A combination of PSO and local search in university course timetabling problem

The university course timetabling problem is a combinatorial optimization problem concerning the scheduling of a number of subjects into a finite number of timeslots in order to satisfy a set of specified constraints. The timetable problem can be very hard to solve, especially when attempting to find a near-optimal solutions, with a large number of instances. This paper presents a combination of particle swarm optimization and local search to effectively search the solution space in solving university course timetabling problem. Three different types of dataset range from small to large are used in validating the algorithm. The experiment results show that the combination of particle swarm optimization and local search is capable to produce feasible timetable with less computational time, comparable to other established algorithms

Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach

عادة ما تكون مشكلة الجدول الزمني للمحاضرات الجامعية (UCTP) هي مشكلة تحسين الإندماجية. يستغرق الأمر جهود يدوية لعدة أيام للوصول إلى جدول زمني مفيد ، ولا تزال النتائج غير جيدة بما يكفي. تُستخدم طرق مختلفة من (الإرشاد أو الإرشاد المساعد) لحل UCTP بشكل مناسب. لكن هذه الأساليب عادةً ما تعطي حلول محدودة. يعالج إطار العمل الاسترشادي العالي هذه المشكلة المعقدة بشكل مناسب. يقترح هذا البحث استخدام محسن سرب الجسيمات استنادا على منهجية الإرشاد العالي (HH PSO) لمعالجة مشكلة الجدول الزمني للمحاضرات الجامعية (UCTP) . محسن سرب الجسيمات PSO يستخدام كطريقة ذات مستوى عالي لتحديد تسلسل الاستدلال ذي المستوى المنخفض (LLH) والذي من ناحية أخرى يستطيع توليد الحل الأمثل. لنهج المقترح يقسم الحل إلى مرحلتين (المرحلة الأولية ومرحلة التحسين). قمنا بتطوير LLH جديد يسمى "أقل عدد ممكن من الغرف المتبقية"  لجدولة الأحداث. يتم استخدام مجموعتي بيانات مسابقة الجدول الزمني الدولية (ITC)  ITC 2002 و ITC 2007 لتقييم الطريقة المقترحة. تشير النتائج الأولية  إلى أن الإرشاد منخفض المستوى المقترح يساعد في جدولة الأحداث في المرحلة الأولية. بالمقارنة مع LLH الأخرى ، الطريقة LLH المقترحة جدولت المزيد من الأحداث لـ 14 و 15 من حالات البيانات من 24 و 20 حالة بيانات من ITC 2002 و ITC 2007 ، على التوالي. تظهر الدراسة التجريبية أن HH PSO تحصل على معدل خرق أقل للقيود في سبع وستة حالات بيانات من ITC 2007 و ITC 2002 ، على التوالي. واستنتج هذا البحث أن LLH المقترحة يمكن أن تحصل على حل معقول وملائم إذا تم تحديد الأولوياتThe university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed approach generates solutions into two phases (initial and improvement). A new LLH named “least possible rooms left” has been developed and proposed to schedule events. Both datasets of international timetabling competition (ITC) i.e., ITC 2002 and ITC 2007 are used to evaluate the proposed method. Experimental results indicate that the proposed low-level heuristic helps to schedule events at the initial stage. When compared with other LLH’s, the proposed LLH schedule more events for 14 and 15 data instances out of 24 and 20 data instances of ITC 2002 and ITC 2007, respectively. The experimental study shows that HH PSO gets a lower soft constraint violation rate on seven and six data instances of ITC 2007 and ITC 2002, respectively. This research has concluded the proposed LLH can get a feasible solution if prioritized

Genetic based discrete particle swarm optimization for elderly day care center timetabling

The timetabling problem of local Elderly Day Care Centers (EDCCs) is formulated into a weighted maximum constraint satisfaction problem (Max-CSP) in this study. The EDCC timetabling problem is a multi-dimensional assignment problem, where users (elderly) are required to perform activities that require different venues and timeslots, depending on operational constraints. These constraints are categorized into two: hard constraints, which must be fulfilled strictly, and soft constraints, which may be violated but with a penalty. Numerous methods have been successfully applied to the weighted Max-CSP; these methods include exact algorithms based on branch and bound techniques, and approximation methods based on repair heuristics, such as the min-conflict heuristic. This study aims to explore the potential of evolutionary algorithms by proposing a genetic-based discrete particle swarm optimization (GDPSO) to solve the EDCC timetabling problem. The proposed method is compared with the min-conflict random-walk algorithm (MCRW), Tabu search (TS), standard particle swarm optimization (SPSO), and a guided genetic algorithm (GGA). Computational evidence shows that GDPSO significantly outperforms the other algorithms in terms of solution quality and efficiency

