Hydraulic Brake Systems for Electrified Road Vehicles: A Down-sizing Approach

Down-sizing hydraulic brake systems can is made possible in electrified road vehicles thanks to the braking torque contribution provided by electric machines. Benefits in terms of weight and cost of the system can be ensured in this way. Nevertheless, appropriate care should be taken not to excessively deteriorate the overall electrical energy recovery capability of the electrified vehicle during braking maneuvers. For this reason, a multi-target optimization framework is developed in this paper to down-size hydraulic brake systems for electrified road vehicles while simultaneously maximizing the braking energy recovery capability of the electrified powertrain. Firstly, hydraulic brake system, electrified powertrain and vehicle chassis are modeled in a dedicated simulation platform. Subsequently, particle-swarm optimization is employed as search algorithm to identify optimal sizing parameters for the hydraulic brake system. Sizing variables particularly include diameter and stroke of the master cylinder, electrically assisted booster diameter, front brake piston diameter and rear brake piston diameter. The simulation of homologation tests for safety standards ensures that retained combinations of sizing parameters complies with regulatory requirements. A case study proves that the developed methodology is flexible and effective at rapidly producing several sub-optimal sizing options for both front-wheel drive and rear-wheel drive layouts for a retained battery electric vehicle

A hybrid algorithm for flexible job-shop scheduling problem with setup times

[EN] Job-shop scheduling problem is one of the most important fields in manufacturing optimization where a set of n jobs must be processed on a set of m specified machines. Each job consists of a specific set of operations, which have to be processed according to a given order. The Flexible Job Shop problem (FJSP) is a generalization of the above-mentioned problem, where each operation can be processed by a set of resources and has a processing time depending on the resource used. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper addresses the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a hybrid algorithm based on genetic algorithm (GA) and variable neighbourhood search (VNS) to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our algorithm against the available ones in terms of solution quality.Azzouz, A.; Ennigrou, M.; Ben Said, L. (2017). A hybrid algorithm for flexible job-shop scheduling problem with setup times. International Journal of Production Management and Engineering. 5(1):23-30. doi:10.4995/ijpme.2017.6618SWORD233051Allahverdi, A. (2015). The third comprehensive survey on scheduling problems with setup times/costs. European Journal of Operational Research, 246(2), 345-378. doi:10.1016/j.ejor.2015.04.004Azzouz, A., Ennigrou, M., & Jlifi, B. (2015). Diversifying TS using GA in Multi-agent System for Solving Flexible Job Shop Problem. Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics. doi:10.5220/0005511000940101Azzouz, A., Ennigrou, M., Jlifi, B., & Ghedira, K. (2012). Combining Tabu Search and Genetic Algorithm in a Multi-agent System for Solving Flexible Job Shop Problem. 2012 11th Mexican International Conference on Artificial Intelligence. doi:10.1109/micai.2012.12Bagheri, A., & Zandieh, M. (2011). Bi-criteria flexible job-shop scheduling with sequence-dependent setup times—Variable neighborhood search approach. Journal of Manufacturing Systems, 30(1), 8-15. doi:10.1016/j.jmsy.2011.02.004Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research, 41(3), 157-183. doi:10.1007/bf02023073Cheung, W., & Zhou, H. (2001). Annals of Operations Research, 107(1/4), 65-81. doi:10.1023/a:1014990729837Fattahi, P., Saidi Mehrabad, M., & Jolai, F. (2007). Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. Journal of Intelligent Manufacturing, 18(3), 331-342. doi:10.1007/s10845-007-0026-8González, M. A., Rodriguez Vela, C., Varela, R. (2013). An efficient memetic algorithm for the flexible job shop with setup times. In Twenty-Third International Conference on Automated, pp. 91-99.Hurink, J., Jurisch, B., & Thole, M. (1994). Tabu search for the job-shop scheduling problem with multi-purpose machines. OR Spektrum, 15(4), 205-215. doi:10.1007/bf01719451Imanipour, N. (2006). Modeling&Solving Flexible Job Shop Problem With Sequence Dependent Setup Times. 