Dynamic Pricing using Heaps

Data Structures

• Heaps: Max-heap for managing high demand areas and adjusting prices dynamically.

Challenges

• Demand-Supply Dynamics: Adjusting prices in real-time based on demand and supply fluctuations.
• Optimized Revenue: Ensuring fair pricing that maximizes revenue.

Benefits

• Fair Pricing: Adjusting prices based on market conditions ensures fairness.
• Revenue Optimization: Maximizing revenue by setting higher prices in high-demand areas.

Algorithm

• Max-Heap Implementation: Using a max-heap to efficiently retrieve and update the highest priced location.

Example Code (C++)

Dynamic Pricing

Space and Time Complexity

Time Complexity:O((k + m) log n)

• k is the number of calls to updatePrice.
• m is the number of calls to getHighestPriceLocation.
• n is the maximum number of elements in the priority queue.

Space Complexity:O(n)

• n is the maximum number of elements in the priority queue at any given time.