Dynamic Queue Time Pricing for Pickup Order Scheduling
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Solution Overview
Problem
Retailers face challenges in providing adequate service levels for pickup orders due to logistical constraints, leading to lengthy wait times for customers as many orders are scheduled during peak periods, overwhelming available resources and personnel.
Innovation Solution
A system that enables customers to view and select pick-up options with associated prices and queue times, using a queuing procedure to calculate wait times and prices based on average service time and inter-arrival rates, dynamically updating these in real-time to distribute demand evenly across time periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If customers schedule orders during peak periods, then service demand is concentrated and easy to predict, but wait times become excessively long and service levels deteriorate
Solution Approach 1:
The system dynamically adjusts pickup time slot availability and pricing based on real-time service capacity and demand patterns. Time slots are not fixed but adaptively modified to balance load across different periods, preventing peak congestion while maintaining service throughput.
Solution Approach 2:
The system changes pricing parameters and time slot parameters based on demand intensity. During peak periods, pricing is adjusted and time slots are modified to encourage redistribution of demand, thereby reducing wait times without sacrificing overall service capacity.
2Productivity
If more personnel are added to service orders during peak periods, then service capacity increases, but operational complexity and costs increase
Solution Approach 1:
The system performs preliminary scheduling and load balancing before peak periods occur. By anticipating demand patterns and pre-allocating resources or adjusting time slots in advance, the system avoids the need for complex real-time resource management during peak service periods.
Solution Approach 2:
The system continuously monitors service capacity utilization and customer wait times, using this feedback to dynamically adjust time slot availability and pricing. This closed-loop control enables the system to maintain optimal service capacity without requiring manual intervention or complex operational structures.
3Loss of time
If time slots are made flexible and dynamically adjusted, then demand distribution improves and wait times reduce, but system complexity increases
Solution Approach 1:
The scheduling system autonomously adjusts time slot parameters and pricing based on embedded algorithms that monitor demand and capacity. This self-service capability eliminates the need for manual scheduling interventions while achieving optimal demand distribution and reduced wait times.
Solution Approach 2:
The system automatically modifies scheduling parameters such as time slot duration, availability windows, and pricing based on real-time conditions. These parameter changes are driven by algorithms that balance demand distribution across time periods, reducing queue times without requiring complex manual management.
Data Source
AI summary
Systems and methods are described which utilize improved scheduling techniques. An electronic scheduling platform enables customers to view and select pick-up options for scheduling orders to be retrieved at a location. Each pick-up option is associated with a price and a queue time. An average service time and an average inter-arrival time is determined for the location. A queuing procedure is executed which calculates queue times and prices for the pick-up options based, at least in part, on the average service time and the average inter-arrival time. The queuing procedure dynamically updates in real-time the calculated queue times and prices for the pick-up options. Instructions are generated for providing an interface that displays at least a portion of the pick-up options with the updated queue times and prices.