UET-scheduling with chain-type precedence constraints
Computers and Operations Research
Scheduling chains to minimize mean flow time
Information Processing Letters
Computers and Biomedical Research
Resource Constrained Chain Scheduling of Uet Jobs on Two Machines
Operations Research
Scheduling Algorithms
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
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In this paper, we outline the development of the core optimization technology used within a decision support tool to help providers and caretakers in constructing catch-up schedules for childhood immunization. These schedules ensure that a child continues to receive timely coverage against vaccine-preventable diseases in the likely event that one or more doses have been delayed. This project was undertaken as part of a collaborative effort between the Centers for Disease Control and Prevention (CDC) and Georgia Institute of Technology. Our aim is to develop a decision support tool that removes from the task of constructing catch-up schedules the tedious combinatorial aspects, while maintaining a level of generality that allows easy accommodation for changes in the existing rules and adding new vaccines to the schedule lineup. We show that the catch-up scheduling problem is NP-hard, and we develop a dynamic programming algorithm that exploits the typical size and structure of the problem to construct optimized schedules almost at the click of a button. In using an optimization-based algorithm, our approach is unique not only in methodology but also in the information, strategy, and advice we can offer to the user. The tool is being advocated by both the CDC and the American Academy of Pediatrics (AAP) as a means of encouraging caretakers and providers to take a more proactive role in ensuring timely vaccination coverage for children, as well as ensuring the accuracy and quality of a catch-up regime.