The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

Performance optimization of a leagility inspired supply chain model: a CFGTSA algorithm based approach

Lean and agile principles have attracted considerable interest in the past few decades. Industrial sectors throughout the world are upgrading to these principles to enhance their performance, since they have been proven to be efficient in handling supply chains. However, the present market trend demands a more robust strategy incorporating the salient features of both lean and agile principles. Inspired by these, the leagility principle has emerged, encapsulating both lean and agile features. The present work proposes a leagile supply chain based model for manufacturing industries. The paper emphasizes the various aspects of leagile supply chain modeling and implementation and proposes a new Hybrid Chaos-based Fast Genetic Tabu Simulated Annealing (CFGTSA) algorithm to solve the complex scheduling problem prevailing in the leagile environment. The proposed CFGTSA algorithm is compared with the GA, SA, TS and Hybrid Tabu SA algorithms to demonstrate its efficacy in handling complex scheduling problems

A Market-based Approach to Multi-factory Scheduling

In this paper, we report on the design of a novel market-based approach for decentralised scheduling across multiple factories. Specifically, because of the limitations of scheduling in a centralised manner -- which requires a center to have complete and perfect information for optimality and the truthful revelation of potentially commercially private preferences to that center -- we advocate an informationally decentralised approach that is both agile and dynamic. In particular, this work adopts a market-based approach for decentralised scheduling by considering the different stakeholders representing different factories as self-interested, profit-motivated economic agents that trade resources for the scheduling of jobs. The overall schedule of these jobs is then an emergent behaviour of the strategic interaction of these trading agents bidding for resources in a market based on limited information and their own preferences. Using a simple (zero-intelligence) bidding strategy, we empirically demonstrate that our market-based approach achieves a lower bound efficiency of 84%. This represents a trade-off between a reasonable level of efficiency (compared to a centralised approach) and the desirable benefits of a decentralised solution

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

Scheduling of non-repetitive lean manufacturing systems under uncertainty using intelligent agent simulation

World-class manufacturing paradigms emerge from specific types of manufacturing systems with which they remain associated until they are obsolete. Since its introduction the lean paradigm is almost exclusively implemented in repetitive manufacturing systems employing flow-shop layout configurations. Due to its inherent complexity and combinatorial nature, scheduling is one application domain whereby the implementation of manufacturing philosophies and best practices is particularly challenging. The study of the limited reported attempts to extend leanness into the scheduling of non-repetitive manufacturing systems with functional shop-floor configurations confirms that these works have adopted a similar approach which aims to transform the system mainly through reconfiguration in order to increase the degree of manufacturing repetitiveness and thus facilitate the adoption of leanness. This research proposes the use of leading edge intelligent agent simulation to extend the lean principles and techniques to the scheduling of non-repetitive production environments with functional layouts and no prior reconfiguration of any form. The simulated system is a dynamic job-shop with stochastic order arrivals and processing times operating under a variety of dispatching rules. The modelled job-shop is subject to uncertainty expressed in the form of high priority orders unexpectedly arriving at the system, order cancellations and machine breakdowns. The effect of the various forms of the stochastic disruptions considered in this study on system performance prior and post the introduction of leanness is analysed in terms of a number of time, due date and work-in-progress related performance metrics

Coupling of centralized and decentralized scheduling for robust production in agile production systems

Individualized products and timely delivery require agile just-in-time manufacturing operations. Scheduling needs to deliver a robust performance with high and stable results even when facing disruptions such as machine failures. Existing approaches often generate predictive schedules and adjust them reactively as disturbances occur. However, the effectiveness of rescheduling approaches highly depends on the available degrees of freedom in the predictive schedule. In the proposed approach, a centralized robust scheduling procedure is coupled with a decentralized reinforcement learning algorithm in order to adjust the required degrees of freedom for a maximally efficient production control in real-time

Information integration platform for CIMS

A new information integration platform for computer integrated manufacturing system (CIMS) is presented, which is based on agent and CORBA. CORBA enhances the system integration because it is an industry-standard for interoperable, distributed objects across heterogeneous hardware and software platform. Agent technology is used to improve intelligence of the integration system. In order to implement the information integration platform, we use a network integration server to integrate the network, design a generic database agent to integrate database, adopt multi-agent based architecture to integrate application, and utilize wrapper as a CORBA object to integrate legacy code.published_or_final_versio

Agent-based distributed manufacturing control: a state-of-the-art survey

Manufacturing has faced significant changes during the last years, namely the move from a local economy towards a global and competitive economy, with markets demanding for highly customized products of high quality at lower costs, and with short life cycles. In this environment, manufacturing enterprises, to remain competitive, must respond closely to customer demands by improving their flexibility and agility, while maintaining their productivity and quality. Dynamic response to emergence is becoming a key issue in manufacturing field because traditional manufacturing control systems are built upon rigid control architectures, which cannot respond efficiently and effectively to dynamic change. In these circumstances, the current challenge is to develop manufacturing control systems that exhibit intelligence, robustness and adaptation to the environment changes and disturbances. The introduction of multi-agent systems and holonic manufacturing systems paradigms addresses these requirements, bringing the advantages of modularity, decentralization, autonomy, scalability and re- usability. This paper surveys the literature in manufacturing control systems using distributed artificial intelligence techniques, namely multi-agent systems and holonic manufacturing systems principles. The paper also discusses the reasons for the weak adoption of these approaches by industry and points out the challenges and research opportunities for the future

An agile and adaptive holonic architecture for manufacturing control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2004. Faculdade de Engenharia. Universidade do Port

Analysis and evaluation of multi-agent systems for digital production planning and control

Industrial manufacturing companies have different IT control functions that can be represented with a so-called hierarchical automation pyramid. While these conventional software systems especially support the mass production with consistent demand, the future project “Industry 4.0” focuses on customer-oriented and adaptable production processes. In order to move from conventional production systems to a factory of the future, the control levels must be redistributed. With the help of cyber-physical production systems, an interoperable architecture must be, implemented which removes the hierarchical connection of the former control levels. The accompanied digitalisation of industrial companies makes the transition to modular production possible. At the same time, the requirements for production planning and control are increasing, which can be solved with approaches such as multi-agent systems (MASs). These software solutions are autonomous and intelligent objects with a distinct collaborative ability. There are different modelling methods, communication and interaction structures, as well as different development frameworks for these new systems. Since multi-agent systems have not yet been established as an industrial standard due to their high complexity, they are usually only tested in simulations. In this bachelor thesis, a detailed literature review on the topic of MASs in the field of production planning and control is presented. In addition, selected multi-agent approaches are evaluated and compared using specific classification criteria. In addition, the applicability of using these systems in digital and modular production is assessed