Distribution network design with big data: model and analysis

Wang, Gang; Gunasekaran, Angappa; Ngai, Eric W.T.

doi:10.1007/s10479-016-2263-8

Cited by 51 publications

(30 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Big data analytics also affects the optimization of service parts in after-sales operations management (Boone et al 2018). In the literature, Wang et al (2018) study a distribution network design optimization problem, with the use of big data. The authors consider the situation in which the supply chain planner can use big data to determine the optimal number of distribution centers and assign customers to them.…”

Section: Supply Chain Managementmentioning

confidence: 99%

Big Data Analytics in Operations Management

Choi

Wallace

Wang

2018

Production and Operations Management

564

298

View full text Add to dashboard Cite

Big data analytics is critical in modern operations management (OM). In this study, we first explore the existing big data‐related analytics techniques, and identify their strengths, weaknesses as well as major functionalities. We then discuss various big data analytics strategies to overcome the respective computational and data challenges. After that, we examine the literature and reveal how different types of big data methods (techniques, strategies, and architectures) can be applied to different OM topical areas, namely forecasting, inventory management, revenue management and marketing, transportation management, supply chain management, and risk analysis. We also investigate via case studies the real‐world applications of big data analytics in top branded enterprises. Finally, we conclude the study with a discussion of future research.

show abstract

Section: Supply Chain Managementmentioning

confidence: 99%

Big Data Analytics in Operations Management

Choi

Wallace

Wang

2018

Production and Operations Management

564

298

View full text Add to dashboard Cite

show abstract

“…Equations (11)- (13) state that each vehicle is initially located at a distribution center, which we denote by 0; after arriving at a delivery point, it has to leave to another destination; finally, all vehicles must arrive at depot n + 1. The inequalities in Equation (14) establish the relationship between the vehicle departure time from a customer and its immediate successor. Finally, constraints in Equation (15) insist that time windows should be observed, and Equation 16shows integrality constraints.…”

Section: Vehicle Routing With a Time Windowmentioning

confidence: 99%

Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS–The Case of Bangkok

Suraraksa

Shin

2019

Applied Sciences

View full text Add to dashboard Cite

A cold chain for perishable fresh products aims to preserve the quality of the products under the control of a predefined temperature range. To satisfy the delivery conditions within appropriate time windows, the most critical operations in cold chain management is the transportation and distribution of fresh products. Due to rapid population growth and increasing demand for high-quality fresh foods, it becomes critical to develop advanced transportation and distribution networks for fresh products, particularly in urban areas. This research aims to design different scenarios based on mathematical models for fresh products transportation and distribution network in the Bangkok metropolitan area using Geographic Information system (GIS). The proposed methodology integrates location–allocation and vehicle routing problem analysis. The performance of all possible scenarios is evaluated and compared by considering the number of required distribution centers and trucks, total travel time, total travel distance, as well as fairness among drivers. The results of the scenario analysis highlight that the alternative scenarios show a better performance as compared to the current network. In addition, the administrator can make a different decision among several alternatives by considering different aspects, such as investment cost, operating cost, and balance of using available resources. Therefore, it may help a public officer to design the fresh products logistics network considering actual demand and traffic conditions in Bangkok.

show abstract

“…Hazen et al (2016) suggested the need to bridge the gap between operations research/supply chain management and big data analytics by synergizing decision-making with quantitative results, transitioning to business analytics, enhancing data quality, diversifying team structure, and defining a structured plan for alternative selection. Recent research on big data sets in the operations management domain includes the studies by Wang et al (2016) which focused on developing a capacitated network design to locate distribution centers for scattered demand points and Tail and Singh (2016) which focused on the facility layout problem. Aloysius et al (2016) investigated the role of technology enablers and privacy inhibitors in big data customer transactions in the realization of competitive advantage by retailers.…”

Section: Summary Of Research Gapsmentioning

confidence: 99%

Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation

et al. 2016

View full text Add to dashboard Cite

The study proposes a cold chain location-allocation configuration decision model for shippers and customers by considering value deterioration and coordination by using big data approximation.Value deterioration is assessed in terms of limited shelf life, opportunity cost, and units of product transportation. In this study, a customer can be defined as a member of any cold chain, such as cold warehouse stores, retailers, and last mile service providers. Each customer only manages products that are in a certain stage of the product life cycle, which is referred to as the expected shelf life. Because of the geographical dispersion of customers and their unpredictable demands as well as the varying shelf life of products, complexity is another challenge in a cold chain.Improved coordination between shippers and customers is expected to reduce this complexity, and this is introduced in the model as a longitudinal factor for service distance requirement. We use big data information that reflects geospatial attributes of location to derive the real feasible distance between shippers and customers. We formulate the cold chain location-allocation decision problem as a mixed integer linear programming problem, which is solved using the CPLEX solver.The proposed decision model increases efficiency, adequately equates supply and demand, and reduces wastage. Our study encourages managers to ship full truck load consignments, to be aware of uneven allocation based on proximity, and to supervise heterogeneous product allocation according to storage requirements.

show abstract

Distribution network design with big data: model and analysis

Cited by 51 publications

References 22 publications

Big Data Analytics in Operations Management

Big Data Analytics in Operations Management

Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS–The Case of Bangkok

Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation

Contact Info

Product

Resources

About