Scheduling and Resource Allocation for Improving Service and Operations Management: Modelling, Solution Methods and Applications (especially in Healthcare)

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This proposal will develop models and solution methods to improve scheduling and resource allocation decisions in service and operations management applications, especially in healthcare. The goal is to achieve better service levels (e.g., shorter wait times (until service day), timely service (at the say of service)), lower cost to service/product providers (e.g., less overtime, lower capacity requirements) and better utilization of resources. There are many real-world applications for the proposal's models and methods in different industries such as service, healthcare and manufacturing. Two main areas are considered: Combined appointment and advance scheduling (CAAS) Generalized multi resource scheduling (GMRS)The aim of CAAS is to find an optimum schedule for a service system where jobs (e.g., customers, patients) arrive to the system randomly (arrival times, quantity, priority and job types) and they are assigned a service day. Jobs wait until the day of the service and job processing times are stochastic. Jobs are processed in a given order in the day of the service. The goal is to find a scheduling policy that minimizes total expected cost (before the day of the service and during the day of the service). The scheduling policy consists of determining day of the service (date) and time of the service (appointment time for a given day of the service). The cost consists of wait time until the day of the service, wait time during the day of the service, idle time of the server, and overtime of the server. I will seek answers to: How to model and solve the CAAS problem? How to solve the CAAS problem with a different objective criterion other than minimizing the expected value (e.g., a percentile of the cost)? How to determine an optimal sequence of jobs for daily processing? How to add an end-resource where all jobs stay for some time after they are processed and before they leave the system?The goal of GMRS is to develop models and solution methods to find a sequencing schedule minimizing processing costs for a given set of jobs (e.g., surgeries) to be processed on multiple machines (e.g., operating rooms) by multiple sets of resources (e.g., surgeons and anesthetists). The output of such a model is the optimum number of machines and optimum job processing schedule (with assigned resources) minimizing the total cost of running machines (regular time and overtime). When a job is processed on a machine, each resource needs to work in a sequential manner and lag times (e.g., a surgeon must wait until preparation and anesthesia are done) between different resources are considered. Further complication is that jobs have different types (e.g., surgical specialty), and resources are based on job types. I will search answers for the following questions: How to model and solve GMRS? How to model and solve a stochastic version GMRS, i.e., job durations stochastic? How to model and solve GMRS with an end-resource?