Discipline: Technology and Engineering
Subcategory: Civil/Mechanical/Manufacturing Engineering
Phuong Mata - Savannah State University
This problem relates to a Distribution Center (DC) in Savannah, Georgia which has hard time determining the optimal inventory of pallets that needs to be stocked internally at their existing DC and in four external warehouses outside of their own internal DC. Traditionally, the DC has experienced a recurring need to enter external warehouse based on their stock levels, and have grappled with the understanding of what the optimal inventory level of their pallets, and not overdo to maintain most productivity potential. It is critical to know the maximum amount of space utilization at which it is counterproductive to the existing DC. The counterproductive results from deep storage as a result of increased space utilization. For instance, increase utilization close to 90% or higher will result in slowing down the storage and retrieval process, because for storage an existing pallet needs to be removed to reach a deep storage space point, only after this removal process can the new pallet be stored. likewise, for retrieval in a deep storage location a pallet that’s blocking it needs to be removed from its current location so that the required pallet stored behind can be retrieved. Due to this phenomenon the DC functions at a slower pace resulting in increased time for storage as well as retrieval per pallet, and thus resulting in lower productivity. We investigate how much on-hand inventory in the internal DC is counterproductive that will result in decreased productivity. We hypothesize that anything beyond 85% capacity utilization is counterproductive at which point storage can be done in external warehouse the internal DC. Additionally, we answer other questions which include: What is the optimal inventory level that results in maximum capacity utilization and productivity, at which point DC can start storing the product in the external buildings? We study this problem and propose solution using simulation optimization technique. SIMIO, a discrete-event simulation tool is used to simulate the current problem. Several scenarios consisting of stochastic demand and supply with varying coefficient of variability are utilized to find the most appropriate near optimal solution for a given set of parameters (demand and supply distribution). The objective is to minimize the overall cost, which includes the amount of time spent by workers to retrieve and store a pallet to and from the location in DC. with maximum productivity and capacity utilization, subject to maximum service level constraint. OptQuest which is an optimization tool is used to determine the optimal values of decision variables, which includes capacity utilization. The research is currently in the phase of model verification and preliminary results are underway. Once the model is verified, a design of experiments is proposed to be conducted. The future research will focus on providing optimal capacity utilization under: (i) family type of products, (ii) three buildings, and (iii) non-seasonal and seasonal demand.
Funder Acknowledgement(s): This study was supported, in part, by a grant from NSF awarded to Jonathan Lambright, Dean of College of Sciences and Technology, and Suman Niranjan, Associate Professor for Operations Management, Director for Interdisciplinary Transportation Studies, Savannah State University, Savannah, GA.
Faculty Advisor: Suman Niranjan, firstname.lastname@example.org
Role: Developed the simulation model. Validated and verified the simulation model for appropriateness. Conducted various numerical experiments, using design of experiments. Conducted statistical analysis.