Optimization of the Collaborative Hub Location Problem with Metaheuristics

By creating new job opportunities and developing the regional economy, the transport of goods generates significant costs, environmental and sanitary nuisances, and high greenhouse gas (GHG) emissions. In this context, collaboration is an interesting solution that can be used to enable companies to overcome some problems such as globalization, economic crisis, health crisis, issues related to sustainability, etc. This study deals with the design of a multiperiod multiproduct three-echelon collaborative distribution network with a heterogeneous fleet. By applying the mixed integer linear problem (MILP) formulations, it was possible to study the three dimensions of sustainability (economic, environmental, and social/societal). Since the examined problem was NP-hard, it was solved using four metaheuristic approaches to minimize the different logistics costs or CO2 emissions. The social/societal aspect evaluated the accident rate and the noise level generated by the freight transport. Four algorithms were developed to achieve our objectives: a genetic algorithm, a simulated annealing, a particle swarm algorithm, and a vibration damping optimization algorithm. Considering a French distribution network, these algorithms overcame the limits of the exact solution method by obtaining optimal solutions with reasonable execution time.

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Location of regional and international hub ports in liner shipping

Maritime Business Review ◽

10.1108/mabr-02-2017-0007 ◽

2017 ◽

Vol 2 (2) ◽

pp. 114-125 ◽

Cited By ~ 2

Author(s):

Jianfeng Zheng ◽

Cong Fu ◽

Haibo Kuang

Keyword(s):

Design Methodology ◽

Location Problem ◽

Programming Model ◽

Liner Shipping ◽

Mixed Integer ◽

Eastern Asia ◽

Hub Location ◽

Hub Location Problem ◽

Content Type ◽

Western Asia

Purpose This paper aims to investigate the location of regional and international hub ports in liner shipping by proposing a hierarchical hub location problem. Design/methodology/approach This paper develops a mixed-integer linear programming model for the authors’ proposed problem. Numerical experiments based on a realistic Asia-Europe-Oceania liner shipping network are carried out to account for the effectiveness of this model. Findings The results show that one international hub port (i.e. Rotterdam) and one regional hub port (i.e. Zeebrugge) are opened in Europe. Two international hub ports (i.e. Sokhna and Salalah) are located in Western Asia, where no regional hub port is established. One international hub port (i.e. Colombo) and one regional hub port (i.e. Cochin) are opened in Southern Asia. One international hub port (i.e. Singapore) and one regional hub port (i.e. Jakarta) are opened in Southeastern Asia and Australia. Three international hub ports (i.e. Hong Kong, Shanghai and Yokohama) and two regional hub ports (i.e. Qingdao and Kwangyang) are opened in Eastern Asia. Originality/value This paper proposes a hierarchical hub location problem, in which the authors distinguish between regional and international hub ports in liner shipping. Moreover, scale economies in ship size are considered. Furthermore, the proposed problem introduces the main ports.

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Hub location problem in round-trip service applications

RAIRO - Operations Research ◽

10.1051/ro/2020125 ◽

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.

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Megaregional Passenger Transportation Hub Location Problem Considering Congestion Effects

PROMET - Traffic&Transportation ◽

10.7307/ptt.v33i4.3666 ◽

2021 ◽

Vol 33 (4) ◽

pp. 551-563

Author(s):

Huang Yan ◽

Xiaoning Zhang

Keyword(s):

Location Problem ◽

Travel Demand ◽

Programming Model ◽

Transportation Network ◽

Network Capacity ◽

Mixed Integer ◽

Hub Location ◽

Hub Location Problem ◽

Passenger Transportation ◽

Congestion Effects

The need to make effective plans for locating transportation hubs is of increasing importance in the megaregional area, as recent research suggests that the growing intercity travel demand affects the efficiency of a megaregional transportation system. This paper investigates a hierarchical facility location problem in a megaregional passenger transportation network. The aim of the study is to determine the locations of hub facilities at different hierarchical levels and distribute the demands to these facilities with minimum total cost, including investment, transportation, and congestion costs. The problem is formulated as a mixed-integer nonlinear programming model considering the service availability structure and hub congestion effects. A case study is designed to demonstrate the effectiveness of the proposed model in the Wuhan metropolitan area. The results show that the congestion effects can be addressed by reallocating the demand to balance the hub utilisation or constructing new hubs to increase the network capacity. The methods of appropriately locating hubs and distributing traffic flows are proposed to optimise the megaregional passenger transportation networks, which has important implications for decision makers.

