scholarly journals Model Hub Median Problem Dengan Batasan Travel Time

2017 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Faisal Ibrahim ◽  
Ahmad Rusdiansyah
Keyword(s):  
Numerical Experiments ◽  
Cost Minimization ◽  
Computational Time ◽  
Hub Location ◽  
Median Problem ◽  
Problem Model ◽  
Excel Solver ◽  
Hub Model ◽  
Spread Sheet ◽  
Total Cost Minimization

This article describes the development of an uncapacitated p-hub median problem model. The established model will be applied in determining the location of the hub. Generally in the median p-hub model problem, it is known that the number of hubs built with the total cost minimization function. In this developed model, there is no limit to the number of hubs and the function of the intended destination is how many hubs are built. The model also looks for where the hub location will be built and which nodes are allocated to each hub. By eliminating the limitation of the number of hubs built, the model adds a total total timeout limit. The numerical experiments are dealing with the model. The solution to solve the model using excel solver. So that it will be designed spread sheet excel appropriate model. In numerical experiments, dummy data representing real systems will be used with the aim of shortening computational time. Numerical experiments are performed in several conditions scenarios. Computational results generate the location of hubs and nodes allocated to each hub with various experimental scenarios.

The Application of Fuzzification for Solving Quadratic Functions

2021 ◽  
Author(s):  
Lunshan Gao
Keyword(s):  
Approximate Solution ◽  
Approximation Algorithm ◽  
Critical Point ◽  
Numerical Experiments ◽  
Optimization Problems ◽  
Local Minimizer ◽  
Quadratic Functions ◽  
Computational Time ◽  
Computational Results ◽  
Average Computational Time

Abstract This paper describes an approximation algorithm for solving standard quadratic optimization problems(StQPs) over the standard simplex by using fuzzification technique. We show that the approximate solution of the algorithm is an epsilon -critical point and satisfies epsilon-delta condition. The algorithm is compared with IBM ILOG CPLEX (short for CPLEX). The computational results indicate that the new algorithm is faster than CPLEX. Especially for infeasible problems. Furthermore, we calculate 100 instances for different size StQP problems. The numerical experiments show that the average computational time of the new algorithm for calculating the first local minimizer is in BigO(n) when the size of the problems is less or equal to 450.

scholarly journals A Branch-Price-and-Cut Procedure for the Discrete Ordered Median Problem

2020 ◽  
Vol 32 (3) ◽  
pp. 582-599 ◽  
Author(s):  
Samuel Deleplanque ◽  
Martine Labbé ◽  
Diego Ponce ◽  
Justo Puerto
Keyword(s):  
Column Generation ◽  
Set Partitioning ◽  
Computational Time ◽  
Median Problem ◽  
Ordered Median ◽  
Set Partitioning Problem ◽  
Continuous Relaxation ◽  
Partitioning Problem

The discrete ordered median problem (DOMP) is formulated as a set-partitioning problem using an exponential number of variables. Each variable corresponds to a set of demand points allocated to the same facility with the information of the sorting position of their corresponding costs. We develop a column generation approach to solve the continuous relaxation of this model. Then we apply a branch-price-and-cut algorithm to solve small- to large-sized instances of DOMP in competitive computational time.

Hub Location and thep-Hub Median Problem

1996 ◽  
Vol 44 (6) ◽  
pp. 923-935 ◽  
Author(s):  
James F. Campbell
Keyword(s):  
Hub Location ◽  
Median Problem

scholarly journals An artificial immune system algorithm for solving the uncapacitated single allocation p-Hub median problem

2021 ◽  
Vol 11 (3) ◽  
pp. 2293
Author(s):  
Fatima Zahraa Grine ◽  
Oulaid Kamach ◽  
Abdelhakim Khatab ◽  
Naoufal Sefiani
Keyword(s):  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Data Sets ◽  
Clonal Selection Algorithm ◽  
Artificial Immune ◽  
Hub Location ◽  
Median Problem ◽  
Solution Quality ◽  
Artificial Immune System Algorithm ◽  
Single Allocation

The present paper deals with a variant of hub location problems (HLP): the uncapacitated single allocation p-Hub median problem (USApHMP). This problem consists to jointly locate hub facilities and to allocate demand nodes to these selected facilities. The objective function is to minimize the routing of demands between any origin and destination pair of nodes. This problem is known to be NP-hard. Based on the artificial immune systems (AIS) framework, this paper develops a new approach to efficiently solve the USApHMP. The proposed approach is in the form of a clonal selection algorithm (CSA) that uses appropriate encoding schemes of solutions and maintains their feasibility. Comprehensive experiments and comparison of the proposed approach with other existing heuristics are conducted on benchmark from civil aeronautics board, Australian post, PlanetLab and Urand data sets. The results obtained allow to demonstrate the validity and the effectiveness of our approach. In terms of solution quality, the results obtained outperform the best-known solutions in the literature.

Diversification-based learning simulated annealing algorithm for hub location problems

2019 ◽  
Vol 26 (6) ◽  
pp. 1995-2016
Author(s):  
Himanshu Rathore ◽  
Shirsendu Nandi ◽  
Peeyush Pandey ◽  
Surya Prakash Singh
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Convergence Rates ◽  
Location Problems ◽  
Computational Time ◽  
Data Sets ◽  
Learning Mechanisms ◽  
Hub Location ◽  
Content Type ◽  
Annealing Algorithm

Purpose The purpose of this paper is to examine the efficacy of diversification-based learning (DBL) in expediting the performance of simulated annealing (SA) in hub location problems. Design/methodology/approach This study proposes a novel diversification-based learning simulated annealing (DBLSA) algorithm for solving p-hub median problems. It is executed on MATLAB 11.0. Experiments are conducted on CAB and AP data sets. Findings This study finds that in hub location models, DBLSA algorithm equipped with social learning operator outperforms the vanilla version of SA algorithm in terms of accuracy and convergence rates. Practical implications Hub location problems are relevant in aviation and telecommunication industry. This study proposes a novel application of a DBLSA algorithm to solve larger instances of hub location problems effectively in reasonable computational time. Originality/value To the best of the author’s knowledge, this is the first application of DBL in optimisation. By demonstrating its efficacy, this study steers research in the direction of learning mechanisms-based metaheuristic applications.

H- Versus P-Version FEM for Shell Analysis

1995 ◽  
Author(s):  
Jacob Fish ◽  
Ravi Guttal
Keyword(s):  
Computational Efficiency ◽  
Numerical Experiments ◽  
Degrees Of Freedom ◽  
Computational Time ◽  
Shell Elements ◽  
Quadrature Scheme ◽  
Assumed Strain ◽  
Shell Analysis ◽  
Speed Up

Abstract Research efforts aimed at optimizing the computational efficiency of the p-method are described. These include (i) a novel quadrature scheme for hierarchical shell elements, (ii) a family of assumed strain hierarchical shell elements, (iii) selective polynomial order escalation for assumed strain elements, and (iv) accelerated multi-grid-like solution procedures. Numerical experiments indicate that with these enhancements it is possible to speed up the overall computational time of p-method for analysis of shells by a factor greater than three for relatively small problems (less than 10,000 degrees of freedom), while computational savings for larger problems are even more significant. It has been found that the performance of the enhanced variant of the p-method for shells is comparable to that of the h-method for low accuracy requirements, and better if higher accuracies are desired.

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