Dynamic Resource Scheduling System for Maintenance Service Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional resource allocation techniques in maintenance service environments fail to optimize technician scheduling, leading to excessive travel time and costs due to inadequate management of time commitments and location assignments.
Innovation Solution
The implementation of a dynamic resource scheduling system that applies specific constraints to a cost function to optimize worker allocation, using Mixed Integer Programming (MIP) and Constraint-Processing (CP) algorithms to minimize travel time, maximize service efficiency, and ensure feasible workforce schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional resource allocation techniques are used, then scheduling is simpler, but travel time and costs increase
Solution Approach 1:
The patent transforms the scheduling problem from a qualitative process to a quantitative optimization problem by defining a cost function with specific parameters (travel time, service time, technician availability). This allows the system to evaluate and compare different scheduling scenarios objectively, minimizing travel time while managing complexity through mathematical formulation.
Solution Approach 2:
The patent replaces manual or heuristic scheduling methods with algorithmic optimization using Mixed Integer Programming (MIP) and Constraint-Processing (CP). This substitution of mechanical/problem-solving approaches with computational algorithms enables the system to handle complexity automatically while delivering optimized schedules that minimize travel time.
2Measurement precision
If more constraints are applied to the cost function, then scheduling accuracy improves, but processing time increases
Solution Approach 1:
The patent divides the scheduling problem into two distinct segments: a MIP phase that handles resource allocation and assignment, and a CP phase that handles temporal constraints and sequencing. This segmentation allows each algorithm to focus on its strength, improving overall accuracy while managing processing time by avoiding the application of all constraints simultaneously in a single complex model.
Solution Approach 2:
The patent implements a dynamic two-phase optimization process where the MIP phase first determines feasible assignments, then the CP phase refines the schedule with temporal constraints. This dynamic approach allows the system to adapt the level of constraint application based on the stage of optimization, achieving high accuracy while controlling processing time through staged refinement.
3Reliability
If individual worker availability is tracked, then schedule feasibility improves, but search space increases
Solution Approach 1:
The patent performs preliminary filtering of worker availability before the main optimization process. By pre-identifying which workers are available during specific time windows and for specific tasks, the system reduces the search space early in the process. This preliminary action maintains schedule feasibility through accurate availability tracking while avoiding the computational burden of evaluating all possible worker-task combinations.
4Loss of time
If location-based assignment is implemented, then travel time decreases, but scheduling complexity increases
Solution Approach 1:
The patent incorporates location as a quantifiable parameter in the cost function, assigning numerical values to travel time between locations. This transformation allows the optimization algorithm to automatically balance location-based travel time against other scheduling constraints, reducing travel time while managing complexity through mathematical optimization rather than manual rule-based assignment.
Data Source
AI summary
Methods and systems for servicing of machines by workers within a period of time. Acquiring for each service an available servicing time, a time duration for servicing, a location of the machine, and a number of workers having appropriate qualifications to be concurrently present for a service. Acquiring for each worker a worker availability, qualifications and location. Determining a cost function representing a service schedule for each worker, wherein an optimization of the cost function is subject to constraints. The constraints include a number of workers with qualifications concurrently present for a service, each worker starts and ends the period of time at the same location and travels independently from other workers. The cost function includes maximizing a number of services to be performed; minimizing a number of workers required to perform servicing for each service; or minimizing a total travel time for each worker to the location.