José-Fernando Camacho-Vallejo

Bilevel Programming and Applications

A great amount of new applied problems in the area of energy networks has recently arisen that can be efficiently solved only as mixed-integer bilevel programs. Among them are the natural gas cash-out problem, the deregulated electricity market equilibrium problem, biofuel problems, a problem of designing coupled energy carrier networks, and so forth, if we mention only part of such applications. Bilevel models to describe migration processes are also in the list of the most popular new themes of bilevel programming, as well as allocation, information protection, and cybersecurity problems. This survey provides a comprehensive review of some of the above-mentioned new areas including both theoretical and applied results.

Solving the Bilevel Facility Location Problem under Preferences by a Stackelberg-Evolutionary Algorithm

This research highlights the use of game theory to solve the classical problem of the uncapacitated facility location optimization model with customer order preferences through a bilevel approach. The bilevel model provided herein consists of the classical facility location problem and an optimization of the customer preferences, which are the upper and lower level problems, respectively. Also, two reformulations of the bilevel model are presented, reducing it into a mixed-integer single-level problem. An evolutionary algorithm based on the equilibrium in a Stackelberg’s game is proposed to solve the bilevel model. Numerical experimentation is performed in this study and the results are compared to benchmarks from the existing literature on the subject in order to emphasize the benefits of the proposed approach in terms of solution quality and estimation time.

A heuristic algorithm for a supply chain's production-distribution planning

In this paper we consider the problem of planning the production and distribution in a supply chain. The situation consists in a set of distribution centers seeking to serve to a set of retailers; these distribution centers are supplied by a set of plants trying to minimize the operation and transportation costs. The problem is formulated as a bilevel mathematical problem where the upper level consists of deciding the amount of product sent from the distribution centers to the retailers trying to minimize the transportation costs and also by considering the costs of acquiring the products that come from the plants. Meanwhile the lower level consists in minimizing the plants? operations cost meeting the demand grouped in the distribution centers. We propose a heuristic algorithm based on Scatter Search that considers the Stackelberg?s equilibrium; numerical tests show that our proposed algorithm improves the existing best known results in the literature.

A Collaborative Supply Chain Management System for a Maritime Port Logistics Chain

In this article we propose a collaborative logistics framework for a Port Logistics Chain (PLC) based on the principlesof Supply Chain Management (SCM) that rely on stakeholders integration and collaboration, providing a referencemodel for the inland coordination of the PLC. A comprehensive literature review was conducted, analyzing severalcases in which SCM practices have been implemented as well as studies related to port development, governance,coordination and best practices associated. This background information was used to identify current gaps in logisticsmanagement practices and potential scopes of intervention within the PLC to suggest a redesign process andconfigure new structures under a collaborative scheme, following the guidelines of SCM.

A heuristic algorithm solving bilevel toll optimization problems

Purpose – One of the most important problems concerning the toll roads is the setting of an appropriate cost for traveling through private arcs of a transportation network. The purpose of this paper is to consider this problem by stating it as a bilevel programming (BLP) model. At the upper level, one has a public regulator or a private company that manages the toll roads seeking to increase its profits. At the lower level, several companies-users try to satisfy the existing demand for transportation of goods and/or passengers, and simultaneously, to select the routes so as to minimize their travel costs. In other words, what is sought is kind of a balance of costs that bring the highest profit to the regulating company (the upper level) and are still attractive enough to the users (the lower level). Design/methodology/approach – With the aim of providing a solution to the BLP problem in question, a direct algorithm based on sensitivity analysis (SA) is proposed. In order to make it easier to move (if nec...

A Genetic Algorithm for the Bi-Level Topological Design of Local Area Networks.

Local access networks (LAN) are commonly used as communication infrastructures which meet the demand of a set of users in the local environment. Usually these networks consist of several LAN segments connected by bridges. The topological LAN design bi-level problem consists on assigning users to clusters and the union of clusters by bridges in order to obtain a minimum response time network with minimum connection cost. Therefore, the decision of optimally assigning users to clusters will be made by the leader and the follower will make the decision of connecting all the clusters while forming a spanning tree. In this paper, we propose a genetic algorithm for solving the bi-level topological design of a Local Access Network. Our solution method considers the Stackelberg equilibrium to solve the bi-level problem. The Stackelberg-Genetic algorithm procedure deals with the fact that the follower’s problem cannot be optimally solved in a straightforward manner. The computational results obtained from two different sets of instances show that the performance of the developed algorithm is efficient and that it is more suitable for solving the bi-level problem than a previous Nash-Genetic approach.