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Distributionally Robust Hub Location

Transportation Science ◽

10.1287/trsc.2019.0948 ◽

2020 ◽

Vol 54 (5) ◽

pp. 1189-1210 ◽

Cited By ~ 3

Author(s):

Shuming Wang ◽

Zhi Chen ◽

Tianqi Liu

Keyword(s):

Convex Hull ◽

Mixed Integer ◽

Hub Location ◽

Hub Location Problem ◽

Mixed Integer Linear Program ◽

Worst Case ◽

Data Set ◽

Second Stage ◽

Robust Model ◽

Distributionally Robust

We study the adaptive distributionally robust hub location problem with multiple commodities under demand and cost uncertainty in both uncapacitated and capacitated cases. The hub location decision anticipates the worst-case expected cost over an ambiguity set of possible distributions of the uncertain demand and cost, and the routing policy, being adaptive to the uncertainty realization, ships commodities through selected hubs. We investigate the adaptivity and tractability of the distributionally robust model under different distributional information about uncertainty. In the uncapacitated case in which demand and cost are independent and costs of different commodities are also mutually independent, the adaptive distributionally robust model is equivalent to a nonadaptive classical robust model and the second-stage routing decision follows an optimal static policy. We then relax the independence assumption and show that the second-stage routing decision follows an optimal scenario-wise policy if either the demand or the cost is supported on a convex hull of given scenarios. We extend our analysis to the capacitated case and show that the second-stage routing decision still follows an optimal scenario-wise policy if the demand is supported on the convex hull of given scenarios. In terms of tractability, for all mentioned cases, we reformulate the distributionally robust model as a moderate-sized mixed-integer linear program, and we recover the associated worst-case distribution by solving a collection of linear programs. Through numerical studies using the Civil Aeronautics Board data set, we demonstrate the advantages of the distributionally robust model by examining its superior out-of-sample performance against the classical robust model and the stochastic model.

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An Endosymbiotic Evolutionary Algorithm for the Hub Location-Routing Problem

Mathematical Problems in Engineering ◽

10.1155/2015/525980 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Ji Ung Sun

Keyword(s):

Evolutionary Algorithm ◽

Vehicle Routing ◽

Solution Method ◽

Programming Model ◽

Mixed Integer ◽

Hub Location ◽

Hub Location Problem ◽

Location Routing Problem ◽

Routing Problem ◽

Location Routing

We consider a capacitated hub location-routing problem (HLRP) which combines the hub location problem and multihub vehicle routing decisions. The HLRP not only determines the locations of the capacitatedp-hubs within a set of potential hubs but also deals with the routes of the vehicles to meet the demands of customers. This problem is formulated as a 0-1 mixed integer programming model with the objective of the minimum total cost including routing cost, fixed hub cost, and fixed vehicle cost. As the HLRP has impractically demanding for the large sized problems, we develop a solution method based on the endosymbiotic evolutionary algorithm (EEA) which solves hub location and vehicle routing problem simultaneously. The performance of the proposed algorithm is examined through a comparative study. The experimental results show that the proposed EEA can be a viable solution method for the supply chain network planning.

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Hierarchical Passenger Hub Location Problem in a Megaregion Area Considering Service Availability

PROMET - Traffic&Transportation ◽

10.7307/ptt.v33i2.3563 ◽

2021 ◽

Vol 33 (2) ◽

pp. 247-258

Author(s):

Huang Yan ◽

Xiaoning Zhang ◽

Xiaolei Wang

Keyword(s):

Travel Demand ◽

Transportation Network ◽

Mixed Integer ◽

Service Availability ◽

Hub Location ◽

Hub Location Problem ◽

Passenger Transportation ◽

Linear Programming Formulation ◽

Wuhan Metropolitan Area ◽

The Impact

The rapid growth of the intercity travel demand has resulted in enormous pressure on the passenger transportation network in a megaregion area. Optimally locating hubs and allocating demands to hubs influence the effectiveness of a passenger transportation network. This study develops a hierarchical passenger hub location model considering the service availability of hierarchical hubs. A mixed integer linear programming formulation was developed to minimize the total cost of hub operation and transportation for multiple travel demands and determine the proportion of passengers that access hubs at each level. This model was implemented for the Wuhan metropolitan area in four different scenarios to illustrate the applicability of the model. Then, a sensitivity analysis was performed to assess the impact of changing key parameters on the model results. The results are compared to those of traditional models, and the findings demonstrate the importance of considering hub choice behavior in demand allocation.

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A P-Hub Location Problem for Determining Park-and-Ride Facility Locations with the Weibit-Based Choice Model

Sustainability ◽

10.3390/su13147928 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7928

Author(s):

Songyot Kitthamkesorn ◽

Anthony Chen ◽

Sathaporn Opasanon ◽

Suwicha Jaita

Keyword(s):

Public Transportation ◽

Choice Model ◽

Transportation Network ◽

Transportation Systems ◽

Mixed Integer ◽

Hub Location ◽

Hub Location Problem ◽

Facility Locations ◽

Park And Ride ◽

Public Transportation Systems

Park and ride (P&R) facilities provide intermodal transfer between private vehicles and public transportation systems to alleviate urban congestion. This study developed a mathematical programming formulation for determining P&R facility locations. A recently developed Weibit-based model was adopted to represent the traveler choice behavior with heterogeneity. The model’s independence of irrelevant alternatives (IIA) property was explored and used to linearize its nonlinear probability. Some numerical examples are provided to demonstrate a feature of the proposed mixed integer linear programing (MILP). The results indicate a significant impact of route-specific perception variance on the optimal P&R facility locations in a real-size transportation network.

