Design of an intelligent support system for fabric quality inspection

Efficient quality management in production process is the key factor for firm’s permanence and prosperity. The rapped globalization development and shortage of resources leading to enhance the efforts toward good raw material exploitation. The present work aims to develop a Decision Support System (DSS) that may provide and facilitate one of the most difficult multi-decision problems that quality managers, in textile manufacturing firm face. In addition, the DSS is developed for a textile manufacturer and it will automate a variety of tasks to improve rolls quality; control the defect distribution on rolls by inventing anew cutting scenarios with respect to inspection results, rolls length, and number of assemblies. The DSS tool, applied for more than 100 fabric lots, will be demonstrated through a short selection of practical case studies

Multi-agent System Models for Distributed Services Scheduling

This thesis investigates the computational and modeling issues involved with developing solutions for distributed service scheduling problems. Compared with traditional manufacturing scheduling, service scheduling poses additional challenges due to the significant customer involvement in service processes. The first challenge is that the service scheduling environment is a distributed environment in which scheduling-related information is scattered among individual identities, such as service providers and customers. The second challenge is that the service scheduling environment is a dynamic environment. Uncertainty in customer demand, customer cancellations and no-shows make the scheduling of services a complex dynamic process. Service scheduling has to be robust and prepared to accommodate any contingencies caused by customer involvement in service production. The third challenge concerns customers’ private information. To compute optimal schedules, ideally, the scheduler should know the complete customer availability and preference information within the scheduling horizon. However, customers may act strategically to protect their private information. Therefore, service scheduling systems should be designed so that they are able to elicit enough of a customer’s private information that will make it possible to compute high quality schedules. The fourth challenge is that in a service scheduling environment, the objectives are complicated and they may even be in opposition. The distributed service scheduling environment enables each agent to have their own scheduling objectives. The objectives of these agents can vary from one to another. In addition to multiple objectives, since agents are self-interested, they are likely to behave strategically to achieve their own objectives without considering the global objectives of the system. Existing approaches usually deal with only a part of the challenges in a specific service domain. There is a need for general problem formulations and solutions that address service scheduling challenges in a comprehensive framework. In this thesis, I propose an integrated service scheduling framework for the general service scheduling problem. The proposed framework uses iterative auction as the base mechanism to tackle service scheduling challenges in distributed and dynamic environments. It accommodates customer’s private information by providing appropriate incentives to customers and it has the potential to accommodate dynamic events. This framework integrates customers’ preferences with the allocation of a provider’s capacity through multilateral negotiation between the provider and its customers. The framework can accommodate both price-based commercial settings and non-commercial service settings. Theoretical and experimental results are developed to verify the effectiveness of the proposed framework. The application of the framework to the mass customization of services and to appointment scheduling are developed to demonstrate the applicability of the general framework to specific service domains. A web-based prototype is designed and implemented to evaluate the scalability of the approach in a distributed environment

A Literature Review on The Design of Intelligent Supply Chain for Natural Fibre Agroindustry

Natural fibre is an environmentally friendly raw material that has a great potential to develop, and is abundantly available in nature [1]. Currently, the growth of natural fibre processing industries in the world has been increasingly important [2]. Processing of abundant natural fibre in both upstream and downstream productions requires effective and collaborative supply chain management in terms of information sharing. Thus, an intelligent system would be implemented in supply chain management from upstream to downstream. Based on review of 46 scientific papers discussing on types of natural fibre, process, technology, and methods, as well as application areas of natural fibre in downstream industries. According to review on different aspects in 55 scientific papers, there were 5 aspects mapped, i.e. supply chain analytic, value chain, performance, collaboration, big data, and decision support system. A concept of 4.0 industry underlies utilization of opportunities for application of supply chain analytic [3]. Upcoming research opportunities include mediating relationship in supply chain network by utilizing Internet of things (IoT) and Big data (BD), in a collaborative relationship to use information sharing. The most possibly contributing research is the development of collaboration between supply chain and genetic algorithm [4]. Integration between production and inventory planning becomes an approach that utilizes Particle swarm optimization (PSO) by developing production planning [5], and production and inventory planning [6]. There is a research opportunity in the design of intelligent supply chain for natural fibre agroindustry by implementing IoT and BD as a tool in supply chain analytic, collaboration through Collaboration prediction forecasting and replenishment (CPFR) that occurs between stakeholders with the aim of improving agroindustry supply chain performance in production integration material and inventory, and performance measurement by integrating the Value chain operation reference (VCOR) model developed in supply chain analytic