2006 International Conference on Service Systems and Service Management. doi:10.1109/icsssm.2006.320680KIM, S. C., & BOBROWSKI, P. M. (1994). Impact of sequence-dependent setup time on job shop scheduling performance. International Journal of Production Research, 32(7), 1503-1520. doi:10.1080/00207549408957019Moghaddas, R., Houshmand, M. (2008). Job-shop scheduling problem with sequence dependent setup times. Proceedings of the International MultiConference of Engineers and Computer Scientists,2, 978-988.Mousakhani, M. (2013). Sequence-dependent setup time flexible job shop scheduling problem to minimise total tardiness. International Journal of Production Research, 51(12), 3476-3487. doi:10.1080/00207543.2012.746480Naderi, B., Zandieh, M., & Fatemi Ghomi, S. M. T. (2008). Scheduling sequence-dependent setup time job shops with preventive maintenance. The International Journal of Advanced Manufacturing Technology, 43(1-2), 170-181. doi:10.1007/s00170-008-1693-0Najid, N. M., Dauzere-Peres, S., & Zaidat, A. (s. f.). A modified simulated annealing method for flexible job shop scheduling problem. IEEE International Conference on Systems, Man and Cybernetics. doi:10.1109/icsmc.2002.1176334Nouiri, M., Bekrar, A., Jemai, A., Niar, S., & Ammari, A. C. (2015). An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. Journal of Intelligent Manufacturing, 29(3), 603-615. doi:10.1007/s10845-015-1039-3Oddi, A., Rasconi, R., Cesta, A., & Smith, S. (2011). Applying iterative flattening search to the job shop scheduling problem with alternative resources and sequence dependent setup times. In COPLAS 2011 Proceedings of the Workshopon Constraint Satisfaction Techniques for Planning and Scheduling Problems, pp. 15-22.Pezzella, F., Morganti, G., & Ciaschetti, G. (2008). A genetic algorithm for the Flexible Job-shop Scheduling Problem. Computers & Operations Research, 35(10), 3202-3212. doi:10.1016/j.cor.2007.02.014Sadrzadeh, A. (2013). Development of Both the AIS and PSO for Solving the Flexible Job Shop Scheduling Problem. Arabian Journal for Science and Engineering, 38(12), 3593-3604. doi:10.1007/s13369-013-0625-ySaidi-Mehrabad, M., & Fattahi, P. (2006). Flexible job shop scheduling with tabu search algorithms. The International Journal of Advanced Manufacturing Technology, 32(5-6), 563-570. doi:10.1007/s00170-005-0375-4Vilcot, G., & Billaut, J.-C. (2011). A tabu search algorithm for solving a multicriteria flexible job shop scheduling problem. International Journal of Production Research, 49(23), 6963-6980. doi:10.1080/00207543.2010.526016Shi-Jin, W., Bing-Hai, Z., & Li-Feng, X. (2008). A filtered-beam-search-based heuristic algorithm for flexible job-shop scheduling problem. International Journal of Production Research, 46(11), 3027-3058. doi:10.1080/00207540600988105Wang, S., & Yu, J. (2010). An effective heuristic for flexible job-shop scheduling problem with maintenance activities. Computers & Industrial Engineering, 59(3), 436-447. doi:10.1016/j.cie.2010.05.016Zandieh, M., Yazdani, M., Gholami, M., & Mousakhani, M. (2009). A Simulated Annealing Algorithm for Flexible Job-Shop Scheduling Problem. Journal of Applied Sciences, 9(4), 662-670. doi:10.3923/jas.2009.662.670Zambrano Rey, G., Bekrar, A., Prabhu, V., & Trentesaux, D. (2014). Coupling a genetic algorithm with the distributed arrival-time control for the JIT dynamic scheduling of flexible job-shops. International Journal of Production Research, 52(12), 3688-3709. doi:10.1080/00207543.2014.881575Zhang, G., Gao, L., & Shi, Y. (2011). An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications, 38(4), 3563-3573. doi:10.1016/j.eswa.2010.08.145Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers & Industrial Engineering, 56(4), 1309-1318. doi:10.1016/j.cie.2008.07.021Zhou, Y., Li, B., & Yang, J. (2005). Study on job shop scheduling with sequence-dependent setup times using biological immune algorithm. The International Journal of Advanced Manufacturing Technology, 30(1-2), 105-111. doi:10.1007/s00170-005-0022-0Ziaee, M. (2013). A heuristic algorithm for solving flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 71(1-4), 519-528. doi:10.1007/s00170-013-5510-zZribi, N., Kacem, I., Kamel, A. E., & Borne, P. (2007). Assignment and Scheduling in Flexible Job-Shops by Hierarchical Optimization. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 37(4), 652-661. doi:10.1109/tsmcc.2007.89749

