Makespan Minimization in Job Shop Scheduling

In industries, the completion time of job problems in the manufacturing unit has risen significantly. In several types of current study, the job's completion time, or makespan, is reduced by taking straight paths, which is time-consuming. In this paper, we used an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm to solve this problem by precisely defining the fault occurrence location in order to rollback. We have used a short-term memory-based rollback recovery strategy to minimise the job's completion time by rolling back to its own short-term memory. The recent movements in Tabu quest are visited using short term memory. As compared to the ACO algorithm, our proposed ACOTS-Cmax solution is more efficient and takes less time to complete

Optimizacija problema raspoređivanja poslova u Industriji 4.0

The Industry 4.0 trend has brought significant transformation in the manufacturing process through digitalization. In Intelligent Manufacturing Systems (IMS) there is an increase in the complexity of scheduling jobs on machines. The scheduling aims to collect data through the support of novel and emerging technologies such as: inclusion of machine sensors, cloud computing, artificial intelligence, big data analytics, scheduling software, etc. In this paper, we present an example of open-source job shop scheduling software (LEKIN) to solve real-time engineering problems in a manufacturing company. With using optimization algorithms and scheduling software there is likely to be a reduction of production costs and minimization of the total order completion time (make span). A test problem is running to evaluate the difference between the implementation of Shifting Bottleneck Heuristic (SBH) and some dispatching rules, such as Earliest Due Date (EDD), First Come First Served (FCFS), and Shortest Processing Time (SPT). The evaluation criteria used were the make span and the total weighted tardiness. The results have shown that the SBH outstripped the dispatching rules.Publishe

Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective

[EN] Based on a scientific literature review in the conceptual domain defined by smart manufacturing scheduling (SMS), this article identifies the benefits and limitations of the reviewed contributions, establishes and discusses a set of criteria with which to collect and structure its main synergistic attributes, and devises a conceptual framework that models SMS around three axes: a semantic ontology context, a hierarchical agent structure, and the deep reinforcement learning (DRL) method. The main purpose of such a modelling research is to establish a conceptual and structured relationship framework to improve the efficiency of the job shop scheduling process using the approach defined by SMS. The presented model orients the job shop scheduling process towards greater flexibility, through enhanced rescheduling capability, and towards autonomous operation, mainly supported by the use of machine learning technology. To the best of our knowledge, there are no other similar conceptual models in the literature that synergistically combine the potential of the specific set of Industry 4.0 principles and technologies that model SMS. This research can provide guidance for practitioners and researchers¿ efforts to move toward the digital transformation of job shops.The research leading to these results received funding from the European Union H2020 Programme ,Belgium with grant agreements No. 825631 "Zero-Defect Manufacturing Platform (ZDMP) ", No. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q) " and 872548 "Fostering DIHs for Embedding Interoperability in Cyber-Physical Systems of European SMEs (DIH4CPS) ", from Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe" and the Regional Department of Innovation, Universities, Science and Digital Society of the Generalitat Valenciana entitled "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065).Serrano-Ruiz, JC.; Mula, J.; Poler, R. (2022). Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective. Journal of Manufacturing Systems. 63:185-202. https://doi.org/10.1016/j.jmsy.2022.03.0111852026

Smart digital twin for ZDM-based job-shop scheduling

[EN] The growing digitization of manufacturing processes is revolutionizing the production job-shop by leading it toward the Smart Manufacturing (SM) paradigm. For a process to be smart, it is necessary to combine a given blend of data technologies, information and knowledge that enable it to perceive its environment and to autonomously perform actions that maximize its success possibilities in its assigned tasks. Of all the different ways leading to this transformation, both the generation of virtual replicas of processes and applying artificial intelligence (AI) techniques provide a wide range of possibilities whose exploration is today a far from negligible sources of opportunities to increase industrial companies¿ competitiveness. As a complex manufacturing process, production order scheduling in the job-shop is a necessary scenario to act by implementing these technologies. This research work considers an initial conceptual smart digital twin (SDT) framework for scheduling job-shop orders in a zero-defect manufacturing (ZDM) environment. The SDT virtually replicates the job-shop scheduling issue to simulate it and, based on the deep reinforcement learning (DRL) methodology, trains a prescriber agent and a process monitor. This simulation and training setting will facilitate analyses, optimization, defect and failure avoidance and, in short, decision making, to improve job-shop scheduling.The research that led to these results received funding from the European Union H2020 Programme with grant agreement No. 825631 Zero-Defect Manufacturing Platform (ZDMP) and Grant agreement No. 958205 Industrial Data Services for Quality Control in Smart Manufacturing (i4Q), and from the Spanish Ministry of Science, Innovation and Universities with Grant Agreement RTI2018-101344-B-I00 "Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)"Serrano Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart digital twin for ZDM-based job-shop scheduling. IEEE. 510-515. https://doi.org/10.1109/MetroInd4.0IoT51437.2021.948847351051

Implementing Industry 4.0: Exploring the literature in a systematic way using text mining

The increasing popularity of digitisation practices and methods by scholars and practitioners alike has been paving the way for industrial transformation. Industry 4.0 has become an accepted trend across various industries, yet despite the increasing number of articles on this topic the complexities of implementation at the firm level remains largely vague and undefined. Therefore, the research presents a review of the social, operational and strategic aspects following the full-text mining of 116 selected articles. The study reveals that digital transformation requires stakeholders and investors to consider implementation through a multi-level and multidisciplinary lens. On this basis the study identifies the social, operational and strategic gaps within the literature and provides recommendations for future studies on implementation

