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AbstractConsider a set of task pairs coupled in time: a first (initial) and second (completion) tasks of known durations with a specified time between them. If the operator or machine performing these tasks is able to process only one at a time, scheduling is necessary to insure that no overlap occurs. This problem has a particular application to production scheduling, transportation, and radar operations (send‐receive pulses are ideal examples of time‐linked tasks requiring scheduling). This article discusses several candidate techniques for schedule determination, and these are evaluated in a specific radar scheduling application.

abstract: Declaration of Conflicts: This project has no conflicts of interest to declare. Context: This project was completed at a federally qualified primary healthcare clinic in Phoenix, Arizona that served patients of all age groups, but primarily cared for the Hispanic population providing primary care, preventative services, family planning, two lab technicians, one promoter, two medical assistant supervisors, five front desk staff, one chief administrative officer, one chief financial officer, two medical directoers who were also providers at the clinic. Problem and Analysis Assessment: During my clinical rotations, I saw the burden a missed patient appointment had not only on the patients themselves, but also on the clinic, providers, and the staff. It caused delay in treatment for patients, and it did not allow other patients that wanted to be seen to be seen. It also increased unnecessary costs and wasted provider time. Thereafter, I met with some of the leadership team and one of the medical directors to determine a solution to reduce the number of missed appointments that were occurring. An educational session was kept to discuss the findings of this problem to the providers and the staff and when surveys were handed out to the patients, providers, and staff to assess their satisfaction with the old scheduling system versus the new scheduling system, they were also provided with a cover letter discussing the project. Intervention: In order for improvements in care to occur, a system process change including the way patients are scheduled must occur. In this case, an open-access scheduling system (OAS) was implemented. OAS allows a patient to schedule an appointment on the 'same-day' or the 'next-day' to be seen. One provider at each of the clinics, each day of the week was available for 'same-day' appointments from 1300-1600. The providers were still available for scheduled appointments using the previous scheduling method. Walk-ins were still accepted, and were scheduled based on patient provider preference; however, if an appointment was not available for their preferred provider, they were typically seen with the provider that was the 'same-day' provider for that day. Strategy for change: Since patients were only allowed to schedule appointments one month in advance, only one month was needed to implement this process change. A recommendation for the future would be to clearly identify the patient encounter type, and label it as a same-day appointment, as this would be helpful when gathering and extracting data for this type of patient group specifically. Measurement of Improvement: Over a three-month period, a data collection plan was used to determine the number of Mas over a three-month period before and after implementation of this change. Satisfaction scores were measured using likert scales for patients, provider, and staff, and a dichotomous scale was used to determine the likelihood of emergency room or urgent care use. A comparison was done to measure revenue during the same time frame. During the three months, a clinically significant decrease in MAs was seen (68% of all patients, providers and staff reported feeling either very satisfied or extremely satisfied with the new scheduling system. Additionally, patients also reported that they were less likely to visit an emergency room(88%) or urgent care (90%) since they were able to be seen the same-day or the next-day by a provider. Effects of changes: An incidental finding occurred during this study - where 877 more patients were seen in the three months during the implementation of this project, compared to the three months prior; which likely resulted in a 41% increase in revenue. Additionally this project, allowed patients that wanted to be seen on the same day, to be seen, and it decreased unnecessary costs associated with emergency room or urgent care visits. Some of the limitations involved included the current political environment, appointment slots that were previously 15 minutes in length (in 2016), increased to 20 minutes in length (in 2017), a language barrier was noted for the patient surveys since English was not the first language for many of the patients who completed the survey (although documents were translated), and the surveys used were not reliable instrument given that a reliable instrument in previous studies could not be found. Lessons learnt: In order to have accuracy of the survey results, it is best for the author of the study to hand out and provide scripture for the survey so that complete data is received from the surveyors. Messages for others: Begin by making a small process change where only one provider allows for the open-access scheduling so that the entire office is not affected by it, and if results begin to look promising then it can be expanded. Additionally, correct labeling of patients as 'same-day' is also important so that additional data can be gathered when needed regarding the 'same-day' patients. Patient/Family/Guardian Involvement: Patients who benefited from the new scheduling system (open-access scheduling) were asked to fill out a survey that asked them to disclose some demographic data and asked them to determine their satisfaction with the new vs old scheduling system and their likelihood of visiting an emergency room or urgent care. Ethics Approval: Arizona State University Institutional Review Board (IRB) Received: September 2017