Iterative restricted space search : a solving approach based on hybridization

Face à la complexité qui caractérise les problèmes d'optimisation de grande taille l'exploration complète de l'espace des solutions devient rapidement un objectif inaccessible. En effet, à mesure que la taille des problèmes augmente, des méthodes de solution de plus en plus sophistiquées sont exigées afin d'assurer un certain niveau d 'efficacité. Ceci a amené une grande partie de la communauté scientifique vers le développement d'outils spécifiques pour la résolution de problèmes de grande taille tels que les méthodes hybrides. Cependant, malgré les efforts consentis dans le développement d'approches hybrides, la majorité des travaux se sont concentrés sur l'adaptation de deux ou plusieurs méthodes spécifiques, en compensant les points faibles des unes par les points forts des autres ou bien en les adaptant afin de collaborer ensemble. Au meilleur de notre connaissance, aucun travail à date n'à été effectué pour développer un cadre conceptuel pour la résolution efficace de problèmes d'optimisation de grande taille, qui soit à la fois flexible, basé sur l'échange d'information et indépendant des méthodes qui le composent. L'objectif de cette thèse est d'explorer cette avenue de recherche en proposant un cadre conceptuel pour les méthodes hybrides, intitulé la recherche itérative de l'espace restreint, ±Iterative Restricted Space Search (IRSS)>>, dont, la principale idée est la définition et l'exploration successives de régions restreintes de l'espace de solutions. Ces régions, qui contiennent de bonnes solutions et qui sont assez petites pour être complètement explorées, sont appelées espaces restreints "Restricted Spaces (RS)". Ainsi, l'IRSS est une approche de solution générique, basée sur l'interaction de deux phases algorithmiques ayant des objectifs complémentaires. La première phase consiste à identifier une région restreinte intéressante et la deuxième phase consiste à l'explorer. Le schéma hybride de l'approche de solution permet d'alterner entre les deux phases pour un nombre fixe d'itérations ou jusqu'à l'atteinte d'une certaine limite de temps. Les concepts clés associées au développement de ce cadre conceptuel et leur validation seront introduits et validés graduellement dans cette thèse. Ils sont présentés de manière à permettre au lecteur de comprendre les problèmes que nous avons rencontrés en cours de développement et comment les solutions ont été conçues et implémentées. À cette fin, la thèse a été divisée en quatre parties. La première est consacrée à la synthèse de l'état de l'art dans le domaine de recherche sur les méthodes hybrides. Elle présente les principales approches hybrides développées et leurs applications. Une brève description des approches utilisant le concept de restriction d'espace est aussi présentée dans cette partie. La deuxième partie présente les concepts clés de ce cadre conceptuel. Il s'agit du processus d'identification des régions restreintes et des deux phases de recherche. Ces concepts sont mis en oeuvre dans un schéma hybride heuristique et méthode exacte. L'approche a été appliquée à un problème d'ordonnancement avec deux niveaux de décision, relié au contexte des pâtes et papier: "Pulp Production Scheduling Problem". La troisième partie a permit d'approfondir les concepts développés et ajuster les limitations identifiées dans la deuxième partie, en proposant une recherche itérative appliquée pour l'exploration de RS de grande taille et une structure en arbre binaire pour l'exploration de plusieurs RS. Cette structure a l'avantage d'éviter l'exploration d 'un espace déjà exploré précédemment tout en assurant une diversification naturelle à la méthode. Cette extension de la méthode a été testée sur un problème de localisation et d'allocation en utilisant un schéma d'hybridation heuristique-exact de manière itérative. La quatrième partie généralise les concepts préalablement développés et conçoit un cadre général qui est flexible, indépendant des méthodes utilisées et basé sur un échange d'informations entre les phases. Ce cadre a l'avantage d'être général et pourrait être appliqué à une large gamme de problèmes

