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It also specifies decision variables for assigning nurses and booking appointments, includes deadline and out-of-window measures, and provides cost and completion-time quantities for optimizing the 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do the document’s notation indices represent?","Question",{"text":62,"@type":63},"They identify key elements in the model, including day, nurse, patient, and slot (and timeslot) used for scheduling and assignment decisions.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Which decision variables are defined for scheduling?",{"text":67,"@type":63},"Binary variables indicate nurse-to-patient assignment in a timeslot and whether each patient is booked for a specific day and slot type (slot, end-slot extension, or post end-slot addition).",{"name":69,"@type":60,"acceptedAnswer":70},"How are penalties and performance metrics represented?",{"text":71,"@type":63},"Scheduling quality uses cost (penalty) terms for different appointment types plus completion-time and makespan quantities to evaluate the produced 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|\n| n | index for the nurse in the task scheduling MIP |\n| p | index for the patient in the appointment scheduling BIP (heuristic) and the task scheduling MIP |\n| s | index for the slot in the appointment scheduling BIP (heuristic) |\n| t | index for the timeslot in the task scheduling MIP\u003Cbr>Input Parameters: |\n| 􀀞×􀀝􀀞 | constant binary matrix indicating nurse availability throughout the (extended) opening hours set of last slots at all stations in the appointment template (not z-app.): end-slots (light-gray) |\n| K | number of stations in the template |\n| M | number of patients that can be simultaneously monitored by one nurse |\n| N | total number of nurses (full-time equivalent) accounted for in the template |\n| Nt | number of nurses available for the setting up or monitoring tasks in timeslot t |\n| S | number of slots in the template |\n| T | number of timeslots in the regular opening hours in a vacant template |\n| an,t | flag (binary parameter): 1 if nurse n available to work in timeslot t |\n| l\u003Cbr>s | duration of slot s |\n| 􀀞̂ | first timeslot of slot s in the template\u003Cbr>Simulation and Other Random Quantities: |\n| A | random variable for the overall number of installments (appointments) in a patient’s regimen |\n| C | random variable for the number of cycles in a patient’s regimen |\n| D | random variable for the number of planned days in a cycle of a patient’s regimen |\n| Dp | deadline for the first appointment of new patient p after receiving the request |\n| Dplan | number of days in the simulation, including both transient and steady-state periods |\n| H | random variable for the cycle duration of a patient’s regimen\u003Cbr>set of requests from new patients being considered at a decision moment on the current day set of requests from returning patients being considered at a decision moment on the current day set of requests being considered at a decision moment on the current day: = 􀀞 ∪ 􀀝 |\n| 􀀞 | desired (planned) day for the next appointment of returning patient p |\n| 􀀞 | required appointment duration for patient p |\n| δ1,p, δ2,p | number of days before or after the planned day with negligible side effects or diminishing results |\n| λ | average rate of the Poisson arrival process for new patients |\n| λhigh | new patient arrival rate corresponding to the service rate of the template: 􀀞􀀝 (􀀜) = 􀀛\u003Cbr>Decision Variables: |\n| v\u003Cbr>p,n,t | binary variable: 1 if nurse n takes care of patient p in timeslot t, and 0 otherwise |\n| xp,d,s | binary variable: 1 if patient p booked for day d in slot s, 0 otherwise |\n| yp,d,s | binary variable: 1 if patient p booked for day d in end-slot s by slot extension, 0 otherwise |\n| zp,d | binary variable: 1 if patient p booked for day d by slot addition, 0 otherwise |\n| 􀀛 n | extra amount of workload assigned to nurse n that is above the average workload of the day (Γ) |\n| ηp,n,t | binary variable: 1 if nurse n starts taking care of patient p in timeslot t\u003Cbr>Intermediary Parameters:\u003Cbr>the indicator function: 1 if statement is true, and 0 otherwise |\n| Bi | constants (parameters) in the BIP to keep various penalties at different levels |\n| Cp,d,s | completion time of appointment p in slot s on day d |\n| 􀀞􀀝􀀜􀀛􀀚 ×􀀙 | binary matrix indicating slot occupancy flags of templates on all days |\n| L | an extremely large value |\n| OOWp,d | number of days that day d is out-of-window for returning patient p |\n| d0 | current day on which decisions are made for appointments on future days |\n| dH | last day considered in the BIP (heuristic), i.e., latest possible day among all patients in |\n| fd,s | flag: 1 if slot s of day d is already booked for a patient and not available at the decision moment |\n| ⋫􀀞 | priority patient p being booked in non-priority slot s |\n\n\n| ⋫􀀞 | priority slot s being booked for ","cbCaiuXHi2hpCcZy","https://ap.wps.com/l/cbCaiuXHi2hpCcZy","pdf",2773241,"English","[{\"question\":\"What do the document’s notation indices represent?\",\"answer\":\"They identify key elements in the model, including day, nurse, patient, and slot (and timeslot) used for scheduling and assignment decisions.\"},{\"question\":\"Which decision variables are defined for scheduling?\",\"answer\":\"Binary variables indicate nurse-to-patient assignment in a timeslot and whether each patient is booked for a specific day and slot type (slot, end-slot extension, or post end-slot addition).\"},{\"question\":\"How are penalties and performance metrics represented?\",\"answer\":\"Scheduling quality uses cost (penalty) terms for different appointment types plus completion-time and makespan quantities to evaluate the produced schedule.\"}]","Planned Diminishing Results - Notation Description | PDF",7]