Perancangan Ulang Tata Letak Fasilitas Lantai Produksi Batik Tulis Pewarna Alam

Batik is an indigenous Indonesian product that is recognized worldwide. One way to preserve and develop batik culture is increasing the batik business productivity effectively, economically, and safely. Increasing the productivity of the batik industry can be realized by minimizing production costs. Reduction of operational costs can be done by designing the layout of the facility. Redesigning the layout and facilities is closely related to the design of the floor production as an object. Layout planning is a way of managing existing facilities on the production floor so that production can run smoothly. This study, aims to redesign the facilities on the floor production at SME Batik Natural Jarum so that they have a more efficient material flow. Arrangements are made for all work stations: raw material storage, production areas, and finishing areas. The purpose of this facility redesign is make the movement of materials run smoothly and reduced material handling costs. The method used in this research is Systematic Layout Planning (SLP). The results of the proposed layout redesign are a reduction in material handling distances and material handling costs by 8%.Batik adalah produk hasil budaya asli Indonesia yang diakui dunia. Salah satu cara melestarikan dan mengembangkan budaya batik adalah dengan meningkatkan produktivitas usaha batik secara efektif, ekonomis, dan aman. Peningkatan produktivitas industri batik dapat direalisasikan dengan meminimasi biaya produksi. Pengurangan biaya operasional dapat dilakukan dengan melakukan perancangan tata letak fasilitas. Merancang ulang tata letak dan fasilitas berkaitan erat dengan perancangan lantai produksi sebagai sebuah objek. Layout planning adalah suatu cara dalam mengatur fasilitas yang ada pada lantai produksi sehingga produksi dapat berjalan dengan lancar. Penelitian ini bertujuan untuk merancang ulang fasilitas pada lantai produksi di UKM Batik Natural Jarum sehingga memiliki aliran material yang lebih efisien. Pengaturan dilakukan untuk semua stasiun kerja mulai dari penyimpanan bahan baku, area produksi, hingga area finishing. Tujuan dari perancangan ulang fasilitas ini adalah agar pergerakan material berjalan dengan lancar sehingga mampu mengurangi ongkos material handling. Metode yang digunakan dalam penelitian ini adalah Systematic Layout Planning (SLP). Hasil perancangan layout usulan dapat menghasilkan pengurangan jarak material handling dan ongkos material handling sebesar 8%

Scheduling in the industry 4.0: a systematic literature review

Industry 4.0 is characterised for being a new way of organising the supply chains, coordinating smart factories that should be capable of a higher adaptivity, making them more responsive to a continuously changing demand. This paper presents a Systematic Literature Review (SLR) with three main objectives. First, to identify in the literature on Industry 4.0, the need for new job scheduling methods for the factories of the digital era. Second, to identify in the literature of scheduling, which of these issues have been accomplished and what are the most critical gaps. Third, to propose a new research agenda on scheduling methodology, that fulfils the needs of scheduling in the field of Industry 4.0. The results show that literature related to the subject of study is rapidly growing and the needs of new methods for job scheduling in the digital factories concern two main ideas. First, the need to create and implement a digital architecture where data can be appropriately processed and second, the need of giving a decentralised machine scheduling solution inside such a framework. Although we can find some studies on small production lines, research with practical results remains scarce in the literature to date

A Genetic-Algorithm-Based Approach for Optimizing Tool Utilization and Makespan in FMS Scheduling

This paper proposes a genetic algorithm approach to solve the identical parallel machines problem with tooling constraints in job shop flexible manufacturing systems (JS-FMSs) with the consideration of tool wear. The approach takes into account the residual useful life of tools and allocates a set of jobs with specific processing times and tooling requirements on identical parallel machines. Two metrics are introduced to evaluate the scheduling decisions and optimize the scheduling process, with the competitive goal of maximizing tool utilization and minimizing production makespan. The proposed approach searches for a set of optimal solutions on the Pareto front that offers the best possible balance between these two objectives, achieving optimal local performance in terms of both makespan and tool utilization. The approach is implemented with a customized genetic algorithm and validated on a real case study from a company operating in the aerospace sector, which confirms its effectiveness in increasing tool utilization and reducing the makespan. The results show that the proposed approach has significant practical implications for the manufacturing industry, particularly in the production of high-value materials such as those in the aerospace sector that require costly tools. This paper contributes to the operational research community by providing advanced scheduling algorithms that can optimize both the makespan and the tool utilization concurrently, improving production efficiency and maintaining competitiveness in the manufacturing industry

Digital Twin for Automatic Transportation in Industry 4.0

[EN] Industry 4.0 is the fourth industrial revolution consisting of the digitalization of processes facilitating an incremental value chain. Smart Manufacturing (SM) is one of the branches of the Industry 4.0 regarding logistics, visual inspection of pieces, optimal organization of processes, machine sensorization, real-time data adquisition and treatment and virtualization of industrial activities. Among these tecniques, Digital Twin (DT) is attracting the research interest of the scientific community in the last few years due to the cost reduction through the simulation of the dynamic behaviour of the industrial plant predicting potential problems in the SM paradigm. In this paper, we propose a new DT design concept based on external service for the transportation of the Automatic Guided Vehicles (AGVs) which are being recently introduced for the Material Requirement Planning satisfaction in the collaborative industrial plant. We have performed real experimentation in two different scenarios through the definition of an Industrial Ethernet platform for the real validation of the DT results obtained. Results show the correlation between the virtual and real experiments carried out in the two scenarios defined in this paper with an accuracy of 97.95% and 98.82% in the total time of the missions analysed in the DT. Therefore, these results validate the model created for the AGV navigation, thus fulfilling the objectives of this paper.SIMinisterio de Ciencia, Innovación y Universidade