**A Systematic Literature Review: Optimization Timetable in Education to Support Work-Life Balance (WLB)** \*\*This is a Double-blind review, please do not include authors information in this version \*\* Received Date: \*date Accepted Date: \*date Published Date: \*date **HIGHLIGHTS** - Ignorance WLB in Timetabling optimization leads the unhealthy. - Optimization academic Timetable use many procedures. - Constructed a SLR to find current optimization and existing technique in Education Timetabling to support WLB. - Identified the significant and gap of optimization in Education Timetabling to support WLB. **ABSTRACT** *Scheduling academic staff timetable is crucial and necessary to avoid redundancy and clash of class between teacher and student timetable. A good timetable allows students and teachers to manage their time and support a good healthy and lifestyle. However, with the scheduling academic staff timetable may use many procedures to get efficiency result. Therefore, this paper provides a gap of study for existing work on Optimization Timetable to Support Work-Life Balance regarding their market commercial and research purposes. The methodology of this study was conducted using Systematic Literature Review (SLR). Result: two findings investigate 1) relevant optimization timetable scheduling used and 2) method for timetable optimization to support Work-Life Balance (WLB). The strengths and weaknesses of the features and utilities behind each study are also presented to provide further understanding of the gaps and weaknesses of each body of research. We conclude that these studies are still insufficient and require further evaluation and improvement.* ***Keywords:** timetable optimization, work-life balance, genetic algorithm, swarm intelligence* # INTRODUCTION Due to their wide range of applications, timetabling problems have received a lot of attention, such as the flight timetable problem, employee timetabling problem, university, or high school timetabling problem. It is, however, more sophisticated than generic scheduling because it involves teachers, students, classrooms, and courses. Besides, due to the numerous constraints, resource constraints, and intricate human aspects involved, course scheduling has always been done manually and take a long time. Using a computer not only consolidates the preferences of the individuals involved, but it also allows for a high level of satisfaction despite the numerous constraints. Thus, this saves a significant amount of time and manpower. As a result, an automation scheduling timetable is required for algorithm selection. Furthermore, Academic staff timetable scheduling is critical to optimise and balance academic staff working hours. As a result, maintaining a work-life balance is essential. However, failure to reach a work-life balance when scheduling teacher timetables may result in poor teaching and learning quality. In this research, extension work by (Yusop, 2022) a review of existing works and tools supporting the optimization timetabling with WLB are presented. The paper is organized as follows: Section 2 describes the background of study; followed by Section 3 presents the Methodology to conduct this research; then Section 4 provides the overall findings and provides discussion of this study; and, lastly, Section 5 of this paper ends with a conclusion and recommendations of future work. # RESEARCH BACKGROUND **Definition of Timetabling** Wren (1995) has defined timetabling as “Timetabling is the allocation, subject to constraints, of given resources to objects being placed in space-time, in such a way as to satisfy as nearly as possible a set of desirable objectives” **Definition of Swarm Intelligence** Swarm intelligence is a relatively recent problem-solving strategy that was identified through simplified social behaviour model simulations of insects and other animals (Bonabeau et al., 1999) . It is inspired by the collective intelligence of swarms of biological populations. The algorithms of ants, bees, wasps, termites, fish in schools, and birds in flocks have all been studied computationally (Caro, n.d.). The Ant Colony Optimization (ACO) algorithm is a meta-heuristic optimization technique inspired by biological systems that computes the shortest path between a source and a destination in order to find optimal solutions. **Definition of Genetic Algorithm** Genetic algorithms (GA), which are a type of gradual developmental algorithm, are influenced by the process of natural selection. Natural selection begins with the selection of suitable individuals from a population (Aminu et al., 2019). GAs is a type of approach that may be used to address these problems quickly. They are founded on genetics and natural selection principles, with the primary notion being that the fittest individuals in a population are selected, and subsequently recombine or mutate into new forms to establish new groupings (Jalal & K Mukhopadhyay, Anal Grasley, 2019). **Definition of Work-Life-Balance** Duxbury (2004) had defined work life balance as a mix of role overload, work-family interference, and family-work interference. Hill, et al.(2001) describes WBL as a person's ability to manage the emotional, behavioural, and temporal demands of paid work, personal, and family duties at the same time. To overcome the problems and issues defined above, this study presents a Systematic Literature Review (SLR), which will be discussed further in the following section. # METHODOLOGY We conducted the SLR in accordance with Barbara Kitchenham's (2007) original guidelines with qualitative research methodology. This SLR consists of three phases, which are (1) Planning the SLR, (2) Conducting the review and (3) Reporting the review. For planning, we initialise the terms and functional of timetabling in education for its factor and issues. Here, Population, Intervention, Comparison, Outcomes and Context criteria (PICOC) to structure the research questions from Barbara Kitchenham 2007) as shown in Tables 1 and 2. The document search technique as shown in Table 3 was used to collect the data from Google Scholar, ScienceDirect, Springer, IEEE Xplore, ACM Digital Library and Scopus and then the selection procedure was carried out systematically based on the steps shown in Table 4 and the inclusion and exclusion criteria shown in Table 5. Following that, we are classifying relevant studies and primary studies in order to conduct the research. Then, as shown in Table 6, data extraction and quality assessments were performed to elicit data from primary studies. Finally, we present the extraction data from nine papers that were used to construct answers to the two RQs. The Kitchenham guidelines are widely used in the preparation of this Systematic literature review report. Each phase is depicted in detail in the section below. **Figure 1:** Conducting SLR **A. Planning** **Table 1:** Summary of PICOC | **Population** | Timetable, worklife balance | | ---------------- | -------------------------------------------------------------------------------- | | **Intervention** | Challenging of timetable scheduling in worklife balance in teaching and learning | | **Comparison** | Existing model, methods and techniques | | **Outcomes** | Prediction of timetable scheduling in WLB, success factors | | **Context** | Empirical in research studies and industry | **Table 2:** Research Questions
Research Objectives Research Questions Motivations
To identify the relevant Timetable optimization in education to support WLB What is Timetable optimization in education to support WLB? Identify the relevant for Timetable optimization in education to support WLB
To identify the gaps and limitations of existing techniques and approaches used to apply Timetable optimization in education to support WLB What are the available approaches or models, or methods used to apply Timetable optimization in education to support WLB?

Explaining the ways to Timetable optimization in education to support WLB.

Identifying approach or tools available to apply Timetable optimization in education to support WLB.
**Table 3:** Searching string | Type searching of Objective 1 | OR | Type searching of Objective 2 | AND | Type searching of Objective 2 | | ----------------------------- | -- | ----------------------------- | --- | ---------------------------------------------------- | | Issues | | Method | | Timetabling optimization in education to support WLB | | Problems | | Technique | | | | Characters | | Approaches | | | | Factor | | | | | **Table 4:** Selection Process | **1** | **RQ is used to generate major searches.** | | ----- | ----------------------------------------------------------------------------------------------------------------------- | | **2** | **Examined the paper title or removed studies that did not fit our search criteria** | | **3** | **To connect result terms in the search string, use the Boolean operator (AND, OR, NOT).** | | **4** | **To identify relevant studies, retrieve the citation and examine the abstract and keywords in the remaining studies.** | | **5** | **The remaining studies were filtered using the inclusion/exclusion criteria listed in Table 5.** | **Table 5:** Inclusion/Exclusion | **Inclusion** | **Exclusion** | | --------------------------------------------------------------------- | ------------------------------------------------------------------ | | Papers focusing on Timetable optimization in education to support WLB | The papers presented do not require peer review. | | Papers describes Timetable optimization in education to support WLB | Papers that present results without providing supporting evidence. | | Systematic literature review | Unrelated studies to the research questions. | | Paper describes Timetable optimization in education to support WLB | Studies not clear | | Paper describes Timetable optimization in education to support WLB | | **Table 6:** Data Extraction
Focus of Search Item of Data Description
General Bibliography Author, year, title, source

Article types

Study aims

Study design

  • Journal/conference

  • paper/technical report

  • The primary study's goal(s)

