Keywords: Educational Timetabling; Integer Programming; Shift assignment


 Scarlett Benson
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1 School Tietabling for Quality Student and Teacher Schedules T. Birbas a ( ), S. Daskalaki b, E. Housos a a Departent of Electrical & Coputer Engineering b Departent of Engineering Sciences University of Patras, Rio Patras GREECE Abstract: The school tietabling proble, although less coplicated than its counterpart for the university, still provides a ground for interesting and innovative approaches that proise solutions of high quality. In this work, a Shift Assignent Proble is solved first and work shifts are assigned to teachers. In the sequel, the actual Tietabling Proble is solved while the optial shift assignents that resulted fro the previous proble help in defining the values for the cost coefficients in the objective function. Both probles are odelled using Integer Prograing and by this cobined approach we succeed in odelling all operational and practical rules that the Hellenic secondary educational syste iposes. The resulting tietables are conflict free, coplete, fully copact and well balanced for the students. They also handle siultaneous, collaborative and parallel teaching as well as blocks of consecutive lectures for certain courses. In addition, they are highly copact for the teachers, satisfy the teachers preferences at a high degree, and assign core courses towards the beginning of each day. Keywords: Educational Tietabling; Integer Prograing; Shift assignent Dr. Birbas is currently the Director for Priary and Secondary Education in the Region of Western Greece. 1
2 1.0 Introduction School Tietabling as a ter refers to the construction of weekly tietables for schools of secondary education (Schaerf, 1999). Forally, in this paper it is defined as the assignent of triplets consisting of teachers, classes and courses to certain tieperiods of each day of the week, subject to constraints and in such way that a cost function is iniized. For the Hellenic gynasiu (the lower levels of secondary education, i.e. high school grades 7 to 9), while the tietabling process was carried out in a sipler way a few years back (Birbas et al., 1997a; 1997b; 1999), current educational trends require ore coplicated schedules to cover special needs and to provide certain flexibility with the curricula offered to the students. Quality, on the other hand, which is an illdefined feature, is always required fro the actual tietables. While it is not easy to ebody quality into the odelling process, it is even ore difficult to quantify it. Many of the quality issues, set forth by the institutions, are turned into hard requireents and the tietablers have to abide by the. The reaining issues are usually declared as desired and then it is iportant to tune the solution ethod so that it approaches as close as possible to solutions of high quality. According to the Hellenic educational syste, the students in the gynasiu for classsections and all students of a given classsection follow the sae schedule for ost part of the week. However, for soe tie periods certain classsections split into saller ones to attend different courses or erge with others to for new classsections just for one or two tie periods. These planning techniques are necessary to handle elective courses or courses like foreign languages taught at different levels or courses that require specially equipped roos that cannot handle the full nuber of students in a classsection. Although necessary, such scheduling requireents create extra difficulties in the tietabling process resulting in tietables that are often quite unsatisfactory. In order to handle the flexibility introduced in the school curricula, our efforts towards autoatic tietabling had to evolve also. Copared to our previous approach that suggested a single Integer Prograing (IP) odel (Birbas et al., 1997a; 1997b), the new odelling approach involves two stages. The first stage is preliinary and assigns work shifts to teachers. In the second stage, the actual tietabling proble is solved, while guided but not constrained by the shift assignents. The central idea of 2
3 the new approach is to create an initial teporal assignent for the teachers based on which part of the day they prefer to teach; then the tietabling proble is used to specify the exact allocation of courses, teachers and classsections to tie periods and days. It is iportant to note that the tietabling proble is solved as a whole and does not break into subprobles, thus the solution will be optial. Of course the notion of work shift does not exist inherently in the school syste, as it does in organisations like hospitals or other work environents. However, the teaching load of a teacher never covers a full day; therefore, it is possible to think of a day s schedule as being split in flexible shifts. The first shift always starts right at the first tie period of a day and the last one always finishes with the last tie period. Interediate shifts ay also exist but this depends on the nuber of teachers and their weekly teaching load. The length of the daily shifts varies for the different teachers of a school and it is fored according to the teaching needs. An additional advantage of the shift assignent stage is that teachers that need to teach siultaneously, collaboratively or in parallel are assigned fro the beginning to the sae shift and this akes the final schedules ore satisfactory. For the actual tietabling proble, at the second stage, we concentrate in odelling all the requireents that result fro school regulations and soe wellaccepted quality rules as hard constraints. The rest of the requireents, which refer ainly to the precedence of core courses in the daily schedules, the preferences of teachers and the copactness of their schedules, are considered as soft (desired) and are left for the objective function. Therefore, our twostage approach devotes quite an effort in assigning to the cost coefficients of the objective function those values that will assure a closer proxiity to the ost desired solutions. The result of this process is tietables of high quality, eaning that teachers have weekly schedules that are as copact as possible, teacher preferences are satisfied as uch as possible, and core courses are taught early in the day. The paper is structured as follows. Section 2 gives a brief literature review on the tietabling proble in general and the school tietabling proble in particular. Ephasis is given to the Integer Prograing approaches. Section 3 gives the details of our odelling approach, the definition of the work shift for the educational environent and the integer prograing odels developed for the two stages. The case study of a real school is provided in section 4 to deonstrate the usability of our approach. Further experientation with five different schools of varying size is also 3
