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.

scholarly journals Hub location problem in round-trip service applications

2020 ◽  
Author(s):  
Omar Kemmar ◽  
Karim Bouamrane ◽  
Shahin Gelareh
Keyword(s):  
Economies Of Scale ◽  
Location Problem ◽  
Service Providers ◽  
Real Life ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Hub Location ◽  
Service Networks ◽  
Hub Location Problem ◽  
Hub And Spoke

In this paper, we introduce a new hub-and-spoke structure for service networks based on round-trips as practiced by some transport service providers. This problem is a variant of Uncapacitated Hub Location Problem wherein the spoke nodes allocated to a hub node form round-trips (cycles) starting from and ending to the hub node. This problem is motivated by two real-life practices in logistics wherein  runaway  nodes and  runaway  connections with their associated economies of scale were foreseen to increase redundancy in the network. We propose a mixed integer linear programming mathematical model with exponential number of constraints. In addition to the separation routines for separating from among exponential constraints, we propose a hyper-heuristic based on reinforcement learning and its comparable counterpart as a variable neighborhood search. Our extensive computational experiments confirm efficiency of the proposed approaches.In this paper, we introduce a new hub-and-spoke structure for service networks based on round-trips as practiced by some transport service providers. This problem is a variant of Uncapacitated Hub Location Problem wherein the spoke nodes allocated to a hub node form round-trips (cycles) starting from and ending to the hub node. This problem is motivated by two real-life practices in logistics wherein  runaway  nodes and  runaway  connections with their associated economies of scale were foreseen to increase redundancy in the network. We propose a mixed integer linear programming mathematical model with exponential number of constraints. In addition to the separation routines for separating from among exponential constraints, we propose a hyper-heuristic based on reinforcement learning and its comparable counterpart as a variable neighborhood search. Our extensive computational experiments confirm efficiency of the proposed approaches.

scholarly journals Green closed-loop supply chain network design: a novel bi-objective chance-constraint approach

2021 ◽  
Vol 55 (2) ◽  
pp. 811-840
Author(s):  
Amin Reza Kalantari Khalil Abad ◽  
Seyed Hamid Reza Pasandideh
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Closed Loop ◽  
Programming Model ◽  
Expected Value ◽  
Chance Constraint ◽  
Chance Constrained Programming ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Closed Loop Supply Chain

In this paper, a novel chance-constrained programming model has been proposed for handling uncertainties in green closed loop supply chain network design. In addition to locating the facilities and establishing a flow between them, the model also determines the transportation mode between facilities. The objective functions are applied to minimize the expected value and variance of the total cost CO2 released is also controlled by providing a novel chance-constraint including a stochastic upper bound of emission capacity. To solve the mathematical model using the General Algebraic Modeling System (GAMS) software, four multi-objective decision-making (MODM) methods were applied. The proposed methodology was subjected to various numerical experiments. The solutions provided by different methods were compared in terms of the expected value of cost, variance of cost, and CPU time using Pareto-based analysis and optimality-based analysis. In Pareto-based analysis, a set of preferable solutions were presented using the Pareto front; then optimality-based optimization was chosen as the best method by using a Simple Additive Weighting (SAW) method. Experimental experiments and sensitivity analysis demonstrated that the performance of the goal attainment method was 13% and 24% better that of global criteria and goal programming methods, respectively.

Maximal covering location problem (MCLP) with fuzzy travel times

2011 ◽  
Vol 38 (12) ◽  
pp. 14535-14541 ◽  
Author(s):  
Soheil Davari ◽  
Mohammad Hossein Fazel Zarandi ◽  
Ahmad Hemmati
Keyword(s):  
Location Problem ◽  
Travel Times ◽  
Maximal Covering

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 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 Mathematical Model and Stochastic Multi-Criteria Acceptability Analysis for Facility Location Problem

2016 ◽  
Vol 28 (3) ◽  
pp. 245-256 ◽  
Author(s):  
Selahattin Karabay ◽  
Erkan Köse ◽  
Mehmet Kabak ◽  
Eren Ozceylan
Keyword(s):  
Mathematical Programming ◽  
Facility Location ◽  
Location Problem ◽  
Programming Model ◽  
Real Life ◽  
Facility Location Problem ◽  
Mixed Integer ◽  
Mathematical Programming Model ◽  
General Management ◽  
New Facilities

This paper studies a real-life public sector facility location problem. The problem fundamentally originated from the idea of downsizing the number of service centres. However, opening of new facilities is also considered in case the current facilities fail to fulfil general management demands. Two operation research methodologies are used to solve the problem and the obtained results are compared. First, a mathematical programming model is introduced to determine where the new facilities will be located, and which districts get service from which facilities, as if there were currently no existing facilities. Second, the Stochastic Multi-criteria Acceptability Analysis-TRI (SMAA-TRI) method is used to select the best suitable places for service centres among the existing facilities. It is noted that the application of mathematical programming model and SMAA-TRI integration approach on facility location problem is the first study in literature. Compression of outcomes shows that mixed integer linear programming (MILP) model tries to open facilities in districts which are favoured by SMAA-TRI solution.

scholarly journals A Parallel Heuristic Method for Optimizing a Real Life Problem (Agricultural Land Investment Problem)

2018 ◽  
Vol 7 (4) ◽  
pp. 168
Author(s):  
Sagvan A. Saleh
Keyword(s):  
Parallel Computing ◽  
Message Passing Interface ◽  
Agricultural Land ◽  
Programming Model ◽  
Heuristic Method ◽  
Real Life ◽  
Neighborhood Search ◽  
Parallel Method ◽  
Real Life Problem ◽  
Investment Problem

This paper proposed a parallel method for solving the Agricultural Land Investment Problem (ALIP), the problem that has an important impact on the agriculture issues. The author is first represent mathematically the problem by introducing a mathematical programming model. Then, a parallel method is proposed for optimizing the problem. The proposed method based on principles of parallel computing and neighborhood search methods. Neighborhood search techniques explore a series of solutions spaces with the aim of finding the best one. This is exploited in parallel computing, where several search processes are performed simultaneously. The parallel computing is designed using Message Passing Interface (MPI) which allows to build a flexible parallel program that can be executed in multicore and/or distributed environment. The method is competitive since it is able to solve a real life problem and yield high quality results in a fast solution runtime.

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