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

Dynamic timetable generator using particle swarm optimization (PSO) method

This paper addresses the usage of Particle Swarm Optimization (PSO) in generating a timetable which the selection of driver and vehicle are based on the concept of PSO. The objectives are simultaneously considered as follow: 1) minimizing the cycle time, 2) regenerate the timetable. Searching for an optimal solution in such of large sized population will be time consuming and thus by presenting the PSO method is able to select the appropriate driver and vehicle with a shorter period. The timetable that is generated will be more appropriate as regenerating function can handle emergency such as breakdown of vehicle. Besides, during the generating of timetable, it also considers constraints which make the task more challenging. The chosen particle during implementing the PSO method should be chosen with fitness nearest to fifty in this system. Thus, the timetable for transport schedule system can be arranged without clashing of driver or vehicle

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

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

A review of key planning and scheduling in the rail industry in Europe and UK

Planning and scheduling activities within the rail industry have benefited from developments in computer-based simulation and modelling techniques over the last 25 years. Increasingly, the use of computational intelligence in such tasks is featuring more heavily in research publications. This paper examines a number of common rail-based planning and scheduling activities and how they benefit from five broad technology approaches. Summary tables of papers are provided relating to rail planning and scheduling activities and to the use of expert and decision systems in the rail industry.EPSR

Particle Swarm Algorithm for Improved Handling of the Mirrored Traveling Tournament Problem

In this study, we used a particle swarm optimization (PSO) algorithm to address a variation of the non-deterministic polynomial-time NP-hard traveling tournament problem, which determines the optimal schedule for a double round-robin tournament, for an even number of teams, to minimize the number of trips taken. Our proposed algorithm iteratively explored the search space with a swarm of particles to find near-optimal solutions. We also developed three techniques for updating the particle velocity to move towards optimal points, which randomly select and replace row and column parameters to find candidate positions close to an optimal solution. To further optimize the solution, we calculated the particle cost function, an important consideration within the problem conditions, for team revenues, fans, and media. We compared our computation results with two well-known meta-Heuristics: a genetics algorithm utilizing a swapping method and a Greedy Randomized Adaptive Search Procedure Iterated Local Search algorithm heuristic on a set of 20 teams. Ultimately, the PSO algorithm generated solutions that were comparable, and often superior, to the existing well-known solutions. Our results indicate that our proposed algorithm could aid in reducing the overall budget expenditures of international sports league organizations, which could enable significant monetary savings and increase profit margins

TIMETABLE SCHEDULING MENGGUNAKAN PARTICLE SWARM OPTIMIZATION

TIMETABLE SCHEDULING MENGGUNAKAN PARTICLE SWARM OPTIMIZATION - Particle Swarm Optimization, hard constraints, soft constraints, penjadwala

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

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