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This paper presents a review pertaining to assignment problem within the education domain, besides looking into the applications of the present research trend, developments, and publications. Assignment problem arises in diverse situations, where one needs to determine an optimal way to assign

Problems related to assignment arise in a range of fields, for example, healthcare, transportation, education, and sports. In fact, this is a well-studied topic in combinatorial optimization problems under optimization or operations research branches. Besides, problem regarding assignment is an important subject that has been employed to solve many problems worldwide [

Assignment problem refers to the analysis on how to assign

The aim of looking into assignment problem is to discover an assignment among two or more sets of elements, which could minimize the total cost of all matched pairs. Relying on the specific structure of the matched sets and the cost function form, the allocation problems can be categorised into quadratic, bottleneck, linear, and multidimensional groups [

The organization of this paper is given as follows: Section

The general aim of assignment problem is to optimize the allocation of resources to demand points where both resources and demand point share equal number [

Optimize

subject to

In relation to every assignment problem, there is a matrix named cost or effectiveness matrix

Moreover, in solving assignment problem, some constraints need to be fulfilled under certain conditions. These constraints are recognized as “hard” constraints as they must adhere to any condition, in which satisfying the condition(s) could generate feasible solution. On the other hand, “soft” constraints are considered as needed, but not crucial. In reality, it is very rare to fulfil all the soft constraints. Usually, the violated soft constraints assessed the solution quality as the objective function (cost function or “fitness” or “penalty”) [

In this review, assignment problem within the education domain is classified into two problems, which are timetabling and allocation problems. As such, this section discusses these two problems, along with the approaches on solving the problems. Furthermore, varied methods have been used in prior studies to solve assignment problem. In fact, there are countless number and a diverse of complex problems that appear in real-life applications that need to be solved. Eventually, this has served as motivation to encourage the development of well-organized procedures to produce good solutions, even if not optimal. Therefore, choosing an appropriate solution is the key of success factor to achieve optimized results. The discussion on approaches in allocation problem is divided into exact method, heuristic and metaheuristic (local search- and population search-based), hybrid, and other techniques.

In fact, according to Martí et al. [

Timetabling problem is considered as a type of assignment problem. A timetable usually provides information about the time for particular events to occur and eventually relates to the resources allocation [

The examination timetabling problem (ETP) is defined as an assignment of a set of examinations to a set of timeslots while simultaneously satisfying several problem constraints. According to Carter and Laporte [

Hence, in considering the problem solution, the hard constraints have to be strictly obeyed under any condition to ascertain solution feasibility. On the other hand, although the soft constraints do not affect the solution feasibility, they must be satisfied as much as possible in order to produce a solution with high quality. In assessing timetable quality, both hard and soft constraints in ETP are described in Table

The hard and soft constraints within the ETP adopted from Qu et al. [

Constraint type | Descriptions |
---|---|

Hard constraints | (i) Student should not sit two examinations at one time. |

(ii) The total number of students in the examination room should not exceed the room capacity. | |

| |

Soft constraints | (i) Examinations that are in conflict should be distributed within the timetable as evenly as possible. |

(ii) Some examinations are required to be scheduled at a particular location or on the same day. | |

(iii) Examinations should be scheduled consecutively. | |

(iv) Examinations with large enrolment size should be scheduled as early as possible. | |

(v) Examinations with limited enrolment should be scheduled into a particular timeslot. | |

(vi) Some examinations are required to be scheduled within a particular timeslot. | |

(vii) Examinations in conflict on the same day should be located nearby. | |

(viii) Examinations should be split over similar locations. | |

(ix) Examinations with the same duration should be allocated the same room. | |

(x) Resource requirements for certain examinations should be met. |

With that, Burke et al. [

Moreover, a broad range of real-world datasets have been introduced in the literature with various practical constraints (see Table

Summary of real-world examination timetabling datasets from different institutions.

Reference | Institution (s) |
---|---|

Ayob et al. [ | Universiti Kebangsaan Malaysia |

| |

Carter et al. [ | Carleton University, Ottawa; Earl Haig Collegiate Institute, Toronto; Ecole des Hautes Etudes Commercials, Montreal; King Fahd University, Dharan; London School of Economics; Ryeson University, Toronto; St. Andrew's Junior High School, Toronto; Trent University, Peterborough, Ontario; Faculty of Arts and Sciences, University of Toronto; Faculty of Engineering, University of Toronto; York Mills Collegiate Institute, Toronto |

| |

Burke et al. [ | University of Nottingham |

| |

Merlot et al. [ | University of Melbourne |

| |

Ergül [ | Middle East Technical University |

| |

Wong et al. [ | Ecole de Technologie Superieure |

| |

Kendall and Hussin [ | University of Technology MARA |

| |

Tounsi [ | Prince Sultan University |

| |

Kahar and Kendall [ | Universiti Malaysia Pahang |

| |

Yong and Yi [ | Hubei University of Technology |

| |

Innet [ | University of the Thai Chamber of Commerce |

| |

Abdul-Rahman et al. [ | Universiti Utara Malaysia |

Real-world timetabling problems are normally complex as they involve various constraints and require significant computational effort. In many practical situations, obtaining a good quality timetable by using the exact method is very challenging, thus leading researchers to opt heuristic approaches. Meanwhile, in academic literature, a variety of timetabling problems and solution methodologies that focus on difficulty and efficiency of the problem solving have been discussed (see Table

Summary of related studies in ETP.

Approach | Reference (s) | |
---|---|---|

Exact method | Integer programming | Qu et al. [ |

Column generation | Woumans et al. [ | |

| ||

Heuristic Technique | Graph colouring heuristics | Ayob et al. [ |

Hyper-heuristics | Burke et al. [ | |

| ||

Metaheuristic technique | ||

Local search based | Hill climbing | Caramia et al. [ |

Tabu search | Sabar et al. [ | |

Variable neighbourhood search | Abdul-Rahman et al. [ | |

Great Deluge | Kahar and Kendall [ | |

Population search based | Memetic algorithm | Ersoy et al. [ |

Genetic algorithm | Pillay and Banzhaf [ | |

| ||

Hybrid | Great Deluge + electromagnetic-like mechanism | Turabieh and Abdullah [ |

Hyper-heuristics + estimation distribution algorithm | Qu et al. [ | |

Harmony search hyper-heuristics | Rankhambe and Kavita [ | |

Bee colony optimization + late acceptance hill climbing + simulated annealing | Alzaqebah and Abdullah [ |

The exact approaches have been used to solve examination timetabling, which evokes mathematical procedures, such as objective function and related constraints. The approaches required to develop a mathematical model should be wisely developed and treated. In fact, the aim of the exact approaches is to obtain an optimal solution, but solving complex problems is computationally expensive. As such, Qu et al. [

In solving this problem, the constructive approach is one that is popular as it incrementally forms a complete solution using construction heuristics [

Additionally, the concept of “squeaky wheel optimization” was initiated by Burke and Newall [

In another study, Abdul-Rahman et al. [

Meanwhile, in solving the ETP at Universiti Malaysia Pahang (UMP), Kahar and Kendall [

Additionally, the local search-based algorithms have successfully solved ETPs. Caramia et al. [

Moreover, Abdul-Rahman et al. [

On top of that, the population search-based algorithms were applied in solving ETPs. A hyper-heuristic methodology, known as “hyperhill-climber”, was proposed by Ersoy et al. [

Other than that, Turabieh and Abdullah [

Course timetabling refers to the process of assigning courses, rooms, students, and lecturers to a fixed time period, typically a working week, while satisfying a given set of constraints [

Typically, while constructing a feasible timetable for a course timetabling problem to satisfy all the hard constraints, the objective function of the problem has to be minimized, which usually reflects the violation of soft constraints. Some instances of the hard and soft constraints of course timetabling problem are presented in Table

The hard and soft constraints within the CTP adopted from Obit [

Constraint type | Descriptions |
---|---|

Hard constraints | (i) The room capacity must be equal to or greater than the number of students attending the event in each timeslot. |

(ii) A student cannot attend two events simultaneously. | |

(iii) Only one event is allowed to be assigned per timeslot in each room. | |

(iv) The room assigned to an event must satisfy the features required by the event. | |

| |

Soft constraints | (i) Students should not have only one event timetabled on a day. |

(ii) Students should not attend more than two consecutive events on a day. | |

(iii) Students should not attend an event in the last timeslot of a day. |

The real-world datasets employed in solving course timetabling problem from various institutions are depicted in Table

Summary of real-world course timetabling datasets from different institutions.

Reference | Institution (s) |
---|---|

Gunawan [ | University in Indonesia |

Oladokun & Badmus [ | University of Ibadan |

Thongsanit [ | Silpakorn University |

Borchani et al. [ | Tunisian University |

Méndez-Díaz et al. [ | A private university in Buenos Aires, Argentina |

Wahid and Hussin [ | Universiti Utara Malaysia |

A number of solutions for course timetabling problems have been discussed in the academic literature (see Table

Summary of studies related to CTP.

