Elias Olivares-Benitez

A metaheuristic approach for selecting a common platform for modular products based on product performance and manufacturing cost

A known strategy to implement Mass Customization is Product Modularization. To take advantage of the benefits of modularity, the selection of a common platform is required. This selection must be done with optimization criteria based on functionality and economics. In this paper we propose metaheuristic procedures to solve the problem of selecting a common platform for a modular product. This selection is based on an aggregate objective function that combines product performance and manufacturing cost. The problem is divided into two hierarchical problems that must be solved sequentially. The mathematical models have a nonlinear integer formulation. Because of the computational complexity to solve optimally these models, metaheuristic procedures are proposed to solve each sub-problem. These procedures are based on Scatter Search and Tabu Search. A case study is presented with a small instance that is solved with these procedures and by total enumeration. The results of the metaheuristic procedures coincide with the optimal values found by total enumeration. The run times are reasonable and it is expected a greater benefit for a larger instance with similar results quality.

Supply chain network design with efficiency, location, and inventory policy using a multiobjective evolutionary algorithm

This study presents a metaheuristic based on a multiobjective evolutionary algorithm to solve a biobjective mixed-integer nonlinear programming model for supply chain design with location-inventory decisions and supplier selection. The supply chain has four echelons with suppliers, plants, distribution centers, and retailers. The decision variables are the opening of plants and distribution centers and the flow of materials between the different facilities, considering a continuous review inventory policy. The conflicting objectives are to minimize total costs on the entire chain, and to maximize a combined value of overall equipment effectiveness from suppliers. Small- and medium-sized scenarios are solved and compared with Pareto fronts obtained with commercial optimization software applying the epsilon-constraint method. The numerical results show the effectiveness of the proposed metaheuristic. The main contributions of this work are a new practical problem that has not been analyzed before, and the development of the evolutionary algorithm.

Extensions to K-Medoids with Balance Restrictions over the Cardinality of the Partitions

The zones design occurs when small areas or basic geographic units (BGU) must be grouped into acceptable zones under the requirements imposed by the case study. These requirements can be the generation of intra-connected and/or compact zones or with the same amount of habitants, clients, communication means, public services, etc. In this second point to design a territory, the selection and adaptation of a clustering method capable of generating compact groups while keeping balance in the number of objects that form each group is required. The classic partitioning stands out (also known as classification by partition among the clustering or classification methods [1]). Its properties are very useful to create compact groups. An interesting property of the classification by partitions resides in its capability to group different kinds of data. When working with geographical data, such as the BGU, the partitioning around medoids algorithms have given satisfactory results when the instances are small and only the objective of distances minimization is optimized. In the presence of additional restrictions, the K-medoids algorithms, present weaknesses in regard to the optimality and feasibility of the solutions. In this work we expose 2 variants of partitioning around medoids for geographical data with balance restrictions over the number of objects within each group keeping the optimality and feasibility of the solution. The first algorithm considers the ideas of k-meoids and extends it with a recursive constructive function to find balanced solutions. The second algorithm searches for solutions taking into account a balance between compactness and the cardinality of the groups (multiobjective). Different tests are presented for different numbers of groups and they are compared with some results obtained with Lagrange Relaxation. This kind of grouping is needed to solve aggregation for Territorial Design problems

Insular Biobjective Routing with Environmental Considerations for a Solid Waste Collection System in Southern Chile

This paper presents a biobjective problem for a solid waste collection system in a set of islands in southern Chile. The first objective minimizes transportation cost and the second one reduces the environmental impact caused by the accumulation of solid waste at the collection points. To solve this problem, biobjective mixed integer linear programming is used. In the model, an itinerary scheme is considered for the visit to the islands. The model decides which collection points are visited per island, the collection pattern, and quantity of solid waste to be collected at each site. The quantity of solid waste is obtained dividing the solid waste generated in the island by the number of collection points selected in that same island and the frequency of visits. For this problem, we considered that the environmental impact function varies through the days during which solid waste is accumulated at each collection point. We present an instance based on real data for a set of islands in Chiloe and Palena regions in southern Chile, in which the deposit node is Dalcahue. We used the epsilon-constraint method and the weighted sum method to obtain the Pareto front, using commercial optimization software.

Technology mapping of the scientific research in biomaterials: a trends study of years 2000–2002

Competitive Technical Intelligence (CTI) is a management tool with increasing application in the last years. In order to analyse information, CTI applies diverse methods such as Technology Mapping (TM), a tool that represents a S&T field in a given period of time as a knowledge network, with the aim of detecting opportunities and trends in specific areas. This article proposes a first approach of TM as part of a CTI process where primary and secondary information sources are analysed. For this purpose, a study to identify the major topics of research in biomaterials from 2000 to 2002 was carried out. After introducing the lector to principles of CTI, scientometrics and co-word analysis, we present our proposal including the process to develop TM. Following the results reveals the major subjects in the groups of artificial and natural biomaterials, organs and tissues, applications and manufacturing processes. Finally, the conclusions are given.

Production Planning for a Company in the Industry of Compact Discs Mass Replications

This chapter addresses a production planning problem for a company that mass-replicates compact discs. In the current situation, the staff of the company creates a production plan for the long term, with low flexibility to make a new plan when new orders arrive. The combination of attributes of the orders and the available machines for the processes generate a high complexity to determine the appropriate production routes and sequencing. To reduce complexity, the computation of a priority index is proposed to combine different attributes of the orders. To optimize the utilization of the production capacities, two approaches were proposed: a Simulation Model and a Linear Programming Model. The priority index is used in both models to promote early scheduling of certain orders to the machines during the planning horizon. The results show that the models proposed deliver production plans in a short time, with a better utilization of the production capacity, and with a focus on improving service level when compared to the current methodology in the company. Additionally, the Linear Programming Model is integrated into an intelligent decision-support system to guarantee the data transfer from the information system of the company and fast execution as often as needed.

Combined Use of Mathematical Optimization and Design of Experiments for the Maximization of Profit in a Four-Echelon Supply Chain

This paper develops a location-allocation model to optimize a four-echelon supply chain network, addressing manufacturing and distribution centers location, supplier selection and flow allocation for raw materials from suppliers to manufacturers, and finished products for end customers, while searching for system profit maximization. A fractional-factorial design of experiments is performed to analyze the effects of capacity, quality, delivery time, and interest rate on profit and system performance. The model is formulated as a mixed-integer linear programming problem and solved by using well-known commercial software. The usage of factorial experiments combined with mathematical optimization is a novel approach to address supply chain network design problems. The application of the proposed model to a case study shows that this combination of techniques yields satisfying results in terms of both its behavior and the obtained managerial insights. An ANOVA analysis is executed to quantify the effects of each factor and their interactions. In the analyzed case study, the transportation cost is the most relevant cost component, and the most relevant opportunity for profit improvement is found in the factor of quality. The proposed combination of methods can be adapted to different problems and industries.

Optimizing Safety Stock Levels in Modular Production Systems Using Component Commonality and Group Technology Philosophy: A Study Based on Simulation

Modular production and component commonality are two widely used strategies in the manufacturing industry to meet customers growing needs for customized products. Using these strategies, companies can enhance their performance to achieve optimal safety stock levels. Despite the importance of safety stocks in business competition, little attention has been paid to the way to reduce them without affecting the customer service levels. This paper develops a mathematical model to reduce safety stock levels in organizations that employ modular production. To construct the model, we take advantage of the benefits of aggregate inventories, standardization of components, component commonality, and Group Technology philosophy in regard to stock levels. The model is tested through the simulation of three years of operation of two modular product systems. For each system, we calculated and compared the safety stock levels for two cases: ( ) under the only presence of component commonality and ( ) under the presence of both component commonality and Group Technology philosophy. The results show a reduction in safety stock levels when we linked the component commonality with the Group Technology philosophy. The paper presents a discussion of the implications of each case, features of the model, and suggestions for future research.