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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.17 No.2 pp.243-248
DOI : https://doi.org/10.7232/iems.2018.17.2.243

A Study on the Group Technology Based University Classroom Coding Scheme

Sungyoul Lee*
Department of Engineering Management, College of Engineering, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia
Corresponding Author, E-mail: syleekr11@gmail.com
January 5, 2017 August 31, 2017 November 21, 2017

ABSTRACT


Effective class to classroom scheduling is critical to perform the academic mission successfully in the University. This practice enables students to take the classes they need in a timely manner and contributes to space utilization as well as both classes and classrooms are scheduled efficiently to support the needs of students, faculty and the institution as a whole. Most institutions handle this scheduling with a manual process coordinating multiple department associates and staff members of the Registration Office. Often, a manual process such as this leads to a number of difficulties and is prone to errors. In light of these challenges, Group Technology (GT) provides a potential answer. GT is a manufacturing technique in which parts having similarities in geometry and manufacturing process are manufactured in one location using a specific set of machines or processes. This paper describes the design of a GT based classroom coding scheme that assists the staff at the University to allocate classrooms for a given semester. This scheme accommodates the most important attributes which identify a specific classroom to be selected. These attributes include classroom size, classroom type, distance from the department, and technology or other room requirements. Consequently, the proposed scheme makes class to classroom allocation issue easy and efficient.



초록


    1. INTRODUCTION

    It is important for classes to be taught in appropriate classrooms. That is, classrooms are expected to provide necessary computer equipment, internet access, and multimedia equipment along with sufficient space for all students enrolled in the class. Class scheduling requires significant advanced planning and coordination, not only within each department but also often with other units to ensure that classes are allocated appropriately to meet interdisciplinary program needs.

    Conversely, GT is based on a general principle that many problems are similar and by grouping similar problems, a single solution can be found to a set of problems, thus saving time and effort (Lee et al., 2014). This study attempts to employ GT, an engineering technique previously used in manufacturing to cut production costs by identifying commonalities in product design and production, in the establishment of a new, more effective classroom coding system. The gist of this study is to propose a new scheme of classroom categorization and coding that will make the allocation task of proper classrooms simple within their pool of assigned classrooms using GT. If we were able to provide a tree diagram that clearly outlines the relationship among various classrooms as well as commonalities in substance among classrooms, such data generated would undoubtedly benefit the staff during classroom allocation each semester (Yoshida, 2007). In case of special allocation like final exams, university staff could easily allocate proper space and appropriately equipped classrooms to all courses needed throughout the entire university based on the proposed classroom code.

    Therefore, as a sample case project, this study will use GT to classify and code all classrooms installed at the men’s campus of the P University in Saudi Arabia, thereby developing a system that will facilitate the scanning of relevant classrooms. Details of the approach are as follows:

    • (1) GT Classification of the Classrooms and Establishment of a Coding System

    • (2) Establishment of a Classroom GT Database

    • (3) Development of a simple allocation algorithm

    • (4) Generating Results: department classroom pool list, time tables sorted by (instructors, departments, student grades in each department, classrooms, etc.)

    2. RESEARCH TRENDS IN CLASSROOM CODING

    A number of studies have been done in the field of classroom scheduling. Most of them have focused on the improvement of the scheduling algorithm (Lukáč, 2013; Bougie, 2012; Bellardo, 2010). However, there was no attempt to classify and code classroom in terms of classroom attributes.

    In most universities, categorization of classrooms installed is largely limited to a simple serial number with a building ID. While such a system may be useful in distinguishing departmental pool of classrooms, it does not provide important information attributing the classroom’s specific characteristics. Consequently, it offers little help for staff wishing to allocate in proper classrooms across various requirements of the courses offered in the semester.

    These days, most classroom scheduling is still done manually. The manual process usually considers the following parameters: days of the week, class hours, type of classroom desired, expected course enrollment and classroom preference. Classrooms are then allocated based on these parameters and on traditional classroom allocation (Needell et al., 2016).