4 attepted in order to study the influence of various factors to the quality of the resulting tietables. Finally, in section 5, we suarise our approach and provide useful conclusions drawn fro it. 2.0 Background and otivation Tietabling represents the ost iportant planning exercise in the school calendar. It not only gives practical expression to the curricular philosophy of the school, it sets, aintains and regulates the teaching and learning pulse of the school and ensures the delivery of quality education for all students [Learning and Teaching Scotland, 2006]. This stateent, written by educators, ephasizes the iportance of the tietabling process for the educational syste and highlights the ultiple objectives of the task. Moreover, it akes clear that while the tietabling practice requires adopting all requireents and constraints that hold uniquely for each institution, quality is an illdefined feature that every institution strives to achieve. A theoretical version of the school tietabling proble has been forulated as a class/teacher atheatical odel (de Werra, 1985). In that odel, pairs of classes and teachers (called lectures) are assigned to tie periods to satisfy a requireent atrix and in such a way that no two teachers or two classes are involved with the sae class or teacher, respectively, during the sae tie period. In such for, the proble is always solvable as long as the required teaching load for any teacher or class does not exceed the length of the tietable (Even et al., 1976). Moreover, in this version or in a couple of its siple variants, the proble is reduced to an edge colouring graph proble and can be solved in polynoial tie. The addition of an objective function that easures the total cost of assignents to an originally search proble, provides one way to easure quality (Junginger, 1986). As it is argued below, quality is a ultiattribute feature and the total cost of assignents in the objective function is usually a weighted average of the iportant quality attributes. The school tietabling proble has been forulated in any and very different ways and has been solved using several analytical or heuristic approaches: Graph colouring (Neufeld and Tartar, 1974; Cangalovic and Schreuder, 1991), network flow techniques (Osterann and de Werra, 1983), genetic algoriths 4
5 (Colorni et al., 1990; 1998; Drexl and Salewski, 1997), constraint prograing (Valouxis and Housos, 2003), siulated annealing (Abrason, 1991), colun generation (Papoutsis, et al., 2003), tiling algoriths (Kingston, 2005) and other local search algoriths (Costa, 1994; Schaerf, 1999b). Of course, the tietabling requireents for the secondary educational level are very diverse for the different countries; therefore, every odel has liited use. In (Lawrie, 1969) an IP odel has been developed to forulate the tietabling proble for high schools in UK. The odel follows a structure developed by the sae author uniquely for the UK educational syste, where the curriculu of pupils is given in ters of layouts. The IP odel in this case is solved using the Goory s ethod of integer fors, while the objective function does not take into account any of the desired quality features. The lack of efficient software tools for the solution of IP odels a few years back has forced researchers away fro suggesting standard IP solution techniques. It is ore than a decade, however, that this obstacle has been reoved and atheatical prograing tools are contributing towards the effort of autoating the tietabling process for high schools (Birbas et al., 1997a) and for the universities (Tripathy, 1992; Diopoulou and Miliotis, 2001; Daskalaki et al., 2004; Daskalaki and Birbas, 2005; Avella and Vasilev, 2005; Schielpfeng and Helber, 2007). While the school tietabling proble is known to be NPcoplete (de Werra, 1997), enhancing a odel with realworld practical or quality features increases even ore the coplexity of the proble. For exaple, copactness is a type of constraint that is usually required in school tietabling for the students but not necessarily for the teachers. The forer is specifically odelled as a hard constraint in several odels presented in the literature (Drexl and Salewski, 1997; Birbas et al., 1997a, 1997b, and 1999; Schaerf, 1999b; Valouxis and Housos, 2003; Papoutsis et al., 2003). Theoretically, deciding whether a tietable that guarantees copactness for the students exists is NPcoplete by itself (Asratian and de Werra, 2002). However, in ost practical situations, the total nuber of tie periods scheduled for each class is uch larger than the total nuber of tie periods scheduled for each teacher; therefore, it is alost certain that there are plenty of feasible solutions that satisfy the student copactness requireent. In contrast, copactness for teachers is not usually required but in several cases it is strongly preferred and recognized as a quality feature (Valouxis and Housos, 2003; Papoutsis et al., 2003). In fact, the two 5
