Application of a continuous supervisory fuzzy control on a discrete scheduling of manufacturing systems.

10 pagesInternational audienceThis paper considers the modelling and simulation of a hierarchical production-flow control system. Particularly, the system capacity allocation has been addressed by a set of distributed and supervised fuzzy controllers. The objective is to adjust the machine's production rates in such a way that satisfies the demand while maintaining the overall performances within acceptable limits. Given the adjusted production rates, the problem of scheduling of jobs is considered at the shop-floor level. In this case, the actual dispatching times are determined from the continuous production rates through a sampling procedure. To deal with conflicts between jobs at a shared machine, a decision for the actual part to be processed is taken using some criteria which represent a measure of the job's priority. A case study demonstrates the efficiency of the proposed control approach

Supervisory Control based Fuzzy Interval Arithmetic Applied for Discrete Scheduling of Manufacturing Systems

6 pagesInternational audienceThis paper considers the modelling and designing of a production-flow scheduler based on fuzzy interval system. Particularly, the supervisory control is built according to the satisfaction degree of conflicting objectives which are quantified by fuzzy intervals. The control system aims at adjusting the machine's production rates in such a way that satisfies the demand while maintaining the overall performances within acceptable limits. At the shop-floor level, the actual dispatching times are determined from the continuous production rates through a sampling procedure. A decision for the actual part to be processed is taken using some criterions which represent a measure of the job's priority. A case study demonstrates the efficiency of the proposed control approach

Agile MPC system linking manufacturing and market strategies

Increasing complexity and interdependency in manufacturing enterprises require an agile manufacturing paradigm. This paper considers a dynamic control approach for linking manufacturing strategy with market strategy through a reconfigurable manufacturing planning and control (MPC) system to support agility in this context. A comprehensive MPC model capable of adopting different MPC strategies through distributed controllers of inventory, capacity, and WIP is presented. A hierarchical supervisory controller (referred to as decision logic unit, DLU) that intakes the high-level strategic market decisions and constraints together with feedback of the current manufacturing system state (WIP, production, and inventory levels) and optimally manages the distributed controllers is introduced. The DLU architecture with its three layers and their different functionalities is discussed showing how they link the higher management level to the operational level to satisfy the required demand. A case study for an automatic PCB assembly factory is implemented to demonstrate the applicability of the whole approach. In addition, a comparative cost analysis study is carried out to compare between the developed agile MPC system and classical-inventory- and capacity-based MPC policies in response to different demand patterns. Results showed that the developed agile MPC policy is as cost effective as the inventory-based MPC policy in demand patterns with steady trends, as cost effective as capacity-based MPC in turbulent demand patterns, and far superior than both classical MPC polices in mixed-demand patterns

Investigation into inspection system utilisation for advanced manufacturing systems.

Masters Degree. University of KwaZulu-Natal, Durban.Varied inspection is an aperiodic inspection utilisation methodology that was developed for advanced manufacturing systems. The inspection scheme was created as a solution to improve manufacturing performance where inspection hinders production, such as cases where inspection time is significantly larger than machining time. Frequent inspection impedes production cycles which result in undesirable blocking, starving, low machine utilisation, increased lead time and work-in-process. The aim of the inspection strategy was to aid manufacturing metrics by adjusting inspection utilisation through multiple control methods. The novelty of the research lies in using an inspection strategy for improved manufacturing performance. Quality control was traditionally viewed as an unintegrated aspect of production. As such, quality control was only used as a tool for ensuring certain standards of products, rather than being used as a tool to aid production. The problem was solved by using the amount of inspection performed as a variable, and changing that variable based on the needs of the manufacturing process. “Inspection intensity” was defined as the amount of inspection performed on a part stream and was based on inputs such as part quality, required production rates, work-in-process requirements among other factors. Varied inspection was executed using a two-level control architecture of fuzzy controllers. Lower level controllers performed varied inspection while an upper level supervisory controller measured overall system performance and made adjustments to lower level controllers to meet system requirements. The research was constrained to simulation results to test the effects of varied inspection on different manufacturing models. Simulation software was used to model advanced manufacturing systems to test the effects of varied inspection against traditional quality control schemes. Matlab’s SimEvents® was used for discrete-event simulation and Fuzzy Logic Toolbox® was used for the controller design. Through simulation, varied inspection was used to meet production needs such as reduced manufacturing lead time, reduced work-in-process, reduced starvation and blockage, and reduced appraisal costs. Machine utilisation was increased. The contribution of the research was that quality control could be used to aid manufacturing systems instead of slowing it down. Varied inspection can be used as a flexible form of inspection. The research can be used as a control methodology to improve the usage of inspection systems to enhance manufacturing performance

A Mathematical Model for HVLV Systems Scheduling and Optimization With Periodic Preventive Maintenance Using (max, +) Algebra

International audienceThe High-Variety, Low-Volume (HVLV) scheduling problem is one of the most arduous combinatorial optimization problems. This paper considers an interesting formulation of the HVLV scheduling problem using (max, +) algebra while periodic Preventive Maintenance (PM) is considered. Maintenance is time based since activities are periodically fixed: maintenance is required after a periodic time interval (all periods are equals on each machine). In this paper, the maintenance tasks of machines are controllable.The jobs and the maintenance operations are scheduled simultaneously. Also, the maintenance operations are scheduled between each other, so that a regular criterion is optimized. To generate feasible schedules, constrained decision variables are incorporated into the (max, +) model. The validity of the proposed approach is illustrated by simulation examples

Scheduling and discrete event control of flexible manufacturing systems based on Petri nets

A flexible manufacturing system (FMS) is a computerized production system that can simultaneously manufacture multiple types of products using various resources such as robots and multi-purpose machines. The central problems associated with design of flexible manufacturing systems are related to process planning, scheduling, coordination control, and monitoring. Many methods exist for scheduling and control of flexible manufacturing systems, although very few methods have addressed the complexity of whole FMS operations. This thesis presents a Petri net based method for deadlock-free scheduling and discrete event control of flexible manufacturing systems. A significant advantage of Petri net based methods is their powerful modeling capability. Petri nets can explicitly and concisely model the concurrent and asynchronous activities, multi-layer resource sharing, routing flexibility, limited buffers and precedence constraints in FMSs. Petri nets can also provide an explicit way for considering deadlock situations in FMSs, and thus facilitate significantly the design of a deadlock-free scheduling and control system. The contributions of this work are multifold. First, it develops a methodology for discrete event controller synthesis for flexible manufacturing systems in a timed Petri net framework. The resulting Petri nets have the desired qualitative properties of liveness, boundedness (safeness), and reversibility, which imply freedom from deadlock, no capacity overflow, and cyclic behavior, respectively. This precludes the costly mathematical analysis for these properties and reduces on-line computation overhead to avoid deadlocks. The performance and sensitivity of resulting Petri nets, thus corresponding control systems, are evaluated. Second, it introduces a hybrid heuristic search algorithm based on Petri nets for deadlock-free scheduling of flexible manufacturing systems. The issues such as deadlock, routing flexibility, multiple lot size, limited buffer size and material handling (loading/unloading) are explored. Third, it proposes a way to employ fuzzy dispatching rules in a Petri net framework for multi-criterion scheduling. Finally, it shows the effectiveness of the developed methods through several manufacturing system examples compared with benchmark dispatching rules, integer programming and Lagrangian relaxation approaches

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research