247 research outputs found

    Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems

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    [EN] Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are ATP-hard. As a result, researchers resort to metaheuristics to obtain effective and efficient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an Automatic Algorithm Design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed MD for three different optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases.Pedro Alfaro-Fernandez and Ruben Ruiz are partially supported by the Spanish Ministry of Science, Innovation, and Universities, under the project "OPTEP-Port Terminal Operations Optimization" (No. RTI2018-094940-B-I00) financed with FEDER funds and under grants BES-2013-064858 and EEBB-I-15-10089. This work was supported by the COMEX project (P7/36) within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office. Thomas Stiitzle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a Research Director.Alfaro-Fernandez, P.; Ruiz GarcĂ­a, R.; Pagnozzi, F.; StĂŒtzle, T. (2020). Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems. European Journal of Operational Research. 282(3):835-845. https://doi.org/10.1016/j.ejor.2019.10.004S8358452823BoĆŒejko, W., Gnatowski, A., NiĆŒyƄski, T., Affenzeller, M., & Beham, A. (2018). Local Optima Networks in Solving Algorithm Selection Problem for TSP. Advances in Intelligent Systems and Computing, 83-93. doi:10.1007/978-3-319-91446-6_9BoĆŒejko, W., Pempera, J., & Smutnicki, C. (2013). Parallel tabu search algorithm for the hybrid flow shop problem. Computers & Industrial Engineering, 65(3), 466-474. doi:10.1016/j.cie.2013.04.007Burke, E. K., Hyde, M. R., & Kendall, G. (2012). Grammatical Evolution of Local Search Heuristics. IEEE Transactions on Evolutionary Computation, 16(3), 406-417. doi:10.1109/tevc.2011.2160401Cahon, S., Melab, N., & Talbi, E.-G. (2004). ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics. Journal of Heuristics, 10(3), 357-380. doi:10.1023/b:heur.0000026900.92269.ecCarlier, J., & Neron, E. (2000). An Exact Method for Solving the Multi-Processor Flow-Shop. RAIRO - Operations Research, 34(1), 1-25. doi:10.1051/ro:2000103Chung, T.-P., & Liao, C.-J. (2013). An immunoglobulin-based artificial immune system for solving the hybrid flow shop problem. Applied Soft Computing, 13(8), 3729-3736. doi:10.1016/j.asoc.2013.03.006Cui, Z., & Gu, X. (2015). An improved discrete artificial bee colony algorithm to minimize the makespan on hybrid flow shop problems. Neurocomputing, 148, 248-259. doi:10.1016/j.neucom.2013.07.056Ding, J.-Y., Song, S., Gupta, J. N. D., Zhang, R., Chiong, R., & Wu, C. (2015). An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem. Applied Soft Computing, 30, 604-613. doi:10.1016/j.asoc.2015.02.006Dubois-Lacoste, J., LĂłpez-Ibåñez, M., & StĂŒtzle, T. (2011). A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems. Computers & Operations Research, 38(8), 1219-1236. doi:10.1016/j.cor.2010.10.008Dubois-Lacoste, J., Pagnozzi, F., & StĂŒtzle, T. (2017). An iterated greedy algorithm with optimization of partial solutions for the makespan permutation flowshop problem. Computers & Operations Research, 81, 160-166. doi:10.1016/j.cor.2016.12.021Gupta, J. N. D. (1988). Two-Stage, Hybrid Flowshop Scheduling Problem. Journal of the Operational Research Society, 39(4), 359-364. doi:10.1057/jors.1988.63Gupta, J. N. D., & Stafford, E. F. (2006). Flowshop scheduling research after five decades. European Journal of Operational Research, 169(3), 699-711. doi:10.1016/j.ejor.2005.02.001Hidri, L., & Haouari, M. (2011). Bounding strategies for the hybrid flow shop scheduling problem. Applied Mathematics and Computation, 217(21), 8248-8263. doi:10.1016/j.amc.2011.02.108Hutter, F., Hoos, H. H., Leyton-Brown, K., & Stuetzle, T. (2009). ParamILS: An Automatic Algorithm Configuration Framework. Journal of Artificial Intelligence Research, 36, 267-306. doi:10.1613/jair.2861Johnson, S. M. (1954). Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly, 1(1), 61-68. doi:10.1002/nav.3800010110Khalouli, S., Ghedjati, F., & Hamzaoui, A. (2010). A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop. Engineering Applications of Artificial Intelligence, 23(5), 765-771. doi:10.1016/j.engappai.2010.01.008KhudaBukhsh, A. R., Xu, L., Hoos, H. H., & Leyton-Brown, K. (2016). SATenstein: Automatically building local search SAT solvers from components. Artificial Intelligence, 232, 20-42. doi:10.1016/j.artint.2015.11.002Li, J., Pan, Q., & Wang, F. (2014). A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem. Applied Soft Computing, 24, 63-77. doi:10.1016/j.asoc.2014.07.005Liao, C.-J., Tjandradjaja, E., & Chung, T.-P. (2012). An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem. Applied Soft Computing, 12(6), 1755-1764. doi:10.1016/j.asoc.2012.01.011Lopez-Ibanez, M., & Stutzle, T. (2012). The Automatic Design of Multiobjective Ant Colony Optimization Algorithms. IEEE Transactions on Evolutionary Computation, 16(6), 861-875. doi:10.1109/tevc.2011.2182651LĂłpez-Ibåñez, M., Dubois-Lacoste, J., PĂ©rez CĂĄceres, L., Birattari, M., & StĂŒtzle, T. (2016). The irace package: Iterated racing for automatic algorithm configuration. Operations Research Perspectives, 3, 43-58. doi:10.1016/j.orp.2016.09.002Marichelvam, M. K., Prabaharan, T., & Yang, X. S. (2014). A Discrete Firefly Algorithm for the Multi-Objective Hybrid Flowshop Scheduling Problems. IEEE Transactions on Evolutionary Computation, 18(2), 301-305. doi:10.1109/tevc.2013.2240304Marichelvam, M. K., Prabaharan, T., & Yang, X. S. (2014). Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Applied Soft Computing, 19, 93-101. doi:10.1016/j.asoc.2014.02.005Marichelvam, M. K., Prabaharan, T., Yang, X. S., & Geetha, M. (2013). Solving hybrid flow shop scheduling problems using bat algorithm. International Journal of Logistics Economics and Globalisation, 5(1), 15. doi:10.1504/ijleg.2013.054428Mascia, F., LĂłpez-Ibåñez, M., Dubois-Lacoste, J., & StĂŒtzle, T. (2014). Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools. Computers & Operations Research, 51, 190-199. doi:10.1016/j.cor.2014.05.020Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91-95. doi:10.1016/0305-0483(83)90088-9Pan, Q.-K., & Dong, Y. (2014). An improved migrating birds optimisation for a hybrid flowshop scheduling with total flowtime minimisation. Information Sciences, 277, 643-655. doi:10.1016/j.ins.2014.02.152Pan, Q.-K., Ruiz, R., & Alfaro-FernĂĄndez, P. (2017). Iterated search methods for earliness and tardiness minimization in hybrid flowshops with due windows. Computers & Operations Research, 80, 50-60. doi:10.1016/j.cor.2016.11.022Pan, Q.-K., Wang, L., Li, J.-Q., & Duan, J.-H. (2014). A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation. Omega, 45, 42-56. doi:10.1016/j.omega.2013.12.004Rajendran, C., & Ziegler, H. (1997). An efficient heuristic for scheduling in a flowshop to minimize total weighted flowtime of jobs. European Journal of Operational Research, 103(1), 129-138. doi:10.1016/s0377-2217(96)00273-1Ruiz, R., & StĂŒtzle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-2049. doi:10.1016/j.ejor.2005.12.009Ruiz, R., & VĂĄzquez-RodrĂ­guez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1-18. doi:10.1016/j.ejor.2009.09.024Sörensen, K. (2013). Metaheuristics-the metaphor exposed. International Transactions in Operational Research, 22(1), 3-18. doi:10.1111/itor.12001Vignier, A., Billaut, J.-C., & Proust, C. (1999). Les problĂšmes d’ordonnancement de type flow-shop hybride : Ă©tat de l’art. RAIRO - Operations Research, 33(2), 117-183. doi:10.1051/ro:1999108Wang, S., Wang, L., Liu, M., & Xu, Y. (2013). An enhanced estimation of distribution algorithm for solving hybrid flow-shop scheduling problem with identical parallel machines. The International Journal of Advanced Manufacturing Technology, 68(9-12), 2043-2056. doi:10.1007/s00170-013-4819-yXu, Y., Wang, L., Wang, S., & Liu, M. (2013). An effective shuffled frog-leaping algorithm for solving the hybrid flow-shop scheduling problem with identical parallel machines. Engineering Optimization, 45(12), 1409-1430. doi:10.1080/0305215x.2012.73778

