Information Processing Apparatus for Profit Maximizing Delivery Scheduling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods do not effectively generate a delivery plan that maximizes profit for businesses transporting products from producers to buyers, as they lack a systematic approach to combine sell and buy conditions with transport medium information to optimize scheduling and profit.
Innovation Solution
An information processing apparatus that combines sell and buy conditions with transport medium information to generate matching candidates and schedules, using algorithms for maximum matching and minimum-cost flow problems to assign tasks to transport mediums, thereby maximizing profit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional delivery plan generation methods are used, then basic transport scheduling can be achieved, but profit maximization cannot be realized
Solution Approach 1:
The patent segments the delivery plan generation into distinct modules: sell condition and buy condition combination generation, transport medium assignment, and scheduling. This segmentation allows complex profit maximization to be achieved through systematic decomposition of the problem into manageable components, each handled by specialized algorithms.
Solution Approach 2:
The patent performs preliminary actions by pre-generating all possible combinations of sell and buy conditions, and pre-calculating transport costs and profits before final scheduling. This preliminary processing enables the system to make optimal profit-maximizing decisions during the actual scheduling phase without re-evaluating all possibilities.
2Productivity
If comprehensive matching of sell and buy conditions is performed, then profit optimization is improved, but calculation time increases
Solution Approach 1:
The patent applies partial action by generating and evaluating only the necessary combinations of sell and buy conditions that lead to profitable transactions. Instead of exhaustively checking all possible combinations, the system uses filtering criteria and heuristics to identify and process only the promising matches, reducing calculation time while maintaining profit optimization.
Solution Approach 2:
The patent changes parameters dynamically during the matching process, adjusting criteria for combination generation based on transport medium characteristics, distances, and profit margins. This adaptive parameter adjustment allows the system to focus computational resources on high-value matches rather than uniformly processing all possibilities.
3Productivity
If transport medium assignment is optimized for profit, then profitability increases, but scheduling complexity increases
Solution Approach 1:
The patent introduces an intermediary matching layer that combines sell conditions, buy conditions, and transport medium information into intermediate matching candidates before final scheduling. This intermediary layer simplifies the scheduling process by pre-resolving complex interactions between multiple factors, transforming a highly complex optimization problem into a more manageable scheduling task.
Data Source
AI summary
According to one embodiment, an information processing apparatus includes processing circuitry. The processing circuitry is configured to combine a sell condition under which a first dealer sells a product and a buy condition under which a second dealer buys the product on a basis of information of a first transport medium to transport the product, and generate matching information including the sell condition and the buy condition combined each other; and assign a transport of the product to the first transport medium by associating the transport with the first transport medium and perform scheduling of the transport on a basis of the matching information.