Multi-Objective On/Off Equipment Scheduling via Iterative Optimization
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Solution Overview
Problem
Current methods for scheduling on/off control of equipment with multiple objectives are inefficient due to exponential complexity and lack of guaranteed optimal solutions, often relying on manual analysis or simplifications that fail to account for real-world complexities and interdependencies in systems like water distribution networks.
Innovation Solution
A system and method that iteratively optimize on/off schedules for equipment by selecting objectives, adjusting margin settings, and generating control signals to balance multiple objectives, ensuring an optimal schedule is achieved through a computer-implemented system using mathematical solvers and iterative optimization processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optimization techniques or meta-heuristics are used to solve multi-objective scheduling problems, then solution quality improves, but computational complexity and cost increase significantly
Solution Approach 1:
The patent segments the scheduling problem into discrete time periods and equipment units, representing the schedule as a combination of binary decision variables for each time-period-equipment triplet. This segmentation transforms the continuous optimization problem into a discrete combinatorial problem that can be systematically explored through iterative evaluation of individual schedule components.
Solution Approach 2:
The patent changes the parameter representation from continuous time and flow variables to discrete binary on/off decisions for each equipment unit at each time period. This parameter transformation enables the use of combinatorial optimization techniques and makes the problem amenable to systematic search methods while maintaining solution quality.
2Device complexity
If manual analysis or intuition-based methods are used to develop operating schedules, then computational resources are saved, but solution optimality and ability to handle multiple objectives deteriorate
Solution Approach 1:
The patent implements self-service through automated iterative optimization that systematically evaluates and improves schedule solutions without requiring manual intervention. The system automatically generates candidate schedules, evaluates them against multiple objectives, and iteratively refines the solution until optimality conditions are met, replacing intuition-based manual analysis with autonomous computational optimization.
Solution Approach 2:
The patent incorporates feedback mechanisms where the evaluation of objective function values guides the iterative improvement process. The system uses feedback from objective evaluations to determine which schedule modifications will improve overall performance, enabling systematic convergence to optimal solutions while handling multiple competing objectives simultaneously.
3Productivity
If simplifications, discretization, or heuristic rules are applied to make the scheduling problem tractable, then computational feasibility improves, but solution accuracy and ability to capture real-world complexities worsen
Solution Approach 1:
The patent applies dynamics by allowing the schedule to be flexibly adjusted through iterative modifications of binary decision variables. Rather than using static simplifications, the system dynamically explores the solution space by systematically switching equipment on/off at different time periods based on real-time objective evaluations, capturing complex interdependencies while maintaining computational feasibility.
Solution Approach 2:
The patent uses partial action by evaluating and optimizing individual equipment schedules and time periods separately through iterative procedures. Rather than attempting to solve the entire complex system simultaneously, the method makes incremental improvements to portions of the schedule, achieving computational feasibility while progressively capturing real-world complexities through repeated partial optimizations.
Data Source
AI summary
An apparatus, method and computer program product for scheduling on/off control of equipment. The method implements an iterative approach that uses one objective and sets a tolerance value on the other objectives. Initially, the method computes the value of the different objective functions given a current feasible solution. The different objective functions are iteratively evaluated with respect to the current solution and a margin is set for every objective at each iteration. The margin is a deviation measure indicating the acceptable range by which each objective can be worsened. The method iteratively optimizes each objective function with respect to one of the objectives, following an order, while enforcing a maximum deviation on the other objective functions by setting them as constraints. The allowable deviation margin is then decreased for all the objective functions. A final output schedule is provided from which signals may be generated to automatically turn on/off the equipment.