A hormone regulation based approach for distributed and on-line scheduling of machines and automated guided vehicles

[EN] With the continuous innovation of technology, automated guided vehicles are playing an increasingly important role on manufacturing systems. Both the scheduling of operations on machines as well as the scheduling of automated guided vehicles are essential factors contributing to the efficiency of the overall manufacturing systems. In this article, a hormone regulation¿based approach for on-line scheduling of machines and automated guided vehicles within a distributed system is proposed. In a real-time environment, the proposed approach assigns emergent tasks and generates feasible schedules implementing a task allocation approach based on hormonal regulation mechanism. This approach is tested on two scheduling problems in literatures. The results from the evaluation show that the proposed approach improves the scheduling quality compared with state-of-the-art on-line and off-line approaches.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was sponsored by the National Natural Science Foundation of China (NSFC) under grant nos 51175262 and 51575264 and the Jiangsu Province Science Foundation for Excellent Youths under grant no. BK2012032. This research was also sponsored by the CASES project which was supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under grant agreement no. 294931.Zheng, K.; Tang, D.; Giret Boggino, AS.; Salido, MA.; Sang, Z. (2016). A hormone regulation based approach for distributed and on-line scheduling of machines and automated guided vehicles. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 232(1):99-113. https://doi.org/10.1177/0954405416662078S99113232

A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems

Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.The project is funded by Department of Science and Technology, Science and Engineering Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCT–Portuguese Foundation for Science and Technology within the R&D Units Projects Scopes: UIDB/00319/2020, UIDP/04077/2020, and UIDB/04077/2020

Lot Streaming in Different Types of Production Processes: A PRISMA Systematic Review

At present, any industry that wanted to be considered a vanguard must be willing to improve itself, developing innovative techniques to generate a competitive advantage against its direct competitors. Hence, many methods are employed to optimize production processes, such as Lot Streaming, which consists of partitioning the productive lots into overlapping small batches to reduce the overall operating times known as Makespan, reducing the delivery time to the final customer. This work proposes carrying out a systematic review following the PRISMA methodology to the existing literature in indexed databases that demonstrates the application of Lot Streaming in the different production systems, giving the scientific community a strong consultation tool, useful to validate the different important elements in the definition of the Makespan reduction objectives and their applicability in the industry. Two hundred papers were identified on the subject of this study. After applying a group of eligibility criteria, 63 articles were analyzed, concluding that Lot Streaming can be applied in different types of industrial processes, always keeping the main objective of reducing Makespan, becoming an excellent improvement tool, thanks to the use of different optimization algorithms, attached to the reality of each industry.This work was supported by the Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project CONIN-P-256-2019, and SENESCYT by grants “Convocatoria Abierta 2011” and “Convocatoria Abierta 2013”

Problem specific heuristics for group scheduling problems in cellular manufacturing

The group scheduling problem commonly arises in cellular manufacturing systems, where parts are grouped into part families. It is characterized by a sequencing task on two levels: on the one hand, a sequence of jobs within each part family has to be identified while, on the other hand, a family sequence has to be determined. In order to solve this NP-hard problem usually heuristic solution approaches are used. In this thesis different aspects of group scheduling are discussed and problem specific heuristics are developed to solve group scheduling problems efficiently. Thereby, particularly characteristic properties of flowshop group scheduling problems, such as the structure of a group schedule or missing operations, are identified and exploited. In a simulation study for job shop manufacturing cells several novel dispatching rules are analyzed. Furthermore, a comprehensive review of the existing group scheduling literature is presented, identifying fruitful directions for future research

