scholarly journals Multi-Period Maximal Covering Location Problem with Capacitated Facilities and Modules for Natural Disaster Relief Services

2021 ◽  
Vol 11 (1) ◽  
pp. 397
Author(s):  
Roghayyeh Alizadeh ◽  
Tatsushi Nishi ◽  
Jafar Bagherinejad ◽  
Mahdi Bashiri
Keyword(s):  
Lower Bounds ◽  
Large Scale ◽  
Location Problem ◽  
Disaster Relief ◽  
Heuristic Method ◽  
Real Life ◽  
Planning Horizon ◽  
Upper Bounds ◽  
Maximal Covering ◽  
Large Scale Problems

The paper aims to study a multi-period maximal covering location problem with the configuration of different types of facilities, as an extension of the classical maximal covering location problem (MCLP). The proposed model can have applications such as locating disaster relief facilities, hospitals, and chain supermarkets. The facilities are supposed to be comprised of various units, called the modules. The modules have different sizes and can transfer between facilities during the planning horizon according to demand variation. Both the facilities and modules are capacitated as a real-life fact. To solve the problem, two upper bounds—(LR1) and (LR2)—and Lagrangian decomposition (LD) are developed. Two lower bounds are computed from feasible solutions obtained from (LR1), (LR2), and (LD) and a novel heuristic algorithm. The results demonstrate that the LD method combined with the lower bound obtained from the developed heuristic method (LD-HLB) shows better performance and is preferred to solve both small- and large-scale problems in terms of bound tightness and efficiency especially for solving large-scale problems. The upper bounds and lower bounds generated by the solution procedures can be used as the profit approximation by the managerial executives in their decision-making process.

scholarly journals The large scale maximal covering location problem

2011 ◽  
Vol 18 (6) ◽  
pp. 1564-1570 ◽  
Author(s):  
M.H. Fazel Zarandi ◽  
S. Davari ◽  
S.A. Haddad Sisakht
Keyword(s):  
Large Scale ◽  
Location Problem ◽  
Maximal Covering

scholarly journals A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes

2020 ◽  
Vol 54 (5) ◽  
pp. 1467-1494
Author(s):  
Binhui Chen ◽  
Rong Qu ◽  
Ruibin Bai ◽  
Wasakorn Laesanklang
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Search Algorithm ◽  
Real Life ◽  
Planning Horizon ◽  
Neighborhood Search ◽  
Variable Neighbourhood Search ◽  
Container Transportation ◽  
Solution Quality ◽  
Routing Problem

This paper studies a real-life container transportation problem with a wide planning horizon divided into multiple shifts. The trucks in this problem do not return to depot after every single shift but at the end of every two shifts. The mathematical model of the problem is first established, but it is unrealistic to solve this large scale problem with exact search methods. Thus, a Variable Neighbourhood Search algorithm with Reinforcement Learning (VNS-RLS) is thus developed. An urgency level-based insertion heuristic is proposed to construct the initial solution. Reinforcement learning is then used to guide the search in the local search improvement phase. Our study shows that the Sampling scheme in single solution-based algorithms does not significantly improve the solution quality but can greatly reduce the rate of infeasible solutions explored during the search. Compared to the exact search and the state-of-the-art algorithms, the proposed VNS-RLS produces promising results.

scholarly journals A new lower bound for the smallest singular value

Filomat ◽  
2019 ◽  
Vol 33 (9) ◽  
pp. 2711-2723
Author(s):  
Ksenija Doroslovacki ◽  
Ljiljana Cvetkovic ◽  
Ernest Sanca
Keyword(s):  
Lower Bound ◽  
Boundary Value Problems ◽  
Lower Bounds ◽  
Large Scale ◽  
Block Structure ◽  
Upper Bounds ◽  
Singular Value ◽  
Ecological Modelling ◽  
Infinity Norm ◽  
Smallest Singular Value

