Two-stage perishable inventory models
Management Science
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Some Origins of Operations Research in the Health Services
Operations Research
Computers and Operations Research - Special issue: Emerging economics
Dynamic Economic Lot Size Model with Perishable Inventory
Management Science
Control Policies For Inventory Systems With Perishable Items: Outsourcing And Urgency Classes
Probability in the Engineering and Informational Sciences
Optimization of Influenza Vaccine Selection
Operations Research
Decision support system induced guidance for model formulation and solution
Decision Support Systems
Managing Patient Service in a Diagnostic Medical Facility
Operations Research
A perishable inventory system with retrial demands and a finite population
Journal of Computational and Applied Mathematics
An Analysis of Pediatric Vaccine Formulary Selection Problems
Operations Research
Supply Chain Coordination and Influenza Vaccination
Operations Research
Dynamic Multipriority Patient Scheduling for a Diagnostic Resource
Operations Research
OR Practice---Catch-Up Scheduling for Childhood Vaccination
Operations Research
Formulations and reformulations in integer programming
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
A demand-responsive decision support system for coal transportation
Decision Support Systems
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The administration of travel vaccines presents a number of operations management challenges. The interplay between shared consumption of multi-dose vaccine packages, rapid spoilage upon opening, the high cost of wastage, and the unique vaccination needs of the patients makes for a very interesting and complex scheduling problem that could benefit from computerized decision support. We compare the performance of a novel binary integer programming model and a genetic algorithm solution technique with conventional scheduling approaches. Computational results show that significant cost savings can be achieved with the DSS while simultaneously considering scheduling preferences of patients and mitigating scheduling inconvenience.