6 copactness requireents ay be conflicting to each other. Therefore, it is iportant to search for copact tietables for teachers only after copactness for students has been fully satisfied. While it is easier to achieve both in situations like the typical classteacher proble, things becoe worse when additional realistic requireents are added. Additional sources of coplexity include special scheduling requireents like the siultaneous or collaborative teaching and parallel courses. In such cases, it is alost ipossible to find fully copact schedules for teachers and the quality of the resulting tietables degrades (Asratian and de Werra, 2002; de Werra et al., 2002). An additional source of coplexity in tietabling probles results fro requireents for consecutive tie periods. Such a constraint, which is very realistic, by itself, turns tietabling probles fro polynoially solvable to NPhard (ten Eikelder and Willeen, 2001). Especially in university tietabling, where a great percentage of the courses require blocks of lectures with two, three or even ore consecutive tie periods, it is quite beneficial to relax this constraint first and try to patch the solution afterwards in soe way (Daskalaki and Birbas, 2005). This approach iproves treendously the required CPU tie and it is therefore recoended for university environents. However, in school tietabling the consecutive tie periods represent just a sall percentage of the courses, thus causing no ajor proble. For the school tietabling proble it is essential to achieve copact schedules for the teachers along with other quality features, thus our approach gives priary iportance to this issue. Beyond school tietabling, the literature in the general area of tietabling and rostering appears quite rich (Burke and Rudova, 2007; Burke and Trick, 2005; Burke and Gausaecker, 2003; Burke and Erben, 2001; Burke and Carter, 1998; Burke and Ross, 1996). The University or Course Tietabling Proble (Carter and Laporte, 1998; Burke and Petrovic, 2002; Petrovic and Burke, 2004) and the Exa Tietabling Proble (Carter and Laporte, 1996; Carter et al., 1996) represent the ost closely related subjects and share siilar solution approaches; however, they carry significant differences and therefore are treated separately by the researchers. Apart fro atheatical prograing, entioned earlier, representative solution approaches to these probles also include graph colouring and its variants (Burke et al., 2003b), tabu search and hyperheuristics (Hertz, 1992; Burke et al., 2003a), 6
7 constraintbased techniques (Deris et al., 1997) and casebased reasoning (Burke et al., 2003c; Burke et al., 2006). 3.0 Modelling approach and proble forulation The following basic assuptions are set forth to describe the fraework for our odels. 1) The school works for I days every week (Monday through Friday) and for J tie periods every day of the week. 2) The school coprises a nuber of classes, which break into K classsections, in total, {A 1, A 2 B 1, B 2 C1, C 2 }. 3) There are L teachers available for teaching. Most of the work full tie, but there is always a percentage of part tiers. The actual teaching load of a teacher depends on his/her seniority and the needs for courses within his/her specialty. 4) Every classsection k requires a tietable that includes all courses designed for it. 5) All students of a given classsection attend the sae courses during ost of the tie periods. For a sall nuber of tie periods, however, soe classsections split into saller sections or reshuffle for attending certain special courses. 6) Most courses require sessions of at ost one tie period per day, however, certain courses ay require blocks of ore than one consecutive tie periods. 7) Classroos are assued preassigned to classsections. With the exception of certain lab work and a sall nuber of special courses, the students attend courses in their dedicated classroo. Assuption (5) refers to courses that require special care for their scheduling. In this odel we handle the following cases:  Siultaneous teaching of different courses fro two teachers to two different groups of students. Depending on the school, offered courses like Coputers or Physics or Technology ay require lab work. If the lab roo cannot fit all students of a section, then two subsections are fored. One subsection attends the underlined course ( 1 ), taught by teacher l *, while the other subsection attends another course ( 2 ) with siilar requireents (Fig. 1a). According to our odelling approach, for every such pair of teachers one is considered to be the basic and the other the nonbasic. For this specific assignent the nonbasic teacher follows the schedule of the basic. 7
8  Collaborative teaching of the sae course by two teachers and for the sae group of students. Again for courses that require lab work, if the lab roo can fit all students of the section, it is not uncoon to assign ore than one teacher to be present in the roo (Fig. 1b). Like in siultaneous teaching, for every pair of teachers that are assigned concurrently to the sae section, one is considered to be the basic and the other the nonbasic.  Parallelis of courses. Courses on foreign languages often are designed for at least two levels (beginners / advanced). Assuing that there is only one English teacher, then students fro two different classsections are joined together to for a new section just for one tie period and attend the beginners level course. The rest of the students fro both classsections for another section and attend a different course, for exaple Physical Education (Fig. 1c). Afterwards, all students return back to their original classsection to continue with their regular schedule. The difference of this scenario fro the previous ones is the reshuffling of two classsections. Still, one of the teachers will be naed as the basic one and the other the nonbasic. Section k SubSection k 1 SubSection k 2 ( 1, l * ) l * : basic teacher ( 2, l ) l : nonbasic teacher (a) Siultaneous teaching of two different courses ( 1 and 2) fro two teachers (l * and l) to two different subsections of a given classsection. Section k ( 1, l * ) l * : basic teacher ( 1, l ) l : nonbasic teacher (b) Collaborative teaching of the sae course 1 fro two teachers (l* and l) to a given classsection. Section k 1 1 Subsection k 1 2 Subsection k 1 Section k 1 ( 1, l * ) l * : basic teacher Section k 2 1 Subsection k 2 2 Subsection k 2 Section k 2 ( 2, l ) l : nonbasic teacher (c) Parallel teaching of two different courses ( 1 and 2) fro two teachers (l * and l) to two newly fored classsections. Figure 1: Courses that require special treatent in scheduling A look in the tietabling literature reveals that in any school systes there are requireents for parallel courses and for siultaneous or collaborative teaching 8