Methods and Formal Models for Healthcare Systems Management

A healthcare system is an organization of people, institutions, and resources that deliver healthcare services to meet the health needs of target populations. The size of the systems, the huge number of agents involved and their different expectations make the management of healthcare systems a tough task which could be alleviated through the use of technology. In this thesis, new methods and formal models for healthcare system management are presented. Particularly, the thesis is divided in two main parts: the first one has to do with the modeling and analysis in hospitals by the use of clinical pathways while the second one deals with the planning and scheduling of patients in the operation rooms.Regarding the modeling and analysis of healthcare systems, depending on different visions and expectations, the system can be treated from different perspectives called facets. In chapter 2, the formal definition and characterization of two facets are given: (1) facet of resource management and (2) handshake between clinical pathways facet. They are obtained by applying to Stochastic Well-formed Nets (colored Petri Nets) modeling the healthcare system a set of relaxations, abstraction and modifications. In the first facet the subclass of S4PR is obtained which is a characteristic model of the resource allocation systems while in the second facet Deterministically Synchronized Sequential Process (DSSP) are considered. Both nets (S4PR and DSSP) are formal subclasses of Petri Nets where net level techniques can be applied.In chapters 3 and 4, we will focus on the liveness of the DSSP systems resulting from the facet of communication between clinical pathways. These kinds of nets are composed by agents (modeling clinical pathways) cooperating in a distributed way by the asynchronous messaging passing through the buffers (modeling the communication channels). In particular two approaches have been proposed.The idea behind the first approach is to advance the buffer consumption to the first conflict transition in the agents. Considering healthcare systems modeled by a DSSP, this means that before a patient starts a clinical pathway, all required information must be available. Unfortunately, this pre-assignment method only works in some particular DSSP structures which are characterized. A more general approach (than buffer pre-assignment) for liveness enforcing in non-live DSSP is given in Chapter. 4. The approach is formalized on two levels: execution and control. The execution level uses the original DSSP structure while for the control level we compute a new net system called the control PN. This net system is obtained from the original DSSP and has a predefined type of structure. The control PN will evolve synchronously with the non-live DSSP ensuring that the deadlock states will not be reached. The states (marking) of the control PN will enable or disable some transitions in the original DSSP, while some transitions in the control PN should fire synchronously with some transitions of the original DSSP.The second part of the thesis deals with surgery scheduling of patients in a hospital department. The Operating Rooms (ORs) are one of the most expensive material resources in hospitals, being the bottleneck of surgical services. Moreover, the aging population together with the improvement in surgical techniques are producing an increase in the demand for surgeries. So, the optimal use of the ORs time is crucial inhealthcare service management. We focus on the planning and scheduling of patients in Spanish hospital departments considering its organizational structure particularities as well as the concerns and specifications of their doctors.In chapter 5, the scheduling of elective patients under ORs block booking is considered. The first criterion is to optimize the use of the OR, the second criterion is to prevent that the total available time in a block will be exceeded and the third criterion is to respect the preference order of the patient in the waiting list. Three different mathematical programming models for the scheduling of elective patients are proposed. These are combinatorial problems with high computational complexity, so three different heuristic solution methods are proposed and compared. The results show that a Mixed Integer Linear Programming (MILP) problem solved by Receding Horizon Strategy (RHS)obtains better scheduling in lowest time.Doctors using the MILP problem must fix an appropriate occupation rate for optimizing the use of the ORs but without exceeding the available time. This has two main problems: i) inexperienced doctors could find difficult to fix an appropriate occupation rate, and ii) the uncertain in the surgery durations (large standard deviation) could results in scheduling with an over/under utilization. In order to overcome these problems, a New Mixed-Integer Quadratic Constrained Programming (N-MIQCP) model is proposed. Considering some probabilistic concepts, quadratic constraints are included in N-MIQCP model to prevent the scheduling of blocks with a high risk of exceeding the available time. Two heuristic methods for solving the N-MIQCP problem are proposed and compared with other chance-constrained approaches in bibliography. The results conclude that the best schedulings are achieved using our Specific Heuristic Algorithm (SHA) due to similar occupation rates than using other approaches are obtained but our SHA respects much more the order of the patients in the waiting list.In chapter 6, a three steps approach is proposed for the combined scheduling of elective and urgent patients. In the first step, the elective patients are scheduled for a target Elective Surgery Time (EST) in the ORs, trying to respect the order of the patients on the waiting list. In the second one, the urgent patients are scheduled in the remaining time ensuring that an urgent patient does not wait more than 48 hours. Finally, in the third step, the surgeries assigned to each OR (elective and urgent) are sequenced in such a way that the maximum time that an emergency patient should wait is minimized. Considering realistic data, different policies of time reserved in the ORs for elective and urgent patients are evaluated. The results show that all ORs must be used to perform elective and urgent surgeries instead of reserving some ORs exclusively for one type of patient.Finally, in chapter 7 a software solution for surgery service management is given. A Decision Support System for elective surgery scheduling and a software tool called CIPLAN are proposed. The DSS use as core the SHA for the scheduling of elective patients, but it has other features related to the management of a surgery department. A software tool called CIPLAN which is based on the DSS is explained. The software tool has a friendly interface which has been developed in collaboration with doctors in the “Lozano Blesa” Hospital in Zaragoza. A real case study comparing the scheduling using the manual method with the scheduling obtained by using CIPLAN is discussed. The results show that 128.000 euros per year could be saved using CIPLAN in the mentioned hospital. Moreover, the use of the tool allows doctors to reduce the time spent in scheduling to use it medical tasks.<br /

Decision Support System for Production Scheduling (DSSPS)

AbstractIn this paper, we study scheduling problems that have batching considerations, and with sequence-dependent setup times for single machine and job shop flow planning. An important focus is the explicit treatment of setup times (costs), missed due dates (tardiness) and wastage as important cost components that impacts the direct and indirect cost. Indirect cost reflects the true cost of an urgent order that caused the machine setup to be re-arranged that can influence business decision on how to priced such order and how on a recurring basis might affect the bottom line profit and lost.[Allahverdi et al., 2006] found that the majority of the earlier papers assumed that the setup time (cost) is negligible or part of the job processing time (cost). This assumption simplifies the analysis however it adversely affects the solution quality. However, there is an increased interest in scheduling problems involving setup times as many recognised that there are tremendous savings when setup times/costs are explicitly incorporated in scheduling decision in various real world industrial environments