scholarly journals Optimization of the Multi-Facility Location Problem Using Widely Available Office Software

Algorithms ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 106
Author(s):  
Petr Němec ◽  
Petr Stodola
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Optimal Location ◽  
Evolutionary Method ◽  
Office Software ◽  
User Knowledge ◽  
Annealing Method

Multi-facility location problem is a type of task often solved (not only) in logistics. It consists in finding the optimal location of the required number of centers for a given number of points. One of the possible solutions is to use the principle of the genetic algorithm. The Solver add-in, which uses the evolutionary method, is available in the Excel office software. It was used to solve the benchmark in 4 levels of difficulty (from 5 centers for 25 points to 20 centers for 100 points), and one task from practice. The obtained results were compared with the results obtained by the metaheuristic simulated annealing method. It was found that the results obtained by the evolutionary method are sufficiently accurate. Their accuracy depends on the complexity of the task and the performance of the HW used. The advantage of the proposed solution is easy availability and minimal requirements for user knowledge.

A simple and effective genetic algorithm for the two-stage capacitated facility location problem

2014 ◽  
Vol 75 ◽  
pp. 200-208 ◽  
Author(s):  
Diogo R.M. Fernandes ◽  
Caroline Rocha ◽  
Daniel Aloise ◽  
Glaydston M. Ribeiro ◽  
Enilson M. Santos ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Two Stage ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location

scholarly journals Combined Simulated Annealing Algorithm for the Discrete Facility Location Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Jin Qin ◽  
Ling-lin Ni ◽  
Feng Shi
Keyword(s):  
Simulated Annealing ◽  
Facility Location ◽  
Location Problem ◽  
Simulated Annealing Algorithm ◽  
Facility Location Problem ◽  
Location Decision ◽  
Previous Algorithm ◽  
Annealing Algorithm ◽  
Discrete Facility Location ◽  
Better Than

The combined simulated annealing (CSA) algorithm was developed for the discrete facility location problem (DFLP) in the paper. The method is a two-layer algorithm, in which the external subalgorithm optimizes the decision of the facility location decision while the internal subalgorithm optimizes the decision of the allocation of customer's demand under the determined location decision. The performance of the CSA is tested by 30 instances with different sizes. The computational results show that CSA works much better than the previous algorithm on DFLP and offers a new reasonable alternative solution method to it.

GENETIC ALGORITHM SOLUTION FOR MULTI-PERIOD TWO-ECHELON INTEGRATED COMPETITIVE/UNCOMPETITIVE FACILITY LOCATION PROBLEM

2008 ◽  
Vol 25 (01) ◽  
pp. 33-56 ◽  
Author(s):  
XUE-FENG WANG ◽  
XIAO-MING SUN ◽  
YANG FANG
Keyword(s):  
Genetic Algorithm ◽  
System Design ◽  
Facility Location ◽  
Distribution System ◽  
Location Problem ◽  
Random Search ◽  
Market Competition ◽  
Regional Distribution ◽  
Facility Location Problem ◽  
Distribution Centers

This paper addresses the multi-period two-echelon integrated competitive/uncompetitive facility location problem in a distribution system design that involves locating regional distribution centers (RDCs) and stores, and determining the best strategy for distributing the commodities from a central distribution center (CDC) to RDCs and from RDCs to stores. The goal is to determine the optimal numbers, locations and capacities of RDCs and stores so as to maximize the total profit of the distribution system. Unlike most of past research, our study allows for dynamic planning horizon, distribution of commodities, configuration of two-echelon facilities, availability of capital for investment, external market competition, customer choice behavior and storage limitation. This problem is formulated as a bi-level programming model and a mutually consistent programming mode, respectively. Since such a distribution system design problem belongs to a class of NP-hard problem, a genetic algorithm-based heuristic (GA) is presented and compared with random search solution and mutually consistent solution (MC) using numerical example. The computational results show that the GA approach is efficient and the values of the performance index were significantly improved relative to the MC.

scholarly journals Optimization of the Weighted Multi-Facility Location Problem Using MS Excel

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 191
Author(s):  
Petr Němec ◽  
Petr Stodola ◽  
Miroslav Pecina ◽  
Jiří Neubauer ◽  
Martin Blaha
Keyword(s):  
Facility Location ◽  
Location Problem ◽  
Dimensional Space ◽  
Continuous Optimization ◽  
Facility Location Problem ◽  
Benchmark Instances ◽  
Hardware Configuration ◽  
Complex Optimization ◽  
Excel Solver ◽  
Office Software

This article presents the possibilities in solving the Weighted Multi-Facility Location Problem and its related optimization tasks using a widely available office software—MS Excel with the Solver add-in. To verify the proposed technique, a set of benchmark instances with various point topologies (regular, combination of regular and random, and random) was designed. The optimization results are compared with results achieved by a metaheuristic algorithm based on simulated annealing principles. The influence of the hardware configuration on the performance achieved by MS Excel Solver is also examined and discussed from both the execution time and accuracy perspectives. The experiments showed that this widely available office software is practical for solving even relatively complex optimization tasks (Weighted Multi-Facility Location Problem with 100 points and 20 centers, which consists of 40 continuous optimization variables in two-dimensional space) with sufficient quality for many real-world applications. The method used is described in detail and step-by-step using an example.

scholarly journals Mathematical Programming Model for the Two-Level Facility Location Problem: The Case of Tanzanian Emergence Maize Distribution Network for 2004–2010 Maize Data

2021 ◽  
Vol 47 (3) ◽  
pp. 1020-1032
Author(s):  
Said A Sima
Keyword(s):  
Mathematical Programming ◽  
Facility Location ◽  
Location Problem ◽  
Programming Model ◽  
Transportation Network ◽  
Distribution Network ◽  
Facility Location Problem ◽  
Optimal Location ◽  
Mathematical Programming Model ◽  
Maize Crop

A two-level facility location problem (FLP) has been studied in the transportation network of emergence maize crop in Tanzania. The facility location problem is defined as the optimal location of facilities or resources so as to minimize costs in terms of money, time, distance and risks with the relation to supply and demand points. Distribution network design problems consist of determining the best way to transfer goods from the supply to the demand points by choosing the structure of the network such that the overall cost is minimized. The three layers, namely production centres (PCs), distribution centres (DCs) and customer points (CPs) are considered in the two-level FLP. The flow of maize from PCs to CPs through DCs is designed at a minimum cost under deterministic mathematical programming model. The four decisions to be made simultaneously are: to determine the locations of DCs (including number of DCs), allocation of CPs to the selected DCs, allocation of selected DCs to PCs, and to determine the amount of maize crop transported from PCs to DCs and then from DCs to CPs. The modelled problem generated results through optimization with respect to optimal location-allocation strategies. The results of the optimized network shows the improvement in costs saving compared to the manually operated existing network. The results show the costs saving of up to 18% which is equivalent to $2,910 thousand (TZS 2.9 billion). Keywords:    Optimization; Maize crop; Transportation network; Deterministic model; Facility location

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