Heuristic solucions to the facility location problem with general Bernoulli demands

In this paper, a heuristic procedure is proposed for the facility location problem with general Bernoulli demands. This is a discrete facility location problem with stochastic demands that can be formulated as a two-stage stochastic program with recourse. In particular, facility locations and customer assignments must be decided here and now, i.e., before knowing the customers who will actually require to be served. In a second stage, service decisions are made according to the actual requests. The heuristic proposed consists of a greedy randomized adaptive search procedure followed by a path relinking. The heterogeneous Bernoulli demands make prohibitive the computational effort for evaluating feasible solutions. Thus the expected cost of a feasible solution is simulated when necessary. The results of extensive computational tests performed for evaluating the quality of the heuristic are reported, showing that high-quality feasible solutions can be obtained for the problem in fairly small computational times.Peer ReviewedPostprint (author's final draft

Exact solutions to a class of stochastic generalized assignment problems

This paper deals with a stochastic Generalized Assignment Problem with recourse. Only a random subset of the given set of jobs will require to be actually processed. An assignment of each job to an agent is decided a priori, and once the demands are known, reassignments can be performed if there are overloaded agents. We construct a convex approximation of the objective function that is sharp at all feasible solutions. We then present three versions of an exact algorithm to solve this problem, based on branch and bound techniques, optimality cuts, and a special purpose lower bound. numerical results are reported.

Outsourcing policies for the Facility Location Problem with Bernoulli Demand

This paper focuses on the Facility Location Problem with Bernoulli Demand, a discrete facility location problem with uncertainty where the joint distribution of the customers' demands is expressed by means of a set of possible scenarios. A two-stage stochastic program with recourse is used to select the facility locations and the a priori assignments of customers to open plants, together with the a posteriori strategy to apply in those realizations where the a priori solution is not feasible. Four alternative outsourcing policies are studied for the recourse action, and a mathematical programming formulation is presented for each of them. Extensive computational experiments have been carried-out to analyze the performance of each of the formulations and to compare the quality of the solutions produced by each of them relative to the other outsourcing policies

A computational comparison of several formulations for the multi-period incremental service facility location problem

The Multi-period Incremental Service Facility Location Problem, which was recently introduced, is a strategic problem for timing the location of facilities and the assignment of customers to facilities in a multi-period environment. Aiming at finding the strongest formulation for this problem, in this work we study three alternative formulations based on the so-called impulse variables and step variables. To this end, an extensive computational comparison is performed. As a conclusion, the hybrid impulse–step formulation provides better computational results than any of the other two formulations

SOLVING FIRE DEPARTMENT STATION LOCATION PROBLEM USING MODIFIED BINARY GENETIC ALGORITHM: A CASE STUDY OF SAMSUN IN TURKEY

Fire and traffic accident are the most common problem in our daily lives. Fire department location for the purpose of easy response and recovery is a significant problem today. The best location of a fire department station determines the rate of recovery of many injured people after traffic accident, fire outbreak, and so on. In this paper, we considered the best location of fire stations. In addition, we also considered where the station is setup. Surveying problem is modeled as a mixed integer programming. It is solved by modified binary genetic algorithm, coding with GAMS. Modified binary GA is different from known GA with respect to binary decision variables. Due to this problem, initial value of the objective function was obtained from known GA. Then finally, the best result was achieved from binary GA

SOLVING FIRE DEPARTMENT STATION LOCATION PROBLEM USING MODIFIED BINARY GENETIC ALGORITHM: A CASE STUDY OF SAMSUN IN TURKEY

Fire and traffic accident are the most common problem in our daily lives. Fire department location for the purpose of easy response and recovery is a significant problem today. The best location of a fire department station determines the rate of recovery of many injured people after traffic accident, fire outbreak, and so on. In this paper, we considered the best location of fire stations. In addition, we also considered where the station is setup. Surveying problem is modeled as a mixed integer programming. It is solved by modified binary genetic algorithm, coding with GAMS. Modified binary GA is different from known GA with respect to binary decision variables. Due to this problem, initial value of the objective function was obtained from known GA. Then finally, the best result was achieved from binary GA

Designing a Reverse Logistics Network for End-of-Life Vehicles Recovery

The environmental factors are receiving increasing attention in different life cycle stages of products. When a product reaches its End-Of-Life (EOL) stage, the management of its recovery process is affected by the environmental and also economical factors. Selecting efficient methods for the collection and recovery of EOL products has become an important issue. The European Union Directive 2000/53/EC extends the responsibility of the vehicle manufacturers to the postconsumer stage of the vehicle. In order to fulfill the requirements of this Directive and also efficient management of the whole recovery process, the conceptual framework of a reverse logistics network is presented. The distribution of new vehicles in an area and also collecting the End-of-Life Vehicles (ELVs) and their recovery are considered jointly. It is assumed that the new vehicles distributors are also responsible for collecting the ELVs. Then a mathematical model is developed which minimizes the costs of setting up the network and also the relevant transportation costs. Because of the complexity of the model, a solution methodology based on the genetic algorithm is designed which enables achieving good quality solutions in a reasonable algorithm run time

Solutions to the facility location problem with general Bernoulli demands

In this work we address the facility location problem with general Bernoulli demands. Extended formulations are proposed for two different outsourcing policies, which allow using sample average approximation for estimating optimal values. In addition, solutions are obtained heuristically and their values compared with the obtained estimates. Numerical results of a series of computational experiments are presented and analyzed.In this work we address the facility location problem with general Bernoulli demands. Extended formulations are proposed for two different outsourcing policies, which allow using sample average approximation for estimating optimal values. In addition, solutions are obtained heuristically and their values compared with the obtained estimates. Numerical results of a series of computational experiments are presented and analyzed.Postprint (published version

An exact approach for the reliable fixed-charge location problem with capacity constraints

Introducing capacities in the reliable fixed charge location problem is a complex task since successive failures might yield in high facility overloads. Ideally, the goal consists in minimizing the total cost while keeping the expected facility overloads under a given threshold. Several heuristic approaches have been proposed in the literature for dealing with this goal. In this paper, we present the first exact approach for this problem, which is based on a cutting planes algorithm. Computational results illustrate its good performancePostprint (published version