Labor Contract Planning Model for Workforce Capacity
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Solution Overview
Problem
Traditional workforce capacity planning methods are inadequate for long-term planning due to their tactical nature and high computational complexity, often requiring extensive calculations and rigid models that are not easily extendable or combinable, making it impractical to perform planning over half a year or more on short-term intervals.
Innovation Solution
A labor contract planning model based on mixed-integer programming (MIP) that uses implicit concrete contract types to generate a contract schedule, reducing computational requirements and enabling long-term workforce capacity planning on shorter intervals, such as daily or hourly updates over a year or more, while maintaining extensibility and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If traditional workforce capacity planning methods are used, then tactical/short-term planning can be performed, but long-term planning becomes impractical due to extensive computational requirements
Solution Approach 1:
The patent segments the planning horizon into discrete time periods (e.g., daily or hourly intervals) and formulates the workforce capacity planning as a sequence of mixed-integer programming problems for each period. This segmentation allows long-term planning to be broken down into manageable computational steps, making it practical to solve for extended time horizons without requiring all computations to occur simultaneously.
Solution Approach 2:
The patent performs preliminary actions by pre-defining contract types, work patterns, and constraint structures before solving the optimization problems. By establishing the mathematical model framework, data structures, and constraint templates in advance, the system reduces the computational burden during actual long-term planning execution, enabling frequent updates over extended periods.
2Adaptability or versatility
If traditional enumeration approach is used to formulate contracts, then all possible contract types can be covered, but computational complexity increases significantly
Solution Approach 1:
The patent creates a universal contract template structure that can represent multiple specific contract types through parameterization. Instead of enumerating each possible contract type separately, the system defines a universal work pattern model with configurable parameters (duration, intensity, skill requirements, etc.) that can instantiate any specific contract type needed, thereby maintaining versatility while reducing computational complexity.
Solution Approach 2:
The patent uses parameter changes to transform the problem from enumerating discrete contract types to optimizing continuous and integer parameters within a unified mathematical model. By representing contracts through adjustable parameters (e.g., start time, end time, skill levels, workload intensity) rather than fixed enumerated types, the system maintains adaptability while enabling efficient mixed-integer programming solutions.
3Productivity
If frequent updates to long-term contract schedules are performed, then workforce capacity can be optimized regularly, but the extensive computations required make this impractical
Solution Approach 1:
The patent implements periodic action by solving the mixed-integer programming problems at regular time intervals (e.g., daily or hourly) throughout the planning horizon. This periodic approach allows the system to perform frequent updates to the contract schedule, adjusting workforce capacity planning regularly while managing computational load by distributing solutions across multiple time periods rather than recalculating everything simultaneously.
Solution Approach 2:
The patent introduces dynamics by making the planning model adaptable to changing conditions over time. The mixed-integer programming formulation allows constraints and objectives to be adjusted dynamically for each time period based on current workforce availability, demand forecasts, and business conditions. This dynamic approach enables frequent updates without requiring complete recalculation of the entire planning horizon, reducing the number of computations needed for each update.
Data Source
AI summary
A method for workforce capacity planning that includes: interpreting one or more labor system input parameters; and determining, based at least in part on the one or more labor system input parameters and a labor contract planning model, a plurality of implicit concrete contract type values. The method further includes: for each of the plurality of implicit concrete contract type values, determining, based at least in part on the labor contract planning model, a number of contracts corresponding to the implicit concrete contract type value; generating, using the labor contract planning model and based at least in part on the plurality of implicit concrete contract type values and their corresponding numbers of contracts, a contract schedule that includes: a plurality of number-of-open-contracts to date pairings, and a plurality of number-of-closed-contracts to date pairings; and transmitting the contract schedule.