Spatio-Temporal Crew Scheduling for Route and Constraint Allocation
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Solution Overview
Problem
Field services management faces challenges in creating efficient spatio-temporal schedules due to labor-intensive operations and complex constraints such as spatial and temporal factors, which are considered an NP-hard problem, making it difficult to optimize crew assignments and task allocation.
Innovation Solution
A method for creating spatio-temporal schedules that considers spatial and temporal constraints, including geographic locations, travel times, and crew availability, using a sequencing engine to maximize user-selected objectives and generate optimal routes for field crews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual or semi-automated processes are used to determine tasks and assign field crews, then flexibility in managing complex constraints is maintained, but labor intensity increases and scheduling efficiency decreases
Solution Approach 1:
The patent introduces a spatio-temporal scheduling system that acts as an intermediary between manual scheduling processes and field operations. This system includes a sequencing engine and assignment engine that automatically handle complex constraint management, reducing labor intensity while maintaining flexibility through configurable parameters and rules-based decision-making
Solution Approach 2:
The scheduling system is segmented into distinct functional modules: a sequencing engine that handles task ordering based on spatial and temporal constraints, and an assignment engine that allocates crews to tasks. This modular architecture reduces overall system complexity by breaking down the complex scheduling problem into manageable, independent components
2Productivity
If spatio-temporal constraints are considered in scheduling, then route optimization and resource utilization improve, but computational complexity increases making the problem NP-hard
Solution Approach 1:
The patent segments the NP-hard spatio-temporal scheduling problem into two separate optimization sub-problems: (1) sequencing workorders based on spatial and temporal constraints, and (2) assigning sequenced workorders to available crews. This decomposition reduces computational complexity by allowing each sub-problem to be solved independently with simpler algorithms rather than requiring solution of the full complex problem simultaneously
Solution Approach 2:
The system performs preliminary sequencing of workorders before crew assignment. By pre-ordering tasks based on spatial proximity and temporal constraints, the system reduces the complexity of the subsequent assignment phase, as crews are then assigned to already-sequenced tasks rather than optimizing both sequencing and assignment simultaneously
3Productivity
If workorders are assigned sequentially to crews with full allocation before selecting next crew, then crew utilization optimizes, but travel time between locations may increase
Solution Approach 1:
The sequencing engine applies local quality optimization by ordering workorders within each crew's route based on spatial proximity and temporal constraints. This ensures that each individual crew route is optimized for minimal travel time while maintaining high utilization, rather than applying a uniform assignment strategy to all crews
Data Source
AI summary
A method for creating a spatio-temporal schedule includes receiving a set of available work crews and a set of workorders to be scheduled, sequencing the set of workorders, wherein each workorder of the set of workorders is associated with a geographic location, a start time and an end time, and iterating through the set of available work crews, assigning a plurality of workorders of the set of workorders fully allocating a current crew before selecting a next crew, until all work crews are fully allocated and/or all workorders are assigned, wherein the assignment of workorders for a given work crew generates a route comprising the plurality of workorders.