Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

Conflict-Free Routing of Mobile Robots

The recent advances in perception have enabled the development of more autonomous mobile robots in the sense that they can operate in a more dynamic environment where obstacles surrounding the robot emerge, disappear, and move. The increased perception of Autonomous Mobile Robots (AMRs) allows them to plan detailed on-line trajectories in order to avoid previously unforeseen obstacles, making AMRs useful in dynamic environments where humans, traditional fork-lifts, and also other mobile robots operate. These abilities contributed to increase automation in logistic applications. This thesis discusses how to efficiently operate a fleet of AMRs and make sure that all tasks are successfully completed.Assigning robots to specific delivery tasks and deciding the routes they have to travel can be modelled as a variant of the classical Vehicle Routing Problem (VRP), the combinatorial optimization problem of designing routes for vehicles. In related research it has been extended to scheduling routes for vehicles to serve customers according to predetermined specifications, such as arrival time at a customer, amount of goods to deliver, etc.In this thesis we consider to schedule a fleet of robots such that areas avoid being congested, delivery time-windows are met, the need for robots to recharge is considered, while at the same time the robots have freedom to use alternative paths to handle changes in the environment. This particular version of the VRP, called CF-EVRP (Conflict-free Electrical Vehicle Routing Problem) is motivated by an industrial need. In this work we consider using optimizing general purpose solvers, in particular, MILP and SMT solvers are investigated. We run extensive computational analysis over well-known combinatorial optimization problems, such as job shop scheduling and bin-packing problems, to evaluate modeling techniques and the relative performance of state-of-the-art MILP and SMT solvers.We propose a monolithic model for the CF-EVRP as well as a compositional approach that decomposes the problem into sub-problems and formulate them as either MILP or SMT problems depending on what fits each particular problem best. The performance of the two approaches is evaluated on a set of CF-EVRP benchmark problems, showing the feasibility of using a compositional approach for solving practical fleet scheduling problems

Bilişim paylaşımı ile gerçek zamanlı üretim planlama ve kontrol sistemi tasarımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Dijital teknolojilerin yaygınlaşması ve hayatın her alanına girmesi ihtiyaçların bireysel kapsamda ele alınmasını sağlamış, rekabeti kişiye özgü çözüm ve ürün üretme boyutuna taşımıştır. Buna bağlı olarak, üretim sistemlerinin gelişimi de çeşitliliği artırmaya ve yönetmeye yönelik olarak devam etmektedir. Bu gelişim ve dönüşüm süreci temel taşlarından birisi kitlesel özelleştime (mass - customization) olan dördüncü sanayi devrimi (Endüstri 4.0) olarak adlandırılmıştır. Dünyanın Endüstri 4.0'a ayak uydurabilmesi için üretim ortamında çeşitliliği ve çeşitliliğe bağlı olarak meydana gelecek değişkenliği yönetebilmesi gerekmektedir. Üretim ortamında, değişkenliğin yönetilebilmesi için geliştirilen yöntemler değişkenlikleri stok tutarak veya zaman toleransları ile çalışarak yönetmektedirler. Bu durum verimliliğin azalmasına ve birim başına düşen sabit maliyetin artmasına neden olmaktadır. Çalışmada, klasik yaklaşımların olumsuz yönlerinen arındırılmış bir üretim planlama yaklaşımı ve modeli önerilmiştir. Önerilen modelin değişkenliklerden etkilenmemesi için model değişken olan miktar parametresi yerine, değişkenliklerden daha az etkilenecek olan zaman parametresi üzerine kurulmuştur. Modelde stok seviyesi yerine stoğun tükenmesine kalan süreye dikkat edilmekte, çizelgeleme sürecinde de üretimin tamamlanmasına kalan süreye ve termin tarihine göre önceliklendirme yapılmaktadır. Model zaman hedeflerine bağlı çalığtığından gerçek zamanlı bir modeldir. Üretim modeli nin gerçek zamanlı olması değişkenliklerden, miktar tabanlı yaklaşıma göre, çok az etkilenmesini sağlamıştır. Yapılan kıyaslama çalışmalarıyla gerçek zamanlı planlama sisteminin üretim ortamındaki değişkenliklerden etkilenmediği ve emniyet stoksuz ortamda, gecikmeleri azaltarak üretimin tamamlanmasını sağladığı ortaya konmuştur. Üstelik bu çıktılar O(n) zaman karmaşıklığına sahip, kısa sürede, sonlanan algoritmalarla elde edilmiştir. Modelin uygulanması algoritmik olarak kolay olsa da, gerçek zamanlı olduğundan, gerçek zamanlı olarak belirlenen işlem döngüsü içerisinde güncel stok ve üretim verisine ihtiyaç duyulmaktadır. Bu veriler Endüstri 4.0 teknolojileriyle elde edilebilen veriler olduğundan, gerçek zamanlı üretim modeli modern üretim sistemlerinde uygulanabilir bir modeldir. Modelin üretim sistemine katkısı, sistemi aynı anda hem itme hem de çekme sistemi gibi çalıştırabilmesidir. Bu sayede üretim sistemi iki biçimde de çalışabilmektedir. Verimli olan stretejiye dinamik olarak geçmek de stok maliyetinin %90'dan fazla azalmasını sağlamıştır.Spread of digital technology in every slice of life provides that the needs have been addressed within the individual scope and also it increases competition to the level of both individual solution and personal production. Accordingly, the development of production systems continues to enhance for managing the diversity. One of the milestones of this development and transformation process is mass customization called the fourth industrial revolution, Industry4.0. Enterprises should be able to overcome with the diversity and variability due to diversity in the production environment in order to keep pace with Industry 4.0. The methods improved in attempt to cope with variability in the production, are keeping inventory or working with time tolerances. In this case, efficiency decreases and overhead cost per unit increases in. A novel production planning approach and a model which is eliminated from negative aspect of conventional methods has been proposed, in this study. The proposed model is based on a time parameter less affected by the variances rather than the quantity in order to avoid being influenced by the changes. The remaining time to stock-out instead of inventory level is taken into account in this model, and prioritization is proceed according to the time remaining to complete the production and due date in the scheduling process. Thus, the model based on a time parameter is a real-time model. Being real-time provides, the model, to be affected from variances less than quantity based methods. It is presented that the real-time model is not affected by the variances in the manufacturing environment, and provides completing manufacturing process with less delays by using no safety stock. Besides, an algorithm having O(n) time complexity provides this result. Though the application of model is easy as algorithmically, the model, being real-time, requires the live inventory and production data within the determined time cycle. Because the data can be gained by the cyber-physical technologies of Industry 4.0, real-time model can be applied to modern production systems. The contribution of this model to production systems is that the model assimilates manufacturing systems as pull or push system at the same time. Selecting the productive strategy dynamically enables the decrease of more than 90% inventory cost