    Currently, the P University uses non-standard classroom identification which consists of only building code and serial number. For instance, ‘FCR 5’ means the fifth classroom at first floor in the administration building and ‘CE 201’ means the first classroom at the second floor of the Communication Engineering Department in Engineering building. After careful study of the P university environment, several parameters concerning the specific probproblem as applied to the P University have been added. (Academic Curricula, 2016) Class hours can be batched into two categories either 50 minutes or 90 minutes. The most common allocation patterns are Sunday, Tuesday, and Thursday for 50 minutes long classes and Monday and Wednesday for 90 minutes long classes. All classes are held on Sunday through Thursday from 8:00 am to 5:00 pm. Six different types of classes and five classroom sizes were identified after careful analysis of specific requests.

    This study, in effect, takes the next logical step in utilizing GT to provide a practical means to categorize and code classrooms according to commonalities. The GT was first introduced by Russian scholar Mitrofanov in the 1950s and has since been put to the wide practical application in the manufacturing industry. This innovative engineering technique was designed to boost economies of size through systematically grouping together parts similar in shape, size, materials, and manufacturing process. (Lee et al., 2014) In this study, we have applied this technique to categorizing all classrooms according to commonalities, thereby achieving systematical grouping of classrooms via factoring in the properties of the classrooms. The practical benefits of such a system are that all classrooms sharing attribute similarities are now grouped together under a similar digit code.

    With the implementation of the GT based classroom coding system, staff will be better able to grasp the characteristics (e.g. size, type, technology or other room requirements) of classrooms installed, and also have access to detailed information (e.g. distance from the department, geographic location, etc.) that will greatly enhance their chances of making the right choice during classroom allocation (Office of the Provost and Executive Vice President for Academic Affairs, 2012).

    3. ESTABLISHMENT OF THE CLASSROOM CODING SCHEME USING GT

    The P University is a relatively small scale institution in Saudi Arabia that includes four colleges and a Preparatory Year Program (PYP). The trial run of classroom coding was conducted on the approximately 100 classrooms currently installed in men’s campus of the P University. To begin with, a comparative analysis of all classrooms installed in P University was conducted, and allotted codes using the GT System. As shown in Table 1, the classroom coding scheme employs a total of 14 digit hybrid coding scheme except for 4 digits of classroom ID number, which entails a hierarchical model based mono code up to the first 4 digits and a chain model based poly code for the remaining 10 digits.

    The classroom ID number is a serial number for the purpose of identification. For instance, we may assign a unique serial number starting from 2000 to the classroom which belongs to the College of Business Administration, starting from 3000 to the one for the College of Computer and Information Science, and so on. Here, to avoid unnecessary confusion, the existing code can be used as a classroom ID number as well such as ‘E 312’ which stands for Engineering building, third floor, and 12th classroom.

    The details of 14 digit GT code are as follows:

    • 1) Primary division (College): Each college is allotted a two digit numerical code. It may often be a building code (E.g.: College of Engineering 30).

    • 2) Secondary division (Department): Each department is allotted a two digit numerical code. (E.g.: Department of Communications and Networks Engineering 10)

    • 3) Type (General/Lab): Each type of classroom is allotted a single digit code. (E.g.: General classroom with blackboard and/or whiteboard 1, Smart classroom with internet access, beam projector, and dedicated PC to instructor 2, Special classroom with U-shape table arrangement 3, PC Lab 4, Special Lab [Chemistry, Physics, English listening, Manufacturing process, etc.] 5, Others [Gymnasium, Auditorium, Islamic prayer room, etc.] 9.

    • 4) Size (Capacity): The seating capacity of the classroom is allotted a single digit numerical code. (E.g.: Less than 15; 1, Between 16 and 20; 2, Between 21 and 30; 3, Between 31 and 50; 4, Over 51; 9).

    • 5) Elevation: the floor level of the building. A two digit numerical code was assigned. (E.g.: 3rd floor 03, 2nd floor underground 92, 4th floor underground 94.)

    • 6) x-coord: Based on (0, 0) position at the westsouth corner of the campus, it represents x coordinate of the classroom. A 3 digit numerical code was assigned to a unit of meter.

    • 7) y-coord: Based on (0, 0) position at the westsouth corner of the campus, it represents y coordinate of the classroom. A 3 digit numerical code was assigned in a unit of meter.

