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

Evaluating the make-to-order performance of production control systems in a re-entrant flow shop

In the current Industry 4.0 era, simulation, along with approaches for dealing with production activity control, is considered of the utmost importance, for instance, in cyber-physical production systems, but also in any other traditional manufacturing environment. In this context, there are varying kinds of production activity control mechanisms that can be explored, and a wide range of contributions are already available. Despite a broad literature on re-entrant flow shops, it is only recently that studies have highlighted the potential impact of workload control—a preeminent approach to production control—on the performance of these shops. Nowadays, shop performance evaluation is also gaining a refreshed importance in order to further align the requisites imposed by Industry 4.0 with sustainability requirements, and thus it is important to explore not just economic but also social-based measures, and this chapter also provides a contribution in this direction by focusing on more internal performance measures of companies, alongside client-oriented ones. Moreover, until now, the application of card-based production control systems in these shops, such as the generic Kanban system, has been largely neglected. Therefore, we examine the performance of the generic Kanban system in this context and compare its performance with that of workload control. The study is carried out using discrete event simulation. Results highlight the effectiveness of the generic Kanban system in this context, despite the best performance offered by workload control.- (undefined

Decision support visualization approach in textile manufacturing a case study from operational control in textile industry

Decision support visualization tools provide insights for solving problems by displaying data in an interactive, graphical format. Such tools can be effective for supporting decision-makers in finding new opportunities and in measuring decision outcomes. In this study, was used a visualization tool capable of handling multivariate time series for studying a problem of operational control in a textile manufacturing plant; the main goal was to identify sources of inefficiency in the daily production data of three machines. A concise rule-based model of the inefficiency measures (i.e. quantitative measures were transformed into categorical variables) was developed and then performed an in-depth visual analysis using a particular technique, the categorical time series plots stacked vertically. With this approach were identified a wide array of production inefficiency patterns, which were difficult to identify using standard quantitative reporting - temporal pattern of best and worst performing machines - and critically, along with most important sources of inefficiency and some interactions between them were revealed. The case study underlying this work was further contextualized within the state of the art, and demonstrates the effectiveness of adequate visual analysis as a decision support tool for operational control in manufacturing.This study was partially conducted at the Psychology Research Centre (UID/PSI/01662/2013), University of Minho, and supported by the Portuguese Foundation for Science and Technology and the Portuguese Ministry of Science, Technology and Higher Education through national funds and co-financed by FEDER through COMPETE2020 under the PT2020 Partnership Agreement (POCI-010145-FEDER-007653). This work was also supported by the following grants: FCT project PTDC/MHC/PCN/1530; FEDER Funds through the "Programa Operacional Factores de Competitividade - COMPETE" program and by National Funds through FCT "Fundacao para a Ciencia e a Tecnologia" under the project: FCOMP-010124-FEDER-PEst-OE/EEI/UI0760/2011, PEst-OE/EEI/UI0760/2014, PEst2015-2020 and UID/CEC/00319/2019

Production scheduling using multi-objective optimization and cluster approaches

Production scheduling is a crucial task in the manufacturing process. In this way, the managers need to make decisions about the jobs production schedule. However, this task is not simple to perform, often requiring complex software tools and specialized algorithms to find the optimal solution. This work considers a multi-objective optimization algorithm to explore the production scheduling performance measure in order to help managers in decision making related to jobs attribution in a set of parallel machines. For this, five important production scheduling performance measures (makespan, tardiness and earliness time, number of tardy and early jobs) were combined into three objective functions and the Pareto front generated was analyzed by cluster techniques. The results presented different combinations to optimize the production process, providing to the manager different possibilities to prioritize the objectives considered.This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021, EXPL/EME-SIS/1224/2021

Bio-inspired multi-objective algorithms applied on production scheduling problems

Production scheduling is a crucial task in the manufacturing process. In this way, the managers must decide the job's production schedule. However, this task is not simple, often requiring complex software tools and specialized algorithms to find the optimal solution. In this work, a multi-objective optimization model was developed to explore production scheduling performance measures to help managers in decision-making related to job attribution under three simulations of parallel machine scenarios. Five important production scheduling performance measures were considered (makespan, tardiness and earliness times, number of tardy and early jobs), and combined into three objective functions. To solve the scheduling problem, three multi-objective evolutionary algorithms are considered (Multi-objective Particle Swarm Optimization, Multi objective Grey Wolf Algorithm, and Non-dominated Sorting Genetic Algorithm II), and the set of optimum solutions named Pareto Front, provided by each one is compared in terms of dominance, generating a new Pareto Front, denoted as Final Pareto Front. Furthermore, this Final Pareto Front is analyzed through an automatic bio-inspired clustering algorithm based on the Genetic Algorithm. The results demonstrated that the proposed approach efficiently solves the scheduling problem considered. In addition, the proposed methodology provided more robust solutions by combining different bio-inspired multi-objective techniques. Furthermore, the cluster analysis proved fundamental for a better understanding of the results and support for choosing the final optimum solution.- This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and EXPL/EME-SIS/1224/2021. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021)