A multi-modal competitive hub location pricing problem with customer loyalty and elastic demand

Mahmoodjanloo, Mehdi; Tavakkoli–Moghaddam, Reza; Baboli, Armand; Jamiri, Atefeh

doi:10.1016/j.cor.2020.105048

Cited by 32 publications

(8 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Regarding the discussion on location objectives, Campbell in 1994 provided an answer by categorising hub location problems into four types based on their objectives: p-hub median, incapacitated hub location, p-hub centre, and hub covering problems [22]. Typically, the fundamental objective of hub location problems is to minimise or maximise one or multiple evaluation criteria, such as maximising service level [23], minimising total cost [12], optimising accessibility [24], and achieving balance and fairness. Meanwhile, considering the total carbon emissions of multimodal transportation networks during site selection is also an important issue to be considered [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Road-Rail Intermodal Hubs Site Selection Based on Road Freight Demand Mining – A Case from Beijing-Tianjin-Hebei Region

OU,

GAN,

et al. 2024

PROMTT

View full text Add to dashboard Cite

This study introduces a holistic framework for optimising road-rail intermodal hub locations based on real regional freight data and railway station information. The primary objective is to enhance railway transportation capacity, thereby facilitating the development of a low-carbon transport system. Research begins by scrutinising the freight landscape in the region, focusing on transport volume, freight intensity, goods types and average delivery distances. Subsequently, data mining techniques, including DBSCAN clustering and frequent itemset mining, are employed to uncover freight demand hotspots across both spatial and temporal dimensions. Based on these findings, a mathematical model for hub location selection is constructed, along with criteria for goods categories suitable for rail transportation. Ultimately, using the Beijing-Tianjin-Hebei region as a case study, 12 road-rail intermodal hubs are identified, along with the main cargo types best suited for rail transport within their respective service areas. This transition is expected to result in an annual reduction of 470,000 tons of regional carbon emissions. The proposed method framework provides valuable guidance and practical insights for the optimisation of freight structures in various regions. Furthermore, it aligns with contemporary environmental and sustainability objectives, contributing to the broader goal of establishing low-carbon transport systems.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Road-Rail Intermodal Hubs Site Selection Based on Road Freight Demand Mining – A Case from Beijing-Tianjin-Hebei Region

OU,

GAN,

et al. 2024

PROMTT

View full text Add to dashboard Cite

show abstract

“…They proposed a chance constraint to control the dissatisfactiion in the waiting time for services according to the Jackson Markov network. Mahmoodjanloo et al [ 13 ] studies the multimodal competitive hub location pricing problem for a new company planning to enter a market where an existing competitor operates its hub-and-spoke network. They propose a scatter search algorithm based on a nested approach to solve the model.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust competitive facility location model with uncertain demand types

2022

PLoS ONE

View full text Add to dashboard Cite

In competitive settings, firms locate their facilities according to customers’ behavior to maximize their market share. A common behavior is consuming from different motivations: one is for convenient demand, and the other is for quality demand. In this behavioral pattern, consumers patronize facilities within convenience for some demands, and patronize high quality facilities beyond convenience range for other demands. This behavior has never been included in competitive facility location problems. Given several other companies’ facilities in the market offering similar products or services, we study how a new entrant company can locate facilities based on this customer behavior to maximize its market share. A two-level robust model for the new entrant company is proposed to locate its facilities by taking into account the uncertainty of the types of customers’ demands. For medium size problems, we propose an equivalent mixed binary linear programming to obtain exact solutions. For large size problems, an exact algorithm (GCKP-A) for solving the inner-level model is given first by exploring the optimal solution. Then a heuristic algorithm is proposed by imbedding (GCKP-A) and 2-opt strategy into the framework of the improved ranking-based algorithm. The performance of the proposed heuristic algorithm is checked for different size problems. The sensitivity analysis of a quasi-real example shows that: (1) in most cases, the uncertainty between two types of demands does not affect the location scheme; (2) the convenience range, the quality range and the quality threshold play an important role in the market share of the new entrant company.

show abstract

“…For the problem of considering both service pricing and pivot point selection in a competitive environment, scholars mainly divided the research on customer demand distribution into the following aspects: path cost [29][30][31][32], price [23,[33][34][35], cost and price [14,16], route cost and transportation time [36][37][38][39][40], cost and service [41,42]. Through the review, it is found that few literature consider both cost and price factors, and the previous studies mostly take distance, cost, and time as the basic parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Referring to Mahmoodjanloo et al [33] on price parameters considered for pricing, the settings of price elasticity of demand and substitution effect coefficients need to consider the intensity of competition between the two enterprises. When studying the entry of new enterprises into the market, if there is no price competition in the market, the price elasticity of demand β is 20; if there is price competition in the market, the price elasticity of demand β is 30, and the substitution effect coefficient γ is 6.…”

Section: Parameter Settingmentioning

confidence: 99%

Hub-and-Spoke Logistics Network Considering Pricing and Co-Opetition

Zhou

Zhao

et al. 2021

Sustainability

View full text Add to dashboard Cite

With the rapid development of the logistics market, the construction of an efficient “channel + hub + network” logistics system, that is, a hub-and-spoke logistics network, is of great importance to enterprises. In particular, how to reduce costs and increase efficiency in both the construction of network facilities and actual operations, and to formulate reasonable prices for the logistics service needs in the entire market are crucial strategies and decisions for enterprises. Under such a background, this article starts from the perspective of duopoly logistics enterprises that jointly build networks and allow the transfer of surplus capacity and carbon credits, and studies the hub-and-spoke logistics network design that also considers the relationship between service pricing and co-opetition. Considering the corporate profit and difficulty of implementation as a whole, the co-opetition is a better choice than the complete competition and perfect cooperation. In addition, the remaining capacity of the company, the transfer of carbon credits, the joint construction and sharing of hubs, and strategic cooperation in the area of corporate common pricing under the price framework agreement are conducive to the realization of an increase in corporate operating profits, a better market share and more favorable pricing.

show abstract

A multi-modal competitive hub location pricing problem with customer loyalty and elastic demand

Cited by 32 publications

References 54 publications

Road-Rail Intermodal Hubs Site Selection Based on Road Freight Demand Mining – A Case from Beijing-Tianjin-Hebei Region

Road-Rail Intermodal Hubs Site Selection Based on Road Freight Demand Mining – A Case from Beijing-Tianjin-Hebei Region

Robust competitive facility location model with uncertain demand types

Hub-and-Spoke Logistics Network Considering Pricing and Co-Opetition

Contact Info

Product

Resources

About