9 (de Gans, 1981; Schaerf, 1999b); therefore, such features are very iportant for the tietabling odels, even though they do add significant coplication to the odels. As explained earlier our proposed solution consists of two stages. In the first stage, the Shift Assignent Proble (SAP) is solved, using an IP odel. The SAP takes as input the teachers preferences for a specific part of each day (declared as early, iddle or late shift), the requireents of the school for siultaneous or collaborative teaching or parallel courses and the school policy for core courses. In the second stage, the actual Tietabling Proble (TP) is solved using a different IP odel, while the values of the cost coefficients in the objective function are deterined with the help of the output fro the previous stage. The objective of the SAP is the assignent of teachers to a inial nuber of work shifts during each day of the week, so that the weekly schedule of the whole school is feasible. It takes into account the fact that teachers never have a fullday teaching assignent and that they prefer copact, instead of sparse, daily schedules. The assignent of teachers to work shifts obeys the following rules:  Every teacher is assigned to exactly one work shift for each day of presence in the school.  Depending on his/her specialty, each teacher teaches either ostly core courses or ostly noncore courses. The first group of teachers should be assigned preferably to early workshifts, while the second group should rather be assigned to later ones. If a teacher teaches both core and noncore courses, then this affects the order of the course assignents within a given work shift.  When it is required fro the school, two or soeties even three specific teachers ay have to be assigned to the sae work shift, so siultaneous, collaborative or parallel teaching can be possible.  During any tie period and as a result during any work shift there should be at least so any teachers present as there are classsections in the school. On the contrary, the TP takes care of all scheduling rules and regulations of the educational syste, which are odelled as constraints of the IP odel. More specifically, the odel provides tietables that carry the following characteristics:  They assign at ost one course to each teacher for every tie period.  They are copact for the students, i.e. there are no epty slots in the student schedules. 9
10  They are coplete in the sense that they cover all courses required by the student curriculu and for the required aount of tie periods per course.  The tietable of each classsection is balanced, in the sense of tie spent at school throughout the week.  All full tie teachers are assigned teaching assignents for each day of the week, in order to guarantee daily presence. Part tie teachers, respectively, are assigned teaching load only for those days that are available to the particular school.  All courses are scheduled for at ost one tie period per day, with the exception of those courses that specifically require consecutive tie periods.  Courses that require consecutive tie periods are scheduled for at ost one ultiperiod session per day and fully cover their weekly requireents.  Occasionally certain courses require to be scheduled siultaneously and ore than one teacher should be scheduled for the sae course. Apart fro the hard constraints that the odels handle, the following soft quality requireents are also considered: Core courses should be scheduled early in the day Teacher preferences should be satisfied as uch as possible Teacher schedules should be as copact as possible In order to create a tietable for a school, the two odels are solved sequentially. Starting fro the SAP and the optial work shifts assignents for each teacher during the week, it is then easier to find an optial solution for the TP that distributes the courses taught by the given teacher to tie periods within the shifts that the specific teacher is assigned to be present. For the solution of the IP odels in both stages the ILOG CPLEX MIP Solver has been used. Table 1. Definition of paraeters Acrony Nae Description TTP Total Tie Periods The total nuber of tie periods to be scheduled in the tietable WTL l Weekly Teaching Load for teacher l The total nuber of tie periods to be assigned to teacher l each week DTL Average Daily Teaching Load for The daily average nuber of tie periods l teacher l assigned to teacher l WTL kl Weekly Teaching Load of teacher l for classsection k The total nuber of tie periods to be assigned to teacher l for classsection k each week WTL kl Weekly Teaching Load of teacher l for classsection k and course The total nuber of tie periods to be assigned to teacher l for course each week TTP k Total Tie Periods for section k The total nuber of tie periods over all courses of classsection k to be assigned each week 10
11 Prior to any odel presentation, Table 1 depicts the acronys, naes and descriptions of certain paraeters that are used further down. It is clear that the following feasibility conditions should hold: (1) L TTP WTL TTP K l l1 k1 k K K M (2) WTL WTL WTL l kl kl k 1 k 1 1 (3) L L M TTP WTL WTL k kl kl l1 l1 1 (4) WTL kl M WTL 1 kl The feasibility conditions (1) (4) hold exactly as they appear above only for those schools that carry no special requireents for the scheduling of their courses. When requests for siultaneous, collaborative or parallel scheduling are present, one should consider only the basic teachers for each such assignent. 3.1 Definition of work shifts for teachers The notion of work shifts in this proble is soewhat different than the shifts as being used in other work environents, for exaple hospitals or superarkets. The ain disparity is the flexibility that should be aintained for the starting and/or ending tie of each work shift that is assigned to an individual. Let us assue for a while that there are no courses with special scheduling requireents. Then the following proposition ust hold for every feasible solution: Proposition 1. At any tie period of a given day, there ust be at least as any teachers present in the school as the nuber of classsections. The proposition ust be true; otherwise, at least one classsection would not be assigned a course during the specified tie period, a situation that violates the requireent for copact student schedules. In order to generate work shifts for the teachers of a school, the concept of the equivalentofateacher (EOT) is introduced. During any day of the week the teaching load of a teacher is always less and soeties uch less than J, the nuber of tie periods of a day, because 1 DTLl J, l. 11
