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Solve a timetabling problem for college courses under various constraints Description A timetabling problem for college courses under various constraints. Further explanation of this example:
This is a conversion of the Mosel example 'Establishing A College Timetable'
Source Files By clicking on a file name, a preview is opened at the bottom of this page.
college_timetable.R ##################################### # This file is part of the # # Xpress-R interface examples # # # # (c) 2022 Fair Isaac Corporation # ##################################### #' --- #' title: "Establishing A College Timetable" #' author: Y. Gu #' date: Jul.2021 #' --- #' #' #' ## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$set(results = "hold") knitr::opts_chunk$set(warning = FALSE, message = FALSE) #' #' #' ## Brief Introduction To The Problem #' #' [This is a conversion of the Mosel example 'Establishing A College Timetable'](https://www.fico.com/fico-xpress-optimization/docs/latest/examples/mosel/ApplBook/I_TimePers/i3school.mos). #' A brief introduction to this problem is given below, and to see the full #' mathematical modeling of this problem you may refer to section 14.3, page 218 of the #' book 'Applications of optimization with Xpress'. #' #' This example is a timetabling problem for college courses. Optimization can provide #' useful help for this kind of problem, while since many subtle sociological and #' psychological constraints are hard to consider in a mathematical model, the solution #' should always be used interactively: the optimization model makes suggestions #' and the human being plans by adjusting the solution to exact needs. #' #' In our example, 9 teachers will give lessons to 2 classes for 5 days per week, and #' each day contains 4 time slots for scheduling lessons. We set binary variables #' 'teach(t,c,l,d)' that take value 1 if the teacher t gives a lesson to class c in #' period l of day d. Our goal is to find a feasible timetable that satisfies all the #' constraints, but we can set objective as minimizing the 'holes' in the timetable, #' i.e., we minimize the sum of courses taught during slots 1 and 4 of every day. #' #' To schedule a feasible timetable, many constraints should be satisfied. Firstly, all #' lessons taught by each teacher to each class must be scheduled. Secondly, a class #' could have at most one course at any time. Similarly, a teacher must not teach more #' than one course at a time slot. To prevent students from getting bored, another #' constraint is specified, where at most one lesson per subject could be taught on the #' same day . #' #' There are also some specific constraints for this example. For example, sport lessons #' have to take place on Thursday afternoon from 14:00 to 16:00. Other specific conditions #' will be discussed when we add rows to the problem in section 'Add Rows'. #' #' The solution to this problem is not unique, and as mentioned before, the one that #' matches the real situation most should be chosen and adjustments could always be made #' if necessary. For mathematical formulations, please refer to the book #' 'Applications of optimization with Xpress' #' #' #' For this example, we need packages 'xpress' and 'dplyr'. Besides, we use the #' function `pretty_name` to give the variables and constraints concise names. #' ## ----Load The Packages And The Function To Give Names------------------------- library(xpress) library(dplyr) pretty_name <- function(prefix, y) { "%s_%s" %>% sprintf(prefix, paste(lapply(names(y), function(name) paste(name, y[name], sep = "_")), collapse = "_")) } #' #' #' Create a new empty problem and give the problem a suitable name. #' ## ----Create The Problem------------------------------------------------------- # create a new problem prob <- createprob() # set the problem name setprobname(prob, "CollegeTimetable") #' #' #' Add the values we need for this example. #' ## ----Data--------------------------------------------------------------------- # 1. classes CLASS <- 1:2 # 2. periods per day Periods <- 1:4 NP <- length(Periods) # 3. days per week Days <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday") ND <- length(Days) # 4. teachers Teachers <- c( "Mr Cheese", "Mrs Insulin", "Mr Map", "Mr Effofecks", "Mrs Derivate", "Mrs Electron", "Mr Wise", "Mr Muscle", "Mrs Biceps" ) # 5. data frame for storing variable indices index.df <- expand.grid(Teachers, CLASS, Periods, Days) names(index.df) <- c("teacher", "class", "period", "day") # 6. lessons per teacher and class Courses <- rep(c(c(1, 3, 2, 0, 4, 3, 1, 1, 0), # lessons for class 1 c(1, 3, 2, 4, 0, 3, 1, 0, 1) # lessons for class 2 ), NP * ND) index.df$courses <- Courses # add Courses as