Issue 191 by Miguel Mata Perez
Distribution is one of the most important processes in a supply chain, given that it represents u... more Distribution is one of the most important processes in a supply chain, given that it represents up to two thirds of company logistics costs and up to 20% of the total cost of products. For that reason, it is essential to optimize the costs of distribution. A steel producer located in Monterrey distributes their products to different parts of Mexico. Currently, the distribution is carried out through empirical knowledge, underusing resources and generating unnecessary costs. The aim is to undertake the distribution process more efficiently. This paper presents an optimization model based on vehicle routing problem (VRP), for the distribution of heavy pipes taking into account the company’s own characteristics, such as: rented heterogeneous fleet, multiple shipments of products, split deliveries and open cycles (meaning that the routes may not necessarily end in the depot).
Papers by Miguel Mata Perez
In this paper a mixed-integer linear programming (MILP) model is studied for the bi-objective pub... more In this paper a mixed-integer linear programming (MILP) model is studied for the bi-objective public R&D projects portfolio problem. The proposed approach provides an acceptable compromise between the impact and the number of supported projects. Lagrangian relaxation techniques are considered to get easy computable bounds for the objectives. The experiments show that a solution can be obtained in less than a minute for instances comprising of up to 25,000 project proposals. This brings significant improvement to the previous approaches that efficiently manage instances of a few hundred projects. Keywords: R&D projects portfolios, mixed integer programming, multi-objective optimization.
In this paper a mixed-integer linear programming (MILP) model is studied for the bi-objective pub... more In this paper a mixed-integer linear programming (MILP) model is studied for the bi-objective public R&D projects portfolio problem. The proposed approach provides an acceptable compromise between the impact and the number of supported projects. Lagrangian relaxation techniques are considered to get easy computable bounds for the objectives. The experiments show that a solution can be obtained in less than a minute for instances comprising of up to 25,000 project proposals. This brings significant improvement to the previous approaches that efficiently manage instances of a few hundred projects. Keywords: R&D projects portfolios, mixed integer programming, multi-objective optimization.
In this paper a mixed-integer linear programming (MILP) model is studied for the bi-objective pub... more In this paper a mixed-integer linear programming (MILP) model is studied for the bi-objective public R&D projects portfolio problem. The proposed approach provides an acceptable compromise between the impact and the number of supported projects. Lagrangian relaxation techniques are considered to get easy computable bounds for the objectives. The experiments show that a solution can be obtained in less than a minute for instances comprising of up to 25,000 project proposals. This brings significant improvement to the previous approaches that efficiently manage instances of a few hundred projects. Keywords: R&D projects portfolios, mixed integer programming, multi-objective optimization.
Journal of Computer and Systems Sciences International, 2011
This paper presents a multi objective MILP model for portfolio selection of research and developm... more This paper presents a multi objective MILP model for portfolio selection of research and development (R&D) projects with synergies. The proposed model incorporates information about the funds assigned to different activities as well as about synergies between projects at the activity and project level. The latter aspects are predominant in the context of portfolio selection of R&D projects in public organizations. Previous works on portfolio selection of R&D projects considered interdepen dencies mainly at the project level. In a few works considering activity level information the models and solution techniques were restricted to problems with a few projects. We study a generalization of our previous model and show that incorporating interdependencies and activity funding information is useful for obtaining portfolios with better quality. Numerical results are presented to demonstrate the efficiency of the proposed approach for large models.
Proceedings of VI ALIO/ …, 2008
Journal of Computer and Systems Sciences International, 2009
Classical and modified Lagrangian bounds for the optimal value of optimization problems with a do... more Classical and modified Lagrangian bounds for the optimal value of optimization problems with a double decomposable structure are studied. For the class of many to many assignment problems, this property of constraints is used to design a subgradient algorithm for solving the modified dual problem. Numerical results are presented to compare the quality of classical and modified bounds, as well as the properties of the corresponding Lagrangian solutions.