    Manual allocation task often considers the following parameters: days of the week, class hours, type of classroom desired, expected course enrollment and classroom preference. Classrooms are then allocated based on these parameters. Once allocated, reallocations are necessary after the first week of the classes to reflect the differences between the real class enrollment and the expected one.

    Figure 1 shows a typical manual sequential allocation process (Vasarhelyi and Sadinha, 1976).

    • 1) Choose a course from the department course list offered.

    • 2) Investigate available classrooms based on technology requirements.

    • 3) Investigate available classrooms based on expected enrollment.

    • 4) Adjust classrooms for the non-fulfilled preferences including faculty preference, time schedule conflicts, etc.

    • 5) Choose the one which is the shortest distance from the department.

    • 6) Allocate classrooms taking the number of sections, addition, and deletion of courses into consideration, etc.

    • 7) Reallocate the classrooms to reflect real enrollment after the first week of the class.

    In summary, availability checks for all possible classrooms that fulfill all the requirements up to that step. Here possible rooms mean the ones that are designated to the specific department. If all other requirements are achieved, the classroom that presents the least distance to the department will be chosen. The allocation is basically done by starting from the first time slot of the day to the last slot until the final course is reached. Here, all classes will be held on Sunday through Thursday in Saudi Arabia.

    Table 2 shows a sample part of the classroom classification and coding scheme which details the classroom designated to the College of Engineering (Academic Curricula, 2016). In the 14 digit code, the first 6 digit is a major code which carries key attributes of the classroom. The remaining 8 digit is a minor code which provides a floor level and relative distance information from the relevant department. As an example, an 18 digit classroom code ‘400130201203320430’ stands for classroom ID number; 4001, College of Engineering; 30, Engineering Management - Construction; 20, ordinary type; 1, size less than 20 seats; 2, third floor; 03, located at the Cartesian coordinate; x = 320m and y = 430m.

    A better way to consistently and effectively allocate classrooms is to use a computer-assisted system that will keep track of all classrooms on campus along with specific details about those rooms that can automatically suggest efficient pairings with the courses offered for a given semester. Thanks to the 14 digit classroom code, the allocation algorithm will be much simpler. The key function of the algorithm is simply to find a match based on the first 6 digits major GT code between the proper classroom and the course requirements.

    The following potential algorithm outlines the prescribed method by which the classroom classification and coding system may be utilized. We assume all required information is handy before the potential program run. These include departmental course list offered with requirements.

    Figure 2 shows a flow chart for the potential allocating algorithm. The program starts with a course within a course list offered in a given semester at a specific department of a college. Once all courses in the department are assigned to proper classrooms, the similar process is repeated up to the ones of the remaining departments of the other colleges.

    The details of the algorithm are as follows:

    • [1] Choose a proper college where the course is offered from the college selection menu.

    • [2] Based on selection in step [1], the associated department list menu will be showed up. Then choose a proper department from it.

    • [3] Choose a proper classroom type required for the course from the given menu of six types.

    • [4] Choose a proper room size required for the course from the given menu of five choices.

    • [5] Choose class hours either 50 minutes or 90 minutes. Here, for convenience, we consider two cases of class hour. The 50 minutes case will be only assigned three times on Sunday, Tuesday, and Thursday respectively. The 90 minutes case will be only assigned two times on Monday and Wednesday respectively. All classes will be held on Sunday through Thursday from 8:00 am to 5:00 pm. For the same course, the same time slots of the corresponding days are initially assigned. For example, 50 minutes - three times per week course would be assigned at a slot of 8:00 to 9:00 am on Sunday, Tuesday, and Thursday at the same time.

    • [6] At this point the first 6 digits of the major code are decided. Based on the 6 digit code, the system is looking up the classroom code database and assigns the matching classroom to the course at the first time slot on the corresponding week day. For the multiple of the same first 6 digit classroom codes, the system automatically chooses the one which is the same level first and then shortest distance from the department by checking the remaining 8 digits. If there is no classroom available at the same level, the program checks next level and then does the same.

    • [7] If there is a course not assigned in the department course list, go to step [3].

    • [8] Print schedule. The schedule may be sorted by the department, by the instructor, by the classroom, or by week day, etc.