12 As a result, if we allow the courses that the given teacher teaches to be scheduled on any tie period of the day, the introduction of epty slots in his/her tietable is unavoidable. The nuber of epty slots in the teachers schedules can be inial if we consider an ideal operation of the school, as follows: On the first period of the day, there are exactly so any teachers present as the nuber of classsections. The teachers proceed with their individual teaching schedules, with no idle period inbetween, until one or ore of the finish for the day. For every teacher that copletes his/her teaching activities, another one starts until soeone else takes over or until the day finishes. The changeover between two teachers ay occur after any period of the day. The subset of teachers that work in a sequential anner for a day will be called the equivalentofateacher (EOT). We can further think of the notion of the virtual classsection for the school, so that an EOT is assigned to one and only one virtual classsection for the whole day. Then a siilar proposition, like the one before, holds for the EOTs and the virtual classsections: Proposition 2. On a given day in a school there should be exactly as any EOTs as the nuber of virtual classsections. This proposition ust also be true for the copactness criterion to be satisfied. In this sense, the tietabling turns into an assignent proble. Tables 2, 3 and 4 deonstrate further the aforeentioned idea. Table 2 presents a possible oneday schedule with seven tie periods for a school that consists of four sections and eight teachers. Teachers 1 and 4 for an EOT by sharing the teaching load for virtual section #1, i.e. the virtual section #1 is assigned to the subset of teachers {1, 4}. Likewise, the subsets of teachers {3, 8}, {6, 2} and {7, 5} for three ore EOTs, which are assigned to virtual sections #2, #3, and #4, respectively. In reality, each teacher switches fro one real classsection to another, so the right part of Table 2 illustrates a possible assignent of the eight teachers to the actual classsections of the school (let s say A1, A2, B1, and C1). Since the sections are four and the teachers eight (a ultiple of four), for every EOT the working day is split in two shifts of different lengths. The work shifts for a school cannot be rigid in nature, but flexible enough to absorb peculiarities of the educational syste. 12
13 Table 2. A typical day schedule for a school with 8 teachers and 4 sections. P1 P2 P3 P4 P5 P6 P7 P1 P2 P3 P4 P5 P6 P7 Teach1 v_section 1 A1 B1 C1 A2 Teach2 v_section 3 C1 A2 A1 B1 Teach3 v_section 2 B1 C1 Teach4 v_section 1 B1 C1 A2 Teach5 v_section 4 C1 A2 A1 Teach6 v_section 3 C1 A2 B1 Teach7 v_section 4 A2 A1 A1 B1 Teach8 v_section 2 A2 A1 A1 B1 C1 Table 3. A typical day schedule for a school with 9 teachers and 4 sections. P1 P2 P3 P4 P5 P6 P7 P1 P2 P3 P4 P5 P6 P7 Teach1 V_section 1 A1 B1 C1 A2 Teach2 v_section 3 C1 A2 A1 B1 Teach3 v_section 2 B1 C1 Teach4 v_section 1 B1 C1 A2 Teach5 v_section 4 C1 A2 A1 Teach6 v_section 3 C1 A2 B1 Teach7 V_section 4 A2 A1 A1 B1 Teach8 v_section 2 A2 A1 A1 Teach9 v_section 2 B1 C1 Table 4. A typical day schedule for a school with 7 teachers and 4 sections. P1 P2 P3 P4 P5 P6 P7 P1 P2 P3 P4 P5 P6 P7 Teach1 v_section 1 A1 B1 C1 A2 A1 Teach2 v_section 2 C1 A2 A1 B1 Teach3 v_section 2 B1 C1 A1 Teach4 v_section 3 B1 C1 A2 Teach5 v_section 4 B1 C1 A2 A1 Teach6 v_section 3 C1 A2 B1 A1 Teach7 v_section 4 v_sect 1 A2 A1 A2 B1 C1 13
14 Siilarly, Table 3 exposes an assignent of shifts for a school with four classsections and nine teachers. In this exaple, the subsets of the teachers {1, 4}, {3, 8, 9}, {6, 2} and {7, 5} for the EOTs. It is noted that since [9/ 4] 2 and 9od4 1, three EOTs are coposed of two teachers and exactly one of three. Therefore, the axiu nuber for the work shifts is three, however only one teacher is assigned to the third shift. For the exaples of Tables 2 and 3, if course requireents for the teachers and classes perit, the teachers ay not have idle periods in their schedule. The last exaple (Table 4) refers to a school with seven teachers and four classsections. This tie [7/ 4] 1 and 7od4 3 ; therefore, one teacher ust carry a fullday assignent and the others will for pairs to share the day. However, if there are not enough courses for a single teacher to fill a work shift of seven tie periods his/her schedule inherently holds idle tie periods. According to this exaple the four EOTs are {1, 7}, {3, 2}, {6, 4} and {7, 5}, i.e. teacher #7 participates in two EOTs and in soe respect it looks as if he/she is assigned two shifts. The nuber and length of work shifts in any given school depends on the nuber of teachers and their availability for teaching (parttie/fulltie), the nuber of classsections and the nuber of courses. The notion of workshifts lies in the heart of the odel to be presented in the next section. As suggested by the three exaples, for fully copact teacher schedules every teacher should be assigned to only one shift. However, additional constraints and planning of the actual courses required by the educational syste often violates this desirable feature. Our approach ais at keeping the nuber of violations at a iniu level. 3.2 Modelling the Shift Assignent Proble (SAP) Two sets of binary variables, one with basic and the other with auxiliary variables are used for odelling the SAP. Variable x i,, l b takes the value of 1, when teacher l is scheduled to work during shift b of day i. Auxiliary variables are introduced only when there is a need for forcing one teacher to be in the sae shift with another teacher to serve in parallel sessions. Therefore, the variable y i, l1, l2, b takes the value of 1 when teachers l 1 and l 2 are both assigned to shift b of day i Constraints for the SAP Five sets of constraints are set forth for this odel. 14