  • Surveys and controlled experiments
RQ1

Comparison

Examples

  • Define Timetable optimization in education to support WLB

  • Examples of Timetable optimization
RQ2

Approach Method

Existing/new/extension

  • Describe the method used Timetable optimization in education to support WLB

  • Explain whether method is new, or already has existing method
**B. Conducting Review** ***Identifying Relevant Studies and Primary Studies*** We began by reviewing the titles of the papers to eliminate any studies that were not clearly related to the research focus. The abstract, keywords, and conclusion were then used to eliminate additional unrelated studies. After applying these two steps, 55 studies remained. We examined these 55 studies and used the inclusion/exclusion criteria listed in Table 5 to select 24 papers as primary studies for this SLR. Further, we used the same selection steps to the reference lists of the selected 24 primary studies to find additional primary studies that are related to the research focus. ***Data Extraction and Quality Assessments*** We extracted data from the primary studies using the data extraction form shown in Table 7. Many primary studies did not provide comprehensive answers in the data extraction form and We extracted the important information provided by the primary studies using the data extraction form. The quality assessment questions in Table 7 were then applied to each primary study that depending on the type of study. We answered our quality assessment questions with 'yes' or 'no.' We used a binary scale because we didn't want to provide a quality score for the studies. **Table 7:** Quality Assessments (QA) and Question Scores(QS) | **QA** | | **QS** | | | | ------ | ------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | | QA1 | Are the review’s inclusion and exclusion criteria described and appropriate? | Y (yes), the inclusion criteria are explicitly defined in the study | P (Partly), the inclusion criteria are implicit. | N (No), the inclusion criteria are not defined and cannot be readily inferred | | QA2 | Is the literature search liable to have covered every single relevant studies? | Y(yes), the authors have either searched 4 or more digital libraries and included additional search strategies or identified and referenced all journals addressing the topic of interest | P (Partly), the authors have search ¾ digital libraries with no extra search strategies or search a defined but restricted set of journals and conference proceedings | N (No), The authors have search up to 2 digital libraries or an extremely restricted set of journals | | QA3 | Did the reviewers assess the quality or validity of included studies? | Y(yes), the authors have explicitly defined quality criteria and extracted them from each primary study | P (Partly), the research question involves quality issues that are addressed by the study | N (No), no explicit quality assessment of individual primary studies has been attempted. | | QA4 | Were the essential information or studies sufficiently depicted? | Y, information is presented about each study | P (Partly), only summary information about primary studies is presented | N, the results of the individual primary studies are not specified | **C. Reporting Review** The data extracted from the 45 primary papers were used to formulate answers to the two research questions given in Table 2. when preparing the SLR report, we closely followed the guidelines provided by Kitchenham & Charters(2007). We present the synthesis of evidence of our SLR, beginning with the analysis from the literature. We also used the selected primary papers to answer the research questions. Table 8 shows the number of studies for quality assessment using the SLR’s level layer. On this paper, we excluded 28 studies with 3 studies being duplicates. After quality assessment of 24 studies, ten studies were chosen for evidence synthesis. **Table 8:** Result of Quality Assessment | **Criteria** | **Study of Paper** | | ---------------------------- | ------------------ | | Before Assessment of Quality | 55 | | Redundance | 3 | | Exclusion | 28 | | After Assessment of Quality | 24 | | Accepted | 10 | | Rejected | 14 | **FINDINGS AND DISCUSSIONS** ***Findings*** We compared the nine existing works on Timetable optimization in education to support WLB. The comparison was made based on methodology, approach, technique, and Timetable optimization in education to support WLB, as shown in Tables 5 and 6. We discussed the paper in more detail below, within the parameters of the research questions defined above. . **5.1. RQ1: What are the factor of Timetable optimization in education?** The comparison analysis of timetable optimization in education is shown in Table 9 below. Genetic Algorithm contributes high number in timetable optimization. However, 3 out of 4 found have combination with othersuch as Rashmi and Arbhishek (2021) developed an approach using prediction algorithm by combining genetic algorithm and particle swarm algorithm. Automated timetable generation enhances the quality of the education institutes and tends to greatly reduce time and manual efforts. Zandavi et al. (2021) proposed a hybrid optimization technique that combines the Nelder-Mead Simplex algorithm with the Non-dominated Sorting Genetic Algorithm to optimise the scheduling problem for distant laboratories to coordinate. Wang et al. (2019) developed a particle swarm optimization technique based on a genetic algorithm, in which the