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

该提案将开发模型和解决方案方法，以改进服务和运营管理应用程序中的调度和资源分配决策，特别是在医疗保健领域。目标是实现更好的服务水平(例如，更短的等待时间(直到服务日)、及时的服务(根据服务的要求))、更低的服务/产品提供商成本(例如，更少的加班、更低的能力要求)和更好的资源利用。该提案的模型和方法在服务、医疗保健和制造业等不同行业有许多现实世界的应用。主要考虑两个方面：*联合预约和提前调度(CAAS)*广义多资源调度(GMRS)*CAAS的目标是为服务系统找到一个最优调度，其中作业(例如，客户、患者)随机到达系统(到达时间、数量、优先级和作业类型)，并且他们被分配一个服务日。作业要等到服务的那一天，作业处理时间是随机的。作业在服务当天按给定的顺序进行处理。目标是找到一种使总预期成本最小的调度策略(在服务当天之前和在服务期间)。调度策略包括确定服务的日期(日期)和服务的时间(服务的给定日期的预约时间)。费用包括服务当天的等待时间、服务当天的等待时间、服务器的空闲时间和服务器的超时。我将寻求以下问题的答案：*如何建模和解决CAAS问题？*如何使用不同的客观标准来解决CAAS问题，而不是最小化期望值(例如，成本的百分数)？*如何确定用于日常处理的最优作业序列？*如何添加终端资源，其中所有作业在被处理之后和离开系统之前停留一段时间？*GMRS的目标是开发模型和解决方法，以找到最小化给定作业集的处理成本的排序计划(例如，手术)由多组资源(例如，外科医生和麻醉师)在多台机器(例如，手术室)上处理。该模型的输出是最优机器数量和最优作业处理计划(在分配资源的情况下)，使机器运行的总成本(正常时间和加班)最小。当在机器上处理作业时，每个资源需要以顺序方式工作，并且考虑不同资源之间的滞后时间(例如，外科医生必须等待，直到准备和麻醉完成)。更复杂的是，工作有不同的类型(例如，外科专业)，资源基于工作类型。我将寻找以下问题的答案：*如何建模和求解GMRS？*如何建模和求解随机版本的GMRS，即作业持续时间随机？*如何建模和求解具有终端资源的GMRS？

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?