Analyzing the Performance of a Hybrid Heuristic for Solving a Bilevel Location Problem under Different Approaches to Tackle the Lower Level

The problem addressed here is a combinatorial bilevel programming problem called the uncapacitated facility location problem with customer’s preferences. A hybrid algorithm is developed for solving a battery of benchmark instances. The algorithm hybridizes an evolutionary algorithm with path relinking; the latter procedure is added into the crossover phase for exploring the trajectory between both parents. The proposed algorithm outperforms the evolutionary algorithm already existing in the literature. Results show that including a more sophisticated procedure for improving the population through the generations accelerates the convergence of the algorithm. In order to support the latter statement, a reduction of around the half of the computational time is obtained by using the hybrid algorithm. Moreover, due to the nature of bilevel problems, if feasible solutions are desired, then the lower level must be solved for each change in the upper level’s current solution. A study for illustrating the impact in the algorithm’s performance when solving the lower level through three different exact or heuristic approaches is made.

A discrete bilevel brain storm algorithm for solving a sales territory design problem: a case study

A sales territory design problem faced by a manufacturing company that supplies products to a group of customers located in a service region is addressed in this paper. The planning process of designing the territories has the objective to minimizing the total dispersion of the customers without exceeding a limited budget assigned to each territory. Once territories have been determined, a salesperson has to define the day-by-day routes to satisfy the demand of customers. Currently, the company has established a service level policy that aims to minimize total waiting times during the distribution process. Also, each territory is served by a single salesperson. A novel discrete bilevel optimization model for the sales territory design problem is proposed. This problem can be seen as a bilevel problem with a single leader and multiple independent followers, in which the leader’s problem corresponds to the design of territories (manager of the company), and the routing decision for each territory corresponds to each follower. The hierarchical nature of the current company’s decision-making process triggers some particular characteristics of the bilevel model. A brain storm algorithm that exploits these characteristics is proposed to solve the discrete bilevel problem. The main features of the proposed algorithm are that the workload is used to verify the feasibility and to cluster the leader’s solutions. In addition, four discrete mechanisms are used to generate new solutions, and an elite set of solutions is considered to reduce computational cost. This algorithm is used to solve a real case study, and the results are compared against the current solution given by the company. Results show a reduction of more than 20% in the current costs with the solution obtained by the proposed algorithm. Furthermore, a sensitivity analysis is performed, providing interesting managerial insights to improve the current operations of the company.

Optimizing a Biobjective Production-Distribution Planning Problem Using a GRASP

This paper addresses a biobjective production-distribution planning problem. The problem is formulated as a mixed integer programming problem with two objectives. The objectives are to minimize the total costs and to balance the total workload of the supply chain, which consist of plants and depots, considering that it represents a company vertically integrated. In order to solve the model, we propose an adapted biobjective GRASP to obtain an approximation of the Pareto front. To evaluate the performance of the proposed algorithm, numerical experimentations are conducted over a set of instances used for similar problems. Results indicate that the proposed GRASP obtains a relatively small number of nondominated solutions for each tested instance in very short computational time. The approximated Pareto fronts are discontinuous and nonconvex. Moreover, the solutions clearly show the compromise between both objective functions.

Approximating solutions to a bilevel capacitated facility location problem with customer's patronization toward a list of preferences

This paper presents a bilevel capacitated facility location problem where customers are allocated to the facilities they patronize based on a predetermined list of preferences. The bilevel problem is composed of an upper level, where a company locates facilities to minimize locating and distributing costs; and a lower level, where customers aim to maximize their preferences by being allocated to the most preferred facilities to get their demands met. The complexity of the lower level problem, which is NP-hard, demands alternatives for obtaining, in general, the followers rational reaction set. Hence, bilevel attainable solutions are defined for solving the bilevel problem in an efficient manner. Moreover, for obtaining valid bounds, a reformulation of the bilevel problem based on the lower levels linear relaxation is performed. Then, a cross entropy method is implemented for obtaining solutions in the upper level; while the lower level is solved in three different manners: by a greedy randomized adaptive procedure based on preferences, by the same procedure but based on a regret cost, and by an exact method (when possible). The conducted experimentation shows the competitiveness of the proposed algorithms, in terms of solution quality and consumed time, despite the complexity of the problems components.