Author ORCID Identifier
0009-0003-6024-9078
Document Type
Dissertation
Date of Award
8-31-2024
Degree Name
Doctor of Philosophy in Industrial Engineering - (Ph.D.)
Department
Mechanical and Industrial Engineering
First Advisor
Sanchoy K. Das
Second Advisor
Layek Abdel-Malek
Third Advisor
Zhiming Ji
Fourth Advisor
Athanassios K. Bladikas
Fifth Advisor
Junmin Shi
Abstract
The fulfillment process for Buy Online Pickup from Store grocery orders (BOPS-Grocery) is particularly challenging, given the large item count per order and the low-profit margins. This research investigates the BOPS-Grocery model and pursues the following objectives: (i) Defining and specifying the BOPS-Grocery fulfillment process. Differentiating the process from classical warehouse order picking., identifying performance objectives, and characterizing design options and facility layout. (ii) Modelling and developing order picking algorithms specific to the straight aisle section. Formulated as an order hatching problem with picker movement minimization. No order splitting, and (iii) modeling and developing order picking algorithms specific to the product island and service requirement sections. Item pick times are not a constant and a function of order variables. Pick time correlations are also modeled. Formulated as an order hatching and routing problem with make span minimization.
BOPS-Grocery Fulfillment: Order picking and delivery of online grocery orders differs significantly from both warehouse picking and traditional retail store picking The primary differentiators are (i) Low gross margins of grocery items, (ii) Large number of items per grocery order, (iii) Variety of item types, and (iv) A large order picking area that is not organized for fast picking. This research reports on a field research study that investigates BOPS-Grocery operations in several large grocery stores. The focus was on order picking and packing operations. The study involved semi-structured interviews and direct observations of order picking activities. The online grocery business must be engineered to minimize order picking costs while ensuring customer satisfaction. BOPS-Grocery operations can be generalized into a common order pick and pack process that can then be used to optimize operations across the industry. The grocery store layout can be classified into three areas, each with unique stocking and picking features. The study documented the online grocery order flow process and highlighted the role of pickers and associated resources.
Straight-Aisle Order Picking: We introduce the G-Picklist problem for order picking in the straight aisle section of a grocery store. Given the low margins of the BOPS-Grocery business, there is a need for readily applicable solutions that improve the picking efficiency and reduce the associated costs. We show that there are two solution objectives: (i) the number of aisle entries and (ii) the picker travel distance. Typically, stores with longer aisles will prefer objective 1, while those with shorter aisles will prefer objective 2. The G-Picklist problem was formulated as an MIP. In the nominal case, grocery stores are using ad hoc rules to create picklists, and this research showed the business utility of an optimized relative to an FCFS rule. For small problems, the performance utility averages 30%, while for a large problem, it averages 25%.
Attribute-Based Island Order Picking: The order-picking literature focuses exclusively on specific or unique items. Grocery item order picking includes a class of items that are characterized by attributes associated with customer preferences. Fresh produce such as fruits and vegetables have natural variations, and each unit of the same item is different. Picking three apples from a pile of gala apples has multiple outcomes in terms of weight, color, and ripeness. Grocery stores want to provide BOPS customers with item-specific attributes that are then transmitted to the pickers. Attribute-based order picking can be challenging, and this is the first research to address the problem. Item pick times are no longer fixed but a function of the number and complexity of the attributes. Non-linear functional models are developed to describe the picking times and establish correlations across multiple orders. These items are typically stocked in an island layout with perimeter access. Both MIP and heuristic solutions to the order picklist problem are presented. The objective is to create order batches that minimize the combined travel and picking times. Experimental results show that heuristics provide high-quality solutions in negligible time.
Service Integrated Order Picking: Service counters are the third layout type in grocery stores. Order picking in this configuration has two significant differences: (i) Each item has an uncertain service time associated with it, and (ii) Pickers are mixed with regular store customers and must be processed through a queue. Both MIP and heuristic solutions to the order picklist problem are presented.
Recommended Citation
Wang, Zijia, "Order-picking strategies and efficiency models for the fulfillment of multi-line e-commerce grocery orders" (2024). Dissertations. 1782.
https://digitalcommons.njit.edu/dissertations/1782
