Award Abstract # 1235061
GOALI/Collaborative Research: Warehouse Integration in Enterprise-Wide Supply Chain Planning under Uncertainty

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: WRIGHT STATE UNIVERSITY
Initial Amendment Date: August 15, 2012
Latest Amendment Date: August 15, 2012
Award Number: 1235061
Award Instrument: Standard Grant
Program Manager: Georgia-Ann Klutke
gaklutke@nsf.gov
 (703)292-2443
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: September 1, 2012
End Date: August 31, 2016 (Estimated)
Total Intended Award Amount: $165,000.00
Total Awarded Amount to Date: $165,000.00
Funds Obligated to Date: FY 2012 = $165,000.00
History of Investigator:
  • Pratik Parikh (Principal Investigator)
    pratik.parikh@louisville.edu
  • Xinhui Zhang (Co-Principal Investigator)
  • Christopher Eifert (Co-Principal Investigator)
Recipient Sponsored Research Office: Wright State University
3640 COLONEL GLENN HWY
DAYTON
OH  US  45435-0002
(937)775-2425
Sponsor Congressional District: 10
Primary Place of Performance: Wright State University
3640 Col Glenn Hwy
Dayton
OH  US  45435-0001
Primary Place of Performance
Congressional District:
10
Unique Entity Identifier (UEI): NPT2UNTNHJZ1
Parent UEI:
NSF Program(s): GOALI-Grnt Opp Acad Lia wIndus,
MANFG ENTERPRISE SYSTEMS
Primary Program Source: 01001213DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 071E, 078E, 1504, 1786
Program Element Code(s): 150400, 178600
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

The research objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) collaborative research award is to develop a set of innovative models and solutions for analyzing the three-way interaction among warehousing, inventory, and transportation decisions. The warehouse-inventory-transportation problem (WITP) for multi-echelon and multi-period supply chains is proposed and modeled through a stochastic integer quadratically-constrained program (SIQCP). Decisions associated with products with differing life-cycles flowing through the supply chain are also considered in the WITP. To solve industry-sized stochastic WITP instances, a seamless integration of decomposition approaches for stochastic integer linear programming (SILP) and continuous convex relaxation techniques for deterministic integer quadratically-constrained programming (IQCP) will be pursued.

If successful, this research will develop novel insights into interactions between the three key supply chain drivers and will prescribe low cost, realistic, plans to supply chain managers. This research will facilitate innovation and excellence in decision analytics at over 600,000 warehouses with annual expenditures of over $100 billion, leading to substantial economic benefit to enterprises both nationally and globally. After successful validation in collaboration with our industry co-PI, opportunities for potential commercialization within the $6.1 billion annual worldwide supply chain software market will be considered. Key findings from this research will be incorporated in an interactive educational website to demonstrate the interplay among warehousing, inventory, and transportation decisions.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Guthrie, B., Parikh, P. J., and Kong, N. "Benchmarking Two Real-World Warehouse Strategies for Two-Product Class Distribution" Proceedings of Industrial and Systems Engineering Research Conference , 2016
Sainathuni, B., Parikh, P. J., Zhang, X., and Kong, N. "The Effect of Picker Blocking on Warehousing and Distribution Decisions" Proceedings of Industrial Engineering Research Conference , 2011
Sainathuni, B., Parikh, P. J., Zhang, X., and Kong, N. "The Warehouse-Inventory-Transportation Problem for Supply Chains" European Journal of Operational Research , v.237 , 2014 , p.690 10.1016/j.ejor.2014.02.007
Sainathuni, B., Parikh, P. J., Zhang, X., and Kong, N. "The Warehouse-Inventory-Transportation Problem for Supply Chains" European Journal of Operational Research , v.237 , 2014 , p.690
Sainathuni, B., Parikh, P. J., Zhang, X.,and Kong, N. "The Warehouse-Inventory-Transportation Problem for Supply Chains" European Journal of Operational Research , v.237 , 2016 , p.690

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The research objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) collaborative research award was to optimize distribution planning for modern day supply chains through their warehouses that deal with distinct product classes. The key question we focused on addressing was, “would proactively incorporating warehousing decisions (workforce and technology) during the planning stage reduce inefficiencies at the operational level?” To address this question, we proposed the warehouse-inventory-transportation problem (WITP), a new integrated problem in the supply chain literature and industry practice that would allow for joint evaluate of three key distribution network drivers, warehouse, inventory, and transportation decisions. We developed a variety of optimization models and algorithms that systematically evaluated the interaction between warehouse, inventory, and transportation decisions for one product class for a deterministic demand, extended this to a two-product class and proposed a sophisticated algorithm to solve industry-sized problems (200+ stores and 1000+ products), and then considered stochastic demand for this specific cases leading to a two-stage stochastic integer quadratically-constrained programming. The key features of these novel models that consider both workforce planning and technology decisions are the warehouse, in conjunction with inbound and outbound transportation and inventory at warehouse and stores. Because of the ability of our algorithms to solve industry-sized problems, we were able to derive several, relevant, practical insights. We incorporate into the problem demand uncertainties associated with both short and long life-cycle products flowing through the warehouse.

The impact of this research can be far-reaching. There are over 600,000 warehouses across thousands of supply chains in the US alone, with annual expenditures of over $100 billion. We have already been disseminating our findings with top food, apparel, and grocery distributors in the US, which has furthered our engagement with those companies in an effort to improve their supply chains. One of the products of this research, a dashboard tool, was implemented at our industry partner’s warehouse. A web-based educational tool that can help both academic faculty and students, and industry practitioners, was hosted on our lab website and disseminated via listservs. Five graduate students were partially funded on this grant and contributed at various stages in the research; 2 PhD and 3 MS. This helped enhanced their written and oral communication skills, their professionalism through interactions with industry co-PIs, and their ability to handle complex problems and find elegant and relevant solutions. While 1 graduated PhD student is already contributing towards the US economy as a Sr Operations Research Analyst, another PhD student recently graduated and serves as an adjunct instructor. Among the 3 MS students, one is working in the Silicon Valley, the other stayed back for his PhD to work on a recently funded NSF grant, and the third MS student is considering pursuing a PhD as well. Findings of this research have now been integrated into our undergraduate and graduate curriculum on supply chain. Our next step would be to explore the translation of these research findings towards commercialization.

Project website (web and desktop tool): http://cecs.wright.edu/research/daol/witp


Last Modified: 12/04/2016
Modified by: Pratik J Parikh

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