Abstract
This document addresses the problem of scheduling and routing a specific number of vehicles to visit a set of customers in specific time windows during a planning horizon. The vehicles have a homogeneous limited capacity and have their starting point and return in a warehouse or initial node, in addition, multiple variants of the classic VRP vehicle routing problem are considered, where computational complexity increases with the increase in the number of customers to visit, as a characteris-tic of an NP-hard problem. The solution method used consists of two connected phases, the first phase makes the allocation through a mixed-integer linear programming model, from which the visit program and its frequency in a determined plan-ning horizon are obtained. In the second phase, the customers are grouped through an unsupervised learning algorithm, the routing is carried out through an Ant Colony Optimization metaheuristic that includes local heu-ristics to make sure com-pliance with the restrictive factors. Finally, we test our algorithm by performance measures using instances of the literature and a comparative model, and we prove the effectiveness of the proposed algorithm.
Highlights
Complexity in business environments grows over time because of technological advances and the dynamics of the economic system, for this reason, organizations of all kinds must constantly work on solutions that allow solving problems at strategic, tactical, and operational levels
This article presents a proposed solution to a periodic vehicle routing problem (PVRP), which considers the minimum frequency of visits to customers in a given planning horizon as well as capacity limitations of the vehicle fleet and the customer’s time windows, through a two-phase iterative model, using optimization techniques in the first phase where the allocation of customers is made for visiting the set of vehicles in each of the periods, while in the second phase performs a grouping by customers and uses the ant colony optimization (ACO) metaheuristic for routing supported with local heuristics for compliance with
This section illustrates the results of the solution method presented to solve a routing problem with capacity and time windows through the comparison with the contribution made by Rodriguez, Correa & López (2015), which corresponds to a two-phase model using exact methods interconnected iteratively through the exit and ending inventory procedures
Summary
Complexity in business environments grows over time because of technological advances and the dynamics of the economic system, for this reason, organizations of all kinds must constantly work on solutions that allow solving problems at strategic, tactical, and operational levels. Among the activities to be carried out at a tactical and operational level is allocating resources in the medium term and or short-term and scheduling tasks such as vehicle routing, which due to the level of detail of information and scalability corresponds to a problem difficult to solve These activities of distribution and transport of goods or services have a high influence on their quality and can mean a high cost for the logistics management of organizations, which added to the context of increased demand for this type of services and competition it requires an adequate level of service and high operational performance in terms of costs. The first periodic vehicle routing problem for a given planning horizon was performed by Russell & Gribbin (1991) where they present an approach for determining effective vehicle routes that satisfy customer service frequencies in the planning horizon, through heuristics in four phases, analysis and initial solution, cost exchange with the traveling salesman problem, cost reduction by addressing real routes and entire modeling for final improvements
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