Flexible Time Modeling for Facility Scheduling
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Solution Overview
Problem
Conventional decision support tools in the oil and gas industry separate planning and scheduling into distinct processes, leading to difficulties in aligning feasible schedules with planning expectations due to divergent time models, where planning is based on economic optimality and scheduling on event-based feasibility.
Innovation Solution
A 'look-ahead' scheduling technique that analyzes multiple time periods to anticipate dominant events, allowing for flexible time modeling that adjusts the schedule to align with planning objectives, using a computer system to aggregate and disaggregate data and apply strategy-based optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If planning and scheduling are separated into distinct processes with different time models, then each process can be optimized independently, but alignment between feasible schedules and planning expectations deteriorates
Solution Approach 1:
The patent merges planning and scheduling into a unified decision support system that uses a common event-based time model. The system integrates both functions while maintaining their distinct objectives through a hierarchical optimization approach where planning provides economic guidance and scheduling ensures operational feasibility within the same temporal framework.
Solution Approach 2:
The system dynamically adjusts the time model from traditional uniform time periods to event-based time segments. This allows the system to adapt the temporal structure to match actual operational events, enabling both planning and scheduling to operate with the same flexible time representation and improving their alignment.
2Productivity
If planning uses a single-period model focusing on economic optimality, then economic goals are achieved, but feasibility of individual events deteriorates
Solution Approach 1:
The system segments the single planning period into multiple event-based time segments, allowing the optimization to consider both economic objectives and operational constraints at different hierarchical levels. This segmentation enables the system to achieve economic optimality while ensuring feasibility within each segment.
Solution Approach 2:
The patent adds a temporal dimension to the planning model by transitioning from aggregated periodic data to event-based time segments. This dimensional change allows the system to simultaneously optimize economic objectives across the entire planning horizon while maintaining feasibility constraints at the event level.
3Reliability
If scheduling uses a multi-period model focusing on event feasibility, then operational feasibility is achieved, but consistency with planning objectives deteriorates
Solution Approach 1:
The system dynamically coordinates multiple time segments through a look-ahead approach that anticipates future events and adjusts scheduling decisions accordingly. This dynamic coordination ensures that event-based scheduling maintains consistency with planning objectives by considering the cumulative impact of scheduling decisions across all segments.
Solution Approach 2:
The system performs preliminary analysis of multiple upcoming events before making scheduling decisions. By anticipating dominant events and their impacts in advance, the system can make scheduling decisions that maintain feasibility while staying consistent with planning objectives, rather than reacting to events as they occur.
4Measurement precision
If schedules are based on event moments rather than preset time periods, then operational accuracy is improved, but coordination with planning time averages deteriorates
Solution Approach 1:
The system changes the fundamental time parameter from uniform periodic intervals to variable event-based segments. This parameter change allows scheduling to achieve higher precision by matching actual event timing while the system maintains alignment with planning averages through aggregation of event-based data across the planning horizon.
Solution Approach 2:
The system uses feedback mechanisms to coordinate event-based scheduling with planning time averages. By continuously monitoring the cumulative effect of event-based decisions and comparing them against planning targets, the system can adjust scheduling to maintain alignment with economic objectives while preserving event-based precision.
Data Source
AI summary
A decision support tool to assist decision-making in the operation of a facility. The decision support tool allows a user to perform planning and scheduling of the events within a facility so that established economic goals do not collide with feasibility of a schedule. This is achieved by flexible time modeling, which introduces a “look-ahead” planning and scheduling technique. This technique analyzes several time periods of the schedule in light of upcoming dominant events, in order for each segment of the schedule to remain as consistent with the planning objectives as possible.