15 α) Uniqueness Every teacher of the school should be assigned to exactly one shift for each day: b x i,, l b 1, i, l i (3.1) where i ={ l: l is a teacher available to the school on day i}. The constraints ensure daily presence of every teacher for all days for which he/she is scheduled to be in that school. b) School policy for the shift assignents The weekly assignents of a given teacher to a specific shift (e.g. orning shift, iddle shift, etc.) cannot exceed a predefined upper liit: i x i,, l b r, l, b (3.2) lb where r lb is the axiu nuber of shifts b allowed per week for teacher l. This liit reflects the policy of each school and ay depend on the teacher s specialty. c) Preassignent of shifts Under certain circustances a teacher ay be allowed the preassignent of a shift: xi,, l b 1, (i, l, b) fix (3.3) where fix ={(i, l, b): teacher l shall be assigned to shift b of day i}. d) Conjugation of shifts (i) Teachers l 1 and l 2 shall be assigned to the sae shifts because their teaching assignents coprise courses that shall be taught in parallel. This rule is necessary when there is a need for siultaneous teaching, collaborative teaching or parallel teaching. The constraints needed in this case are: x i, l, b xi, l, b 0, i, b, (l 1,l 2 )V 1 V 2 ql q 1 l (3.4a) where V 1 ={(l 1,l 2 ) : l 1 and l 2 are teachers that need to teach siultaneously to different subsections or collaboratively to the sae section}, V 2 ={(l 1,l 2 ) : l 1 and l 2 are teachers teaching in parallel to two different class sections} and ql i indicate the weekly tieperiod requireent for the course that the teacher l i (i = 1, 2) teaches and requires conjugation of shifts. 15
16 Constraints (3.4a) safeguard the assignent of teachers l 1 and l 2 to the sae shift on a given day. xi, l, b xi, l, b yi, l, l, b 0, i, b, (l 1,l 2 )V 1 ql 1 ql 2 (3.4b) Constraints (3.4b) are ipleented only in the case of siultaneous or collaborative teaching for linking the auxiliary variables y i, l1, l2, b with x i,, l b. i b y q i, l1, l2, b l1 (l 1,l 2 )V 1 ql 1 ql 2 (3.4c) Lastly, constraints (3.4c) ensure that exactly ql 1 shifts are allocated to teacher l 1, who is the teacher of the course with the sallest requireent in ters of tie periods per week. (ii) Teachers l 1 and l 2 shall be assigned to the sae shifts because their teaching assignents coprise courses that shall be taught in parallel; however, teacher l 3 also requires siultaneous teaching with l 1 during soe other periods. In this case, the teachers l 2 and l 3 are assigned to consecutive shifts: x x 0, i, b, (l 1 ;l 2, l 3 ) V 3 (3.4d) i, l1, b i, l3, b bb1 bb1 where V 3 ={(l 1 ;l 2, l 3 ): l 1 is required to teach soe course at the sae tie with teacher l 2 and another course at the sae tie with teacher l 3 } Constraints (3.4d) allow the siultaneous teaching of teachers l 1 and l 2 for certain tie periods and afterwards the siultaneous teaching of teachers l 1 and l 3. e) Copleteness During any shift of the day, the nuber of teachers required to be present shall be equal to the nuber of classsections of the school. For the cases of collaborative teaching for a given course, this nuber changes, necessitating the following constraints: For all workshifts of any given day, except of the last one, the following constraints are set forth: x y K, i, and b = 1,,B1 (3.5a) i, l, b i, l1, l2, b l ( l1, l2 ) V 1 where K is the nuber of the classsections and Β is the nuber of the daily shifts. For the last shift B, however, only the reaining teachers ay be assigned: 16
17 x y, i (3.5b) i, l, B i, l1, l2, b l ( l1, l2) V 1 b { B} where υ is defined as i.e. L B K Objective function Lod K and the nuber of shifts is defined as the ceiling of L/K, The objective function for this proble is a linear cost function: (3.6) Miniise ci, l, b xi, l, b ii ll bb The coefficients c i,, l b are defined in a way that reflects the teachers preferences for specific shifts for all days. The lower the value for c i,, l b, the higher the preference for shift b. An exaple of such a value syste appears in Table 5. A high value for a particular cost coefficient prevents, but does not prohibit the assignent of its corresponding variable to the final solution. Table 5. Possible values for the cost coefficients c i,, l b Rank of preference Cost of Preference First preference 50 Second preference 120 Third preference Modelling the Tietabling Proble The tietabling proble, as it is odelled in this section, takes responsibility for the assignent of subjects, teachers and class sections to the tie periods of a week, taking into account all functional rules iposed by a given school syste, typical for the Hellenic educational syste and any other countries. Furtherore, the solution of the SAP guides the process of assigning values to the cost coefficients in the objective function based on the shifts that each teacher is assigned for each day of the week. Two sets of binary variables are again eployed. The first consists of the basic variables denoted by x i, j, k, l,, where i, j, k, l, and. x i, j, k, l, is assigned the value of 1, when course, taught by teacher l to the classsection k, is scheduled for the jth period of day i. The second set consists of the auxiliary variables y,,,, i t k h, used only for those courses, which require sessions of consecutive periods. y i, t, k, h, takes the value of 1, when course, is scheduled for h consecutive 17
18 periods on day i for classsection k, with t being the 1 st period for this assignent. The IP odel ebodies as constraints all standard scheduling rules for the tietabling process and any nonstandard ones iposed fro regulations of the educational syste we have studied Constraints The constraints are presented in three different groups. The first group are the standard constraints, naed as such because they take care of rules that play a fundaental role in all tietabling probles and appear in nearly all forulations. The second group consists of several nonstandard constraints in the sense that any of these constraints appear in tietabling probles, however not all schools operate under these rules. The third group refers to the special scheduling requireents that certain courses ay require. Requireents like consecutive teaching periods result fro the nature of certain courses, while others result fro an effort for the educational syste to be ore flexible and adaptable to students needs. Throughout the presentation of the constraints, a nuber