An optimization approach for predictive-reactive job shop scheduling of reconfigurable manufacturing systems

The manufacturing industry is now moving forward rapidly towards reconfigurability and reliability to meet the hard-topredict global business market, especially job-shop production. However, even if there is a properly planned schedule for production, and there is also a technique for scheduling in Reconfigurable Manufacturing System (RMS) but job-shop production will always come out with errors and disruption due to complex and uncertainty happening during the production process, hence fail to fulfil the due-date requirements. This study proposes a generic control strategy for piloting the implementation of a complex scheduling challenge in an RMS. This study is aimed to formulate an optimization-based algorithm with a simulation tool to reduce the throughput time of complex RMS, which can comply with complex product allocations and flexible routings of the system. The predictive-reactive strategy was investigated, in which Genetic Algorithm (GA) and dispatching rules were used for predictive scheduling and reactivity controls. The results showed that the proposed optimization-based algorithm had successfully reduced the throughput time of the system. In this case, the effectiveness and reliability of RMS are increased by combining the simulation with the optimization algorithm

A critical analysis of job shop scheduling in context of industry 4.0

Scheduling plays a pivotal role in the competitiveness of a job shop facility. The traditional job shop scheduling problem (JSSP) is centralized or semi-distributed. With the advent of Industry 4.0, there has been a paradigm shift in the manufacturing industry from traditional scheduling to smart distributed scheduling (SDS). The implementation of Industry 4.0 results in increased flexibility, high product quality, short lead times, and customized production. Smart/intelligent manufacturing is an integral part of Industry 4.0. The intelligent manufacturing approach converts renewable and nonrenewable resources into intelligent objects capable of sensing, working, and acting in a smart environment to achieve effective scheduling. This paper aims to provide a comprehensive review of centralized and decentralized/distributed JSSP techniques in the context of the Industry 4.0 environment. Firstly, centralized JSSP models and problem-solving methods along with their advantages and limitations are discussed. Secondly, an overview of associated techniques used in the Industry 4.0 environment is presented. The third phase of this paper discusses the transition from traditional job shop scheduling to decentralized JSSP with the aid of the latest research trends in this domain. Finally, this paper highlights futuristic approaches in the JSSP research and application in light of the robustness of JSSP and the current pandemic situation

Controlo da atividade de produção em ambientes dinâmicos com recurso a sistemas autónomos

Dissertação de mestrado em Engenharia de SistemasAs empresas industriais enfrentam ambientes de produção cada vez mais difíceis e complexos. Fatores externos à empresa, como variações ao nível dos produtos e das quantidades solicitadas, bem como fatores internos, decorrentes de avarias, setups e tempos de processamento variáveis, entre outros, representam grandes desafios para o Controlo da Atividade de Produção (PAC – Production Activity Control). Lidar com estes aspetos de dinâmica e complexidade ao nível do PAC é crucial para a eficiência dos sistemas de produção. No entanto, os métodos correntemente utilizados baseados em abordagens centralizadas de planeamento nem sempre são adequados para lidar com ambientes de produção complexos e dinâmicos. O controlo autónomo da produção (APC - Autonomous Production Control) representa uma alternativa que visa melhorar o desempenho dos sistemas de produção pela reação rápida e flexível às mudanças ou alterações que possam ocorrer nos sistemas de produção. Para tal o APC transfere o poder de decisão para objetos logísticos (e.g., máquinas, transportadores, trabalho, etc.) “inteligentes” e distribuídos. No âmbito deste trabalho são estudados dois métodos para o controlo autónomo da produção, QLE (Queue Lenght Estimator) e PHE (Pheromones), em diferentes ambientes produtivos. É também proposta uma nova regra de despacho, baseada numa regra existente, para a sequenciação dos trabalhos em fila de espera das máquinas. Os métodos para o controlo autónomo da produção foram testados usando a simulação discreta, com vista a melhor perceber o seu comportamento, procurando encontrar formas de melhorar o seu desempenho. Os resultados obtidos mostram um desempenho superior do método QLE, nos diferentes ambientes produtivos considerados. Os resultados mostram ainda que é possível melhorar o desempenho deste método pela inclusão de informação relativa a avarias e a tempos restantes de processamento (no momento da decisão) no processo de tomada de decisão. Os resultados obtidos mostram ainda que a regra de despacho proposta tem um desempenho superior ao das restantes, com as quais foi comparada. O estudo realizado tem importantes implicações para a prática industrial e para a investigação nesta área.Industrial companies face even more difficult and complex production environments. Factors external to the company, such as variations in the products and quantities requested, as well as internal factors, due to failures, setups and variable processing times, among others, represent major challenges for the Production Activity Control (PAC). To deal with these dynamic and complex aspects it is crucial the use of PAC to have a good efficiency in production systems. However, the methods currently used are based on centralized planning approaches and are not always adequate to deal with complex and dynamic production environments. Autonomous Production Control (APC) is an alternative that aims to improve the performance of production systems by rapid and flexible reaction to changes that may occur in production systems. To this end, the APC transfers the power of decision to logistic objects (e.g., machines, conveyors, orders, etc.) "intelligent" and distributed. In this work two methods for the independent control of production, QLE (Queue Lenght Estimator) and PHE (Pheromones), are studied in different productive environments. A new dispatch rule, based on an existing rule, is also proposed for the sequential queuing jobs of the machines. The methods for autonomous production control were tested using discrete simulation to better understand their behavior, looking for ways to improve their performance. The results show a superior performance of the QLE method, in the different productive environments considered. The results also show that it is possible to improve the performance of this method by including information regarding failures and remaining processing times (at the time of decision) in the decision-making process. The results also show that the proposed dispatch rule performs better than the others, with which it was compared. The study has important implications for industrial practice and for research in this area

Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202