Approach | Reference (s) | |
---|---|---|

Exact method | Integer linear programming | Oladokun and Badmus [ |

Decomposition approach | Vermuyten et al. [ | |

| ||

Heuristic technique | Integer linear programming based heuristics | Méndez-Díaz et al. [ |

| ||

Metaheuristic technique | ||

Local search based | Simulated annealing | Ceschia et al. [ |

Hyper-heuristics iterated local search | Soria-Alcaraz et al. [ | |

Two stage metaheuristics | Lewis and Thompson [ | |

Local search-based algorithm with adaptive mechanism | Nagata [ | |

Variable neighborhood descent | Borchani et al. [ | |

Tabu search with sampling and perturbation + SA with reheating | Goh et al. [ | |

Nonlinear great deluge hyper-heuristics with reinforcement learning | Obit et al. [ | |

Population search based | Ant colony optimisation | Nothegger et al. [ |

Genetic algorithm | Song et al. [ | |

| ||

Hybrid | Integer programming + greedy heuristics + modified simulated annealing | Gunawan et al. [ |

Genetic algorithm + local search | Badoni et al. [ | |

Artificial bee colony algorithm + hill climbing optimizer | Bolaji et al. [ | |

Integer programming + greedy heuristics | Ghiani et al. [ | |

Hybrid multi-objective genetic algorithm + hill climbing + simulated annealing | Akkan and Gülcü [ | |

Harmony search with great deluge | Wahid and Hussin [ |

Other than that, by using the ILP-based heuristics, a post-enrolment course timetabling problem at a private university in Buenos Aires, Argentina, was solved by Méndez-Díaz et al. [

Meanwhile, Ceschia et al. [

Accordingly, Nagata [

Moreover, in the course timetabling problem domain, some researchers proposed population-based metaheuristic approaches. For instance, Nothegger et al. [

Additionally, Gunawan et al. [

The school timetabling problem (STP) is about generating school timetable that usually follows a cycle every week for all classes, in which the objective is to avoid teachers from attending two classes at the same time. In school timetabling, students are normally preassigned, while only teachers and rooms need to be assigned in the timetabling problem. As such, Cerdeira-Pena et al. [

In fact, the two types of constraints, which are hard and soft constraints, had been used to solve STP. With that, the fact that a teacher can only teach a lesson to one group at a time and the fact that a group cannot accept more than one lesson at a time by different teachers are examples of hard constraints. Usually, soft constraints depend on preferences by the teacher or are based on school policies, such as to minimize the number of periods for the teacher, to minimize the gaps within the timetable, and to avoid some subjects restriction at a prescribe time. Furthermore, Santos et al. [

The hard and soft constraints related to STP adopted from Cerdeira-Pena et al. [

Constraint type | Descriptions |
---|---|

Hard constraints | (i) Overlaps: avoid the possibility of a class been taught by more than one teacher in the same period and avoid classes sharing resources (i.e., a classroom, lab, etc.), where classes which involved the same group could be assigned to the same period. |

(ii) Simultaneity: two classes are defined as simultaneous classes if they are taught by different teachers at the same time. | |

(iii) Unavailability: it considers periods when a class cannot be given or when a teacher cannot teach. | |

(iv) Consecutiveness: this constraint checks whether a distribution of hours for a pair teacher-class is followed. For example, some practical lessons should be taught in two consecutive periods of time. | |

| |

Soft constraints | (i) Overuse: it refers to the number of periods per day in which a teacher gives its lessons, over its specified maximum of periods per day. |

(ii) Underuse: when teachers have preferences on their minimum number of periods per day, it indicates the number of periods under such minimum. | |

(iii) Holes: consider the number of | |

(iv) Splits: consider the number of periods between two nonconsecutive assignments to the same class in the same day. | |

(v) Groups: assuming a specified maximum of periods per day for an association teacher-class, it considers the number of exceeding periods in such day. This constraint is only considered if a maximum number of consecutive periods (related with | |

(vi) Undesired: assuming that there are periods in which a teacher would prefer not to teach, this constraint indicates the number of such periods where that teacher is assigned a class. This constraint is the |

The real-world datasets that had been used in solving STP are shown in Table

Summary of real-world school timetabling datasets from different institutions.

Reference | Institution (s) |
---|---|

Cerdeira-Pena et al. [ | Secondary school (I.E.S. Menendez Pidal) in A Coruna-Spain |

Santos et al. [ | Brazilian high schools |

Boland et al. [ | Australian high school |

Beligiannis et al. [ | Greek high schools |

Birbas et al. [ | Hellenic secondary school |

Ribić and Konjicija [ | Croatian secondary school |

Moura and Scaraficci [ | Brazilian high school |

Minh et al. [ | Vietnam high schools |

As for the school timetabling field, several solutions were retrieved from the literature as described in Table

Summary of studies related to STP.

Approach | Reference (s) | |
---|---|---|

Exact method | Integer Linear Programming | Boland et al. [ |

Mixed Integer Programming | Santos et al. [ | |

| ||

Heuristic technique | Hyper-heuristics | Pillay [ |

| ||

Metaheuristic technique | ||

Local search based | Simulated annealing | Zhang et al. [ |

Greedy Randomized Adaptive Search Procedure (GRASP) | Moura and Scaraficci [ | |

Tabu search | Minh et al. [ | |

Population search based | Evolutionary algorithm | Beligiannis et al. [ |

Particle Swarm Optimization | Tassopoulos et al. [ | |

| ||

Hybrid | Random Non-Ascendent Method (RNA) + genetic algorithm | Cerdeira-Pena et al. [ |

Simulated annealing + Tabu search | Yongkai et al. [ | |

| ||

Others | Neural network | Smith et al. [ |

For example, Boland et al. [

Other than that, the hyper-heuristic based evolutionary algorithm (EA) method was employed by Pillay [

Similarly, Yongkai et al. [

Other than that, Beligiannis et al. [

Meanwhile, the hybrid algorithm was also applied in STP. Cerdeira-Pena et al. [

The study using machine learning such as neural network within education domain also appears in the literature. Example of a previous study on neural network was proposed by Smith et al. [

Carrasco and Pato [

Allocation problem has been considered as a type of assignment problem. In fact, the allocation problem has been cited widely as a fundamental combinatorial optimization problem under optimization or operation research branch. The allocation problem is a famous problem discussed in the literature with various types of applications, especially within the education domain. This problem is categorised into three subproblems, which are (Section

The student-project allocation problem (SPAP) is related to assigning a person to a particular project or cases based on preference or interest of student and lecturer [

Thus, in order to solve the problem, both hard and soft constraints need to be considered. Table

The hard and soft constraints related to SPAP.

Constraint type | Descriptions | Reference (s) |
---|---|---|

Hard constraints | (i) Preference lists. | Teo and Ho [ |

(ii) Capacity constraints. | Anwar and Bahaj [ | |

(iii) Every student must be allocated one and only one project. | Anwar and Bahaj [ | |

(iv) A project can be allocated to at most one student. | Anwar and Bahaj [ | |

(v) Total number of cases available must not be more than the total number of students. | Ghazali and Abdul-Rahman [ | |

(vi) Maximum number of students in handling a case. | Ghazali and Abdul-Rahman [ | |

(vii) Maximum number of cases that can be handled by certain students. | Ghazali and Abdul-Rahman [ | |

| ||

Soft constraints | (i) Assigning a number of students to many cases. | Ghazali and Abdul-Rahman [ |

Furthermore, numerous real-world SPAPs have been broadly introduced in the literature with variants of measurement and more real-world constraints, as presented in Table

Summary of real-world SPAP from different institutions.

Reference | Institution (s) |
---|---|

Anwar and Bahaj [ | University of Southampton |

Ramli and Bakar [ | Universiti Utara Malaysia |

Ghazali and Abdul-Rahman [ | Internship students at Law Firm in Malaysia |

Dye [ | University of York |

The methodologies that have solved SPAP have been discussed in the academic literature (see Table

Summary of related studies to SPAP.

Approach | Reference (s) | |
---|---|---|

Exact method | Integer programming | Anwar and Bahaj [ |

Goal programming | Li et al. [ | |

Mixed integer linear programming | Calvo-Serrano et al. [ | |

| ||

Metaheuristic technique | ||

Local search based | Simulated annealing | Ghazali and Abdul-Rahman [ |

Population search based | Genetic algorithm | Harper et al. [ |

| ||

Hybrid | 0-1 integer programming + AHP | Ramli and Bakar [ |

| ||

Other techniques | Constraint Logic Programming | Dye [ |

Linear time algorithm | Abraham et al. [ | |

Maximum stable matching to preferences over pair | Abu El-Atta and Moussa [ | |

Maximum stable matching to preferences over projects | Manlove and O’Malley [ |

Other than that, the local search-based and population search-based algorithms were applied in solving SPAP as well. A study carried out by Ghazali and Abdul-Rahman [

