José-Luis Martínez-Flores

Logistics Planning for the Synchronization of Key Functional Areas of a Latin American Bottling Company

Currently, organizations of all kinds are forced to compete providing value to their customers effectively. Within any organization, the supply chain plays a strategic role that determines the efficiency and competitiveness of the same through the optimization of its processes in order to maximize the profitability of the organization and meet the requirements of its customers. Generally, companies base their operations on a sales forecast whose level of variation directly impacts the performance of production planning, material planning, and the management of their inventories. Faced with a state of uncertainty in demand, the control of inventory of their products becomes complex when it comes to materials with a limited lifetime for the food industry. Therefore, the objective of this case study focuses on defining the most appropriate forecasting method for a finished product from a beverage bottling company settled in Mexico that smooths the variability of the demand. The company experiences a forecast variability of minimum 135% with respect to its actual sales based on naive systems. In addition, it is also considered convenient to synchronize the demand of the finished product with the optimal inventory level of its raw materials through the analysis of different stochastic inventory models like a periodic and continuous review in order to avoid MXN 14’805,188.00 in spills due to the expiration date. As a result of this proposal, we obtained better forecasting reducing the variability up to 95%, and the developer of new inventory policies can ensure the availability of materials for production lines at 98%.

Risk-based Strategic Inventory Supply Model for New Products

Companies must maintain a high competitive level to stay in the global market. This objective is even more complex when considering the introduction of new products (NPs) in the market because these present greater uncertainty and sudden changes in demand. Because of this, an agile Supply Chain (SC) is required to respond quickly to the dynamic behavior of the NP. Thus, the present work contributes with a Risk-based Strategic Inventory Supply Model for New Products (RSISNP) that integrates diverse inventory management tools to address the introduction of NPs within the SC. For this purpose, a comprehensive study was performed with 36 managers of related companies to determine the most important factors that may affect the supply of these products. Then, these factors were integrated as cost elements within an adapted periodic-continuous review strategy which is the basis for the RSISNP model. When tested on a case study it was found that the proposed model can reduce excess-of-inventory and obsolescence costs for NPs for different type of demands and life cycles. Thus, for professionals in the logistics and production area, this model can represent a tool to improve the response of the SC to these factors throughout the development process and launch of NPs.

An Evolutive Tabu-Search Metaheuristic Approach for the Capacitated Vehicle Routing Problem

The Capacitated Vehicle Routing Problem (CVRP) is one of the most important problems in the field of Logistics and Distribution Networks. This is due to the fact that many real-world transportation applications can be modeled as a CVRP. However, because the CVRP is of NP-hard complexity, just instances with approximately 100 vertices (or demand points) can be solved exactly. Hence, heuristic and metaheuristic approaches have been studied to provide feasible (or near-optimal) solutions for large CVRP instances. This chapter presents the design of a metaheuristic in order to provide near-optimal solutions for large CVRP instances. The proposed metaheuristic integrates a two-stage solution process: first, a set of feasible CVRP routes are obtained by means of a Tabu-Search (TS) algorithm, and second, a Genetic Algorithm (GA) is integrated to improve the feasibility of each CVRP route. The performance of this Evolutive Tabu-Search (E-TS) metaheuristic was assessed on well-known large CVRP instances. Results showed average approximation errors up to 5.27% from best-known benchmarks. Final assessment was performed on a real-world routing problem with more than 240 points leading to an improved distribution network. These results corroborate the suitability of the proposed metaheuristic for large CVRP instances.

Decision Model for the Pharmaceutical Distribution of Insulin

In this work, we studied the problem of the efficient distribution of insulin, as it is a product of first necessity for the Mexican citizens with diabetes. In contrast to other pharmaceutical products, insulin must be transported with optimal conditions which restrict its distribution time. Similarly, warehousing and production costs must be considered in the supply, storage and distribution decisions given its obsolescence or damage risks. To address this problem, we propose a decision model that integrates distribution within the supply and storage aspects of the insulin supply chain. For this case, we considered variables such as warehousing costs, transportation costs and times, and decisions regarding route and inventory planning. For the development of the integrated decision model, we extended on the capacitated vehicle routing problem (CVRP) model and the Economic Order Quantity (EOQ) model of logistics and supply chain management. When the integrated model was tested on a distribution instance of 100 pharmaceutical retailers, we obtained a more suitable distribution of insulin considering time restrictions and operational warehousing and transportation costs. Thus, the proposed model can be considered for improvement of distribution decisions for other products or retailers in the pharmaceutical industry.

Dynamic Reduction-Expansion Operator to Improve Performance of Genetic Algorithms for the Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is an important routing problem within the transportation industry. However, finding optimal solutions for this problem is not easy due to its computational complexity. In this work, a novel operator based on dynamic reduction-expansion of minimum distance is presented as an initial population strategy to improve the search mechanisms of Genetic Algorithms (GA) for the TSP. This operator, termed as , consists of four stages: (a) clustering to identify candidate supply/demand locations to be reduced, (b) coding of clustered and nonclustered locations to obtain the set of reduced locations, (c) sequencing of minimum distances for the set of reduced locations (nearest neighbor strategy), and (d) decoding (expansion) of the reduced set of locations. Experiments performed on TSP instances with more than 150 nodes provided evidence that can improve convergence of the GA and provide more suitable solutions than other approaches focused on the GA’s initial population.

Logistic model for the facility location problem on ellipsoids

The facility location problem (FLP) consists of finding the best location for a facility (service center) to efficiently meet the demands of client points. Studies on the FLP have considered flat or spherical surfaces for computation of distances between these entities. However, the Earth’s surface is not flat or perfectly spherical. This article considers the ellipsoid as a more appropriate model of the Earth’s surface and adapts the FLP to address this assumption. The optimization of the adapted FLP was performed using Solver. Results on geographic instances provided important insights about the logistic financial aspect of the ellipsoid assumption.