Hi,   I'm working on a code based on VRP and I have got answer that is making sense with the following code proc optmodel; /* Sets and Parameters */ set P = 1..5; /* Set of 5 patients */ NUM n = card(P); number d{P,P} = [ 0 42.00467295 44.15950104 23.43200516 41.38762733 42.00467295 0 3.50018558 18.70469082 0.767077531 44.15950104 3.50018558 0 21.17540754 3.583323032 23.43200516 18.70469082 21.17540754 0 18.13469416 41.38762733 0.767077531 3.583323032 18.13469416 0 ] ; /* Distance between patients */ number service{P}; /* Service time for each patient */ number w1 = 23.25; /* Telehealth wage per hour */ number w2 = 26; /* Fixed wage for traveling nurse (for 40 hours) */ number speed = 60; /* Vehicle speed (km/h) */ number c_fuel = 0.2; /* Fuel cost per km */ number T_max = 40; /* Maximum working hours */ service[1] = 21; service[2] = 21.5; service[3] = 17; service[4] = 19.5; service[5] = 13; /* Decision Variables */ var x{P} binary; /* 1 if telehealth nurse serves patient, 0 otherwise */ var y{P,P} binary; /* 1 if traveling nurse route used between patients i and j */ var u{P} >= 0; /* Order of visits for each location */ /* Objective Function: Minimize total cost */ minimize TotalCost = /* Telehealth nurse cost: service time * hourly wage */ sum{i in P} x[i] * service[i] * w1 + /* Traveling nurse fixed wage */ sum{i in P} (1 - x[i]) * 40 * w2 + /* Fuel cost for traveling routes */ sum{i in P, j in P: i < j} y[i,j] * d[i,j] * c_fuel; /* Constraints */ /* Each patient must be assigned to either a telehealth or traveling nurse */ con AssignPatient{i in P}: x[i] + sum{j in P: i ne j} y[i,j] = 1; /* Limit total working hours */ con WorkingHours_travelling: sum{i in P, j in P: i ne j} y[i,j] * (d[i,j] / speed) + sum{i in P} (1 - x[i]) * (service[i] / 2) <= T_max; con WorkingHours_tele: sum{i in P} x[i] * service[i] <= T_max; /* Vehicle capacity constraint: no more than 8 traveling nurses */ con VehicleLimit: sum{i in P} (1 - x[i]) <= 8; /* Solve the problem */ solve; /* Print results */ print TotalCost; print x y u; quit;   However, the current code doesn't tell me what is the order of vehicle travelling so I am adding another variable u in for the order plus some flow conservation and sub tour elimination. What I added in worked well before with a simple VRP problem. However, it seems to mess up and make my model give Infeasible solution. I am not sure if what i have added is incorrect or it was my original code that is somehow wrong so it is not compatible with what I've added in.   NUM n = card(P); var u{P} >= 0; /* Order of visits for each location */ /* Flow constraints for VRP */ /* Define flow in and out of all nodes */ impvar FlowOut {i in P} = sum {j in P: i ne j} y[i,j]; impvar FlowIn {j in P} = sum {i in P: i ne j} y[i,j]; /* Arrival and Departure Constraints */ con Arrival_con {i in P}: FlowIn[i] = 1; con Departure_con {j in P}: FlowOut[j] = 1; /* Subtour elimination constraints */ con Subtour_elimination_con {i in P, j in P: i ne j}: u[i] - u[j] + n * y[i,j] <= n - 1; con u_restrict1_con {i in P}: u[i] >= 1; con u_restrict2_con {i in P}: u[i] <= n - 1; print x y u; ... View more

Hello,   I'm trying to create pdf output that includes multiple runs of sgplot so that the same global header and footer appears on every page. I can get them to appear on pages with only text, but am not sure how to display them on pages with sgplot output. The sgplots themselves have titles and footers within their borders. In the example below, the header and footnote print on the title page. I would like them to also appear in the subsequent page with the sgplot output. The actual report would have many sgplot calls.   I appreciate anyone's insight on this. Thanks.     /* Create test dataset */ data test1; input var $ rating1 survey $ source $ section $; datalines; f_pr_inst_mean 68 Faculty Acme U Program f_pr_national_mean 79 Faculty National Program s_pr_inst_mean 81 Student Acme U Program s_pr_national_mean 78 Student National Program f_fa_inst_mean 78 Faculty Acme U Faculty f_fa_national_mean 86 Faculty National Faculty s_fa_inst_mean 89 Student Acme U Faculty s_fa_national_mean 83 Student National Faculty f_in_inst_mean 77 Faculty Acme U Institution f_in_national_mean 69 Faculty National Institution s_in_inst_mean 66 Student Acme U Institution s_in_national_mean 76 Student National Institution f_le_inst_mean 89 Faculty Acme U Leadership f_le_national_mean 78 Faculty National Leadership f_ed_inst_mean 75 Faculty Acme U Education f_ed_national_mean 84 Faculty National Education s_ed_inst_mean 83 Student Acme U Education s_ed_national_mean 73 Student National Education ; run; /* Output to pdf */ %let school = "Acme U"; goptions reset=all device=SASPRTC; ods _ALL_ close; %let folder = M:\Engagement Surveys - 2024\Scratch\; %let filename = %sysfunc(dequote(&school)); %let extension = .pdf; ods pdf (id=1) file="&folder&filename&extension"; options nodate; /* Title page with header and footer */ proc odstext; ods escapechar='~'; title j=r "~S={preimage='C:\Users\nhurwitz\OneDrive - ACAPT\Documents\CoE logo.png' just=c}"; footnote justify=c height=6pt color=black "©2024: Engagement Survey from American Council of Academic Physical Therapy (ACAPT – www.acapt.org/excellence) All rights reserved – September 2024)"; p '2024 ENGAGEMENT SURVEY MEMBER REPORT:' / style = {font_weight=bold font_size=18pt just=c width=100% verticalalign=top}; p &school / style = {font_weight=bold font_size=16pt just=c width=100%}; /* Sgplot on separate page - no header or footer currently */ ods proclabel = "Section Charts"; proc sgplot data=test1 description="Faculty Survey Response by Section: Institution vs. National"; styleattrs DATACOLORS=(CX94c25b CX8e4133); title 'Faculty Survey Response by Section: Institution vs. National'; where survey = 'Faculty'; vbar section / response=rating1 datalabel=rating1 group=source groupdisplay=cluster clusterwidth=.50 name="vbars"; yaxis label='Level of Agreement' values=(0, 33, 67, 100, 110) valuesdisplay=("Disagree (0)" "Somewhat Disagree (33)" "Somewhat Agree (67)" "Agree (100)"); xaxis display=(nolabel); keylegend "vbars" / title=""; footnote height=.75 "Bar values represent the average responsive value of students or faculty members across questions in the section"; ods html(id=1) close;     ... View more

Hello SAS Community, I am working on a project where I need to match subjects based on age using PROC PSMATCH.   However, I require some flexibility in the matching criteria. Specifically, I need to match subjects such that their ages can differ by up to 0.1 years.   I found that the EXACT statement in PROC PSMATCH specifies classification variables to be matched exactly, but this does not meet my requirement for age matching with a small tolerance.   Does anyone know how to implement this in PROC PSMATCH? Any guidance or examples would be greatly appreciated! Thank you in advance for your help! ... View more

My SAS code  is generating semantic error. It is saying ERROR: The array element 'travel_time[1,2]' has no value at line 228 column 57.   I am not sure what that means. I believe the data is completed and has been loaded in correctly.    /* Patient data: duration of treatment and revenue */ data patient_data; input i duration revenue; datalines; 1 12 200 2 45 181 3 90 255 4 26 150 5 75 220 6 60 200 7 45 180 8 120 300 9 90 250 10 30 158 ; run; /* Travel times between patients (symmetric for simplicity) */ data travel_times; input i j t; datalines; 1 2 15 1 3 20 1 4 25 1 5 30 1 6 3.5 1 7 11 1 8 26 1 9 12 1 10 26 2 3 10 2 4 18 2 5 22 2 6 10 2 7 3 2 8 4 2 9 5.5 2 10 15 3 4 12 3 5 15 3 6 10 3 7 25 3 8 30 3 9 22 3 10 12 4 5 8 4 6 23 4 7 10 4 8 7.5 4 9 6.5 4 10 48 5 6 28 5 7 10 5 8 15 5 9 16 5 10 45 6 7 28 6 8 45 6 9 30 6 10 25 7 8 12 7 9 10 7 10 4 8 9 3.5 8 10 15 9 10 30 1 1 0 2 2 0 3 3 0 4 4 0 5 5 0 6 6 0 7 7 0 8 8 0 9 9 0 10 10 0 ; run; /* Generate full travel time matrix */ data travel_times_full; set travel_times; output; t_temp = t; t = t_temp; i_temp = i; i = j; j = i_temp; output; run; /* Add diagonal elements (travel time to self = 0) */ data travel_times_final; set travel_times_full; do i = 1 to 10; j = i; t = 0; output; end; run; /* Nurse and vehicle data */ data nurse_data; input H_t H_c c_t c_c c_v n_t; datalines; 480 480 30 40 20 8 ; run; /* Macro to set the number of telehealth nurses */ %let n_t = 20; /* Use PROC OPTMODEL to solve the problem */ proc optmodel; /* Define sets */ set PATIENTS = 1..10; set TELEHEALTH_NURSES = 1..&n_t; set TRAVEL_CAREGIVERS = 1..8; set VEHICLES = 1..4; /* Define parameters */ number d{PATIENTS}; number travel_time{PATIENTS, PATIENTS}; number H_t; number H_c; number r{PATIENTS}; number c_t; number c_c; number c_v; /* Read data */ read data patient_data into [i] d=duration r=revenue; read data travel_times_final into [i j] travel_time=t; read data nurse_data into H_t H_c c_t c_c c_v; /* Define variables */ var X{PATIENTS, TELEHEALTH_NURSES} binary; var Y{PATIENTS, TRAVEL_CAREGIVERS} binary; var Z{PATIENTS, PATIENTS, TRAVEL_CAREGIVERS, VEHICLES} binary; var U{PATIENTS} integer >= 1 <= card(PATIENTS); /* Objective function */ max Profit = sum{i in PATIENTS} (r[i] * (sum{tn in TELEHEALTH_NURSES} X[i,tn] + sum{c in TRAVEL_CAREGIVERS} Y[i,c])) - sum{tn in TELEHEALTH_NURSES} (c_t * sum{i in PATIENTS} d[i] * X[i,tn]) - sum{c in TRAVEL_CAREGIVERS} (c_c * (sum{i in PATIENTS} d[i] * Y[i,c] + sum{i in PATIENTS, j in PATIENTS, v in VEHICLES} travel_time[i,j] * Z[i,j,c,v])) - sum{v in VEHICLES} (c_v * sum{c in TRAVEL_CAREGIVERS, i in PATIENTS, j in PATIENTS} travel_time[i,j] * Z[i,j,c,v]); /* Constraints */ con Patient_Assignment{i in PATIENTS}: sum{tn in TELEHEALTH_NURSES} X[i,tn] + sum{c in TRAVEL_CAREGIVERS} Y[i,c] = 1; con Telehealth_Hours{tn in TELEHEALTH_NURSES}: sum{i in PATIENTS} d[i] * X[i,tn] <= H_t; con Travel_Caregiver_Hours{c in TRAVEL_CAREGIVERS}: sum{i in PATIENTS} d[i] * Y[i,c] + sum{i in PATIENTS, j in PATIENTS, v in VEHICLES} travel_time[i,j] * Z[i,j,c,v] <= H_c; con Vehicle_Capacity{v in VEHICLES}: sum{c in TRAVEL_CAREGIVERS, i in PATIENTS, j in PATIENTS} Z[i,j,c,v] <= 2; con Flow_Conservation{i in PATIENTS, c in TRAVEL_CAREGIVERS}: sum{j in PATIENTS, v in VEHICLES} Z[i,j,c,v] = Y[i,c]; con Flow_Conservation_Reverse{i in PATIENTS, c in TRAVEL_CAREGIVERS}: sum{j in PATIENTS, v in VEHICLES} Z[j,i,c,v] = Y[i,c]; con Subtour_Elimination{i in PATIENTS, j in PATIENTS, c in TRAVEL_CAREGIVERS: i ne j}: U[i] - U[j] + 1 <= card(PATIENTS) * (1 - sum{v in VEHICLES} Z[i,j,c,v]); con Ordering_Link{i in PATIENTS, c in TRAVEL_CAREGIVERS}: U[i] >= 1 + card(PATIENTS) * (Y[i,c] - 1); /* Solve the model */ solve; /* Print results */ print X Y Z U; quit; ... View more

In SAS, column length is critical. SAS numeric columns have a default length of 8 bytes, allowing the storage of up to 16 digits. In contrast, SAS character columns can vary from 1 to 32,767 bytes, with 1 byte typically equating to 1 character. *Note: This applies to single-byte encoding. If using UTF-8 encoding, some characters may require up to 3 bytes. This variability is common with non-English characters.   While numeric columns generally don't need length adjustments, character columns often do to prevent truncation of values. The usual approach to altering column length involves the LENGTH statement in the DATA step. However, this method can disrupt the order of your columns. Why does this happen, and how can we fix it while maintaining the original column order? The issue stems from the Program Data Vector (PDV), but don't worry—we can resolve it using PROC SQL. ... View more