum duration of a route (0 means 'unbounded'), Q = maximum load of a vehicle,The next lines contain, for the depot and each customer, the following information:i x y d q f a list wherei = customer number (0 corresponds to the depot),x = x coordinate,y = y coordinate,d = service duration,q = demand,f = frequency of visit,a = number of possible visit combinations,list = list of all possible visit combinations.Each visit combination is coded with the decimal equivalent of the corresponding binary bit string. For example, in a 5-day period, the code 10 which is equivalent to the bit string 01010 means that a customer is visited on days 2 and 4. (Days are numbered from left to right.)THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV