The Three-Objective Optimization Model of Flexible Workshop Scheduling Problem for Minimizing Work Completion Time, Work Delay Time, and Energy Consumption

In recent years, the optimal design of the workshop schedule has received much attention with the increased competition in the business environment. As a strategic issue, designing a workshop schedule affects other decisions in the production chain. The purpose of this thesis is to design a three-objective mathematical model, with the objectives of minimizing work completion time, work delay time and energy consumption, considering the importance of businesses attention to reduce energy consumption in recent years. The developed model has been solved using exact solution methods of Weighted Sum (WS) and Epsilon Constraint (Ɛ) in small dimensions using GAMS software. These problems were also solved in large-scale problems with NSGA-II and SFLA meta-heuristic algorithms using MATLAB software in single-objective and multi-objective mode due to the NP-Hard nature of this group of large and real dimensional problems. The standard BRdata set of problems were used to investigate the algorithms performance in solving these problems so that it is possible to compare the algorithms performance of this research with the results of the algorithms used by other researchers. The obtained results show the relatively appropriate performance of these algorithms in solving these problems and also the much better and more optimal performance of the NSGA-II algorithm compared to the performance of the SFLA algorithm

Dynamic adjustment of dispatching rule parameters in flow shops with sequence dependent setup times

Decentralized scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence dependent setup times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs

A production scheduling simulation model for improving production efficiency

Abstract: A real manufacturing system of an electronic company was mimicked by using a simulation model. The effects of dispatching rules and resources allocations on performance measures were explored. The results indicated that the dispatching rules of shortest processing time (SPT) and earliest due date are superior to the current rule of first in first out adopted by the company. A new combined rule, the smallest quotient of dividing shortest remaining processing time (SRPT) by SPT (SRPT/SPT_Min), has been proposed and demonstrated the best performance on mean tardiness time under the current resources situation. The results also showed that using fewer resources can increase their utilization, but it increases the risk of delivery tardiness as well, which in turn will damage the organization's reputation in the long run. Some suggestions for future work were presented

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

Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

From MDPI via Jisc Publications RouterHistory: accepted 2021-07-08, pub-electronic 2021-07-15Publication status: PublishedFunder: National Natural Science Foundation of China; Grant(s): 51875420, 51875421In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness

A Knowledge Enriched Computational Model to Support Lifecycle Activities of Computational Models in Smart Manufacturing

Due to the needs in supporting lifecycle activities of computational models in Smart Manufacturing (SM), a Knowledge Enriched Computational Model (KECM) is proposed in this dissertation to capture and integrate domain knowledge with standardized computational models. The KECM captures domain knowledge into information model(s), physics-based model(s), and rationales. To support model development in a distributed environment, the KECM can be used as the medium for formal information sharing between model developers. A case study has been developed to demonstrate the utilization of the KECM in supporting the construction of a Bayesian Network model. To support the deployment of computational models in SM systems, the KECM can be used for data integration between computational models and SM systems. A case study has been developed to show the deployment of a Constraint Programming optimization model into a Business To Manufacturing Markup Language (B2MML) -based system. In another situation where multiple computational models need to be deployed, the KECM can be used to support the combination of computational models. A case study has been developed to show the combination of an Agent-based model and a Decision Tree model using the KECM. To support model retrieval, a semantics-based method is suggested in this dissertation. As an example, a dispatching rule model retrieval problem has been addressed with a semantics-based approach. The semantics-based approach has been verified and it demonstrates good capability in using the KECM to retrieve computational models

Proposta de modelo de simulação na indústria automotiva como ferramenta da indústria 4.0

Orientador: Prof. Dr. Gustavo Valentim LochDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa : Curitiba, 28/08/2020Inclui referências: p. 81-84Resumo: Desde as primeiras simulações computacionais na década de 1950, muito se aperfeiçoou tanto pelo aspecto de capacidade computacional como pelas linguagens de programação. Mais recentemente, em 2011, ocorreu a introdução do conceito de Indústria 4.0, sendo a simulação descrita como uma importante ferramenta da manufatura. Por meio dela pode-se avaliar medidas de desempenho e otimizar a produção e a qualidade do produto, sem a necessidade de testes no processo produtivo. A presente dissertação apresenta uma metodologia de criação de um simulador através da linguagem de programação c# aplicado em uma indústria automotiva. Mais especificamente, foi utilizada como base uma linha de produção do centro de usinagem configurada como Flow shop flexível. Nesta linha grande parte dos processos são automatizados e cada etapa possui inspeções de qualidade, manutenções preventivas e um operador responsável. A otimização do processo se dá por meio de propostas de melhoria de processo para minimizar o tempo de máquinas paradas. Os experimentos foram realizados de forma offline com o intuito de identificar os potenciais ganhos com a ferramenta, onde foi simulado o impacto de cada modelo produzido assim como as paradas não programadas. Ao final do trabalho é identificado os benefícios da adoção da linguagem de programação, assim como é traçado uma estratégia para a implementação online para o auxílio da tomada de decisão quanto a alocação da mão de obra. Destaca-se que foi o primeiro grande projeto de simulação desenvolvido no processo estudado e os ganhos observados podem ser utilizados como justificativa para a utilização dessa ferramenta poderosa na implantação de conceitos da indústria 4.0 tanto na fábrica do estudo realizado como em outras. Palavras-chave: Simulação de eventos discretos. Indústria 4.0. Otimização. Flow shop flexível. Gêmeo DigitalAbstract: Since the first computational simulations in the 1950s, much has been improved both in terms of computational capacity and programming languages. More recently, in 2011, the concept of Industry 4.0 was introduced, with simulation being described as an important manufacturing tool. Through it, you can evaluate performance measures and optimize production and product quality, without the need for tests in the production process. The present dissertation presents a methodology for creating a simulator using the C # programming language applied in an automotive industry. More specifically, a machining center production line configured as a flexible Flow shop. In this line most of the processes are automated and each stage has quality inspections, preventive maintenance, and a responsible operator. The optimization of the process takes place through proposals for process improvement to minimize downtime. The experiments were carried out offline in order to identify the potential gains with the simulator, where the impact of each model produced was simulated as well as the unscheduled stops. At the end of the work, the benefits of adopting the programming language are identified, as well as a strategy for online implementation is outlined to assist decision making regarding the allocation of labor. It is noteworthy that it was the first major simulation project developed in the studied process and the observed gains can be used as a justification for the use of this powerful tool in the implementation of industry 4.0 concepts, both in the study factory and in others. Keywords: Simulation. Industry 4.0. Optimization. Flexible Flow shop. Digital Twi