Robust dynamic schedule coordination control in the supply chain

International audienceCoordination plays crucial role in supply chain management. In this paper, we extend the existing body of literature on supply chain coordination by representing a robust schedule coordination approach. A hybrid discrete/continuous flow shop supply chain with job shop processes at each supplier stage is studied. For this purpose, the developed scheduling model comprises operations control (for customer order fulfillment dynamics), channel control (production machine and transportation dynamics), resource control (material supply dynamics), and flow control (processing and shipment dynamics) with multiple objectives. Based on the scheduling model, we introduce a robust analysis of schedule coordination in the presence of disruptions in capacities and supply. The application of attainable sets opens a possibility to analyse schedule coordination dynamics under disruptions. The results provide insights of how to integrate the coordination issues into schedule robustness analysis. We exemplify the developed approach for the case of two-stage supply chain coordination, and derive managerial insights for both considered scheduling problem and application of dynamic control methods to supply chain coordination in general

Étude de la flexibilité opérationnelle d’un flow shop par des algorithmes génétiques

Dans un récent article nous avons resolu le problème de la flexibilité opérationnelle d’un « flow shop » en prenant en compte la flexibilité à l’aide d’une méthode exacte. Ce qui a permis d’obtenir une solution optimale, mais au prix d’un temps de résolution relativement grand, trop important pour une gestion d’atelier en temps réel. Le problème à résoudre est de caractériser les leviers de flexibilité liés à la variation des durées opératoires et des dates de livraison des articles d’un atelier de production à cheminement unique afin d’assurer la flexibilité opérationnelle du système. Dans le présent travail, nous utilisons cette fois les algorithmes génétiques pour approcher la solution optimale en recherchant un temps de résolution plus court. Il s’agit dans cet article d’exposer cette résolution et de présenter une comparaison avec celle de la méthode exacte précédemment utilisé

A Pareto-Based Adaptive Variable Neighborhood Search for Biobjective Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Different from most researches focused on the single objective hybrid flowshop scheduling (HFS) problem, this paper investigates a biobjective HFS problem with sequence dependent setup time. The two objectives are the minimization of total weighted tardiness and the total setup time. To efficiently solve this problem, a Pareto-based adaptive biobjective variable neighborhood search (PABOVNS) is developed. In the proposed PABOVNS, a solution is denoted as a sequence of all jobs and a decoding procedure is presented to obtain the corresponding complete schedule. In addition, the proposed PABOVNS has three major features that can guarantee a good balance of exploration and exploitation. First, an adaptive selection strategy of neighborhoods is proposed to automatically select the most promising neighborhood instead of the sequential selection strategy of canonical VNS. Second, a two phase multiobjective local search based on neighborhood search and path relinking is designed for each selected neighborhood. Third, an external archive with diversity maintenance is adopted to store the nondominated solutions and at the same time provide initial solutions for the local search. Computational results based on randomly generated instances show that the PABOVNS is efficient and even superior to some other powerful multiobjective algorithms in the literature