A human centred hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustment

Manufacturing activities and production control are constantly growing. Despite this, it is necessary to improve the increasing variety of scheduling and layout adjustments for dynamic and flexible responses in volatile environments with disruptions or failures. Faced with the lack of realistic and practical manufacturing scenarios, this approach allows simulating and solving the problem of job shop scheduling on a production system by taking advantage of genetic algorithm and particle swarm optimization algorithm combined with the flexibility and robustness of a multi-agent system and dynamic rescheduling alternatives. Therefore, this hybrid decision support system intends to obtain optimized solutions and enable humans to interact with the system to properly adjust priorities or refine setups or solutions, in an interactive and user-friendly way. The system allows to evaluate the optimization performance of each one of the algorithms proposed, as well as to obtain decentralization in responsiveness and dynamic decisions for rescheduling due to the occurance of unexpected events.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Learning-based scheduling of flexible manufacturing systems using ensemble methods

Dispatching rules are commonly applied to schedule jobs in Flexible Manufacturing Systems (FMSs). However, the suitability of these rules relies heavily on the state of the system; hence, there is no single rule that always outperforms the others. In this scenario, machine learning techniques, such as support vector machines (SVMs), inductive learning-based decision trees (DTs), backpropagation neural networks (BPNs), and case based-reasoning (CBR), offer a powerful approach for dynamic scheduling, as they help managers identify the most appropriate rule in each moment. Nonetheless, different machine learning algorithms may provide different recommendations. In this research, we take the analysis one step further by employing ensemble methods, which are designed to select the most reliable recommendations over time. Specifically, we compare the behaviour of the bagging, boosting, and stacking methods. Building on the aforementioned machine learning algorithms, our results reveal that ensemble methods enhance the dynamic performance of the FMS. Through a simulation study, we show that this new approach results in an improvement of key performance metrics (namely, mean tardiness and mean flow time) over existing dispatching rules and the individual use of each machine learning algorithm

A distributed approach for AGV scheduling

Se adjuntan 6 archivos de Simio como soporte que contienen 6 modelos desarrollados durante el trabajo de grado. Además, se anexa un link que redirecciona a un sitio web seguro (Microsoft Stream) dónde se encuentra un video explicativo del modelo final de Simio desarrollado para el trabajo. Adicionalmente se adjuntan 2 archivos Excel, uno que contiene los modelos estáticos desarrollados (heurística y metaheurística) para validación del modelo final y otro que contiene análisis estadísticos realizados Por último, se anexan todos los documentos solicitados por la dirección de Trabajo de Grado en formato PDF junto con 2 adicionales que corresponden a memoria de cálculos para validaciones estadísticas y resultados de modelos estáticos.The implementation of Industry 4.0, where robotics mix with information and communication technologies to increase efficiency in Flexible Manufacturing Systems (FMS), is at its peak. Automated Guided Vehicles (AGVs) have become increasingly popular because they increase transportation flexibility, reducing transportation costs and overall process times. The AGV scheduling problem has been mostly pointed towards time optimization only using centralized approaches where the scheduling of production does not change and it is considered static. FMS in real life are dynamic environments that demand flexibility, as well as reactivity, to deal with changes in production conditions, such as machine breakdowns, rush orders, layout changes, lack of raw materials, among others. Therefore, there is a need for a dynamic approach to the AGV scheduling problem that addresses real life unexpected situations more efficiently, aiming for time saving at the same time. The purpose of this project is to design and implement, in a simulation environment, a distributed approach to the AGV scheduling problem that deals better with real-life FMS changing conditions. Results show that although our approach is based on the MSM heuristic, good performance measures in real time were obtained comparing with other optimization algorithms.Ingeniero (a) IndustrialPregrad