Esnek atölye tipi hücre çizelgeleme problemleri için çok amaçlı matematiksel model ve genetik algoritma ile çözüm önerisi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Günümüz rekabetçi iş ortamında, müşteriler daha düşük maliyetle daha yüksek kalitede çeşitli ürünleri satın almak istemektedir. İmalat firmaları, talep çeşitliliğini karşılamak için yüksek derecede ürün çeşitliliğine ve küçük imalat parti büyüklüğüne ihtiyaç duymaktadır. Üretimdeki ürün çeşitlilikleri uzun hazırlık ve taşıma süreleri, karmaşık çizelgeleme problemleri gibi birçok probleme neden olmaktadır. Geleneksel imalat sistemleri, bu tip değişikliklere cevap vermede yeterince esnek değilken Hücresel Üretim Sistemleri üreticilerin bu ihtiyaçlarına cevap verebilecek özelliklere sahiptir. Ayrıca gerçek hayat problemlerinin çoğunda, bir parçanın bazı ya da bütün operasyonları birden fazla makinede işlem görebilmekte ve bazen de bu operasyonlar bir makineyi ya da iş merkezini birden fazla kez ziyaret etmektedir. Bu seçenek sisteme esneklik kazandırırken bu kadar karmaşık bir üretim sisteminin başarılı ve doğru bir şekilde işletilebilmesi kaynakların etkin kullanılmasını da gerektirmektedir. Bu çalışma, istisnai parçaları, hücrelerarası hareketleri, hücrelerarası taşıma sürelerini, sıra bağımlı parça ailesi hazırlık sürelerini ve yeniden işlem gören parçaları dikkate alarak hücresel imalat ortamında esnek atölye tipi çizelgeleme probleminin çözümüne dair bir matematiksel model ve çözüm yöntemi sunmaktadır. Mevcut bilgilerimiz ışığında yapılan bu çalışma Esnek Atölye Tipi Hücre Çizelgeleme Probleminde (EATHÇP) çok amaçlı matematiksel model ve meta-sezgiselinin kullanımı için ilk girişimdir. Bununla birlikte gerçek hayat uygulamaları için EATHÇP süreci, birçok çelişen amacı dikkate almayı gerektirdiği için ele alınan skalerleştirme metodu pratik uygulama ve teorik araştırma açısından oldukça önemlidir. Önerilen karma tamsayılı doğrusal olmayan matematiksel modelle küçük ve orta boyutlu problemler çözülebilmektedir. Büyük boyutlu problemlerin çözümü, doğrusal olmayan modellerle makul zamanlarda olamayacağı ya da çok uzun süreceği için konik skalerleştirmeli çok amaçlı matematiksel modeli kullanan bir Genetik Algoritma (GA) meta-sezgisel çözüm yöntemi önerilmiştir. GA yaklaşımının en iyi veya en iyiye yakın çözüme ulaşmasına etki eden parametrelerin en iyi kombinasyonu belirlemek amacı ile bir deney tasarımı gerçekleştirilmiştir. Bu tez çalışması için Eskişehir Tülomsaş Motor Fabrikası'nda bir uygulama çalışması yürütülmüştür. Yürütülen bu çalışma, altı farklı amaç ağırlık değerleri kullanılarak hem konik skalerleştirmeli GA yaklaşımı ile hem de ağırlıklı toplam skalerleştirmeli GA yaklaşımı ile çözülmüştür. Amaç ağırlıklarının beşinde çok amaçlı konik skalerleştirme GA yaklaşımının daha baskın sonuçlara ulaşabildiği vurgulanmıştır. Ayrıca, önerilen çok amaçlı modelin gerçek hayat problemleri için de makul zamanda uygun çözümler üretebildiği gösterilmiştir.In today's highly competitive business environment, customers desire to buy various products with higher quality at lower costs. Manufacturing firms require a high degree of product variety and small manufacturing lot sizes to meet the demand variability. The product variations in manufacturing cause many problems such as lengthy setup and transportation times, complex scheduling. Cellular Manufacturing Systems contain the characteristics, which will respond to the needs of manufacturers, even though Conventional Manufacturing Systems are not flexible enough to respond to changes. In addition, in most real life manufacturing problems, some or all operations of a part can be processed on more than one machine, and sometimes operations may visit a machine or work center more than once. It is necessary to use resources effectively in order to run such a complex production system successfully. In this study, a mathematical model and a solution approach that deals with a flexible job shop scheduling problem in cellular manufacturing environment is proposed by taking into consideration exceptional parts, intercellular moves, intercellular transportation times, sequence-dependent family setup times, and recirculation. To the best of our knowledge, this is the first attempt to use multi-objective mathematical model and meta-heuristic approach for a Flexible Job Shop Cell Scheduling Problem (FJCSP). However, in the real-life applications, the scalarization method considered is highly important in terms of theoretical research and practical application because the FJCSP process is not easy because of many conflicting objectives. The proposed mixed integer non-linear model can be used for solving small and middle scaled problems. Solution of large scaled problems is not possible in reasonable time or takes too long time, so a Genetic Algorithm (GA) meta-heuristic approach that uses a multi-objective mathematical model with conic scalarization has been presented. An experimental design was used to determine the best combination of parameters which are affected performance of genetic algorithm to achieve optimum or sub-optimum solution. In this thesis study, a case study was conducted in Tülomsaş Locomotive and Engine Factory in Eskişehir. This study was solved by using both conic scalarization GA approach and weighted sum scalarization GA approach with six different weights of objective. It is emphasized that the multi-objective conic scalarization GA approach has better quality than other approach for five different weights of objective. In addition, it has been shown that the multi-objective model could also obtain optimum results in reasonable time for the real-world problems