The aim of this paper is to obtain new lower bounds for the smallest singular value for some special subclasses of nonsingularH-matrices. This is done in two steps: first, unifying principle for deriving new upper bounds for the norm 1 of the inverse of an arbitrary nonsingular H-matrix is presented, and then, it is combined with some well-known upper bounds for the infinity norm of the inverse. The importance and efficiency of the results are illustrated by an example from ecological modelling, as well as on a type of large-scale matrices posessing a block structure, arising in boundary value problems.

scholarly journals The large-scale dynamic maximal covering location problem

2013 ◽  
Vol 57 (3-4) ◽  
pp. 710-719 ◽  
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Soheil Davari ◽  
Seyyed Ali Haddad Sisakht
Keyword(s):  
Large Scale ◽  
Location Problem ◽  
Maximal Covering

Modelling and analysis of uncertain hub maximal covering location problem in the presence of partial coverage

2021 ◽  
Vol 40 (5) ◽  
pp. 9987-10002
Author(s):  
Junbin Wang ◽  
Zhongfeng Qin
Keyword(s):  
Location Problem ◽  
Programming Model ◽  
Real Life ◽  
Expected Value ◽  
Neighborhood Search ◽  
Chance Constrained Programming ◽  
Travel Times ◽  
Maximal Covering ◽  
Partial Coverage ◽  
Deterministic Equivalent

The hub maximal covering location problem aims to find the best locations for hubs so as to maximize the total flows covered by predetermined number of hubs. Generally, this problem is defined in the framework of binary coverage. However, there are many real-life cases in which the binary coverage assumption may yield unexpected decisions. Thus, the partial coverage is considered by stipulating that the coverage of an origin-destination pair is determined by a non-increasing decay function. Moreover, as this problem contains strategic decisions in long range, the precise information about the parameters such as travel times may not be obtained in advance. Therefore, we present uncertain hub maximal covering location models with partial coverage in which the travel times are depicted as uncertain variables. Specifically, the partial coverage parameter is introduced in uncertain environment and the expected value of partial coverage parameter is further derived and simplified with specific decay functions. Expected value model and chance constrained programming model are respectively proposed and transformed to their deterministic equivalent forms. Finally, a greedy variable neighborhood search heuristic is presented and the efficiency of the proposed models is evaluated through computational experiments.

A New Method for Determining Optimal Locations of Bike Stations to Maximize Coverage in a Bike Share System Network

2020 ◽  
Author(s):  
Rabab Salih-Elamin ◽  
Haitham Al-Deek
Keyword(s):  
Critical Point ◽  
Location Problem ◽  
Real Life ◽  
New Method ◽  
Centrality Measures ◽  
Maximal Covering ◽  
Demand Coverage ◽  
Specific Distance ◽  
Bike Share

A new method that combines maximal covering location problem and bike station importance is utilized to find optimal locations of bike stations and maximize Bike Share System (BSS) demand coverage within a specific distance. This method uses multi-criteria that focus on centrality measures and bike station’s importance. Results of applying the new method to a real-life BSS network demonstrated that BSS efficiency improved after removing the least important bike stations up to a critical point after which efficiency started to deteriorate as the network became disconnected. The new method can be used to improve management of BSS networks.

scholarly journals A new family of conjugate gradient coefficient with application

2018 ◽  
Vol 7 (3.28) ◽  
pp. 36
Author(s):  
Norrlaili Shapiee ◽  
Mohd Rivaie ◽  
Mustafa Mamat ◽  
Puspa Liza Ghazali
Keyword(s):  
Conjugate Gradient ◽  
Large Scale ◽  
Optimization Problems ◽  
Real Life ◽  
Standard Test ◽  
Test Problems ◽  
Inexact Line Search ◽  
New Family ◽  
Unconstrained Optimization Problems ◽  
Large Scale Problems

Conjugate gradient (CG) methods are famous for their utilization in solving unconstrained optimization problems, particularly for large scale problems and have become more intriguing such as in engineering field. In this paper, we propose a new family of CG coefficient and apply in regression analysis. The global convergence is established by using exact and inexact line search. Numerical results are presented based on the number of iterations and CPU time. The findings show that our method is more efficient in comparison to some of the previous CG methods for a given standard test problems and successfully solve the real life problem.  