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

Fair allocation of operations and makespan minimization for multiple robotic agents

We study the problem of allocating a set of indivisible operations to a set of agents in a fair and efficient manner while also minimizing the makespan. We first present the Operation Trading Algorithm that generates allocations satisfying the DEQx (Duplicated Equitability up to any operation) fairness criterion while also guaranteeing an upper bound of 2 on the makespan for identical agents. The algorithm also guarantees an upper bound of 1.618 for 2 uniformly related agents and (1+√(4n−3))/2 for n uniformly related agents. The pairwise approach used in this algorithm has the added advantages of being decentralizable, reactive and robust. A new protocol named as the Decentralized Random Group Formation (DRGF) Protocol is presented for implementing the Operation Trading Algorithm in a decentralized manner and for dealing with communication failures. We then define a relaxed version of the DEQ1 (Duplicated Equitability upto some operation) fairness criterion called partial-DEQ1. A market-based algorithm is presented to achieve partial-DEQ1 along with Pareto Optimality. Following this, it is shown that the algorithm also guarantees an upper bound of 1.618 on the makespan for 2 non-identical agents. Parametric pruning further improves the upper bound to 1.5, which is theoretically the best possible upper bound. To the best of our knowledge, these are the first algorithms designed to achieve the mentioned fairness criteria. The algorithms additionally guarantee upper bounds on the makespan. Finally, we show the efficacy of the algorithms in generating allocations with near optimal makespans by numerically evaluating the algorithms on randomly generated problem instances

