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Hopeful Journeys Educational Center faces a daily task of assigning tutors to students subject to myriad complex rules and restrictions. The organization’s mission, which is to provide individualized education to students with autism spectrum disorders and other developmental disabilities, as well as its limited operating budget and day-to-day resource/demand variability, makes this a uniquely challenging scheduling problem. When we first communicated with Hopeful Journeys, the organization was in critical need of an efficient methodology for producing daily schedules to replace its existing time-consuming and error-prone manual approach. This paper describes the fully open-source, Excel-based optimization tool we developed to support Hopeful Journeys’ mission. Our work illustrates the potential to use freely available operations research tools within a “rapid prototyping” approach to provide immediate impact to organizations that lack the resources to utilize commercial software or professional consultants.

Abstract

The methodology described in this article enables a type of holistic fleet optimization that simultaneously considers the composition and activity of a fleet through time as well as the design of individual systems within the fleet. Often, real‐world system design optimization and fleet‐level acquisition optimization are treated separately due to the prohibitive scale and complexity of each problem. This means that fleet‐level schedules are typically limited to the inclusion of predefined system configurations and are blind to a rich spectrum of system design alternatives. Similarly, system design optimization often considers a system in isolation from the fleet and is blind to numerous, complex portfolio‐level considerations. In reality, these two problems are highly interconnected. To properly address this system‐fleet design interdependence, we present a general method for efficiently incorporating multi‐objective system design trade‐off information into a mixed‐integer linear programming (MILP) fleet‐level optimization. This work is motivated by the authors' experience with large‐scale DOD acquisition portfolios. However, the methodology is general to any application where the fleet‐level problem is a MILP and there exists at least one system having a design trade space in which two or more design objectives are parameters in the fleet‐level MILP.