a vector 'courses' to 'index.df' for future use #' #' #' Create binary variables 'teach' and set objective as described in introduction. #' ## ----Add Columns-------------------------------------------------------------- # create binary variables 'teach' for each teacher, each class and each period. Also # create a vector 'teach' in 'index.df' to store the indices of 'teach' index.df$teach <- index.df %>% apply(1, function(x) xprs_newcol( prob, lb = 0, ub = 1, coltype = "B", objcoef = NULL, name = paste0(x[c("teacher", "class", "period", "day")], collapse = "_") )) # set objective: minimize the number of "holes" in the class timetables, i.e., minimize the courses # being placed in periods 1 and 4. colidx <- (index.df %>% filter(period == 1 | period == 4))$teach chgobj(prob, colind = colidx, objcoef = rep(1, length(colidx))) #' #' #' Add the constraints described in introduction and some specific constraints related to #' the teachers in this example. #' ## ----Add Rows, results='hide'------------------------------------------------- # 1. all lessons taught by the teacher t to class c must be scheduled index.df %>% group_by(teacher, class) %>% group_map( ~ xprs_newrow( prob, colind = .x$teach, rowcoef = rep(1, nrow(.x)), rowtype = 'E', rhs = .x$courses[1], name = pretty_name("allcourses", .y) ) ) # 2. a class has at most one course at any time index.df %>% group_by(class, period, day) %>% group_map( ~ xprs_newrow( prob, colind = .x$teach, rowcoef = rep(1, nrow(.x)), rowtype = 'L', rhs = 1, name = pretty_name("onecourse", .y) ) ) # 3. a teacher must not teach more than one lesson at a time index.df %>% group_by(teacher, period, day) %>% group_map( ~ xprs_newrow( prob, colind = .x$teach, rowcoef = rep(1, nrow(.x)), rowtype = 'L', rhs = 1, name = pretty_name("oneteacher", .y) ) ) # 4. at most one two-hour lesson per subject is taught on the same day index.df %>% group_by(teacher, class, day) %>% group_map( ~ xprs_newrow( prob, colind = .x$teach, rowcoef = rep(1, nrow(.x)), rowtype = 'L', rhs = 1, name = pretty_name("samesubject", .y) ) ) # 5. the specific conditions for this example # Note that these constraints require us to fix some 'teach' variables to 0 or 1, and # to realize this we change the bounds of these variables. An equivalent way to # achieve this is to set additional constraints such as: teach(t,c,l,d) = 0. # 5.1 the sport lessons taught by Mr Muscle and Mrs Biceps have to take place on Thursday afternoon # from 14:00 to 16:00 (time slot 3 on Thursday) colidx <- c((index.df %>% filter(period==3 & teacher=="Mr Muscle" & class==1 & day=="Thursday"))$teach, (index.df %>% filter(period==3 & teacher=="Mrs Biceps" & class==2 & day=="Thursday"))$teach) chgbounds( prob, colind = colidx, bndtype = rep("L", length(colidx)), bndval = rep(1, length(colidx)) ) # 5.2 no course may be scheduled during Monday period 1 colidx <- (index.df %>% filter(period==1 & day=="Monday"))$teach chgbounds( prob, colind = colidx, bndtype = rep("U", length(colidx)), bndval = rep(0, length(colidx)) ) # 5.3 Mr Effofecks does not teach on Monday morning (time slots 1 & 2 on Monday) colidx <- (index.df %>% filter(teacher=="Mr Effofecks" & (period==1 | period==2 ) & day=="Monday"))$teach chgbounds( prob, colind = colidx, bndtype = rep("U", length(colidx)), bndval = rep(0, length(colidx)) ) # 5.4 Mrs Insulin does not teach on Wednesday colidx <- (index.df %>% filter(teacher=="Mrs Insulin" & day=="Wednesday"))$teach chgbounds( prob, colind = colidx, bndtype = rep("U", length(colidx)), bndval = rep(0, length(colidx)) ) #' #' #' Now we can solve the problem. #' ## ----Solve The Problem-------------------------------------------------------- setoutput(prob) summary(xprs_optimize(prob)) #' #' #' Display the solutions here. #' ## ----The Solutions------------------------------------------------------------ index.df$solution <- xprs_getsolution(prob) # timetable for class 1 c1_timetable <- as.data.frame(matrix("-", nrow = NP, ncol = ND)) names(c1_timetable) <- Days c1_lessons <- index.df %>% filter(class == 1 & solution == 1) for (i in 1:nrow(c1_lessons)) { day <- as.character(c1_lessons$day[i]) period <- c1_lessons$period[i] c1_timetable[period, day] <- as.character(c1_lessons$teacher[i]) } print("The timetable of class 1 is:") c1_timetable cat("\n") # timetable for class 2 c2_timetable <- as.data.frame(matrix("-", nrow = NP, ncol = ND)) names(c2_timetable) <- Days c2_lessons <- index.df %>% filter(class == 2 & solution == 1) for (i in 1:nrow(c2_lessons)) { day <- as.character(c2_lessons$day[i]) period <- c2_lessons$period[i] c2_timetable[period, day] <- as.character(c2_lessons$teacher[i]) } print("The timetable of class 2 is:") c2_timetable #' #' | |||||||||||

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