Ferramenta de apoio ao escalonamento da produção

A imprevisibilidade e instabilidade dos mercados obrigam cada vez mais resposta rápida por parte das empresas. Todas as indústrias têm necessidade de investimento no sentido aprimorar as suas respostas, que deriva do crescimento do nível de competitividade dos mercados em que estão inseridas. A eficiência da sua produção é um dos fatores que mais condiciona a rentabilidade de uma organização. Tendo isto em conta, há cada vez maior necessidade na eficácia do planeamento e programação da produção que permita maximizar os recursos instalados. O escalonamento da produção consiste na alocação e sequenciação das tarefas nas respetivas máquinas com o desígnio de encontrar a melhor sequência para o processamento das mesmas. A presente dissertação tem por objetivo a criação de uma ferramenta de apoio à decisão, com intuito de colmatar as dificuldades no escalonamento da produção. Determinando uma afetação e sequenciação das tarefas às máquinas, minimizando uma determinada medida de desempenho. Procedeu-se à descrição do problema de escalonamento, identificando formas de resolução e otimização dos diferentes tipos de problemas. Centrou-se os esforços na resolução de todos os tipos de problema de escalonamento, incluindo os problemas em Job-Shop, que consistem num conjunto de operações que têm de ser executadas numa máquina pré-determinada, obedecendo a um determinado sequenciamento com tempos pré-definidos. São caracterizados por uma complexidade NP-hard e sendo o protótipo capaz de resolver este tipo de problema fica automaticamente habilitado a resolver qualquer tipo de problema do escalonamento da produção. A meta-heurística escolhida que permitiu a resolução dos problemas foi o Simulated Annealing, sendo escolhida por apresentar a vantagem de convergir para uma solução ótima, quando existe uma minuciosa escolha dos seus parâmetros combinado um arrefecimento muito lento. A finalidade desta meta-heurística foi construída para a minimização do makespan, ou seja, na minimização do tempo de fluxo total. O framework desenvolvido utiliza o SA, mas podem ser implementadas outras meta-heurísticas sem alterações significativas do modelo. Como forma de demonstrar as potencialidades do framework foram analisadas instâncias de Flow- -Shop e Job-Shop, quem têm por base conjuntos de problemas padronizados, previamente definidos por autores da área científica em questão. Conclui-se a viabilidade da ferramenta criada e, em articulação com os procedimentos de otimização escolhidos, constituindo assim um fator diferenciador para as organizações e permitindo uma melhoria no desempenho dos recursos disponíveis.The increasing unpredictability and instability of the markets requires a quick response from companies. All industries have a need for investment in order to improve their responses, which stems from the continuous growth in the level of competitiveness of the markets in which they operate. One of the factors that most affects the profitability of an organization is the efficiency of its production. Bearing this in mind, there is an increasing need for effective production planning and scheduling to maximize the installed resources. The scheduling of production consists of the allocation and sequencing of tasks on the respective machines, with the aim of finding the best sequence for processing them. This dissertation aims to create a decision support tool, in order to overcome the difficulties in scheduling production. Determining the assignment and sequencing of tasks to the machines, minimizing a certain measure of performance. The scheduling problem was described, identifying ways to solve and optimize the different types of problems. Efforts were focused on solving all types of scheduling problems, including job shop problems, which consist of a set of operations that have to be performed on a pre-determined machine, following a specific sequence with pre-defined times. They are characterized by an NPhard complexity and being the prototype capable of solving this type of problem, it is automatically enabled to solve any type of production scheduling problem. The chosen meta-heuristic that allowed the resolution of the problems was Simulated Annealing, being chosen because it has the advantage of converging to an optimal solution, when there is a meticulous choice of parameters combined with very slow cooling. The purpose of this metaheuristic was built to minimize the makespan, that is, to minimize the total flow time. The developed framework uses SA, but other meta-heuristics can be implemented without significant changes to the model. As a way to demonstrate the framework’s potential, instances of flow shop and job shop were analyzed, which are based on sets of standardized problems, previously defined by authors of the scientific area in question. The viability of the created tool is concluded and, in articulation with the chosen optimization procedures, constituting a differentiating factor for the organizations and allowing an improvement in the performance of the available resources

A study of the relationship between flexible working arrangements, job environment, job communication, and employee performance

Flexible working arrangements, job communication, and job environment are some of the prominent variables that impacted employee performance in the organization, especially during the COVID-19 period. However, most of the previous research did not include all three factors in the same study and did not highlight the link amid the variables and the performance of the employees. Furthermore, the majority of previous research in the Selangor area focuses on the governmental sector rather than the private sector, especially in the mobile and accessories retail chain industry. Hence, this study aims to focus on the relationship between flexible working arrangements, job environment, job communication, and employee performance in mobile and accessories retail chain company at Selangor. The study was conducted on private sector employees in mobile and accessories retail chain company at Selangor. The convenient sampling technique was selected to obtain data from 201 private sector employees of mobile and accessories retail chain company at Selangor. Referring to the results of this study, the employees’ performance of private sector employees in mobile and accessories retail chain company at Selangor is at the highest level. The study also found a positive relationship among flexible working arrangements, job communication, job environment, and employee performance. However, the study discovered that only job communication and job environment have the greatest impact on employee performance. The study's implications are examined. Finally, when the study was completed, the limitations of the study were determined. There are also recommendations for the organization, employees, and future researchers for a betterunderstanding of the future