Planeamento de sistemas de produção celulares

Doutoramento em Gestão IndustrialUm sistema de produção celular (SPC) consiste numa forma de organização dos recursos produtivos sendo estes agrupados em células independentes, dedicadas ao processamento de famílias de componentes com necessidades de processamento similares. Estes sistemas podem ser implementados de duas formas: fisicamente e logicamente. No primeiro caso as máquinas são dispostas em células de fabrico de maneira a melhorar a eficiência da implantação em termos de fluxo, no segundo caso as máquinas são afetadas a famílias de componentes, formando células virtuais não sendo, contudo, a sua localização física alterada. Neste trabalho são apresentados os problemas associados à definição de um sistema de produção celular, nomeadamente nas fases de planeamento, controlo e avaliação. Devido à complexidade e ao impacto no desempenho da organização das decisões tomadas na fase de planeamento do sistema produtivo, os problemas inerentes a um projeto desta natureza são aprofundados, sendo apresentados algoritmos para a resolução dos mesmos. Os procedimentos desenvolvidos tratam os seguintes problemas: (i) formação de células de fabrico, (ii) balanceamento de células de fabrico, (iii) formação de células virtuais numa implantação funcional, (iv) formação de células virtuais numa implantação distribuída, e (v) projeto da implantação fabril considerando secções com configuração geométrica fixa. Dada a natureza combinatória dos problemas considerados, foram utilizadas metaheurísticas na sua resolução. A principal contribuição deste trabalho para a investigação no planeamento de sistemas de produção celulares resulta do facto de os modelos e heurísticas desenvolvidos incluírem um conjunto de aspetos de grande relevância para a sua aplicação a ambientes industriais, ultrapassando algumas das limitações práticas inerentes a outros procedimentos reportados na literatura relevante.A cellular manufacturing system (CMS) is a type of production system that organizes the production resources into independent cells taking into account the similarities in the manufacturing sequences of the different parts assigned to those cells. This type of production system can be implemented in two ways: physically or logically. In the first situation, machines are grouped into cells with the purpose of improving the layout efficiency in terms of flow. In the second option, machines are allocated to part families, originating virtual cells, but their physical location is not altered. The first part of this work consists in a revision of the problems associated to a cellular manufacturing system, namely regarding its design, control and evaluation. The problems inherent to the design phase are analyzed with greater detail due to their importance and impact on the system’s performance. Due to the complexity and significance of those problems, they were further analysed and algorithms were developed in order to contribute to their solution. The models address the following problems: (i) cell formation, (ii) production cell balancing, (iii) virtual cell formation in a functional layout, (iii) virtual cell formation in a distributed layout, and (v) facility layout considering objects with a fixed geometric shape. Due to the combinatorial nature of the problems, metaheuristics were used in order to solve them. The main contribution of this work, towards the furthering of investigation in cellular manufacturing systems design, derives from the development of models and heuristics that include a set of features that are highly relevant for its implementation in a real world industrial setting, overcoming, in this way, some of the practical limitations inherent to other known procedures

Fractal architecture for 'leagile' networked enterprises.

The manufacturing environment and markets in recent times are becoming increasingly dynamic, diverse and unpredictable, due mainly to fast evolution of products and technology, erratic customer behaviour and high consumerism and an increasingly shorter lead-time. The burden of the impact falls on organisational structures built on centralized, rigid manufacturing architecture, because they cannot cope or adapt to the highly uncertain or unpredictable nature of the market. Enterprises who wish to survive these challenges need to rethink their business and manufacturing models, and most importantly reinvent their tactical, operational and organizational formulas to leverage their strategic long term visions.Newer manufacturing systems to curb the effects of this upheaval have to promote an entirely decentralised, flexible, distributed, configurable and adaptable architecture to ameliorate this condition. Many philosophies are proposed and studied towards planning, monitoring, and controlling the 21st century manufacturing system. These include - Bionic manufacturing system (BMS), Holonic manufacturing system (HMS), Fractal manufacturing system (FrMS), Responsive manufacturing etc.This research program focuses on the FrMS, which has vast conceptual advantageous features among these new philosophies, but its implementation has proved very difficult. FrMS is based on autonomous, cooperating, self-similar agent called fractal that has the capability of perceiving, adapting and evolving with respect to its partners and environment. The fractal manufacturing configuration uses self regulating, organisational work groups, each with identical goals and within its own area of competence to build up an integrated, holistic network system of companies. This network yields constant improvement as well as continuous checks and balances through self-organising control loops. The study investigates and identifies the nature, characteristic features and feasibility of this system in comparison to traditional approaches with a detailed view to maximising the logistical attribute of lean manufacturing system and building a framework for 'leagile' (an integration of lean and agile solutions) networked capabilities. It explores and establishes the structural characteristic potentials of Fractal Manufacturing Partnership (FMP), a hands-on collaboration between enterprises and their key suppliers, where the latter become assemblers of their components while co-owning the enterprise's facility, to create and achieve high level of responsiveness. It is hoped that this architecture will drive and harness the evolution from a vertically integrated company, to a network of integrated, leaner core competencies needed to tackle and weather the storm of the 21st century manufacturing system

