Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Reactive scheduling using a multi-agent model: the SCEP framework

Multi-agent systems have been successfully applied to the scheduling problem for some time. However, their use often leads to poorly unsatisfactory disappointing results. A new multi-agent model, called supervisor, customers, environment, producers (SCEP), is suggested in this paper. This model, developed for all types of planning activities, introduces a dialogue between two communities of agents leading to a high level of co-operation. Its two main interests are the following: first it provides a more efficient control of the consequences generated by the local decisions than usual systems to each agent, then the adopted architecture and behaviour permit an easy co-operation between the different SCEP models, which can represent different production functions such as manufacturing, supply management, maintenance or different workshops. As a consequence, the SCEP model can be adapted to a great variety of scheduling/planning problems. This model is applied to the basic scheduling problem of flexible manufacturing systems, andit permits a natural co-habitation between infinite capacity scheduling processes, performedby the manufacturing orders, and finite capacity scheduling processes, performed by the machines. It also provides a framework in order to react to the disturbances occurring at different levels of the workshop

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Supply chain control: Trade-offs and system requirements

The official published version can be accessed from the link below.A paper describes the underlying forces which drive change in manufacturing enterprises and supply chains. It sets out the complexities in modern capitalism and global economics and illustrates the trade-offs that can be made. IT systems which are required to assist improvements to both customer service and enterprise manufacturing performance are explained, alluding to the special case for the semiconductor industry. Arguments are presented showing how the new tools being developed with the ESPRIT project 20544, X-CITTIC, will satisfy the control needs for a virtual enterprise. This paper describes the underlying forces which drive change in manufacturing enterprises and supply chains. It sets out the complexities in modem capitalism and global economics and illustrates the trade-offs that can be made. IT systems which are required to assist improvements to both customer service and enterprise manufacturing performance are explained alluding to the special case for the semiconductor industry. Finally it shows how the new tools being developed with the ESPRIT project 20544, XCITTIC, will satisfy the control needs for a virtual enterprise

A Predictive-reactive Approach for JSP with Uncertain Processing Times

The paper is supported by the Asia-Link project funded by the European Commission (CN/ASIA-LINK/024 (109093)), the National Natural Science Foundation of China (50705076, 50705077), and the National Hi-Tech R&D Program of China (2007AA04Z187)JSP with discretely controllable processing times (JSP-DCPT) that are perturbed in a turbulent environment is formulated, based on which, a time-cost tradeoff based predictive-reactive scheduling approach is proposed for solving the problem. In the predictive scheduling process, on the basis of a proposed three-step decomposition approach for solving JSP-DCPT, a solution initialization algorithm is presented by incorporating a hybrid algorithm of tabu search and simulated annealing and a fast elitist non-dominated sorting genetic algorithm; in the reactive scheduling process, Pareto-optimal schedules are generated, among which every schedule that is not dominated by any initial schedule can be selected as the responding schedule so as to maintain optimality of the objective that is to minimize both the makespan and the cost. Experimental simulations demonstrate the effectiveness of the proposed approach