Next, a hybrid algorithm was used in solving SPAP. Ramli and Bakar [

Meanwhile, Dye [

The new student allocation problem (NSAP) is a clustering problem in allocating new students to their corresponding class with minimum intelligence gap by sorting method: a group of new students with similar ranking and assigned into the same class. Table

The hard constraints within the NSAP adopted from Zukhri and Omar [

Constraint type | Descriptions |
---|---|

Hard constraints | (i) Capacity of students in each class. |

(ii) A group of new students with similar ranking assigned to the same class. |

The real-world datasets broadly introduced to NSAP and some real-world NSAP from various institutions are presented in Table

Summary of real-world NSAP from different institutions.

Reference | Institution (s) |
---|---|

Zukhri and Omar [ | Islamic University of Indonesia |

Hassim et al. [ | Politeknik Ungku Omar |

Rad et al. [ | 177 existing university majors in Iran |

Ma et al. [ | Singaporean Ministry of Education |

Table

Summary of studies related to NSAP problem.

Approach | Reference (s) | ||
---|---|---|---|

Metaheuristic technique | Population search-based | Genetic algorithm | Zukhri and Omar [ |

| |||

Hybrid | | Rad et al. [ | |

| |||

Other techniques | Data mining | Ma et al. [ | |

Fuzzy | Susanto [ | ||

Subtractive Clustering | Yadav and Ahmed [ | ||

Analytical Hierarchy Process | Hassim et al. [ | ||

Bayesian approach | Yadav [ |

Besides, by using data mining, Ma et al. [

Next, Yadav and Ahmed [

However, some researchers proposed the hybrid algorithm to solve NSAP. For example, Rad et al. [

The space allocation problem (SAP) refers to a problem to allocate resources to space areas, for example, allocating rooms and at the same time satisfying several requirements and constraints [

As such, several sets of constraints had been involved in SAP, as highlighted in past studies (see Table

The hard and soft constraints within the SAP.

Constraint type | Descriptions | Reference (s) |
---|---|---|

Hard constraints | (i) Classroom capacity. | Frimpong and Owusu [ |

(ii) Room’s availability at different time of the day and room request. | Gosselin and Truchon [ | |

| ||

Soft constraints | (i) Events from the same course occurring the same day must be allocated as close to each other as possible with respect to their geographical location. | Bagger et al. [ |

(ii) If an event is split into multiple rooms, these must also be as close to each other as possible in the geographical sense |

Some real-life datasets that have been broadly introduced to SAP are presented in Table

Summary of real-world SAP from different institutions.

Reference | Institution (s) |
---|---|

Burke et al. [ | University of Nottingham, Nottingham Trent University and University of Wolverhampton |

| |

Beyrouthy et al. [ | University in Sydney Australia |

| |

Adewumi and Ali [ | Nigeria Universities |

| |

Bagger et al. [ | Technical University of Denmark (DTU) |

| |

Frimpong and Owusu [ | Premier Nurse’s Training College, Kumasi |

| |

Phillips et al. [ | University of Auckland, New Zealand |

| |

Abdullah et al. [ | Universiti Teknologi Malaysia |

Varying solution approaches have been proposed in the academic literature (see Table

Summary of studies related to SAP.

Approach | Reference (s) | |
---|---|---|

Exact method | Linear programming | Gosselin and Truchon [ |

Integer programming | Phillips et al. [ | |

| ||

Metaheuristic technique | ||

Local search based | Tabu search | Burke et al. [ |

Variable neighbourhood search | Constantino [ | |

Population search based | Genetic algorithm | Adewumi and Ali [ |

| ||

Hybrid | Hill climbing + SA + Tabu search + GA | Burke et al. [ |

Iterative improvement + SA + Tabu search + GA) | Landa-Silva Dario [ | |

Hill climbing + SA | Beyrouthy et al. [ | |

IP + SA | Beyrouthy et al. [ | |

Mixed Integer Program (MIP) + C# | Bagger et al. [ | |

| ||

Other techniques | MS EXCEL + SPSS | Abdullah et al. [ |

Web-based system | Navuduri [ |

Meanwhile, Burke et al. [

Besides, some researchers applied the hybrid technique in solving SAP. Burke et al. [

Moving on, Abdullah et al. [

Diverse methods have been proposed for assignment problem in education domain within these recent times. In fact, researchers have worked really hard to adapt several optimization procedures to solve these problems. Moreover, it is usually not easy to formulate the actual problem that suits a particular solution procedure and there will be a lot of work to be done to fully utilize the solution quality. As such, several studies are still under progress in determining effective solutions for assignment problems.