scholarly journals A Parallel Mixture of SVMs for Very Large Scale Problems

2002 ◽  
Vol 14 (5) ◽  
pp. 1105-1114 ◽  
Author(s):  
Ronan Collobert ◽  
Samy Bengio ◽  
Yoshua Bengio
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Support Vector ◽  
Small Subset ◽  
Training Algorithm ◽  
Classification Problems ◽  
Data Set ◽  
Large Scale Problems ◽  
Life Problems ◽  
Vector Machines

Support vector machines (SVMs) are the state-of-the-art models for many classification problems, but they suffer from the complexity of their training algorithm, which is at least quadratic with respect to the number of examples. Hence, it is hopeless to try to solve real-life problems having more than a few hundred thousand examples with SVMs. This article proposes a new mixture of SVMs that can be easily implemented in parallel and where each SVM is trained on a small subset of the whole data set. Experiments on a large benchmark data set (Forest) yielded significant time improvement (time complexity appears empirically to locally grow linearly with the number of examples). In addition, and surprisingly, a significant improvement in generalization was observed.

scholarly journals Urban Free-Space Optical Network Optimization

2020 ◽  
Vol 10 (21) ◽  
pp. 7872
Author(s):  
Revital Marbel ◽  
Boaz Ben-Moshe ◽  
Tal Grinshpoun
Keyword(s):  
Genetic Algorithm ◽  
Optical Networks ◽  
Free Space ◽  
Large Scale ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Real Life ◽  
Free Space Optical ◽  
Time Performance ◽  
Run Time

This paper presents a set of graph optimization problems related to free-space optical communication networks. Such laser-based wireless networks require a line of sight to enable communication, thus a visibility graph model is used herein. The main objective is to provide connectivity from a communication source point to terminal points through the use of some subset of available intermediate points. To this end, we define a handful of problems that differ mainly in the costs applied to the nodes and/or edges of the graph. These problems should be optimized with respect to cost and performance. The problems at hand are shown to be NP-hard. A generic heuristic based on a genetic algorithm is proposed, followed by a set of simulation experiments that demonstrate the performance of the suggested heuristic method on real-life scenarios. The suggested genetic algorithm is compared with the Euclidean Steiner tree method. Our simulations show that in many settings, especially in dense graphs, the genetic algorithm finds lower-cost solutions than its competitor, while it falls behind in some settings. However, the run-time performance of the genetic algorithm is considerably better in graphs with 1000 nodes or more, being more than twice faster in some settings. We conclude that the suggested heuristic improves run-time performance on large-scale graphs and can handle a wider range of related optimization problems. The simulation results suggest that the 5G urban backbone may benefit significantly from using free-space optical networks.

scholarly journals The capacitated maximal covering location problem with heterogeneous facilities and vehicles and different setup costs: An effective heuristic approach

2021 ◽  
pp. 79-90 ◽  
Author(s):  
Masoud Hatami Gazani ◽  
Seyed Armin Akhavan Niaki ◽  
Seyed Taghi Akhavan Niaki
Keyword(s):  
Location Problem ◽  
Heuristic Method ◽  
Optimal Solution ◽  
Heuristic Approach ◽  
Mixed Integer ◽  
Solution Technique ◽  
Maximal Covering ◽  
Setup Costs ◽  
Constructive Heuristic ◽  
Solution Methods

In this research, a maximal covering location problem (MCLP) with real-world constraints such as multiple types of facilities and vehicles with different setup costs is taken into account. An original mixed integer linear programming (MILP) model is constructed in order to find the optimal solution. Since the problem at hand is shown to be NP-hard, a constructive heuristic method and a meta-heuristic approach based on genetic algorithm (GA) are developed to solve the problem. To find the most effective solution technique, a set of problems of different sizes is randomly generated and solved by the proposed solution methods. Computational results demonstrate that the heuristic method is capable of producing optimal or near-optimal solutions in a rational execution time.

Sign in / Sign up

Export Citation Format

Share Document