A Systematic Approach of Scheduling and Evaluting Quality by using Fuzzy Logic in Manufacturing (original) (raw)

Scheduling By Using Fuzzy Logic in Manufacturing

This paper represents the scheduling process in furniture manufacturing unit. It gives the fuzzy logic application in flexible manufacturing system. Flexible manufacturing systems are production system in furniture manufacturing unit. FMS consist of same multipurpose numerically controlled machines. Here in this project the scheduling has been done in FMS by using fuzzy logic tool in Matlab software. The fuzzy logic based scheduling model in this paper will deals with the job and best alternative route selection with multi-criteria of machine. Here two criteria for job and sequencing and routing with rules. This model is applicable to the scheduling of any manufacturing industry.

Fuzzy Logic Based Scheduling of Flexible Manufacturing System

Flexible manufacturing systems (FMS) are production systems consisting of identical multipurpose numerically controlled machines (workstations), automated material handling system, tools and load and unload stations, inspection stations, storage areas and a hierarchical control system. The latter has the task of coordinating and integrating all the components of the whole system for automatic operations. The basic problem in FMS operations is scheduling of jobs on the different machines. This problem can be solved through various approaches. One of the techniques is fuzzy logic. This technique answers a problem in a probabilistic way by values ranging between 0 and 1.in my problem, I have taken two priority conditions-job priority and route priority and the scheduling has been done accordingly. The results are compared with shortest processing time and more time is consumed using shortest processing time technique. This shows that fuzzy scheduler can be used as one of the powerful tool in FMS scheduling problem.

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

2017

This 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.

Fuzzy Logic in Flexible Manufacturing System for Production Scheduling

Fuzzy logic, although a mathematical technique, defines its behavioral framework through a compact linguistic rule base. It has the ability to concurrently consider multiple criteria and to model human experience in the form of simple rules. Furthermore, the advantage of the fuzzy logic system approach is that it incorporates both numerical and linguistic variables. By applying a fuzzy logic to overcome the dynamic scheduling problems in an FMS environment. The fuzzy based scheduling is designed to solve the problem of selecting the best job assignment for a given job which is the sub-problem of scheduling in a Flexible manufacturing system (FMS).

Programming of Job Shop Production Systems with Fuzzy Logic

TECCIENCIA, 2019

This article proposes a decision-making algorithm based on the "fuzzy logic" as an optimization technique, which allows to find a good solution to the problem of determining the priority (sequencing) of service or manufacture of jobs in the programming of intermittent production systems, Job Shop. The combinatorial nature and complexity of the problem motivates the exploration of other alternatives solutions to the traditionally used. Initially, the fuzzy logic controller structure (number of input variables, rules and output) is determined in accordance with the objective functions to be optimized. Triangular membership functions are selected for the batch size, the delivery date, the processing time, the number of tools required in each operation, and the priority of the processing the jobs. The fuzzy rules base is defined, and the controller model is formulated (fuzzification, evaluation and defuzzification). The algorithm is developed in Matlab's ®Simulink ®Fuzzy logic toolbox, achieving better results than those obtained with other methods.

Research Outline on Reconfigurable Manufacturing System Production Scheduling Employing Fuzzy Logic

International Journal of Information and Electronics Engineering, 2012

This paper presents a research project carry out at Faculty of Engineering, University Putra Malaysia in the area of fuzzy scheduling. The fuzzy based scheduling model, in this paper, will only deal with the job assignment problem in Reconfigurable Manufacturing System (RMS). The model will select the best alternative machine with multi-criteria scheduling through an approach based on a fuzzy logic. This paper aim to show the framework of research and methodology, which aspire to be constructing based on.

Analysis of Performance of Fuzzy Logic-Based Production Scheduling by Simulation

Lecture Notes in Computer Science, 2005

In this paper, a new fuzzy logic-based approach to production scheduling in the presence of uncertain disruptions is presented. The approach is applied to a real-life problem of a pottery company where the uncertain disruption considered is glaze shortage. This disruption is defined by two parameters that are specified imprecisely: number of glaze shortage occurrences and glaze delivery time. They are modelled and combined using standard fuzzy sets and level 2 fuzzy sets, respectively. A predictive schedule is generated in such a way as to absorb the impact of the fuzzy glaze shortage disruption. The schedule performance measure used is makespan. Two measures of predictability are defined: the average deviation and the standard deviation of the completion time of the last job produced on each machine. In order to analyse the performance of the predictive schedule, a new simulation tool FPSSIM is developed and implemented. Various tests carried out show that the predictive schedules have good performance in the presence of uncertain disruptions.

Research outline on Reconfigurable Manufacturing Systems Production Scheduling using Fuzzy Logic

Use of Fuzzy Logic Approaches in Scheduling of FMS: A Review

2011

Scheduling in an flexible manufacturing systems(FMS) environment is more complex and difficult than a conventional manufacturing environment. Therefore, determining an optimal schedule and controlling an FMS is considered a difficult task. To achieve high performance for an FMS, a good scheduling system should make a right decision at a right time according to system conditions. Fuzzy logic approaches easily deal with uncertain and incomplete information, and human experts knowledge can be easily coded into fuzzy rules. Due to these reasons, fuzzy logic approaches are very effective for scheduling of flexible manufacturing systems. This work presents a review on use of fuzzy logic approaches in scheduling of flexible manufacturing systems. Keywords— Fuzzy logic; Scheduling; FMS.

A Systematic Approach of Scheduling and Evaluting Quality by using Fuzzy Logic in Manufacturing (original) (raw)

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