position vector and genetic evolution operators are reconstructed based on each train's departure and arrival timings at stations. The latest studied by Guerriero and Guido (2022) developed a novel optimization to improve employees’ satisfaction and ensure the best work/life balance possible, an alternative partition of a workday into shifts to the usual two shifts, morning, and afternoon. Therefore, we believe that combination of algorithms is still limited to optimize the timetable scheduling specifically in teacher or lecturer timetable in university. **Table 9:** Timetable optimization in education | Year | Name | Optimization | Particle Swarm Optimization | Simplex Algorithm | Genetic Algorithm | Swarm Algorithm | Constraint Satisfaction Problems (CSP) | Whale Optimization Algorithm (WOA) | Ant Colony | Bee Colony | | ---- | ------------------------- | ------------ | --------------------------- | ----------------- | ----------------- | --------------- | -------------------------------------- | ---------------------------------- | ---------- | ---------- | | 2003 | (Sandhu, 2001) | | | | | | ✔ | | | | | 2012 | (Larsen, 2012) | ✔ | | | | | | | | | | 2018 | (Liu et al., 2018) | | | | | | | | | ✔ | | 2018 | (Uslu et al., 2018) | | | | ✔ | | | | ✔ | | | 2019 | (Wang et al., 2019) | | ✔ | | ✔ | | | | | | | 2019 | (Knutsäter & Sandh, 2019) | | ✔ | | | | | | | | | 2020 | (Omar et al., 2020) | | | | | | | ✔ | | | | 2020 | (Zandavi et al., 2021) | | | ✔ | ✔ | | | | | | | 2020 | (Guerriero & Guido, 2022) | ✔ | | | | | | | | | | 2021 | (Rashmi & Abhishek, 2021) | | ✔ | | ✔ | | | | | | | | Total | 2 | 3 | 1 | 4 | 0 | 1 | 1 | 1 | 1 | **5.2. RQ2: What are the available approaches or models, or methods used to apply Timetable optimization in education to support WLB?** **To provide a better WLB, the optimization of timetable schedule need to through analyse and must be involve many elements inclusive of WLB attributes. The comparison features based on methodology/approach/technique as shown in Table 10. Based on Table 10, it founds that Model is high contribution for applying the timetable optimization. However, to developed scheduling timetable, a proper requirement must be considered to encourage the positive effectiveness and success of optimization that able to support work-life balance. Further, there are work by Larsen (2012) and** Guerriero & Guido (2022) **support WLB but current works and tools do not provide a proper approach such as there is lack of tool developed to support the WLB. Thus, it is necessary to have a tool for supporting WLB specifically in academic level.** **Table 10:** Comparison of approaches or models, or methods used to apply Timetable optimization in education to support WLB | | | | | Optimization | | | | | | ---- | ------------------------- | --- | | ------------ | ------ | -------- | --------- | ---- | | Year | Name | WLB | | Model | Method | Approach | Framework | Tool | | 2003 | (Sandhu, 2001) | | | ✔ | | | | | | 2012 | (Larsen, 2012) | ✔ | | ✔ | | | | | | 2018 | (Liu et al., 2018) | | | ✔ | | | | | | 2018 | (Uslu et al., 2018) | | | ✔ | | | | | | 2019 | (Wang et al., 2019) | | | ✔ | | | | | | 2019 | (Knutsäter & Sandh, 2019) | | | ✔ | | | | | | 2020 | (Omar et al., 2020) | | | ✔ | | | | | | 2020 | (Zandavi et al., 2021) | | | ✔ | | | | | | 2020 | (Ramos & Galleto, 2020) | ✔ | | | | | | | | 2020 | (Guerriero & Guido, 2022) | ✔ | | ✔ | | | | | | 2021 | (Rashmi & Abhishek, 2021) | | | | | | ✔ | | | 2021 | (Tucker & Folkard, 1968) | ✔ | | ✔ | | | | | | | Total | 4 | | 10 | | | 1 | | ***Discussions*** We reviewed ten studies based on SLR results and compared them in terms of models, methods, approaches, and frameworks. We discovered that most of the studies investigated similar topics such as Genetic Algorithm and Particle Swarm Algorithms in optimization of timetable scheduling in education. Based on our findings, as shown in Tables 9 and 10, this study discovered that various models are used. by optimize the timetable that supporting swarm intelligence. Based on the analysis, the analysis support genetic algorithm and analysis are required process to optimize the timetable. Further, most of tools is lack to support timetable. In this respect, this research believes that there is study related to the optimization for scheduling timetable but there is lack of method to work-life balance for academic timetable where automation scheduling of academic timetable. Thus, producing a tool to support WLB with integration of optimization techniques crucial and could help the educators’ WLB. The development of algorithm of optimization in WLB specifically in educational setting should require a thorough study that include WLB attributes and optimization. **CONCLUSION AND RECOMMENDATIONS** Timetable scheduling is important and crucial to support WLB of teachers and educators in School or University. To keep them consistent to deliver work in good health, WLB attributes and Optimization timetable scheduling may considered. This allows the teacher and educators have sustain and less overload work. Therefore, these papers provided the Systematic Literature Review (SLR) methodology for optimization timetable to support WLB and research fields. We have reported the findings and analyses of different studies, which showed that optimization of timetable scheduling in education with WLB is limited. The strengths and weaknesses provide an understanding of the limitations of existing tools. 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