of sets usually subsets of the ain sets are utilized in order to liit down the nuber of equations and the nuber of variables in the odel. These sets are defined as follows: kl ={ : is a course that teacher l teaches for classsection k} * kl ={ : is any regular course that teacher l teaches for classsection k}, where the ter regular refers to all courses that do not require any special type of scheduling. si l = {(, k, l * ): is a course that teacher l teaches for a part of section k siultaneously with another course taught by the basic teacher l * } col l = {(, k, l * ): is a course that teacher l teaches for section k in collaboration with the basic teacher l * } cons = {(, k, h ): is a course of section k that needs to be scheduled in block(s) of h consecutive periods} paral = {[( a, b ); (k a, k b ); (l a, l b )]: a is a course that teacher l a teaches for section k a that needs to be scheduled always in parallel to b a course taught by teacher l b for section k b } 18
19 = {[( a, k a, l a ; b, k b, l b ;...; w, k w, l w ) (l a )]:,,..., are courses that excl a b w teachers la, lb,..., lw teach to sections ka, kb,..., kw, respectively, however no ore than a certain nuber of the ay be scheduled in the sae day or tie period} fix = {(i a, j a, a, k a, l a ): a is a course that teacher l a teaches to section k a and should be scheduled on day i a and in period j a } l = {i : i is any day of the week for which teacher l is available for the school} l = { k : k is a classsection of the school to which teacher l teaches at least one course} In addition, we define the axiu stretch, a paraeter that is used in the odel: Definition: Maxiu Stretch for classsection k, denoted by ax S k, is the axiu nuber of teaching periods that section k ay have during any day of the week. ax S k ay be set equal to J, the length of each day, however, in order to create ore balanced tietables for the classes, it is preferable to set a different upper liit for each section of the school. Therefore, ax S k equals to TTP k I. In general, however, it holds that S J, k. ax k A. Standard Constraints The constraints of this group ake sure that the resulting tietables are conflictfree and contain all courses required for scheduling. A1. Uniqueness Every teacher ay be assigned at ost one course and one classsection in a given period with the exception of indicated courses that require ore than one instructor. x x 1, l, j, i i, j, k, l, * i, j, k, l, * * si col l kl (,, ) l l k k l (3.7) A2. Copleteness for students All courses in the curriculu of a classsection should appear in the tietable for the required nuber of teaching periods. l 19
20 x x TTP, k i, j, k, l, * i, j, k, l, * si col k kl l k (, k, l ) l l l l i jj l ii jj k (3.8) A3. Copleteness for teachers All courses assigned to a given teacher should appear in the tietable for the required nuber of teaching periods. x x WTL kl, k, l i, j, k, l, * i, j, k, l, * * si col kl (, k, l ) l l l i j i j (3.9) A4. Copleteness for courses The teaching periods assigned to a given course over a whole week should add up to the weekly requireents for the specific course. il j x i, j, k, l, WTL kl k, l k,, (3.10) kl In a given odel, not all of the copleteness constraints are needed. The feasibility conditions in the beginning of section 3.0 ay guarantee that only the last (Eq. 3.10) is sufficient to satisfy all others. B. NonStandard (Operational) Constraints. In this group of constraints several rules that quite often are requested fro the educational systes are presented. B1. Copactness for students The tietable of every classsection should not carry epty slots during the week k * kl l k * kl l k * kl l x i, j, k, l, + * i j k l * si col (, k, l ) l l x ax k i, S, k, l, j x i, j, k, l, x,,,, + ax * k * si col (, k, l ) l l x = 1, k, i, j=1,..., ( S 1) i, S, k, l, + * i j k l * si col (, k, l ) l l j x,,,, 1, i, k ax k (3.11a) (3.11b) S, i, k (3.12) Constraints (3.11) ensure that for each section there is exactly one course scheduled for any given period (except ay be the last one) of any day. Constraints (3.12), on the other hand, checks whether all courses of section k are scheduled within the axiu stretch allowed for the section. The requireent for copact student schedules appears ax k 20
21 in any school tietabling probles and this is a ajor difference between university and school tietabling practice. B2. Daily presence of teachers Many schools require daily presence fro the fulltie teaching staff in the school, with the exception of the parttie staff that declare ahead of tie the days of availability. Therefore, every teacher should be assigned at least one teaching period of each day that is available to the specific school. l * kl j k x i, j, k, l, + * i, j, k, l, * si col (, k, l ) l l j x 1, l, i l, (3.13) Not all schools carry such a requireent. Instead, soe schools allow for one dayoff in the week or they carry soe different policy for presence in the school. B3. Unifor distribution of courses Any given course ay be scheduled for at ost one teaching period per day of the week, unless it requires ore teaching periods than the days of the week or there is a special request for ultiple or consecutive hours, in which cases they are scheduled accordingly. x j i, j, k, l, 1, i, k, l k, kl (3.14) C. Special Requireents This last group of constraints are provided to handle special requireents that soeties appear in connection with certain courses. These requireents increase the coplexity of the proble. In our case, it does increase significantly the nuber of equations and the nuber of variables. However, we have applied this IP odelling for any schools in our country of quite varying sizes and still the proble was solvable. C1. Consecutiveness of teaching periods The school tietabling process should accoodate special requireents for certain courses to be taught in ultiperiod slots at ost once a day for a given class section. Let be a course that requires a session of h consecutive periods and l the teacher that teaches the course for section k. Then the following should apply: y h *,,,, i t k h x th1 i, j, k, l, i, t, k, h, jt y +h 1, (,k,h )M con,i l, t Jh +1 (3.15a) 21
