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Point‐of‐Dispensing Location and Capacity Optimization via a Decision Support System
Author(s) -
RamirezNafarrate Adrian,
Lyon Joshua D.,
Fowler John W.,
Araz Ozgur M.
Publication year - 2015
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.12323
Subject(s) - computer science , point (geometry) , queueing theory , operations research , set (abstract data type) , genetic algorithm , action (physics) , decision support system , mathematical optimization , operations management , data mining , machine learning , economics , mathematics , computer network , physics , geometry , quantum mechanics , programming language
Dispensing of mass prophylaxis can be critical to public health during emergency situations and involves complex decisions that must be made in a short period of time. This study presents a model and solution approach for optimizing point‐of‐dispensing (POD) location and capacity decisions. This approach is part of a decision support system designed to help officials prepare for and respond to public health emergencies. The model selects PODs from a candidate set and suggests how to staff each POD so that average travel and waiting times are minimized. A genetic algorithm (GA) quickly solves the problem based on travel and queuing approximations (QAs) and it has the ability to relax soft constraints when the dispensing goals cannot be met. We show that the proposed approach returns solutions comparable with other systems and it is able to evaluate alternative courses of action when the resources are not sufficient to meet the performance targets.