Determination of Routing and Sequencing in a Flexible Manufacturing System Based on Fuzzy Logic

AbstractThis paper is concerned with scheduling in Flexible Manufacturing Systems (FMS) using a Fuzzy Logic (FL) approach. Four fuzzy input variables; machine allocated processing time, machine priority, machine available time and transportationpriority are defined. The job priority is the fuzzy output variable, showing the priority status of a job to be selected for next operation on a machine. The model will first assign operation of parts to machines under the given production plan and then determine the input sequence of the assigned operations for each machine based on a multi-criteria scheduling scheme. A complete fuzzy scheduling algorithm is developed to solve the operation allocation and operation scheduling problems in FMS environments aiming to approach the objectives of minimizing mean flowtime, maximizing machine utilization and balancing machine usage. The test results demonstrate the superiority of the fuzzy logic approach in most performance measures.

Plant Location Selection for Food Production by Considering the Regional and Seasonal Supply Vulnerability of Raw Materials

A production capacity analysis considering market demand and raw materials is very important to design a new plant. However, in the food processing industry, the supply uncertainty of raw materials is very high, depending on the production site and the harvest season, and further, it is not straightforward to analyze too complex food production systems by using an analytical optimization model. For these reasons, this study presents a simulation-based decision support model to select the right location for a new food processing plant. We first define three supply vulnerability factors from the standpoint of regional as well as seasonal instability and present an assessment method for supply vulnerability based on fuzzy quantification. The evaluated vulnerability scores are then converted into raw material supply variations for food production simulation to predict the quarterly production volume of a new food processing plant. The proposed selection procedure is illustrated using a case study of semiprocessed kimchi production. The best plant location is proposed where we can reduce and mitigate risks when supplying raw material, thereby producing a target production volume steadily

Control of an industrial desktop robot using computer vision and fuzzy rules

Desktop robots are suitable for various production line systems in industrial applications like dispensing, soldering, screw tightening, pick’n place, welding or marking. Despite their capabilities to meet diverse requirements, they have to be programmed off-line using waypoints and path information. Misalignments in the workspace location during loading cause injuries in the workpiece and tool. Further, in modern flexible industrial production, machinery must adapt to changing product demands, both to the simultaneous production of different types of workpieces and to product styles with short life cycles. In this paper, visual data processing concepts on the basis of fuzzy logic are applied to enable an industrial desktop robot to raise its flexibility and address these problems that limit the production rate of small industries. Specifically, a desktop robot performing dispensing tasks is equipped with a CCD camera. Visual information is used to autonomously change previously off-line stored robot programs for known workpieces or to call worker’s attention for unknown/misclassified workpieces. A fuzzy inference classifier based on a fuzzy grammar, is used to describe/identify workpieces. Fuzzy rules are automatically generated from features extracted from the workpiece under analysis. Regarding the evaluation of the system performance, different types of workpieces were tested and a good rate performance, higher than 90%, was achieved. The obtained results illustrate both the flexibility and robustness of the proposed solution as well as its capabilities for good classification of workpieces. The overall system is being implemented in an industrial environment

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

Computer vision and fuzzy rules applied to an industrial desktop robot

Purpose – Desktop robots are suitable for production line systems in industrial applications. Despite their capabilities to meet diverse requirements, they have to be programmed off-line using waypoints and path information. Misalignments in the workspace location during loading cause injuries in the workpiece and tool. Further, in flexible industrial production, machinery must adapt to changing product demands, both to the simultaneous production of different types of workpieces and to product styles with short life cycles. In this paper, visual data processing concepts on the basis of fuzzy logic are applied to enable an industrial desktop robot to raise its flexibility and address these problems that limit the production rate of small industries. Design/methodology/approach – In this paper, a desktop robot performing dispensing tasks is equipped with a computer vision system. Visual information is used to autonomously change previously off-line stored robot programs for known workpieces or to call worker’s attention for unknown/ misclassified workpieces. A fuzzy inference classifier based on a fuzzy grammar, is used to describe/identify workpieces. Fuzzy rules are automatically generated from features extracted from the workpiece under analysis. Findings – Different types of workpieces were tested and a good rate performance, higher than 90 per cent, was achieved. The obtained results illustrate both the flexibility and robustness of the proposed solution as well as its capabilities for good classification of workpieces. Practical implications – The overall system is being implemented in an industrial environment. Originality/value – The paper reports a piece of solid work which indicates clearly that the work is suitable for industrial utilization

A fuzzy multiobjective algorithm for multiproduct batch plant: Application to protein production

This paper addresses the problem of the optimal design of batch plants with imprecise demands and proposes an alternative treatment of the imprecision by using fuzzy concepts. For this purpose, we extended a multiobjective genetic algorithm (MOGA) developed in previousworks, taking into account simultaneously maximization of the net present value (NPV) and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The former is computed by comparing the fuzzy computed production time to a given fuzzy production time horizon and the latter is based on the additional fuzzy demand that the plant is able to produce. The methodology provides a set of scenarios that are helpful to the decision’s maker and constitutes a very promising framework for taken imprecision into account in new product development stage

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

Production/maintenance cooperative scheduling using multi-agents and fuzzy logic

Within companies, production is directly concerned with the manufacturing schedule, but other services like sales, maintenance, purchasing or workforce management should also have an influence on this schedule. These services often have together a hierarchical relationship, i.e. the leading function (most of the time sales or production) generates constraints defining the framework within which the other functions have to satisfy their own objectives. We show how the multi-agent paradigm, often used in scheduling for its ability to distribute decision-making, can also provide a framework for making several functions cooperate in the schedule performance. Production and maintenance have been chosen as an example: having common resources (the machines), their activities are actually often conflicting. We show how to use a fuzzy logic in order to model the temporal degrees of freedom of the two functions, and show that this approach may allow one to obtain a schedule that provides a better compromise between the satisfaction of the respective objectives of the two functions

ARTMAP Neural Networks for Information Fusion and Data Mining: Map Production and Target Recognition Methodologies

The Sensor Exploitation Group of MIT Lincoln Laboratory incorporated an early version of the ARTMAP neural network as the recognition engine of a hierarchical system for fusion and data mining of registered geospatial images. The Lincoln Lab system has been successfully fielded, but is limited to target I non-target identifications and does not produce whole maps. Procedures defined here extend these capabilities by means of a mapping method that learns to identify and distribute arbitrarily many target classes. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of canonical algorithms and a benchmark testbed has enabled the evaluation of candidate recognition networks as well as pre- and post-processing and feature selection options. The resulting mapping methodology sets a standard for a variety of spatial data mining tasks. In particular, training pixels are drawn from a region that is spatially distinct from the mapped region, which could feature an output class mix that is substantially different from that of the training set. The system recognition component, default ARTMAP, with its fully specified set of canonical parameter values, has become the a priori system of choice among this family of neural networks for a wide variety of applications.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); Office of Naval Research (N00014-01-1-0624

Scheduling uncertain orders in the customer–subcontractor context

Within the customer–subcontractor negotiation process, the first problem of the subcontractor is to provide the customer with a reliable order lead-time although his workload is partially uncertain. Actually, a part of the subcontractor workload is composed of orders under negotiation which can be either confirmed or cancelled. Fuzzy logic and possibility theory have widely been used in scheduling in order to represent the uncertainty or imprecision of processing times, but the existence of the manufacturing orders is not usually set into question. We suggest a method allowing to take into account the uncertainty of subcontracted orders. This method is consistent with list scheduling: as a consequence, it can be used in many classical schedulers. Its implementation in a scheduler prototype called TAPAS is described. In this article, we focus on the performance of validation tests which show the interest of the method