22 y Jh 1 i, t, k, h, 1, (, k, h ) con, i l (3.15b) t 1 y Jh 1 i, t, k, h, = ν, (, k, h ) con (3.16) iil t1 where ν is the nuber of the ultiperiod slots required for course every week. Constraints 3.15a force h basic variables x i, j, k, l, that refer to consecutive tie periods to take the value of 1, while constraints 3.15b ensure that only one block of consecutive periods ay be assigned in any given day. Finally, constraint 3.16 indicates that there should be exactly ν of these blocks for the whole week. C2. Parallelis of courses As explained in the Introduction, this constraint is activated when the students of two different sections (k 1 and k 2 ) are rearranged in order to for two new sections that attend two different courses ( 1 and 2 ) assigned to teachers l 1 and l 2, respectively. So, it is only needed to schedule the two courses 1 and 2 for the sae tie periods. x x 0, [( 1, 2 ); (k 1, k 2 ); (l 1, l 2 )] par, i,, j (3.17) i, j, k1, l1, 1 i, j, k2, l2, 2 C3. Mutual exclusiveness of courses Soeties it is desirable for two or ore courses to be scheduled at different tie periods or days or ore generally only up to n r of the to be scheduled at the sae tie. In this case the following constraints are appropriate. (, k, l) excl x i, j, k, l, n, i, j (3.18) r Given there is no index for the classroos in the decision variables, these constraints are iportant in order to avoid clashes for the laboratories or specially equipped roos. C4. Preassignent of certain courses Lastly, preassignents of certain courses to specific tie periods are easily handled by the following constraints. x = 1, (i, j, k, l, ) i, j,k,l, fix (3.19) Objective function The objective function for the tietabling proble is a cost function, with two distinct ters. The cost coefficients c i, j, k, l, represent the cost of assigning course 22
23 Cost coefficient Cost coefficient taught by teacher l for section k to period j of day i. Siilarly, the cost coefficients a refer to the cost of scheduling course for section k on day i and for the i, t, k, h, periods t up to t +h 1, given that course requires h consecutive tie periods. Therefore, the objective of the IP odel is to iniize the function: c x a i, j, k, l, i, j, k, l, i, t, k, h, i, t, k, h, i j l k i (, k, h ) t l kl cons t y (3.20) where P t = { t : t is a possible value for the 1 st tie period of the h consecutive tie periods for course cons } (1st shift) Tie Period (a) Core Noncore (2nd shift) 150 Core Noncore Tie period (b) Figure 2. Penalty functions for assigning values to cost coefficients in a 2shift proble The cost coefficients c i, j, k, l, take values with the help of the output fro the SAP and certain penalty functions like the ones shown in Figs. 2 and 3. Fig. 2 shows an exaple of such penalty functions for a school that operates with two shifts per day. For a given day and for a core course, if the teacher is assigned to the first shift (by the SAP) then the corresponding cost coefficient (in the TP) will be assigned the values indicated by the solid line in Fig. 2a for the different tieperiods of the day. Since it is preferable for the core courses to be scheduled as early as possible during the day, we provide low values for the first three periods, ediu values for the fourth period and high or very high (not shown in the figure) for the rest of the day. If the solution fro the SAP assigns the teacher to the second shift, then the corresponding cost coefficients ay take the values of the solid line in Fig. 2b. For the second shift, the fifth and fourth periods are preferable for the core courses and are assigned the lowest values, while the 23
24 Cost coefficient Cost coefficient Cost coefficient sixth and seventh are in the ediulow range. The rest of the periods are assigned very high values (not shown) in order to avert assignents of core courses to these periods. On the contrary, for a noncore course, if the teacher is assigned to the first or the second shift then the corresponding cost coefficients ay be assigned the values indicated by the dotted lines in Figs. 2a and 2b, respectively. Core (1st shift) Noncore (2nd shift) Core Noncore Tie Period (a) Tie Period (b) (3rd shift) Tie Period (c) Core Noncore Figure 3. Penalty functions for assigning values to cost coefficients in a 3shift proble Siilarly, Fig. 3 depicts a siilar set of penalty functions that could be used for assigning values to the cost coefficients of the TP objective function when a school operates with three shifts for the teaching staff. The rationale behind these functions is siilar to those in Fig. 2. Finally, Fig. 4 displays an exaple penalty function that could be used for assigning values to the a i, t, k, h, cost coefficients of the TP objective 24
25 Cost coefficient function for a course that requires two consecutive tie periods for its teaching. The chart suggests that it is ost preferable for this course to start either on the second tie period, otherwise on the third or fifth period. It is quite clear that all penalty functions ay take different shapes and each school ay introduce its own policy for the desired assignents Tie periods Figure 4. Penalty function for assigning values to cost coefficients for courses that require two consecutive tie periods 4.0 Experiental Results In order to deonstrate the effectiveness of the odelling approach just presented, the results of a case study are shown in this section. The study concerns an existing secondary school with six classsections, two sections for each class, i.e. {A1, A2, B1, B2, C1, and C2}. Twentythree teachers are serving in the school, sixteen fulltie and seven parttie. For the tietable of this school, 95 courses ought to be assigned to 5 days of the week, with 7 periods per day. The proble has been solved using the approach presented in section 3 and the results are presented below. The solution fro the SAP indicated the work shifts, where each teacher should be assigned for each day of the week, based on individual preferences, the needs of the school and the policy about core and noncore courses. Then, using the penalty functions of Fig. 3, the values for the coefficients in the objective function of the TP were assigned and the proble was solved. For both probles, the MIP solver of ILOG CPLEX 10.1 has been used. 25
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