Bulk Product Allocation and Blending Optimization
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Solution Overview
Problem
Current approaches to optimizing bulk product transportation fail to consider inventory management, product blending, and maximize net profit, particularly when dealing with multiple types and qualities of products with non-constant supply and demand rates, using a heterogeneous fleet of vessels.
Innovation Solution
A computer application that optimizes product allocation, transportation routing, and vehicle scheduling to maximize total net profit margin by considering inventory, production, and consumption schedules, allowing for product blending during transit, and utilizing a heterogeneous fleet of vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current transportation optimization approaches are used, then routing and scheduling can be optimized, but inventory management and product blending are not considered
Solution Approach 1:
The patent combines transportation routing optimization with inventory management and product blending capabilities into a single integrated system. The optimization model simultaneously considers vehicle routing, inventory levels at supply and demand locations, and product blending decisions, allowing all these functions to work together rather than in isolation.
Solution Approach 2:
The system is designed to handle multiple product types with different qualities and specifications within a single framework. It can manage heterogeneous fleets of vehicles, handle various inventory policies, and perform product blending to meet different demand requirements, making the system universally applicable to diverse transportation and inventory management scenarios.
2Productivity
If transportation optimization focuses on routing and scheduling, then vehicle efficiency improves, but net profit maximization is not achieved
Solution Approach 1:
The patent changes the objective function from cost minimization to net profit maximization. The model incorporates product prices, transportation costs, inventory holding costs, and blending costs to calculate net profit. By adjusting these parameters and their relationships in the optimization model, the system identifies solutions that maximize profit rather than simply minimizing transportation costs.
3Ease of operation
If a homogeneous fleet is used, then operational simplicity is maintained, but flexibility in handling multiple product types is reduced
Solution Approach 1:
The system assigns specific characteristics to different vehicles in the heterogeneous fleet, such as capacity, speed, and suitability for different product types. Each vehicle has localized qualities that make it appropriate for specific tasks, allowing the system to optimize vehicle-product matching while maintaining operational simplicity through automated assignment decisions.
4Device complexity
If constant supply and demand rates are assumed, then model simplicity is maintained, but real-world variability is not captured
Solution Approach 1:
The patent implements dynamic supply and demand rates that can vary over time within the planning horizon. The model allows supply rates from different locations and demand rates at different locations to change from period to period, capturing real-world variability. This dynamic approach replaces static constant rate assumptions with time-varying parameters that reflect actual operational conditions.
Data Source
AI summary
A computer application loaded on a computer readable medium, a computer apparatus comprising the same, and process employing the same, is described herein. The computer application, when executed, causes a computer to optimize, for maximum net profit margin, the product allocation, transportation routing, transportation vehicle/route scheduling and, optionally, blending, of bulk products that are produced by and loaded from supply locations and delivered to and consumed by demand locations, using a heterogeneous fleet of transportation vehicles over a pre-defined period of time.