The timetabling problem was classified into three subproblems, which were examination timetabling problem, course timetabling problem, and school timetabling problem. As observed, most researchers applied the heuristic techniques, which is graph colouring heuristics, to solve ETP. However, there is an idea for future work for solving a more complex problem as mentioned in Ayob et al. [

Prior studies of course timetabling problem showed that various techniques have been implemented to address the problem. The proposed approach usually depended on the complexity of the problem, while the complexity of the problem relied on the problem size, and the difficulty of the problem was related to varied constraints and preferences. However, most of the publications in the literature on course timetabling problem focused on testing the benchmark problem, in comparison to the real-world problem. When compared with ETP, various preferences have been introduced in the problem, while the course timetabling problem mostly catered only for basic requirement of the problem. Thus, this problem has bigger opportunity to be investigated by researchers in order to understand more on the problem requirement and preferences. Hence, this bridges the gap between the benchmark problem and the real cases of the problem. In terms of techniques proposed to overcome the problem, local search technique and their hybridization have the potential to be further investigated. The variation of the approach is mostly focused on local search approach and not for any swarm-based approach, except that for Bolaji et al. [

Meanwhile, in solving STP, many researchers proposed various techniques. The problems turned narrower when the main aim was to avoid teachers from having to attend two events at one time. Therefore, the researchers tended to understand more on the real cases of the problem. As for the techniques proposed, the local search-based technique has potential to be further explored since most of the literature focused in producing good solution quality. Furthermore, the latest trend also showed that IP model is also popular in solving STP. Most of the studies proposed an algorithm in order to give an optimum solution to the problem.

As for allocation problem, it had been classified into three subproblems: student-project allocation problem (SPAP), new student allocation problem (NSAP), and space allocation problem (SAP). Most researchers applied the exact method such as integer programming models to obtain an optimum solution to the problem. The main aspect in SPAP is to satisfy the constraints. As noted, most of the publications in the literature on SPAP focused on the capacity constraints and preferences of entities (students and lecturers). Furthermore, the latest trend also showed that stable matching problem, which considered both preferences of the entities towards projects, is also popular in order to solve STP. By applying stable matching problem in NSAP, it represents how real-life preferences can really occur, besides increasing the satisfaction of both entities in order to solve allocation problem.

In fact, past studies concerning NSAP showed the proposal of various techniques. When compared with SPAP, it includes assigning a person to a particular project or case based on preference or interest of student and lecture, while NSAP catered to the clustering problem of new student based on ranking (intelligence gap). As observed, most researches focused on solving the real problem of NSAP by applying different techniques. For example, Ma et al. [

Lastly, past studies in SAP showed that various approaches have been employed to solve the real problem of SAP. Most researchers tended to hybrid a number of techniques to solve SAP. Based on this review, it is found that the hybrid approaches produced better results when tested on real-world datasets compared to other single approaches in the literature. Burke et al. [

The assignment problem is a combinatorial optimization problem that is flexible as it can be used as an approach to model any real-world problem. In fact, several components in assignment problem have been explored, for example, the constraints and solution methodology used within the education domain. As such, this paper presents the review of assignment problems discussed in the previous literature within educational activities. Not only that, this paper provides several solution approaches in solving assignment problem, where various approaches were introduced in solving these types of problems.

Moreover, it is very important to choose the right approaches in solving the problem so as to obtain an optimal or near optimal solution depending on the complexity of the problem. Based on past literature that had been reviewed, the heuristic and metaheuristic approaches were the top trend to solve the assignment problem because these approaches produced good, but not certainly optimal solution. It was also found that numerous proposed approaches that have been discussed are significant to be used and have been adapted in some real-world situations. The assignment problem will remain an endless puzzle in the future as its flexibility in diverse applications that can be applied into real-world situations.

The authors declare that there are no conflicts of interest regarding the publication of this paper.

The work reported in this paper was supported by the Ministry of Higher Education of Malaysia under the Exploratory Research Grant Scheme, S/O code 12826, and Universiti Utara Malaysia (UUM) under the University Grant S/O code 13415. The authors are grateful and would like to thank UUM for the financial support received during the research period that had enabled to produce this piece of work.