Journal of Computer and Systems Sciences International, 2010
There are often many ways in which a given problem can be relaxed in a Lagrangian fashion. It is ... more There are often many ways in which a given problem can be relaxed in a Lagrangian fashion. It is not obvious a priori, which relaxation produces the best bound. Moreover, a bound may appear to be the best for a certain data set, while being among the worst for another problem instance. We consider here an opti mization problem over the set of Lagrangian relaxations with the objective to indicate the relaxation produc ing the best dual bound. An iterative technique to solve this problem is proposed based on constraints gener ation scheme. The approach is illustrated by a computational study for a class of the two stage capacitated facility location problem.
Journal of Computer and Systems Sciences International, Apr 27, 2009
Classical and modified Lagrangian bounds for the optimal value of optimization problems with a do... more Classical and modified Lagrangian bounds for the optimal value of optimization problems with a double decomposable structure are studied. For the class of many to many assignment problems, this property of constraints is used to design a subgradient algorithm for solving the modified dual problem. Numerical results are presented to compare the quality of classical and modified bounds, as well as the properties of the corresponding Lagrangian solutions.
A Lagrangian based heuristic is proposed for many-to-many assignment problems taking into account... more A Lagrangian based heuristic is proposed for many-to-many assignment problems taking into account capacity limits for task and agents. A modified Lagrangian bound studied earlier by the authors is presented and a greedy heuristic is then applied to get a feasible Lagrangian-based solution. The latter is also used to speed up the subgradient scheme to solve the modified Lagrangian dual problem. A numerical study is presented to demonstrate the efficiency of the proposed approach.
In the two-stage capacitated facility location problem a single product is produced at some plant... more In the two-stage capacitated facility location problem a single product is produced at some plants in order to satisfy customer demands. The product is transported from these plants to some depots and then to the customers. The capacities of the plants and depots are limited. The aim is to select cost minimizing locations from a set of potential plants and depots. This cost includes fixed cost associated with opening plants and depots, and variable cost associated with both transportation stages. In this work a Lagrangian relaxation is analyzed and a Lagrangian heuristic producing feasible solutions is presented. The results of a computational study are reported.
Distribution is one of the most important processes in a supply chain, given that it represents u... more Distribution is one of the most important processes in a supply chain, given that it represents up to two thirds of company logistics costs and up to 20% of the total cost of products. For that reason, it is essential to optimize the costs of distribution. A steel producer located in Monterrey distributes their products to different parts of Mexico. Currently, the distribution is carried out through empirical knowledge, underusing resources and generating unnecessary costs. The aim is to undertake the distribution process more efficiently. This paper presents an optimization model based on vehicle routing problem (VRP), for the distribution of heavy pipes taking into account the company's own characteristics, such as: rented heterogeneous fleet, multiple shipments of products, split deliveries and open cycles (meaning that the routes may not necessarily end in the depot).
In this paper we show the importance of applying mathematical optimization when designing the dis... more In this paper we show the importance of applying mathematical optimization when designing the distribution network in a supply chain, specifically in making decisions related location of facilities and inventory management, which are associated with different levels of planning but are closely related. The addressed problem is an extension of the classic capacitated facility location problem. The distinguishing features are: the inventory management, the presence of multiple plants, and the single source constraints in both echelons. A key issue is that demand at each distribution center is a function of the demands at the retailers assigned, which is a random variable whose value is not known at the time of designing the network. We focus on the mathematical modeling of the problem and the evaluation of the performance of the developed models, so, it can be observed the troubles that arise when modeling supply chains that consider different types of decisions. Keywords: Supply chain, location and inventory problem, mixed integer nonlinear programming, mixed integer linear programming. RESUMEN En este artículo se muestra la importancia de la optimización matemática en el diseño de una cadena de suministros, específicamente en la toma de decisiones dentro de un problema de localización de instalaciones y un problema de inventarios. Dichas decisiones pertenecen a diferentes niveles de planeación aun así se encuentran estrechamente relacionadas. El problema es una extensión del clásico problema de localización de instalaciones capacitadas. Las características destacadas son: el manejo de inventarios, la presencia de múltiples plantas y las restricciones de única fuente en ambos niveles de la cadena. Un punto clave en la investigación consiste en definir la demanda de los centros de distribución como función de la demanda de los minoristas asignados, la cual es una variable aleatoria, cuyo valor es desconocido al momento de diseñar la red de distribución. Nos enfocamos en la modelación matemática del problema y en la evaluación del desempeño de los modelos desarrollados, de manera que es posible observar la dificultad que involucra modelar cadenas de suministros que consideran diferentes tipos de decisiones.
