scholarly journals Development of a Nonlinear Integer Optimization Model for Tenant Mix Layout in a Shopping Centre

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Hongyue Lv ◽  
Ting-Kwei Wang
Keyword(s):  
Optimization Model ◽  
Spatial Clustering ◽  
Spillover Effect ◽  
Main Function ◽  
Integer Optimization ◽  
Shopping Centre ◽  
Physical Information ◽  
Clustering Effect ◽  
Configuration Scheme ◽  
Tenant Mix

The tenant mix layout of shopping malls affects shopper consumption behaviour and the performance of malls. The main function of the tenant mix layout is to increase store sales by increasing footfall. However, although existing studies have shown the importance of the spatial clustering effect and the physical information about tenants, the authors of those studies did not properly consider both the spatial clustering effect and the physical information about tenants at the meantime. Through this study, we aimed to maximize the spillover effect of the stores in the shopping centre while considering both the spatial clustering effect and physical information about tenants. Therefore, we present a problem called the tenant mix problem, which is to determine the optimal tenant configuration scheme for existing shopping centre space segmentation to maximize the rental income of a shopping centre. To solve this problem, a nonlinear integer optimization model with defined characteristics was proposed and solved using a genetic algorithm. A shopping centre case study is also presented to verify the performance of the model.

scholarly journals Spatial-Temporal Effects of PM2.5 on Health Burden: Evidence from China

2019 ◽  
Vol 16 (23) ◽  
pp. 4695 ◽  
Author(s):  
Zeng ◽  
Du ◽  
Zhang
Keyword(s):  
Direct Effect ◽  
Spatial Clustering ◽  
Spillover Effect ◽  
Spatial Spillover ◽  
Regulatory Policies ◽  
Health Burden ◽  
Lag Effect ◽  
Pm2.5 Exposure ◽  
The Impact ◽  
Spatial Spillover Effect

By collecting the panel data of 29 regions in China from 2008 to 2017, this study used the spatial Durbin model (SDM) to explore the spatial effect of PM2.5 exposure on the health burden of residents. The most obvious findings to emerge from this study are that: health burden and PM2.5 exposure are not randomly distributed over different regions in China, but have obvious spatial correlation and spatial clustering characteristics. The maximum PM2.5 concentrations have a significant positive effect on outpatient expense and outpatient visits of residents in the current period, and the impact of PM2.5 pollution has a significant temporal lag effect on residents’ health burden. PM2.5 exposure has a spatial spillover effect on the health burden of residents, and the PM2.5 concentrations in the surrounding regions or geographically close regions have a positive influence on the health burden in the particular region. The impact of PM2.5 exposure is divided into the direct effect and the indirect effect (the spatial spillover effect), and the spatial spillover effect is greater than that of the direct effect. Therefore, we conclude that PM2.5 exposure has a spatial spillover effect and temporal lag effect on the health burden of residents, and strict regulatory policies are needed to mitigate the health burden caused by air pollution.

High-speed rail cost recovery time based on an integer optimization model

2014 ◽  
Vol 49 (5) ◽  
pp. 634-647 ◽  
Author(s):  
Shoupeng Tang ◽  
Stephen D. Boyles ◽  
Nan Jiang
Keyword(s):  
Optimization Model ◽  
Recovery Time ◽  
High Speed ◽  
Cost Recovery ◽  
Integer Optimization ◽  
High Speed Rail

scholarly journals Detecting Customer Trends for Optimal Promotion Targeting

2020 ◽  
Author(s):  
Lennart Baardman ◽  
Setareh Borjian Boroujeni ◽  
Tamar Cohen-Hillel ◽  
Kiran Panchamgam ◽  
Georgia Perakis
Keyword(s):  
Optimization Model ◽  
Problem Definition ◽  
Customer Relationships ◽  
Mixed Integer ◽  
Percentage Error ◽  
Integer Optimization ◽  
Demand Model ◽  
Support Tool ◽  
Demand Models ◽  
Transactional Data

Problem definition: Retailers have become increasingly interested in personalizing their products and services such as promotions. For this, we need new personalized demand models. Unfortunately, social data are not available to many retailers because of cost and privacy issues. Thus, we focus on the problem of detecting customer relationships from transactional data and using them to target promotions to the right customers. Academic/practical relevance: From an academic point of view, this paper solves the novel problem of jointly detecting customer trends and using them for optimal promotion targeting. Notably, we estimate the causal customer-to-customer trend effect solely from transactional data and target promotions for multiple items and time periods. In practice, we provide a new tool for Oracle Retail clients that personalizes promotions. Methodology: We develop a novel customer trend demand model distinguishing between a base purchase probability, capturing factors such as price and seasonality, and a customer trend probability, capturing customer-to-customer trend effects. The estimation procedure is based on regularized bounded variables least squares and instrumental variable methods. The resulting customer trend estimates feed into the dynamic promotion targeting optimization problem, formulated as a nonlinear mixed-integer optimization model. Though it is nondeterministic polynomial-time hard, we propose a greedy algorithm. Results: We prove that our customer-to-customer trend estimates are statistically consistent and that the greedy optimization algorithm is provably good. Having access to Oracle Retail fashion client data, we show that our demand model reduces the weighted-mean absolute percentage error by 11% on average. Also, we provide evidence of the causality of our estimates. Finally, we demonstrate that the optimal policy increases profits by 3%–11%. Managerial implications: The demand model with customer trend and the optimization model for targeted promotions form a decision-support tool for promotion planning. Next to general planning, it also helps to find important customers and target them to generate additional sales.

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