Monte Carlo Tree Search in Finding Feasible Solutions for Course Timetabling Problem

We are addressing the course timetabling problem in this work. In a university, students can select their favorite courses each semester. Thus, the general requirement is to allow them to attend lectures without clashing with other lectures. A feasible solution is a solution where this and other conditions are satisfied. Constructing reasonable solutions for course timetabling problem is a hard task. Most of the existing methods failed to generate reasonable solutions for all cases. This is since the problem is heavily constrained and an effective method is required to explore and exploit the search space. We utilize Monte Carlo Tree Search (MCTS) in finding feasible solutions for the first time. In MCTS, we build a tree incrementally in an asymmetric manner by sampling the decision space. It is traversed in the best-first manner. We propose several enhancements to MCTS like simulation and tree pruning based on a heuristic. The performance of MCTS is compared with the methods based on graph coloring heuristics and Tabu search. We test the solution methodologies on the three most studied publicly available datasets. Overall, MCTS performs better than the method based on graph coloring heuristic; however, it is inferior compared to the Tabu based method. Experimental results are discussed

An effective hybrid local search approach for the post enrolment course timetabling problem

We address the post enrolment course timetabling (PE-CTT) problem in this paper. PE-CTT is known as an NP-hard problem that focuses on finding an efficient allocation of courses onto a finite number of time slots and rooms. It is one of the most challenging resources allocation problems faced by universities around the world. This work proposes a two-phase hybrid local search algorithm to address the PE-CTT problem. The first phase focuses on finding a feasible solution, while the second phase tries to minimize the soft constraint violations of the generated feasible solution. For the first phase, we propose a hybrid of Tabu Search with Sampling and Perturbation with Iterated Local Search. We test the proposed methodology on the hardest cases of PE-CTT benchmarks. The hybrid algorithm performs well and our results are superior compared to the recent methods in finding feasible solutions. For the second phase, we propose an algorithm called Simulated Annealing with Reheating (SAR) with two preliminary runs (SAR-2P). The SAR algorithm is used to minimize the soft constraint violations by exploiting information collected from the preliminary runs. We test the proposed methodology on three publicly available datasets. Our algorithm is competitive with the standards set by the recent methods. In total, the algorithm attains new best results for 3 cases and new best mean results for 7 cases. Furthermore, it is scalable when the execution time is extended

Halal Logo Branding to Attract Muslim Tourists

Purpose: The Halal logo is the brand of a tourist attraction for Muslim tourists. With the branding  Halal, the visitors will assume if the food and drinks are Halal to eat or drink. This paper aims to introduce branding the Halal logo display on Halal tourism activities in Indonesia.   Theoretical framework:  The approach used is communication semiotics. The data were obtained by an online-based survey of respondents who had travelled in Indonesia, particularly in tourist destination areas where the majority of the population was non-Muslim.   Design/methodology/approach:  The approach used is communication semiotics. The data were obtained by an online-based survey of respondents who had travelled in Indonesia, particularly in tourist destination areas where the majority of the population was non-Muslim.   Findings: This paper concludes that the way tourists determine food and at least Halal is by finding a restaurant with a Halal logo, searching the internet about Halal restaurants, asking the hotel staff about Halal restaurants and asking friends or family who has visited the same tourist destination about Halal restaurants in the area to visit   Research, Practical & Social implications:  The representation of Halal in a restaurant is the restaurants having a Halal certificate marked by a Halal logo label and those having no a Halal certificate with a Halal logo but offering Halal food.   Originality/value:  This research contributed that the logo label represents Halal food although not all restaurants have Halal certificates. The Halal logo label is the branding that attracts Muslim tourists.