Front Cover -- Contents -- Preface -- Authors -- Chapter 1: Introduction -- Chapter 2: Fuzzy Simulated Metamorphosis Algorithm -- Chapter 3: Fuzzy Simulated Evolution Algorithm -- Chapter 4: Fuzzy Grouping Genetic Algorithm -- Chapter 5: Fuzzy Grouping Particle Swarm Optimization -- Chapter 6: Fuzzy Simulated Metamorphosis Algorithm for Nurse Scheduling -- Chapter 7: Fuzzy Simulated Evolution Algorithms for Nurse Rerostering -- Chapter 8: Fuzzy Particle Swarm Optimization for Physician Scheduling -- Chapter 9: Fuzzy Grouping Genetic Algorithm for Homecare Staff Scheduling -- Chapter 10: Fuzzy Grouping Particle Swarm Optimization for Care Task Assignment -- Chapter 11: Future Trends and Research Prospects in Healthcare Operations -- Appendix: Fuzzy Set Theory Concepts -- Back Cover.

Front Cover -- Repetitive Project Scheduling: Theory and Methods -- Copyright Page -- Contents -- 1 Basic Concept -- 1.1 Projects -- 1.2 Repetitive Construction Projects -- 1.3 Characteristics of Repetitive Activities and Projects -- 1.3.1 Repetitive and Nonrepetitive Activities -- 1.3.2 Typical and Non-Typical Activities -- 1.3.3 Resource Continuity Constraints -- 1.3.4 Distance Constraints -- 1.3.5 Hard and Soft Logic Relations -- 1.4 Network Planning Techniques -- 1.4.1 Critical Path Method -- 1.4.2 Plan Evaluation and Review Technique -- 1.5 Existing Scheduling Techniques for Repetitive Construction Projects -- 2 Line-of-Balance Technique -- 2.1 Introduction -- 2.2 Basic Concept and Representation -- 2.2.1 Crew Synchronization -- 2.2.2 Optimum Crew Size and Natural Rhythm -- 2.3 Integrated CPM-LOB Method -- 2.3.1 Meeting a Given Deadline -- 2.3.2 Number of Crews Determination -- 2.3.3 Drawing LOB Schedule -- 2.3.4 Example Application -- 2.4 Comments and Future Research -- 2.5 Conclusion -- 3 Controlling Path Analysis in Repetitive Scheduling Method -- 3.1 Introduction -- 3.2 Basic Representation of RSM -- 3.3 Method for Determining Controlling Path -- 3.3.1 Determining pCPs with Time Constraint -- 3.3.2 Determining pCPs with Distance Constraint -- 3.3.3 Determining pCPs with Multiple Constraints -- 3.3.4 Determining pCPs with Constraint with Bar Activity and Block Activity -- 3.3.5 Identifying the Controlling Path -- 3.4 Types of Sub-Activities -- 3.5 Project Duration Determination -- 3.6 Case Study -- 3.7 Discussion -- 3.8 Conclusion and Prospects -- 4 Conversion of Repetitive Scheduling Model to Network Model -- 4.1 Introduction -- 4.2 Method for Converting RSM to Network Model -- 4.2.1 Conversion of Activities -- 4.2.2 Conversion of Logical Relations -- 4.2.3 Conversion of Precedence Relations -- 4.2.4 Displaying the Spatial Information

The purpose of this project is to develop a computer program that will schedule state cars that will insure each car gets at least seven hundred and fifty miles each month. This program will be accessible to each employee, they will be able to go to their individual computer or any computer in the office and schedule a car. This program will eliminate problems that happen now such as shortage in clerical staff and save time for workers.

AbstractThe U.S. Navy Prowler aircraft is designed for electronic surveillance and countermeasures. In this paper, we describe the problem of scheduling Prowler crew training, and we present two integer programming models to solve it. The first model maximizes the number of aviators trained above 75% in each mission area, subject to the available number of flights, over a single month. The second model distinguishes peacetime from mobilization, and minimizes the number of flights done in mobilization subject to the available number of flights in peacetime. Our models distinguish different types of crew and allow more than one qualification to be earned on a given flight. We give numerical results using real data, comparing our results to the actual readiness of a squadron. We found that crew readiness of Prowler squadrons can be increased by 10%, simply by better scheduling. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 289–305, 2003.

Scheduling is defined by Baker as, "the allocation of resources over time to perform a collection of tasks". The term facilities is often used instead of resources and the tasks to be performed may involve a variety of different operations.