    A modified migrating bird optimization for university course timetabling problem

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    University course timetabling problem is a dilemma which educational institutions are facing due to various demands to be achieved in limited resources. Migrating bird optimization (MBO) algorithm is a new meta-heuristic algorithm which is inspired by flying formation of migrating birds. It has been applied successfully in tackling quadratic assignment problem and credit cards fraud detection problem. However, it was reported that MBO will get stuck in local optima easily. Therefore, a modified migrating bird optimization algorithm is proposed to solve post enrolment-based course timetabling. An improved neighbourhood sharing mechanism is used with the aim of escaping from local optima. Besides that, iterated local search is selected to be hybridized with the migrating bird optimization in order to further enhance its exploitation ability. The proposed method was tested using Socha’s benchmark datasets. The experimental results show that the proposed method outperformed the basic MBO and it is capable of producing comparable results as compared with existing methods that have been presented in literature. Indeed, the proposed method is capable of addressing university course timetabling problem and promising results were obtained

    Modeling and Solving Flow Shop Scheduling Problem Considering Worker Resource

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    In this paper, an uninterrupted hybrid flow scheduling problem is modeled under uncertainty conditions. Due to the uncertainty of processing time in workshops, fuzzy programming method has been used to control the parameters of processing time and preparation time. In the proposed model, there are several jobs that must be processed by machines and workers, respectively. The main purpose of the proposed model is to determine the correct sequence of operations and assign operations to each machine and each worker at each stage, so that the total completion time (Cmax) is minimized. Also this paper, fuzzy programming method is used for control unspecified parameter has been used from GAMS software to solve sample problems. The results of problem solving in small and medium dimensions show that with increasing uncertainty, the amount of processing time and consequently the completion time increases. Increases from the whole work. On the other hand, with the increase in the number of machines and workers in each stage due to the high efficiency of the machines, the completion time of all works has decreased. Innovations in this paper include uninterrupted hybrid flow storage scheduling with respect to fuzzy processing time and preparation time in addition to payment time. The allocation of workers and machines to jobs is another innovation of this article

    Internet of Things in urban waste collection

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    Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving

    Ordonnancement d’ateliers en prĂ©sence d’opĂ©rateurs

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    La thĂ©orie de l’ordonnancement a, depuis son avĂšnement, suscitĂ© un grand intĂ©rĂȘt de la part de chercheurs, de scientifiques, mais aussi d’industriels. Ceci est dĂ» Ă  la grande variĂ©tĂ© de problĂšmes rĂ©els pouvant ĂȘtre modĂ©lisĂ©s sous forme de problĂšmes d’ordonnancement. En effet, ce domaine peut trouver des applications aussi bien en gestion d’horaires qu’en informatique, ou encore en environnement de production. Les Ă©tudes relativement rĂ©centes dans le domaine ont vu l’introduction du paramĂštre humain et sa considĂ©ration dans la prise de dĂ©cision portant sur les ressources matĂ©rielles d’un problĂšme d’ordonnancement. La prĂ©sente thĂšse traite des problĂšmes d’ordonnancement d’ateliers avec opĂ©rateurs. Dans lesdits ateliers, des tĂąches devront ĂȘtre exĂ©cutĂ©es par plusieurs machines selon un ordre qui dĂ©pend du type d’atelier. Pour ce faire, lesdites tĂąches utilisent simultanĂ©ment un opĂ©rateur et une machine. Le nombre d’opĂ©rateurs ainsi que leurs placements, i.e. leurs affectations aux machines, dĂ©pendra du modĂšle d’affectation choisi. Tout d’abord, nous considĂ©rons un problĂšme de flow shop de permutation avec temps de rĂ©glages. Nous supposons que le nombre d’opĂ©rateurs est Ă©gal au nombre de machines et qu’ils s’occupent des opĂ©rations de rĂ©glage. Nous utilisons pour la rĂ©solution la mĂ©taheuristique Migrating Birds Optimization. Nous apportons des amĂ©liorations Ă  l’algorithme de base et en prĂ©sentons quatre versions, ce qui nous permet d’obtenir des rĂ©sultats de relativement bonne qualitĂ© avec des configurations diffĂ©rentes qui apportent de la flexibilitĂ© lors de la prise de dĂ©cision. Par la suite, nous Ă©tudions des problĂšmes oĂč le nombre d’opĂ©rateurs est infĂ©rieur au nombre de machines. Nous Ă©tudions trois types d’ateliers : les flow shops, les job shops et les open shops. Nous commençerons d’abord par l’étude de complexitĂ© de nos problĂšmes. Nous prĂ©sentons d’abord des cas rĂ©solubles en temps polynomial et exhibons les mĂ©thodes permettant de les rĂ©soudre. Pour les cas difficiles, nous proposons des mĂ©thodes de rĂ©solution ainsi que des bornes infĂ©rieures. Les rĂ©sultats montrent que les mĂ©thodes proposĂ©es donnent de bons rĂ©sultats, souvent proches des bornes thĂ©oriques. Since its advent, scheduling theory has generated great interest amidst researchers, scientists but also industrialists. This is due to the great diversity of real problems that can be modeled as scheduling problems. Indeed, this field can find applications in timetabling, computer science but also in production systems. Recent studies in the field have introduced the human resources and considered them in decision making processes involving the material resources of scheduling problems. This thesis deals with scheduling shop problems with operators. In the aforementioned shops, tasks are to be processed according to orderings that depend on the type of shop. In order to do so, the tasks need simultaneously an operator and a machine. The number of operators and their positions in the shop, i.e. their assignements to machines, depends on the chosen assignment mode. First, we consider a permutation flow shop problem with setup times. We assume that the number of operators is equal to the number of machines and that they handle setup operations. We use the metaheuristic called the Migrating Birds Optimization to solve this problem. We improve the basic algorithm and present four versions, which allows us to obtain results of good quality with different structures, which provides flexibility in decision making. Next, we study problems where the number of operators is less than the number of machines. We study three types of shops : flow shops, job shops and open shops.We first start by studying the complexity of our problems. Then we present well-solvable cases as well as their solution methods. For some N P-hard cases, we propose solution methods and a lower bound. The results show that the proposed methods provide good results, often close to the theoretical lower bounds

    Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

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    Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems

    No-wait open shop com flexibilidade de operadores

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    Orientador: Prof. Dr. Cassius Tadeu ScarpinTese (doutorado) - Universidade Federal do ParanĂĄ, Setor de Tecnologia, Programa de PĂłs-Graduação em MĂ©todos NumĂ©ricos em Engenharia. Defesa : Curitiba, 29/09/2021Inclui referĂȘncias: p. 97-103Área de concentração: Programação MatemĂĄticaResumo: O Problema de Open Shop pode ser definido como um problema de Programação da Produção, em que os itens nĂŁo possuem rotas de processamento prĂ©-definidas. A implicação da nĂŁo existĂȘncia de sequencias prĂ©-definidas gera, para o modelo matemĂĄtico de Programação Linear Inteira Mista (PLIM) proposto, o aumento do espaço de solução e, por conseguinte, a complexidade em encontrar soluçÔes para o problema. AplicaçÔes de Open Shop sĂŁo encontradas, por exemplo, em centros de exames mĂ©dicos e laboratoriais, oficinas mecĂąnicas, centros de controle de qualidade, entre outros. Quando a produção de itens se dĂĄ de modo contĂ­nuo, tem-se a produção denominada sem espera (no-wait), verificada em ambientes cujas propriedades fĂ­sicoquĂ­micas do material processado sĂŁo alteradas caso haja espera entre as mĂĄquinas, como na indĂșstria farmacĂȘutica. Esse trabalho visa apresentar um modelo PLIM que aborde a temĂĄtica do Open Shop com restriçÔes de flexibilidade de operadores e de no-wait, a fim de minimizar o tempo de fluxo total, expresso pela diferença entre o instante de tĂ©rmino do processamento do item e a data de liberação deste. PropĂ”e-se uma ponderação em relação Ă  restrição de recursos, no caso a mĂŁo de obra, flexĂ­vel e multifacetada, e a restrição de no-wait, relacionada a produção contĂ­nua de itens. Com o propĂłsito de validar o modelo, diversos cenĂĄrios foram criados, testando-se diferentes nĂ­veis de flexibilidade dos operadores, avaliando o impacto desta na obtenção de maior factibilidade do problema. Os resultados obtidos refletem que o aumento de apenas uma habilidade pode reduzir sensivelmente a inviabilidade do modelo. CenĂĄrios em que o operador domina apenas uma habilidade possuem infactibilidade prĂłxima a 50%. AlĂ©m disso, devido Ă  complexidade resolutiva do modelo proposto, instĂąncias com mais de cinco itens majoritariamente nem sequer apresentam solução inicial vĂĄlida dentro do tempo limite proposto.Abstract: The Open Shop Problem can be defined as a Production Scheduling problem, in which the items do not have predefined processing routes. The implication of the nonexistence of predefined sequences generates, for the proposed MILP (Mixed Integer Linear Programming) Problem, the increase of the solution space and, therefore, the complexity of finding solutions for the problem. Applications of Open Shop are found, for example, in medical and laboratory examination centers, mechanical workshops, quality control centers, among others. When the job production is continuous, we have no-wait processing, which is encountered in environments where the physical and chemical properties of the processed material are altered if there is wait between machines, such as pharmaceutical industry. This work aims to present a MILP model which addresses the Open Shop theme with operator flexibility and no-wait in order to minimize the total flowtime, expressed by the difference between completion time of a job and its release date. We propose weighing with regards to the resource constraints, in the case of flexible and multi-skilled labor, and no-wait, related to job continuous production. With respect to validate the model, several scenarios were created, addressing different degrees of worker flexibility, assessing its impact on problem feasibility. The obtained results highlight that the addition of only one skill can pronouncedly reduce the model unavailability. Scenarios where the worker masters only one skill possess infeasibility levels close to 50%. Furthermore, due to the solving complexity of the proposed model, benchmarks with more than five jobs mostly not even present a valid initial solution within the designed time limit