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A leader-follower single allocation hub location problem under fixed markups

Filomat ◽

10.2298/fil2008463c ◽

2020 ◽

Vol 34 (8) ◽

pp. 2463-2484

Author(s):

Dimitrije Cvokic

Keyword(s):

Location Problem ◽

State Of The Art ◽

Mixed Integer ◽

Hub Location ◽

Hub Location Problem ◽

Hub And Spoke ◽

Solution Approach ◽

Feasible Solutions ◽

S Model ◽

Robust Variant

This study examines a scenario in which two competitors, called a leader and a follower, sequentially create their hub and spoke networks to maximize their profits. It is assumed that a non-hub node can be allocated to at most one hub. The pricing is regulated with a fixed markup. Demand is split according to the logit model, and customers patronize their choice of route by a price. Two variants of this Stackelberg competition are addressed: deterministic and robust. In both cases, it was shown how to present the problem as a bi-level mixed-integer non-linear program. When it comes to the deterministic variant, a mixed-integer linear reformulation of the follower?s model is given. For the robust variant, it is shown how to reformulate the follower?s program as a mixed-integer conic-quadratic one. The benefits of these reformulations are that they allow the usage of state-of-the-art solvers in finding feasible solutions. As a solution approach for the leader, an alternating heuristic is proposed. Computational experiments are conducted on the set of Cinstances and thoroughly discussed, providing some managerial insights.

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On Multistage Multiscale Stochastic Capacitated Multiple Allocation Hub Network Expansion Planning

Mathematics ◽

10.3390/math9243177 ◽

2021 ◽

Vol 9 (24) ◽

pp. 3177

Author(s):

Laureano F. Escudero ◽

Juan F. Monge

Keyword(s):

Optimal Solution ◽

Scenario Tree ◽

Mixed Integer ◽

Planning Problem ◽

Hub Location ◽

Hub Location Problem ◽

Modeling Framework ◽

Solution Quality ◽

Network Expansion ◽

Expansion Planning

The hub location problem (HLP) basically consists of selecting nodes from a network to act as hubs to be used for flow traffic directioning, i.e., flow collection from some origin nodes, probably transfer it to other hubs, and distributing it to destination nodes. A potential expansion on the hub building and capacitated modules increasing along a time horizon is also considered. So, uncertainty is inherent to the problem. Two types of time scaling are dealt with; specifically, a long one (viz., semesters, years), where the strategic decisions are made, and another whose timing is much shorter for the operational decisions. Thus, two types of uncertain parameters are also considered; namely, strategic and operational ones. This work focuses on the development of a stochastic mixed integer linear optimization modeling framework and a matheuristic approach for solving the multistage multiscale allocation hub location network expansion planning problem under uncertainty. Given the intrinsic difficulty of the problem and the huge dimensions of the instances (due to the network size of realistic instances as well as the cardinality of the strategic scenario tree and operational ones), it is unrealistic to seek an optimal solution. A matheuristic algorithm, so-called SFR3, is introduced, which stands for scenario variables fixing and iteratively randomizing the relaxation reduction of the constraints and variables’ integrality. It obtains a (hopefully, good) feasible solution in reasonable time and a lower bound of the optimal solution value to assess the solution quality. The performance of the overall approach is computationally assessed by using stochastic-based perturbed well-known CAB data.

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Impact of the Locus of Causality and Internal Control on the Social Utility of Causal Explanations

Swiss Journal of Psychology ◽

10.1024/1421-0185/a000020 ◽

2010 ◽

Vol 69 (3) ◽

pp. 173-179 ◽

Cited By ~ 12

Author(s):

Samantha Perrin ◽

Benoît Testé

Keyword(s):

Internal Control ◽

Three Dimensions ◽

Social Utility ◽

External Control ◽

Job Interview ◽

Locus Of Causality ◽

Causal Explanations ◽

Professional Success ◽

The Social ◽

Different Types

Research into the norm of internality ( Beauvois & Dubois, 1988 ) has shown that the expression of internal causal explanations is socially valued in social judgment. However, the value attributed to different types of internal explanations (e.g., efforts vs. traits) is far from homogeneous. This study used the Weiner (1979 ) tridimensional model to clarify the factors explaining the social utility attached to internal versus external explanations. Three dimensions were manipulated: locus of causality, controllability, and stability. Participants (N = 180 students) read the explanations expressed by appliants during a job interview. They then described the applicants on the French version of the revised causal dimension scale and rated their future professional success. Results indicated that internal-controllable explanations were the most valued. In addition, perceived internal and external control of explanations were significant predictors of judgments.

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