Schedulazione di una linea flessibile di tipo Open Job-Shop : il caso Fendi

L’argomento di questa tesi è l’ideazione e lo studio di un opportuno modello di scheduling in grado di rappresentare il funzionamento della catena di pianificazione produttiva per la sede fiorentina dell’azienda di alta moda Fendi. Una volta scelto tale modello, abbiamo implementato un opportuno modulo software che realizzi la schedulazione in maniera automatica sfruttando le condizioni iniziali imposte. Il modulo software è stato realizzato in collaborazione con la software house EasySystem, nell’ambito di un tirocinio aziendale promosso da Fendi. EasySystem lavora da molti anni nel settore delle aziende pellettiere ed ha maturato una grande esperienza nella realizzazione di pacchetti software ad hoc per questa specifica tipologia di industrie. Saranno infatti discusse nel dettaglio tutta una serie di peculiarità che rendono tali problemi difficilmente gestibili tramite i software gestionali commerciali più diffusi come Sap o Oracle Dynamics. Una volta realizzato, il software per l’analisi dello scheduling sarà integrato all’interno di un tool gestionale più ampio, in uso nella sede fiorentina di Fendi. Tale tool, di nome EasyGest, realizzato proprio da EasySystem, rappresenta ad oggi uno standard delle piccole e medie aziende pellettiere operanti in Toscana nelle province di Firenze, Arezzo e Siena. Alla conclusione della fase di sviluppo è seguito un periodo di testing effettuato all’interno del reparto produttivo di Fendi e di quelli di due dei suoi principali fornitori: Borgo s.r.l. e CM Monini. The topic of this thesis is the design and study of an appropriate scheduling model able to represent the functioning of the chain of production planning for the Florentine headquarters company of high fashion Fendi. Once this model was chosen, we have realised a software module that implements the scheduling automatically using the initial conditions imposed. The software module was developed in collaboration with the software house EasySystem under an internship sponsored by Fendi. EasySystem worked for many years in the field of leather goods companies and has a wealth of experience in the development of software packages tailored to this specific type of industries. It will be discussed in detail a number of features that make it difficult to manage these problems through business management software popular as SAP or Oracle Dynamics. Once it has completed, the software for the analysis of scheduling will be integrated into a management tool in wider use in the Florentine site of Fendi. This tool, named Easygest, made just by EasySystem, is now a standard for small and medium-sized businesses operating in Tuscany leather goods in the provinces of Florence, Arezzo and Siena. At the end of the development phase, it is followed a period of testing carried out within the production department of Fendi and those of two of its main suppliers: Borgo Ltd. and CM Monini

Design of a solution technique based on an integral approach for the Flexible Open-Flow Shop scheduling problem

In manufacturing industries, scheduling is a form of decision-making that plays a crucial role. The determination of the methods by which a set of jobs must be manufactured in order to seek specific goals leads to the development of different schedule techniques. However, scheduling depends on the type of workshop or manufacturing environment such as open shop, job shop and flow shop. There are cases that more than one environment for the same manufacturing process could coexist. This project deals with a specific scheduling problem in which each job is processed under the combination of two shop environments; the first one is related to an open shop while the second one corresponds to a flow shop; this problem is called the Flexible open-flow shop (FOFS). These types of scheduling problems present NP-hardness, meaning the neediness of sophisticated algorithms to find solutions in reasonable computational times. Additionally, are commonly solved separately or by approximating into another workshop, leaving the interaction of both environments irrelevant. Thus, the main objective of this project is to design solution techniques based on an integral approach to minimize the maximum completion time also known as makespan.Ingeniero (a) IndustrialPregrad

Aplicación de la meta-heurística colonia de hormigas para la resolución de problemas multi-objetivo de programación de la producción en Flowshops híbridos (flexibles)

150 páginasCon el fin de mejorar los niveles de competitividad, las empresas de manufactura y de servicio están obligadas a la implementación constante de procedimientos formales que les permitan optimizar sus procesos. En ese sentido, en lo referente a las operaciones de manufactura, la logística de producción, y más específicamente la programación de operaciones, juega un papel importante en cuanto al uso eficiente de los recursos. La programación de operaciones (scheduling, en inglés) es una rama de la optimización combinatoria que consiste en la asignación de recursos para la realización de un conjunto de actividades con el fin de optimizar uno o varios objetivos. Debido a la complejidad intrínseca en la mayoría de los problemas de programación de la producción, los cuales son del tipo NP-duro (esto es, el tiempo que requieren para resolver un caso particular de un problema crece en el peor de los casos de manera exponencial con respecto al tamaño del problema), los métodos exactos convencionales de resolución tales como: programación lineal, entera y mixta, entre otros, no son eficientes en términos del tiempo de cálculo para llegar a la solución óptima. Por lo tanto, se hace necesario el uso de enfoques alternativos para resolver este tipo de problemas en un tiempo razonablemente corto para el tomador de decisiones, sobre todo aquellas que se toman diariamente. Dentro de estos enfoques se encuentran las metaheurísticas, que consisten en procedimientos formales desarrollados con el fin de superar esta dificultad que se presenta con los métodos tradicionales. Los procedimientos meta-heurísticos más comunes para la resolución de problemas combinatorios son: los algoritmos genéticos, la búsqueda tabú, la colonia de hormigas y el recocido simulado entre otros

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Design and analysis of algorithms for solving a class of routing shop scheduling problems

Ph.DDOCTOR OF PHILOSOPH