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling

Development of production planning and control models for virtualmanufacturing cells

U doktorskoj disertaciji su predstavljeni modeli za planiranje i upravljanje proizvodnim sistemom organizovanim putem virtuelnih proizvodnih ćelija. U modelima su razvijeni postupci određivanja dužine planskog perioda i broja faza obrade ili montaže. U modelima je razvijen postupak kreiranja virtuelnih proizvodnih ćelija imajući u vidu zahteve planskog i upravljačkog sistema. Modeli omogućuju rekonfiguraciju ćelija usled analize opterećenja tehnoloških sistema i mogućnosti realizacije terminskih planova proizvodnje. Modeli su ispitani na primerima dva proizvodna preduzeća sa teritorije Republike Srbije.Doctoral dissertation presents models for production planning and control of virtual manufacturing cells. Models present developed procedure for the determination of the production planning period and the number of processing or assembly stages. Models also present the process of creating virtual manufacturing cells, bearing the requirements of the production planning and control system.The models enable the reconfiguration of cells due to the analysis of machines workload and due to the analysis of the production schedule. The models were tested on the examples of two production companies from the Republic of Serbia

Proposition d'une méthodologie multicritère pour la résolution du problème d'ordonnancement d'un projet avec prise en compte des compétences et des ressources

RÉSUMÉ: Cette recherche porte sur le problème d'ordonnancement d'un projet avec contraintes de ressources (RCPSP). Son objectif consiste à étudier deux extensions du problème de base en développant une méthode de résolution du RCPSP à critères multiples qui prend en compte les compétences maîtrisées par les ressources et les compétences requises par chaque activité du projet. Le projet étudié se base sur celui proposé par Montoya (2012), il se compose de quatre activités, de quatre ressources et de trois compétences. La résolution du problème se présente en deux étapes. D'abord, le logiciel d'optimisation à critères multiples Midaco est utilisé afin d'obtenir des solutions de Pareto optimisant la durée ainsi que le coût du projet. Pour cette recherche, le logiciel Midaco est utilisé avec le logiciel de calcul Matlab, dans lequel le code représentant le problème est créé. À la fin de cette première étape, les meilleurs ordonnancements des activités selon les ressources et les compétences disponibles sont retenus afin de passer à l'étape suivante. Parmi tous les essais réalisés, sept d'entre eux sont retenus, proposant treize solutions optimales. Ces points de Pareto retenus passent à l'étape suivante, la sélection de la solution de meilleur compromis à l'aide du logiciel d'aide à la décision Prométhée. Cet outil permet de sélectionner la meilleure solution de compromis selon les critères définis par l'utilisateur. Mis à part la durée et le coût du projet, le temps perdu est le troisième critère étudié, il s'agit du temps d'inactivité d'une ressource entre deux activités. Pour effectuer le choix de la solution finale, les trois critères sont pris en considération à poids égaux. D'autres simulations sont effectuées pour des poids différents afin d'observer l'évolution du rangement. Cette étude contribue à la recherche en proposant une méthode de résolution pour deux extensions du problème d'ordonnancement d'un projet avec contraintes de ressources, les objectifs multiples et les compétences multiples. -- Mot(s) clé(s) en français : RCPSP, objectifs multiples, compétences multiples, gestion de projet, métaheuristique, Midaco, Prométhée, optimisation, points de Pareto. -- ABSTRACT: This research is about resource-constrained project scheduling problem (RCPSP). The objective is to study two extensions of the basic problem by developing a method to solve the multi-objective RCPSP while considering the skills mastered by the resources and the skills required by each project activity. The studied project is based on the one proposed by Montoya (2012). It consists of four activities, four resources and three skills. The problem is solved in two steps. First, the multi-criteria optimization software Midaco is used to obtain Pareto points, used to optimize the duration and the cost of the project. For this search, the Midaco software is used with Matlab, in which the code representing the problem is created. At the end of this first step, the best schedules according to available resources and skills are kept in order to take the next step. Among all the tests carried out, seven of them are selected, proposing thirteen optimal solutions. The second step is to select the best compromise solution by using the Promethee decision support software. This tool allows you to select the best compromise solution according to the criteria defined by the user. Cost, duration and lost time are the three criteria studied. The last one is about the inactivity time of a resource between two activities. To choose the best solution, these three criteria are taken into account with equal weights. This study contributes to the research by proposing a resolution method for two extensions of the RCPSP, the mutli-objective and the multi-skill optimizations. -- Mot(s) clé(s) en anglais : RCPSP, multi-objective, multi-skill, project management, meta-heuristic, Midaco, Promethee, optimization, Pareto points