11 research outputs found
A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem
The Job Shop Scheduling Problem (JSP) is a combinatorial optimization problem cataloged as type NP-Hard. To solve this problem, several heuristics and metaheuristics have been used. In order to minimize the makespan, we propose a Memetic Algorithm (MA), which combines the exploration of the search space by a Genetic Algorithm (GA), and the exploitation of the solutions using a local search based on the neighborhood structure of Nowicki and Smutnicki. The genetic strategy uses an operation-based representation that allows generating feasible schedules, and a selection probability of the best individuals that are crossed using the JOX operator. The results of the implementation show that the algorithm is competitive with other approaches proposed in the literature
Meta-learning computational intelligence architectures
In computational intelligence, the term \u27memetic algorithm\u27 has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a \u27meme\u27 has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as \u27memetic algorithm\u27 is too specific, and ultimately a misnomer, as much as a \u27meme\u27 is defined too generally to be of scientific use. In this dissertation the notion of memes and meta-learning is extended from a computational viewpoint and the purpose, definitions, design guidelines and architecture for effective meta-learning are explored. The background and structure of meta-learning architectures is discussed, incorporating viewpoints from psychology, sociology, computational intelligence, and engineering. The benefits and limitations of meme-based learning are demonstrated through two experimental case studies -- Meta-Learning Genetic Programming and Meta- Learning Traveling Salesman Problem Optimization. Additionally, the development and properties of several new algorithms are detailed, inspired by the previous case-studies. With applications ranging from cognitive science to machine learning, meta-learning has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning --Abstract, page iii
Memetic algorithms for solving job-shop scheduling problems
The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. In this paper, we developed a memetic algorithm (MA) for solving JSSPs. Three priority rules were designed, namely partial re-ordering, gap reduction and restricted swapping, and used as local search techniques in our MA. We have solved 40 benchmark problems and compared the results obtained with a number of established algorithms in the literature. The experimental results show that MA, as compared to GA, not only improves the quality of solutions but also reduces the overall computational time
Atölye tipi çizelgeleme problemlerinde evrimsel algoritmalar ile yapay arı kolonisi algoritmasının bĂŒtĂŒnleĆik bir yaklaĆımı
06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan âYĂŒkseköÄretim Kanunu Ä°le Bazı Kanun Ve Kanun HĂŒkmĂŒnde Kararnamelerde DeÄiĆiklik Yapılması Hakkında Kanunâ ile 18.06.2018 tarihli âLisansĂŒstĂŒ Tezlerin Elektronik Ortamda Toplanması, DĂŒzenlenmesi ve EriĆime Açılmasına Ä°liĆkin Yönergeâ gereÄince tam metin eriĆime açılmıĆtır.Hayatımızın birçok alanında çok önemli bir yeri olan çizelgeleme problemlerinin çözĂŒmĂŒ ile ilgili olarak yıllardır çok ciddi çalıĆmalar yapılmıĆtır. Bu çalıĆmaların yapılmasında ĆĂŒphesiz en bĂŒyĂŒk sebep, mevcut çizelgeye göre daha iyilerinin geliĆtirilmesini saÄlamaya çalıĆmak ve daha bĂŒyĂŒk kazanç ve verimlilikler ortaya koymaktır. Bundan dolayı, doÄru ve etkin bir çizelgeleme, hem insanlar hem de iĆletmeler için bĂŒyĂŒk önem arz etmektedir. Bu baÄlamda özellikle son yıllarda çizelgeleme problemlerinin çözĂŒmĂŒnde sezgisel algoritmaların araĆtırmacılar tarafından yoÄun bir biçimde kullanıldıÄı görĂŒlmektedir. Bu tez çalıĆmasında, atölye tipi çizelgeleme problemlerinin çözĂŒmĂŒnĂŒn eniyilemesi için bĂŒtĂŒnleĆik bir yaklaĆım önerilmiĆtir. Bu baÄlamda sĂŒrĂŒ zekĂąsına dayalı sezgisel algoritmalardan olan yapay arı kolonisi algoritması ile evrimsel algoritmalar bĂŒtĂŒnleĆik yaklaĆım için kullanılmıĆtır. Ănerilen metot, atölye tipi çizelgeleme ile ilgili data setlerine uygulanmÄ±Ć ve elde edilen sonuçlar ortalama baÄıl hata yĂŒzdesi (ARPE) ile ortalama baÄıl sapma yĂŒzdesi (ARPD) kriterleri kullanılarak, karınca kolonisi optimizasyon (ACO) tekniÄi, kuĆ sĂŒrĂŒsĂŒ algoritması (PSO) ve diferansiyel geliĆim (DE) algoritması ile kıyaslanmıĆtır. Sonuçlar kıyaslanırken, parametrik ve parametrik olmayan testler kullanılarak metotlar arasında istatistiksel olarak anlamlı farklar olup olmadıÄı kurulan hipotezlerle araĆtırılmıĆtır. ARPE kriterine göre, önerilen yaklaĆım ile ACO tekniÄi sonuçları arasında istatistiksel olarak anlamlı farklar gözlemlenirken, önerilen metot ile PSO ve DE algoritmalarının sonuçları arasında ise istatistiksel olarak anlamlı farklar olmadıÄı görĂŒlmĂŒĆtĂŒr. Yapılan testler sonucunda, önerilen metot ile elde edilen ARPE deÄeri, ACO metodu ile elde edilen ARPE deÄerinden 4,3 puan (yĂŒzdesel deÄiĆim olarak) daha dĂŒĆĂŒk olduÄundan daha etkin bir netice