Schedule optimization, however, is one of the most challenging optimization problems due to variations in types of work and operations. This variation makes solutions hard to generalize and therefore hard to scale. Additionally, the mathematical complexity of optimization equations and the number of decision variables mean models take a long time to run. To be truly useful, optimization models need to operate in almost real time so that they can react to changes such as employee sick days and unexpected demand surges. While classic optimization models have been around for decades, the advent of new technologies in AI and cloud infrastructure allows for the rapid development and deployment of tools that bring deep analytics and optimization engines to the scheduling process. These tools have also reduced the cost of deploying an end-to-end schedule optimization solution and can sit on top of existing work management systems. Additionally, using AI improves the quality and functionality of scheduling in a number of ways: — offering the most optimal solution given a range of interdependent constraints and dynamic, ever-changing demand
— Emergency jobs have high importance but low predictability and may require a crew to be immediately reallocated from another work site. These schedule “break-ins” require real- time juggling of crews and often cause churn and rework for schedulers. Smart scheduling can help block off capacity for these emergent break-ins via dynamic schedule loading. For example, only 60 to 70 percent of capacity may be allocated in a given week if algorithms predict, based on historical data, that 30 to 40 percent of time will need to be spent on emergency jobs. Smart scheduling can also identify the optimal crew to address the emergency job based on factors such as geographic proximity and the priority and state of the crew’s current job. — Short-cycle jobs can typically be completed within the day. They range in complexity: some jobs may require one crew for an hour or two, while others—such as hydro-vacuum excavation—may require several crews for a full day alongside coordination with third-party contractors. The scheduled duration for a short- cycle job may often be several hours more or less than the actual requirement, leading to either a schedule backlog or underutilization. Smart scheduling can better estimate the durations of these jobs using a combination of historical performance and factor-driven adjustments. For example, data on local soil composition can be used to estimate the time needed to dig. — Long-cycle jobs may require multiple days to complete, and the main challenge is to ensure continuity by scheduling the same crews for the whole duration. These jobs often come with multiple crews and pieces of equipment, plus third-party contractors, which means that smart scheduling can ease the significant mental burden on schedulers. Schedulers need to coordinate the availabilities of crew, materials, and equipment ahead of time to ensure that all components are ready on the day when the work is to be done. Depending on the type of job, schedulers may need to create crews of different sizes—generally one to four full-time
— providing a consistent, systematic approach with no human bias
— delivering significantly faster computation than manual processes, which improves the ability to adapt to unexpected changes in operations
— lowering HR requirements, which frees up capacity to focus on other areas
Smart scheduling offers benefits for utility companies For electric and gas utilities, scheduling is a central function that matches demand for services with the crews, materials, and equipment needed to perform those services. Utilities have a variety of different work types—including emergency jobs, short-cycle jobs, and long-cycle jobs—with varying scheduling dynamics. Smart scheduling provides benefits for each work type:
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