Location Decision‐Making of Equipment Manufacturing Enterprise under Dual‐Channel Purchase and Sale Mode

Xu, Wei; Liu, Lina; Zhang, Qingshan; Liu, Peng

doi:10.1155/2018/3797131

Cited by 20 publications

(12 citation statements)

References 10 publications

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“…Although there are few studies on the factors influencing the number of tourists, most researchers agreed that per capita disposable income [17][18][19] and per capita gross domestic product (GDP) [20][21][22] had a significant impact on the tourist volume. Eeckels et al [23] used spectral analysis to examine the relationship between cyclical component of GDP and tourist volume.…”

Section: Factors Influencing the Tourist Volumementioning

confidence: 99%

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

Wang

et al. 2020

Teh. vjesn

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Tourist volume is increasing with the expansion of the scale of tourism, and improving the prediction of tourist volume is helpful for tourism managers to make decisions. Internet search index can be applied to predict the behavior of users, which is widely used in the study of tourist volume prediction and infectious disease prediction. However, the high dimension and correlation of Internet search index tends to reduce the accuracy of the models, which increases the average prediction error of common time-series models. The dynamic factor model (DFM) proposed in our study can be used to solve the problem. This study selects 23 variables and introduces the generalized dynamic factor model (GDFM) to predict tourist volume. The model cannot only reduce the dimensionality of high-dimensional Internet search index data, but also reflects the dynamic correlation between Internet search index data. The results show that the prediction accuracy is improved in our method, and the prediction accuracy of tourist volume is improved by over 10%, with an average error of only 4.3% when compared with the neural network (NN) model. Our study not only provides implications for decision-makers to predict tourist volume timely and accurately, but also helps companies understand tourist' behavior and make the best strategic decisions.

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Section: Factors Influencing the Tourist Volumementioning

confidence: 99%

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

Wang

et al. 2020

Teh. vjesn

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“…Step 2. According to (15), randomly generate the first generation of wolves, and record the location of each wolf.…”

Section: ) the Steps Of The Immune Wolf Colony Algorithmmentioning

confidence: 99%

A New Hybrid Algorithm for Cold Chain Logistics Distribution Center Location Problem

2020

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According to the perishable characteristics of refrigerated food and the objective of minimizing the total cost, the mathematical optimization model of cold chain logistics distribution center location problem is established by introducing such constraints as the freshness and time window. In order to solve the problems of slow convergence and easy to fall into local optimal solution in the process of the traditional wolf colony optimization, an immune wolf colony hybrid algorithm is proposed to solve the location problem of distribution center. In this hybrid algorithm, the idea of vaccination of immune algorithm is introduced into the wolf colony algorithm. By adjusting the antibody concentration and selecting immune operator, the diversity of the wolf colony algorithm is improved, and then the search space of the solution is expanded; the convergence speed and solution accuracy of the wolf colony algorithm are improved by using immune memory cells and immune vaccine. The simulation results show that the immune wolf colony algorithm can quickly converge to the global optimal solution and optimize the location model of logistics distribution center. The algorithm has good feasibility and robustness. INDEX TERMS Cold chain logistics, distribution center location, immune algorithm, wolf colony algorithm.

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“…The clustering procedures include clustering by passenger picking-up time and by the O-D matrix. The outputs are the groups of demands with clustered pick-up time and/or originations and destinations [25].…”

Section: Figure 1 Inputs Modules Processes and Outputs Of The Metmentioning

confidence: 99%

Improving Bus Transit Services for Disabled Individuals: Demand Clustering, Bus Assignment, and Route Optimization

Qiao

2020

IEEE Access

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Bus transit provides shorter-distance public transportation services, which are subject to various disability discrimination acts with various dedicated features. The Americans with Disabilities Act (ADA) requires that disabled individuals shall have equal rights to receive fare bus transit services, including fixed-route and door-todoor bus services. Most previous studies were mainly focused on policy aspects as part of the efforts of disability rights. The proper planning of demand requests from disabled individuals has been a critical issue but has gained insufficient attention. The existing methodologies in planning route for special transit buses for disabled individuals normally do not consider passengers' waiting time, lack sufficient flexibility, and have strict restrictions on the total number of served destinations. This paper proposes a four-module based methodology for the planning of bus transit, including demand information collection, demand clustering, transit bus assignment, and a linear programming-based route planning with different objective functions. Houston MetroLift bus transit service was employed as an example to illustrate the proposed method. Three scenarios during the route planning module were designed in this case study: (1) planning for pre-timed shortest distance, (2) planning for the pre-timed shortest waiting time of passengers, and (3) flexible planning. Results showed that scenario 1 obtained the shortest total travel time and the highest benefit for bus providers, scenario 2 is with the shortest average waiting time, while scenario 3 is real-time based with longer total travel time and longer waiting time. Scenarios 2 and 3 consider the special needs of disabled passengers.

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Location Decision‐Making of Equipment Manufacturing Enterprise under Dual‐Channel Purchase and Sale Mode

Cited by 20 publications

References 10 publications

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

A New Hybrid Algorithm for Cold Chain Logistics Distribution Center Location Problem

Improving Bus Transit Services for Disabled Individuals: Demand Clustering, Bus Assignment, and Route Optimization

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