Juan Sepúlveda

Bio: Juan Sepúlveda is an academic researcher from National University of Colombia. The author has contributed to research in topics: Vehicle routing problem & Heuristic (computer science). The author has an hindex of 2, co-authored 2 publications receiving 7 citations.

An algorithm for the routing problem with split deliveries and time windows (SDVRPTW) applied on retail SME distribution activities

Abstract: In this paper, particular conditions of retail trade SMEs was analyzed, identifying not enough financial resources for using powerful tools for solve vehicle routing problem (VRP). On the other hand, in literature revised could not be identified studies about application of current approaches for solving VRP in SMEs. Additionally because of high cost, commercial software do not fit investment budget of those companies. Through a simple insertion heuristic for VRP with split deliveries and time windows (SDVRPTW), developed on an accessible technology platform like Microsoft® Excel™, was validated that SDVRPTW is an appropriate approach for solving vehicle routing problem on retail trade SMEs. Computational results show that the heuristic proposed can reduce about 50% the fleet size.

Comparación entre árboles de regresión CART y regresión lineal

Abstract: Linear regression is the most widely used method in statistics to predict values of continuous variables due to its easy interpretation, but in many situations the suppositions to apply the model are not met and some users tend to force them leading them to erroneous conclusions. CART regression trees is a regression alternative that does not require suppositions on the data to be analyzed and is a method of easy interpretation of results. This work compares predictive levels of linear regression with CART through simulation. In general, it was found that when the correct linear regression model is adjusted to the data, the prediction error of linear regression is always lower than that of CART. It was also found that when linear regression model is erroneously adjusted to the data, the prediction error of CART is lower than that of linear regression only when it has a sufficiently large amount of data.

Branch-and-Cut for the Split Delivery Vehicle Routing Problem with Time Windows

Abstract: The split delivery vehicle routing problem with time windows (SDVRPTW) is a notoriously hard combinatorial optimization problem. First, it is hard to find a useful compact mixed-integer programming...

Vehicle routing with time windows : optimization and approximation

Abstract: A survey of solution methods for routing problems with time window constraints. Among the problems considered are the traveling salesman problem, the vehicle routing problem, the pickup and delivery problem, and the dial-a-ride problem. Optimization algorithms that use branch and bound, dynamic programming and set partitioning, and approximation algorithms based on construction, iterative improvement and incomplete optimization are presented.

Compact Formulations for Split Delivery Routing Problems

Abstract: Split delivery routing problems are concerned with serving the demand of a set of customers with a fleet of capacitated vehicles at minimum cost, where a customer can be served by more than one vehicle if beneficial. They generalize traditional variants of routing problems and have applications in commercial and humanitarian logistics. Previously, formulations involving only commonly used arc-based variables have provided only relaxations for split delivery variants, as the possibility of visiting customers more than once introduces modeling challenges. The only known compact formulations are based on variables indexed by vehicle or by visit number and perform poorly when using general-purpose integer programming software. We present compact formulations that avoid the use of these types of variables and that can model split delivery routing problems with and without time windows. Computational experiments demonstrate their superior performance over existing compact formulations. We also develop a branch-and-cut algorithm that balances the efficiency derived from a relaxed formulation with the strength derived from one of the proposed formulations and demonstrate its efficacy on a large set of benchmark instances. The algorithm solves 95 instances to proven optimality for the first time and improves the best known lower and/or upper bound for many other instances.

Modelos y metodologías de credit score para personas naturales: una revisión literaria

Abstract: This paper provides a literature review on risk scoring models for credit granting in personal banking The methods by Abdou & Pointon (2011), Glennon, Kiefer, Larson, & Choi (2008), and Saavedra-Garcia (2010) are considered The aim is to create a sorting scheme to explain the multiple mathematical and econometrical models used for credit scoring and to produce an up-to-date list supported by scholars and experts in the field