Modelizado para la planeación de la producción y la logística directa e inversa de una cadena de suministro cuyo abastecimiento depende parcialmente de los materiales de retorno

Abstract

Production planning model: Mathematical model which represents all the facilities in each one of the levels of the supply chain and their geographical localization. It lets to determine production and stock levels and delivery quantities in order to satisfy the demands in a planning horizon of several periods. xvThis model has capacity constraints for both production and storage, constraints of demand, compatibility between products and facilities because not all the products can be in all facilities, and constraints of direct deliveries among determinate nodes in the supply chain (particularly between plants and retailers), especially.Vehicle routing model: mathematical model that represents products distribution from different source nodes to destine nodes, it use a heterogeneous fleet, in a time period.One of the model assumptions is that a node acts as product emitting or recipient (facility plants or retailers). However, an intermediate node can take one of these roles depending if is the origin or destine of goods. This model has fleet use constraints, volume, and mass capacity vehicle constraints, fluxes in the supply chain. A vehicle can visit several destine nodes after leave from a source node, and a node can be visited by one or more vehicles (because of compatibility conditions between products and nodes or by economical aspects) and there are soft and strong time windows.Integrated logistics and production management model: It is a model that join the two previous models, because of it, it is possible to plan production, inventories, and vehicle routing for a horizon time of several periods.Three proposed models have the objective of minimizing the total cost and are evaluated with testing instances.The Models were built using Gurobi 6.5.1 optimization model and Python2.7 programming language. Moreover, it was built a constructive heuristics and a genetic algorithm using Python programming language and Pyevolve software, as an alternative to solving routing vehicles, to reduce execution time.xvi Índice generalAgradecimientos ix Resumen xi Resum xiii Summary xv Índice general xvii Índice de Tablas xxi Índice de Figuras xxv 1 Introducción1.1 Antecedentes y estado actual del tema Ruteo de vehículosRespecto del problema de ruteo de vehículos (VRP), éste ha sido estudiado teniendo en cuenta múltiples variantes entre las cuales están el considerar o no límites en la capacidad, relaciones con producción, entregas y recogidas de manera simultánea, ventanas de tiempo, entre otros aspectos.Bolduc, Laporte, Renaud, y Boctor (2010) proponen un modelo de ruteo de vehículos que tiene en cuenta los calendarios de demanda y de producción (VRPPDC), sin restricciones de capacidad, por medio de búsqueda tabú.Los problemas que tienen en cuenta ventanas de tiempo han sido bastante estudiados, trabajos como el de X. Li, Leung, y Tian (2012) en el cual se definen subproblemas elementales de ruta más corta con restricciones de recursos y el de Chabrier ( 2006) en el cual se resuelve este tipo de problema por medio de método...