    A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem

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    The Combinatorial Optimization Problem (COPs) is one of the branches of applied mathematics and computer sciences, which is accompanied by many problems such as Facility Layout Problem (FLP), Vehicle Routing Problem (VRP), etc. Even though the use of several mathematical formulations is employed for FLP, Quadratic Assignment Problem (QAP) is one of the most commonly used. One of the major problems of Combinatorial NP-hard Optimization Problem is QAP mathematical model. Consequently, many approaches have been introduced to solve this problem, and these approaches are classified as Approximate and Exact methods. With QAP, each facility is allocated to just one location, thereby reducing cost in terms of aggregate distances weighted by flow values. The primary aim of this study is to propose a hybrid approach which combines Discrete Differential Evolution (DDE) algorithm and Tabu Search (TS) algorithm to enhance solutions of QAP model, to reduce the distances between the locations by finding the best distribution of N facilities to N locations, and to implement hybrid approach based on discrete differential evolution (HDDETS) on many instances of QAP from the benchmark. The performance of the proposed approach has been tested on several sets of instances from the data set of QAP and the results obtained have shown the effective performance of the proposed algorithm in improving several solutions of QAP in reasonable time. Afterwards, the proposed approach is compared with other recent methods in the literature review. Based on the computation results, the proposed hybrid approach outperforms the other method

    Decentralized algorithm of dynamic task allocation for a swarm of homogeneous robots

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    The current trends in the robotics field have led to the development of large-scale swarm robot systems, which are deployed for complex missions. The robots in these systems must communicate and interact with each other and with their environment for complex task processing. A major problem for this trend is the poor task planning mechanism, which includes both task decomposition and task allocation. Task allocation means to distribute and schedule a set of tasks to be accomplished by a group of robots to minimize the cost while satisfying operational constraints. Task allocation mechanism must be run by each robot, which integrates the swarm whenever it senses a change in the environment to make sure the robot is assigned to the most appropriate task, if not, the robot should reassign itself to its nearest task. The main contribution in this thesis is to maximize the overall efficiency of the system by minimizing the total time needed to accomplish the dynamic task allocation problem. The near-optimal allocation schemes are found using a novel hybrid decentralized algorithm for a dynamic task allocation in a swarm of homogeneous robots, where the number of the tasks is more than the robots present in the system. This hybrid approach is based on both the Simulated Annealing (SA) optimization technique combined with the Discrete Particle Swarm Optimization (DPSO) technique. Also, another major contribution in this thesis is the formulation of the dynamic task allocation equations for the homogeneous swarm robotics using integer linear programming and the cost function and constraints are introduced for the given problem. Then, the DPSO and SA algorithms are developed to accomplish the task in a minimal time. Simulation is implemented using only two test cases via MATLAB. Simulation results show that PSO exhibits a smaller and more stable convergence characteristics and SA technique owns a better quality solution. Then, after developing the hybrid algorithm, which combines SA with PSO, simulation instances are extended to include fifteen more test cases with different swarm dimensions to ensure the robustness and scalability of the proposed algorithm over the traditional PSO and SA optimization techniques. Based on the simulation results, the hybrid DPSO/SA approach proves to have a higher efficiency in both small and large swarm sizes than the other traditional algorithms such as Particle Swarm Optimization technique and Simulated Annealing technique. The simulation results also demonstrate that the proposed approach can dislodge a state from a local minimum and guide it to the global minimum. Thus, the contributions of the proposed hybrid DPSO/SA algorithm involve possessing both the pros of high quality solution in SA and the fast convergence time capability in PSO. Also, a parameters\u27 selection process for the hybrid algorithm is proposed as a further contribution in an attempt to enhance the algorithm efficiency because the heuristic optimization techniques are very sensitive to any parameter changes. In addition, Verification is performed to ensure the effectiveness of the proposed algorithm by comparing it with results of an exact solver in terms of computational time, number of iterations and quality of solution. The exact solver that is used in this research is the Hungarian algorithm. This comparison shows that the proposed algorithm gives a superior performance in almost all swarm sizes with both stable and small execution time. However, it also shows that the proposed hybrid algorithm\u27s cost values which is the distance traveled by the robots to perform the tasks are larger than the cost values of the Hungarian algorithm but the execution time of the hybrid algorithm is much better. Finally, one last contribution in this thesis is that the proposed algorithm is implemented and extensively tested in a real experiment using a swarm of 4 robots. The robots that are used in the real experiment called Elisa-III robots

    Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

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    The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications
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