vermiĆtir. ARPD kriterine göre ise, önerilen yaklaĆım ile diÄer tĂŒm algoritmaların sonuçları arasında istatistiksel olarak anlamlı farklar olduÄu yapılan testlerle ortaya konmuĆtur. Yapılan testler sonucunda, önerilen metot ile elde edilen ARPD deÄeri, ACO metodu ile elde edilen ARPD deÄerinden 6,3 puan, PSO metodu ile elde edilen ARPD deÄerinden 0,6 puan, DE metodu ile elde edilen ARPD deÄerinden ise 0,7 puan daha dĂŒĆĂŒk olduÄundan daha kararlı ve etkin neticeler vermiĆtir. Yapılan testler sonucunda, çizelgelemesi yapılacak olan iĆ veya makine sayısının 20 ve 20'den az olduÄu durumlarda önerilen metodun çok daha hızlı ve etkin sonuçlar verdiÄi gözlemlenmiĆtir.There have been a lot of research made about solution of scheduling problems that have a very important place in many areas of our lives for years. The cause of these researches is to develop better than the current schedule and achieve greater profits. Therefore, there is great importance of efficient scheduling for both humans and businesses. In this context, heuristic algorithms are used extensively by researchers for solving scheduling problems in recent years. In this dissertation study, an integrated approach has been developed for optimizing the solution of job shop scheduling problems. In this context, artificial bee colony algorithm and evolutionary algorithms are used for the integrated approach. The proposed hybrid method has been applied to data sets related to job shop scheduling. The obtained results have been compared with the results of different optimization techniques that these techniques are ant colony optimization (ACO), particle swarm optimization (PSO) and differential evolution algorithm (DE) using the average relative error percentage (ARPE) and average relative percentage deviation (ARPD) criteria. It has investigated whether statistically significant differences among methods using parametric and non-parametric tests with the founded hypotheses for the comparisons. According to the ARPE criterion, statistically significant differences have been obtained between the results of the recommended approach and ACO technique. According to the same criterion, statistically significant differences have not been observed between the result of the proposed method with PSO and DE algorithms. ARPE value of the recommended approach yielded 4.3 points (as percentage changes) more effective than ARPE value of the ACO technique according to the results of the tests. According to the ARPD criterion, statistically significant differences have been obtained between the results of the recommended approach and other all techniques. According to the results of the tests, ARPD value of the proposed method yielded more effective and stable of 6.3 points than ARPE value of the ACO technique, of 0.6 points than ARPE value of the PSO algorithm, of 0.7 points than ARPE value of the DE algorithm. According to the results of the tests, it observed that the proposed method has much faster and more effective results in conditions less than 20 number of machines or jobs which will be scheduling
Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold:
Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction
A study of evolutionary multiobjective algorithms and their application to knapsack and nurse scheduling problems
Evolutionary algorithms (EAs) based on the concept of Pareto dominance seem the most suitable technique for multiobjective optimisation. In multiobjective optimisation, several criteria (usually conflicting) need to be taken into consideration simultaneously to assess a quality of a solution. Instead of finding a single solution, a set of trade-off or compromise solutions that represents a good approximation to the Pareto optimal set is often required. This thesis presents an investigation on evolutionary algorithms within the framework of multiobjective optimisation. This addresses a number of key issues in evolutionary multiobjective optimisation. Also, a new evolutionary multiobjective (EMO) algorithm is proposed. Firstly, this new EMO algorithm is applied to solve the multiple 0/1 knapsack problem (a wellknown benchmark multiobjective combinatorial optimisation problem) producing competitive results when compared to other state-of-the-art MOEAs.
Secondly, this thesis also investigates the application of general EMO algorithms to solve real-world nurse scheduling problems. One of the challenges in solving real-world nurse scheduling problems is that these problems are highly constrained and specific-problem heuristics are normally required to handle these constraints. These heuristics have considerable influence on the search which could override the effect that general EMO algorithms could have in the solution process when applied to this type of problems. This thesis outlines a proposal for a general approach to model the nurse scheduling problems without the requirement of problem-specific heuristics so that general EMO algorithms could be applied. This would also help to assess the problems and the performance of general EMO algorithms more fairly