In this paper a mixed-integer linear programming (MILP) model is studied for the bi-objective pub... more In this paper a mixed-integer linear programming (MILP) model is studied for the bi-objective public R&D projects portfolio problem. The proposed approach provides an acceptable compromise between the impact and the number of supported projects. Lagrangian relaxation techniques are considered to get easy computable bounds for the objectives. The experiments show that a solution can be obtained in less than a minute for instances comprising of up to 25,000 project proposals. This brings significant improvement to the previous approaches that efficiently manage instances of a few hundred projects. Keywords: R&D projects portfolios, mixed integer programming, multi-objective optimization.
Journal of Computer and Systems Sciences International, 2009
Classical and modified Lagrangian bounds for the optimal value of optimization problems with a do... more Classical and modified Lagrangian bounds for the optimal value of optimization problems with a double decomposable structure are studied. For the class of many to many assignment problems, this property of constraints is used to design a subgradient algorithm for solving the modified dual problem. Numerical results are presented to compare the quality of classical and modified bounds, as well as the properties of the corresponding Lagrangian solutions.
Journal of Computer and Systems Sciences International, 2010
There are often many ways in which a given problem can be relaxed in a Lagrangian fashion. It is ... more There are often many ways in which a given problem can be relaxed in a Lagrangian fashion. It is not obvious a priori, which relaxation produces the best bound. Moreover, a bound may appear to be the best for a certain data set, while being among the worst for another problem instance. We consider here an opti mization problem over the set of Lagrangian relaxations with the objective to indicate the relaxation produc ing the best dual bound. An iterative technique to solve this problem is proposed based on constraints gener ation scheme. The approach is illustrated by a computational study for a class of the two stage capacitated facility location problem.
Journal of Computer and Systems Sciences International, 2011
This paper presents a multi objective MILP model for portfolio selection of research and developm... more This paper presents a multi objective MILP model for portfolio selection of research and development (R&D) projects with synergies. The proposed model incorporates information about the funds assigned to different activities as well as about synergies between projects at the activity and project level. The latter aspects are predominant in the context of portfolio selection of R&D projects in public organizations. Previous works on portfolio selection of R&D projects considered interdepen dencies mainly at the project level. In a few works considering activity level information the models and solution techniques were restricted to problems with a few projects. We study a generalization of our previous model and show that incorporating interdependencies and activity funding information is useful for obtaining portfolios with better quality. Numerical results are presented to demonstrate the efficiency of the proposed approach for large models.
In this paper a mixed-integer linear programming (MILP) model is studied for the bi-objective pub... more In this paper a mixed-integer linear programming (MILP) model is studied for the bi-objective public R&D projects portfolio problem. The proposed approach provides an acceptable compromise between the impact and the number of supported projects. Lagrangian relaxation techniques are considered to get easy computable bounds for the objectives. The experiments show that a solution can be obtained in less than a minute for instances comprising of up to 25,000 project proposals. This brings significant improvement to the previous approaches that efficiently manage instances of a few hundred projects. Keywords: R&D projects portfolios, mixed integer programming, multi-objective optimization.
A Lagrangian based heuristic is proposed for many-to-many assignment problems taking into account... more A Lagrangian based heuristic is proposed for many-to-many assignment problems taking into account capacity limits for task and agents. A modified Lagrangian bound studied earlier by the authors is presented and a greedy heuristic is then applied to get a feasible Lagrangian-based solution. The latter is also used to speed up the subgradient scheme to solve the modified Lagrangian dual problem. A numerical study is presented to demonstrate the efficiency of the proposed approach.
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Issue 191 by Miguel Mata Perez
Papers by Miguel Mata Perez