A comparison of combinatory methods and GIS based MOLA (IDRISI®) for solving multi-objective land use assessment and allocation problems

The aim of this study was to provide an informed choice among two combinatory methods and GIS based MOLA module in IDRISI® by comparing their performance in solving a hypothetical Multi-Objective Land use Assessment and Allocation (MOLAA)problem. Among the combinatory methods, Simulated Annealing and Tabu Search algorithms were chosen for study. The application of Simulated Annealing has already been demonstrated in solving a MOLAA problem but Tabu Search has not been used to a MOLAA problem before. The Kioloa Region of New South Wales, Australia was chosen for designing a hypothetical MOLAA problem due to availability and access to the digital datasets at the Australian National University. The MOLAA problem was formulated for accomplishing six land use objectives by allocating the area to four land use types, that is, conservation, agriculture, forestry and development, using altogether 1 7 criteria, including 16 factors and one constraint. The criteria maps were classified in ordinal, continuous and fuzzy scale and combined by using Weighted Linear Combination to produce land use suitability models for each land use type. The ordinal and continuous land use suitability models were used in solving the problem by applying the MOLA module. In order to apply the combinatory methods, all three land use suitability models, that is, ordinal, continuous and fuzzy, were transferred to cost suitability models where the lowest cost value represented the best suitability and the highest cost value represented the lowest suitability in the interval data set. Three initial input solutions generated by the random, cheapest and greatest difference methods were used for optimising by applying both algorithms. Both combinatory methods maximized overall land use suitability with better spatial compactness by allocating each land unit with the most suitable land use with the lowest cost. At the land use level, MOLA exhibited a bias towards land uses with lower area requirement and allocates more suitable land units to them. Though the MOLA module is highly efficient in solving large grid MOLAA problem, the combinatory methods deliver a solution close to the near-optimal solution with better compactness in an acceptable time frame. Hence, the combinatory methods have been shown to be appropriate choice to solve MOLAA problems. The solutions were not significantly different at their mean cost functions between Simulated Annealing and Tahu Search at the appropriate parameters. Among the cost suitability models, both algorithms performed better in the fuzzy models in the large MOLAA problem. The initial input solution influenced the performance of the algorithms. The algorithms produced better results in the cheapest and greatest difference initial input solution in the medium grid MOLAA problem whereas the cost function was more improved using the random initial input solution in the large grid. Although there is no significant difference in the mean cost functions between Simulated Annealing and Tahu Search, the previous one is found more efficient in solving large grid MOLAA problem. For the same values of compactness factors, Simulated Annealing produced more spatially compact land use allocation than Tahu Search. Thus decision makers/land use planners or consultants could obtain a better decision alternative to a land use allocation problem by applying Simulated Annealing with the recommended appropriate annealing schedule and initial input cost suitability model. This study recommends further research in Tahu Search to find an effective attribute for a Tahu list, to be applied to a MO LAA problem

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities