Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources

This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. The processing time and the number of required resources for each job rely on the machine that does the processing. Each job j needed units of resources (rjm) during its time of processing on a machine m. These additional resources are limited, and this made the UPMR a difficult problem to solve. In this study, the maximum completion time of jobs makespan must be minimized. Here, we proposed genetic algorithm (GA) to solve the UPMR problem because of the robustness and the success of GA in solving many optimization problems. An enhancement of GA was also proposed in this work. Generally, the experiment involves tuning the parameters of GA. Additionally, an appropriate selection of GA operators was also experimented. The guide genetic algorithm (GGA) is not used to solve the unspecified dynamic UPMR. Besides, the utilization of parameters tuning and operators gave a balance between exploration and exploitation and thus help the search escape the local optimum. Results show that the GGA outperforms the simple genetic algorithm (SGA), but it still didn't match the results in the literature. On the other hand, GGA significantly outperforms all methods in terms of CPU time

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

A decentralized metaheuristic approach applied to the FMS scheduling problem

La programación de FMS ha sido uno de los temas más populares para los investigadores. Se han entregado varios enfoques para programar los FMS, incluidas las técnicas de simulación y los métodos analíticos. Las metaheurísticas descentralizadas pueden verse como una forma en que la población se divide en varias subpoblaciones, con el objetivo de reducir el tiempo de ejecución y el número de evaluaciones, debido a la separación del espacio de búsqueda. La descentralización es una ruta de investigación prominente en la programación, por lo que el costo de la computación se puede reducir y las soluciones se pueden encontrar más rápido, sin penalizar la función objetivo. En este proyecto, se propone una metaheurística descentralizada en el contexto de un problema de programación flexible del sistema de fabricación. La principal contribución de este proyecto es analizar otros tipos de división del espacio de búsqueda, particularmente aquellos asociados con el diseño físico del FMS. El desempeño del enfoque descentralizado se validará con los puntos de referencia de programación de FMS.FMS scheduling has been one of the most popular topics for researchers. A number of approaches have been delivered to schedule FMSs including simulation techniques and analytical methods. Decentralized metaheuristics can be seen as a way where the population is divided into several subpopulations, aiming to reduce the run time and the numbers of evaluation, due to the separation of the search space. Decentralization is a prominent research path in scheduling so the computing cost can be reduced and solutions can be found faster, without penalizing the objective function. In this project, a decentralized metaheuristic is proposed in the context of a flexible manufacturing system scheduling problem. The main contribution of this project is to analyze other types of search space division, particularly those associated with the physical layout of the FMS. The performance of the decentralized approach will be validated with FMS scheduling benchmarks.Ingeniero (a) IndustrialPregrad

Metaheuristics for single and multiple objectives production scheduling for the capital goods industry

In the capital goods industry, companies produce plant and machinery that is used to produce consumer products or commodities such as electricity or gas. Typical products produced in these companies include steam turbines, large boilers and oil rigs. Scheduling of these products is difficult due to the complexity of the product structure, which involves many levels of assembly and long complex routings of many operations which are operated in multiple machines. There are also many scheduling constraints such as machine capacity as well as operation and assembly precedence relationships. Products manufactured in the capital goods industry are usually highly customised in order to meet specific customer requirements. Delivery performance is a particularly important aspect of customer service and it is common for contracts to include severe penalties for late deliveries. Holding costs are incurred if items are completed before the due date. Effective planning and inventory control are important to ensure that products are delivered on time and that inventory costs are minimised. Capital goods companies also give priority to resource utilisation to ensure production efficiency. In practice there are tradeoffs between achieving on time delivery, minimising inventory costs whilst simultaneously maximising resource utilisation. Most production scheduling research has focused on job-shops or flow-shops which ignored assembly relationships. There is a limited literature that has focused on assembly production. However, production scheduling in capital goods industry is a combination of component manufacturing (using jobbing, batch and flow processes), assembly and construction. Some components have complex operations and routings. The product structures for major products are usually complex and deep. A practical scheduling tool not only needs to solve some extremely large scheduling problems, but also needs to solve these problems within a realistic time. Multiple objectives are usually encountered in production scheduling in the capital goods industry. Most literature has focused on minimisation of total flow time, or makespan and earliness and tardiness of jobs. In the capital goods industry, inventory costs, delivery performance and machine utilisation are crucial competitive. This research develops a scheduling tool that can successfully optimise these criteria simultaneously within a realistic time. ii The aim of this research was firstly to develop the Enhanced Single-Objective Genetic Algorithm Scheduling Tool (ESOGAST) to make it suitable for solving very large production scheduling problems in capital goods industry within a realistic time. This tool aimed to minimise the combination of earliness and lateness penalties caused by early or late completion of items. The tool was compared with previous approaches in literature and was proved superior in terms of the solution quality and the computational time. Secondly, this research developed a Multi-Objective Genetic Algorithm Scheduling Tool (MOGAST) that was based upon the development of ESOGAST but was able to solve scheduling problems with multiple objectives. The objectives of this tool were to optimise delivery performance, minimise inventory costs, and maximise resource utilisation simultaneously. Thirdly, this research developed an Artificial Immune System Scheduling Tool (AISST) that achieved the same objective of the ESOGAST. The performances of both tools were compared and analysed. Results showed that AISST performs better than ESOGAST on relatively small scheduling problems, but the computation time required by the AISST was several times longer. However ESOGAST performed better than the AISST for larger problems. Optimum configurations were identified in a series of experiments that conducted for each tool. The most efficient configuration was also successfully applied for each tool to solve the full size problem and all three tools achieved satisfactory results.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development of a Multi-Objective Scheduling System for Complex Job Shops in a Manufacturing Environment

In many sectors of commercial operation, the scheduling of workflows and the allocation of resources at an optimum time is critical; for effective and efficient operation. The high degree of complexity of a “Job Shop” manufacturing environment, with sequencing of many parallel orders, and allocation of resources within multi-objective operational criteria, has been subject to several research studies. In this thesis, a scheduling system for optimizing multi-objective job shop scheduling problems was developed in order to satisfy different production system requirements. The developed system incorporated three different factors; setup times, alternative machines and release dates, into one model. These three factors were considered after a survey study of multiobjective job shop scheduling problems. In order to solve the multi-objective job shop scheduling problems, a combination of genetic algorithm and a modified version of a very recent and computationally efficient approach to non-dominated sorting solutions, called “efficient non-dominated sort using the backward pass sequential strategy”, was applied. In the proposed genetic algorithm, an operation based representation was designed in the matrix form, which can preserve features of the parent after the crossover operator without repairing the solution. The proposed efficient non-dominated sort using the backward pass sequential strategy was employed to determine the front, to which each solution belongs. The proposed system was tested and validated with 20 benchmark problems after they have been modified. The experimental results show that the proposed system was effective and efficient to solve multi-objective job shop scheduling problems in terms of solution quality

Fitness Proportionate Niching: Harnessing The Power Of Evolutionary Algorithms For Evolving Cooperative Populations And Dynamic Clustering

Evolutionary algorithms work on the notion of best fit will survive criteria. This makes evolving a cooperative and diverse population in a competing environment via evolutionary algorithms a challenging task. Analogies to species interactions in natural ecological systems have been used to develop methods for maintaining diversity in a population. One such area that mimics species interactions in natural systems is the use of niching. Niching methods extend the application of EAs to areas that seeks to embrace multiple solutions to a given problem. The conventional fitness sharing technique has limitations when the multimodal fitness landscape has unequal peaks. Higher peaks are strong population attractors. And this technique suffers from the curse of population size in attempting to discover all optimum points. The use of high population size makes the technique computationally complex, especially when there is a big jump in fitness values of the peaks. This work introduces a novel bio-inspired niching technique, termed Fitness Proportionate Niching (FPN), based on the analogy of finite resource model where individuals share the resource of a niche in proportion to their actual fitness. FPN makes the search algorithm unbiased to the variation in fitness values of the peaks and hence mitigates the drawbacks of conventional fitness sharing. FPN extends the global search ability of Genetic Algorithms (GAs) for evolving hierarchical cooperation in genetics-based machine learning and dynamic clustering. To this end, this work introduces FPN based resource sharing which leads to the formation of a viable default hierarchy in classifiers for the first time. It results in the co-evolution of default and exception rules, which lead to a robust and concise model description. The work also explores the feasibility and success of FPN for dynamic clustering. Unlike most other clustering techniques, FPN based clustering does not require any a priori information on the distribution of the data

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Evolutionary Computation Strategies applied to the UA-FLP

En la presente tesis doctoral se desarrollan dos aproximaciones distintas al problema de distribución en planta de áreas desiguales (UA-FLP). En primer lugar, se trata de incorporar el conocimiento del diseñador experto a los algoritmos clásicos de optimización, de forma que, además de buscar buenas soluciones desde el punto de vista cuantitativo, por ejemplo minimizando el flujo de materiales, se introduzca la posibilidad de que el diseñador aporte su experiencia y preferencias personales. Para facilitar la intervención humana en el proceso de búsqueda de soluciones, se ha utilizado un procedimiento de clustering, el cual permite clasificar las soluciones subyacentes en el conjunto de búsqueda, de forma que se presente al diseñador un número suficientemente representativo y, a la vez, evitándole una fatiga innecesaria. Además, en esta primera propuesta se han implementado dos técnicas de niching, denominadas Deterministic Crowding y Restricted Tournament Selection. Estas técnicas tienen la capacidad de mantener ciertas propiedades dentro de la población de soluciones, preservar múltiples nichos con soluciones cercanas a los óptimos locales, y reducir la probabilidad de quedar atrapado en ellos. De esta manera el algoritmo se enfoca simultáneamente en más de una región (nicho) en el espacio de búsqueda, lo cual es esencial para descubrir varios óptimos en una sola ejecución. Por otro lado, en la segunda aproximación al problema, se ha implementado una estrategia evolutiva paralela, muy útil para los problemas de alta complejidad en los que el tiempo de ejecución con un enfoque evolutivo secuencial es prohibitivo. La propuesta desarrollada, denominada IMGA, está basada en un algoritmo genético paralelo de grano grueso con múltiples poblaciones o islas. Este enfoque se caracteriza por evolucionar varias subpoblaciones independientemente, entre las que se intercambian individuos, haciendo posible explorar diferentes regiones del espacio de búsqueda, al mismo tiempo que se mantiene la diversidad de la población, permitiendo la obtención de buenas y diversas soluciones. Con ambas propuestas se han realizado experimentos que han arrojado resultados muy satisfactorios, encontrando buenas soluciones para un conjunto de problemas bien conocidos en la bibliografía. Estos buenos resultados han permitido la publicación de dos artículos indexados en el primer decil del ranking JCR (Journal Citation Reports).The present doctoral thesis develops two different approaches to the Unequal Area Facility Layout Problem (UA-FLP). The first approach encompasses the designer’s knowledge on classic optimization of algorithms in pursuance of good quantitative solutions (e.g. minimizing the materials flow) and also opens the possibility to include the contribution of the designer by means of his expertise and personal preferences. A clustering procedure has been used to facilitate human intervention in the process of finding solutions. This allows the underlying solutions to be classified in the search in order to present the designer with sufficiently representative solutions and, at the same time, avoiding unnecessary fatigue. In addition, two niching techniques have been implemented, called Deterministic Crowding and Restricted Tournament Selection. These techniques have the ability to maintain certain properties within the solutions space, preserve multiple niches with solutions close by local optimums, and reduce the probability of being trapped in them. In this way, the algorithm focuses simultaneously on more than one region (niche) in the search space, which is essential to discover several optimums in a single execution. The second approach to the problem comprises the implementation of a parallel evolutionary strategy. This method is useful for problems of high complexity in which the execution time using a sequential evolutionary approach is prohibitive. The proposal developed, called IMGA (Island Model Genetic Algorithm), is based on a parallel genetic algorithm of multiple-population coarse-grained. This is characterized by evolving several subpopulations independently among which individuals are exchanged. Different regions of the search space can be explored while the diversity of the population is maintained. Satisfactory and diverse solutions have been obtained as a result of this method. Experiments with both proposals have been carried out with satisfactory results, providing good solutions for a set of problems well known in the literature. These results were already published in two papers indexed in the first decile of the JCR (Journal Citation Reports) ranking