Santiago-Omar Caballero-Morales

Building Cost Function 3D Benchmarks to Improve the Economic Statistical Design of Control Charts

Shewhart or control charts are important Statistical Process Control (SPC) techniques used for prompt detection of failures in a manufacturing process and minimization of production costs which are modelled with nonlinear functions (cost functions). Heuristic methods have been used to find the chart’s parameters integrated within the cost function that best comply with economic and statistical restrictions. However heuristic estimation is highly dependent on the size of the search space, the set of initial solutions, and the exploration operators. In this paper the 3D analysis of the cost function is presented to more accurately identify the search space associated with each parameter of control charts and to improve estimation. The parameters estimated with this approach were more accurate than those estimated with Hooke and Jeeves (HJ) and Genetic Algorithms (GAs) under different failure distributions. The results presented in this work can be used as a benchmark to evaluate and improve the performance of other heuristic methods.

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.