Global Optimization Using Local Search Approach for Course Scheduling Problem

Course scheduling problem is a combinatorial optimization problem which is defined over a finite discrete problem whose candidate solution structure is expressed as a finite sequence of course events scheduled in available time and space resources. This problem is considered as non-deterministic polynomial complete problem which is hard to solve. Many solution methods have been studied in the past for solving the course scheduling problem, namely from the most traditional approach such as graph coloring technique; the local search family such as hill-climbing search, taboo search, and simulated annealing technique; and various population-based metaheuristic methods such as evolutionary algorithm, genetic algorithm, and swarm optimization. This article will discuss these various probabilistic optimization methods in order to gain the global optimal solution. Furthermore, inclusion of a local search in the population-based algorithm to improve the global solution will be explained rigorously

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

Data-Driven Optimization of Public Transit Schedule

Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these,this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.Comment: 20 pages, 6 figures, 2 table

Performance Analyses of Graph Heuristics and Selected Trajectory Metaheuristics on Examination Timetable Problem

Examination timetabling problem is hard to solve due to its NP-hard nature, with a large number of constraints having to be accommodated. To deal with the problem effectually, frequently heuristics are used for constructing feasible examination timetable while meta-heuristics are applied for improving the solution quality. This paper presents the performances of graph heuristics and major trajectory metaheuristics or S-metaheuristics for addressing both capacitated and un-capacitated examination timetabling problem. For constructing the feasible solution, six graph heuristics are used. They are largest degree (LD), largest weighted degree (LWD), largest enrolment degree (LE), and three hybrid heuristic with saturation degree (SD) such as SD-LD, SD-LE, and SD-LWD. Five trajectory algorithms comprising of tabu search (TS), simulated annealing (SA), late acceptance hill climbing (LAHC), great deluge algorithm (GDA), and variable neighborhood search (VNS) are employed for improving the solution quality. Experiments have been tested on several instances of un-capacitated and capacitated benchmark datasets, which are Toronto and ITC2007 dataset respectively. Experimental results indicate that, in terms of construction of solution of datasets, hybridizing of SD produces the best initial solutions. The study also reveals that, during improvement, GDA, SA, and LAHC can produce better quality solutions compared to TS and VNS for solving both benchmark examination timetabling datasets

Perancangan Sistem Informasi Penjadwalan Resource Perguruan Tinggi Menggunakan Metode Particle Swarm Optimization (PSO)

Sistem informasi manajemen penjadwalan kuliah merupakan suatu sistem berfokus pada pengelolaan data akademik dan constraints dalam upaya mengoptimalkan penggunaan resource yang tersedia dan terhindar dari bentrok, sehingga informasi yang dihasilkan efektif. Hasil dari informasi tersebut dapat membantu perguruan tinggi dalam merencanakan penggunaan ruangan, dan melakukan pengembangan program studi pada Politeknik Negeri Bengkalis. Tujuan penelitian ini adalah membuat sistem informasi penjadwalan kuliah untuk pemanfaatan resource pada perguruan tinggi menggunakan particle swarm optimization. Data resource dan constraint diimplementasikan menggunakan sistem informasi penjadwalan dengan pendekatan algoritma PSO. Hasil analisa data menggunakan algoritma PSO dengan menggabungkan enam hard constraint dan dua soft constraint belum dapat menghasilkan solusi yang optimal, karena masih terdapat bentrok dosen-timeslot (soft1), namun tanpa menggabungkan kedua soft constraint dapat menghasilkan solusi yang optimal dalam penggunaan ruangan, dimana solusi terbaik dengan nilai fitness (0,333), c1 (2,0), c2 (2,0), w (0,2), dan maksimal iterasi 10 dari solusi yang diinginkan. Hasil akhir penelitian adalah sistem informasi manajemen penjadwalan kuliah berbasis web (lokal) dan desktop untuk pemanfaatan resource yang menghasilkan informasi jadwal kuliah dan penggunaan ruangan pada perguruan tinggi