    • [9] If there is a department left to be scheduled, go to step [1]. Otherwise, stop.

    Once all courses have been assigned to classrooms, some time slots can be exchanged manually according to special preference or requirement while avoiding conflicts

    4. CONCLUSIONS AND ANTICIPATED EFFECTS

    This study describes the establishment of a computer based classroom categorization and coding system using GT applied to the approximately 100 classrooms that are currently located on the men’s campus of the P University in Saudi Arabia. Although the main purview of this study was limited to classrooms of relatively small scale university, the system can be equally effectively extended to classrooms installed in other large scale universities. The system is specifically designed as an aid for the responsible staff members during class allocation to the classroom at the start of each new semester. Since the system makes it possible for the relevant staffs to easily search, assign, and select an appropriate classroom to be fit to the requirement of the course or faculty, the university is able to save efforts to schedule classrooms and ultimately achieve to assign spaces efficiently. For students and faculty members, the system provides an optimum assignment in terms of walking distance and specific course requirement.

    The anticipated effects of the system are as follows:

    • 1) Decreasing allocation effort involved: reduces reliance on cumbersome manual trial and error assignment.

    • 2) Optimum classroom assignment: assigns an appropriate classroom to course in terms of walking distance and course requirement.

    • 3) Detailed classroom descriptions: provides all the fundamental information necessary for responsible staffs to make educated classroom choices. More specifically, in case of a special allocation for the final examination, the proper allocation can be easily achieved all over the university simply based on classroom code.

    • 4) Contribution to the standardization of classroom coding: laying the foundation for the standardization of classroom coding more suited for the ease of computerization.

    The significance of this study is to introduce a new approach which utilizes the concept of GT to classroom allocation problem. Because this study focused on the feasibility of the proposed system, the trial run under this study was limited to a relatively simple and small part of the population. However, further study needs to be conducted on a whole population appropriate to analyze the efficiency and validity of the proposed system in the future. Combined with the development of GT based course classification (Lee et al., 2014) ultimately makes it possible for the proposed system to be fully automated.

    Figure

    IEMS-17-243_F1.gif

    A typical manual sequential allocation process.

    IEMS-17-243_F2.gif

    A flow chart for the potential allocating algorithm.

    Table

    14 Digits coding scheme

    A sample part of the classroom classification and coding scheme

    REFERENCES

    1. Academic Curricula (2016) University Bulletin., P University in Saudi Arabia,
    2. H.A. Bellardo (2010) Preference driven university course scheduling system, Master Thesis, California Polytechnic State University, San Luis Obispo, USA.,
    3. P. Bougie (2012) A web-based classroom allocation system, Master Thesis, The Department of Computer Science and the Faculty of the University of Wisconsin- La Crosse, USA.,
    4. S.Y. Lee , H. Yu , J.A. Ahn , G.E. Park , W.S. Choi (2014) The development of a trial curriculum classification and coding system using group technology., Journal of Engineering Education Research, Vol.17 (4) ; pp.43-47
    5. M. LukA ? (2013) Course timetabling at Masaryk University in the UniTime system, Master Thesis, Masaryk University Faculty of Informatics, Czech.,
    6. D.M. Needell (2016) Software requirements specification of a university class cheduler, cited 2017 Jan 5, Available from:, http://www1.cmc.edu/pages/faculty/DNeedell/papers/ucs_specs.pdf
    7. Office of the Provost and Executive Vice President for Academic Affairs (2012) Class and Classroom Scheduling Policy, Ann Arbor Campus at the University of Michigan, cited 2018 Jun 19, Available from:, https://www.provost.umich.edu/space/instruct/ClassClassroomSchedulingPolicy.pdf
    8. M.A. Vasarhelyi , J.C. Sadinha (1976) Classroom Allocation: A Heuristic Application of APL, Graduate school of business administration, University of Southern California, cited 2017 Jan 5, Available from:, http://raw.rutgers.edu/MiklosVasarhelyi/Resume%20Articles/Unpublished%20Papers/classroom%20allocation1976.pdf
    9. M. Yoshida (2007) Structuring and visualising the engineering knowledge: Basic principles, methods and the application to the UTs engineering curriculum, Proceedings of the APRU DLI 2007,
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