A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems

Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite

Improved local search approaches to solve the post enrolment course timetabling problem

In this work, we are addressing the post enrollment course timetabling (PE-CTT) problem. We combine different local search algorithms into an iterative two stage procedure. In the first stage, Tabu Search with Sampling and Perturbation (TSSP) is used to generate feasible solutions. In the second stage, we propose an improved variant of Simulated Annealing (SA), which we call Simulated Annealing with Reheating (SAR), to improve the solution quality of feasible solutions. SAR has three features: a novel neighborhood examination scheme, a new way of estimating local optima and a reheating scheme. SAR eliminates the need for extensive tuning as is often required in conventional SA. The proposed methodologies are tested on the three most studied datasets from the scientific literature. Our algorithms perform well and our results are competitive, if not better, compared to the benchmarks set by the state of the art methods. New best known results are provided for many instances

Modification of Flow Structure Over a Van Model by Suction Flow Control to Reduce Aerodynamics Drag

Automobile aerodynamic studies are typically undertaken to improve safety and increase fuel efficiency as well as to  find new innovation in automobile technology to deal with the problem of energy crisis and global warming. Some car companies have the objective to develop control solutions that enable to reduce the aerodynamic drag of vehicle and  significant modification progress is still possible by reducing the mass, rolling friction or aerodynamic drag. Some flow  control method provides the possibility to modify the flow separation to reduce the development of the swirling structures around the vehicle. In this study, a family van is modeled with a modified form of Ahmed\u27s body by changing the orientation of the flow from its original form (modified/reversed Ahmed body). This model is equipped with a suction on the rear side to comprehensively examine the pressure field modifications that occur. The investigation combines computational and experimental work. Computational approach used  a commercial software with standard k-epsilon flow turbulence model, and the objectives was  to determine the characteristics of the flow field and aerodynamic drag reduction that occurred in the test model. Experimental approach used load cell in order to validate the aerodynamic drag reduction obtained by computational approach. The results show that the application of a suction in the rear part of the van model give the effect of reducing the wake and the vortex formation. Futhermore, aerodynamic drag reduction close to 13.86% for the computational approach and 16.32% for the experimental have been obtained

Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

This survey paper provides an overview of current developments for the Portfolio Optimisation Problem (POP) based on articles published from 2018 to 2022. It reviews the latest solution methodologies utilised in addressing POPs in terms of mechanisms and performance. The methodologies are categorised as Metaheuristic, Mathematical Optimisation, Hybrid Approaches, Matheuristic and Machine Learning. The datasets (benchmark, real-world, and hypothetical) utilised in portfolio optimisation research are provided. The state-of-the-art methodologies for benchmark datasets are presented accordingly. Populationbased metaheuristics are the most preferred techniques among researchers in addressing the POP. Hybrid approaches is an emerging trend (2018 onwards). The OR-Library is the most widely used benchmark dataset for researchers to compare their methodologies in addressing POP. The research challenges and opportunities are discussed. The summarisation of the published papers in this survey provides an insight to researchers in identifying emerging trends and gaps in this research area

Hybrid particle swarm optimization with particle elimination for the high school timetabling problem

In this paper, a PSO-based algorithm that hybridized Particle Swarm Optimization (PSO) and Hill Climbing (HC) is applied to high school timetabling problem. This hybrid has two features, a novel solution transformation and particle elimination. The proposed methodologies are tested on the XHSTT-2014 dataset (which is relatively new for the school timetabling problem) plus other additional instances. The experimental results show that the proposed algorithm is efective in solving small and medium instances compared to standalone HC and better than the conventional PSO for most instances. In a comparison to the state of the art methods, it achieved the lowest mean of soft constraint violations for 7 instances and the lowest mean of hard constraint violations for 1 instance

A Survey of university course timetabling problem: perspectives, trends and opportunities

The timetabling problem is common to academic institutions such as schools, colleges or universities. It is a very hard combinatorial optimisation problem which attracts the interest of many researchers. The university course timetabling problem (UCTTP) is difficult to address due to the size of the problem and several challenging hard and soft constraints. Over the years, various methodologies were proposed to solve UCTTP. The purpose of this survey paper is to provide the most recent scientific review of the methodologies applied to UCTTP. The paper unveils a classification of methodologies proposed in recent years based on chronology and datasets used. Perspectives, trends, challenges and opportunities in UCTTP are also presented. It is observed that meta-heuristic approaches are popular among researchers. This is followed closely by hybrid methodologies. Hyper-heuristic approaches are also able to produce effective results. Another observation is that the state-of-art methodologies in the scientific literature are not fully utilised in a real-world environment perhaps due to the limited flexibility of these methodologies