scholarly journals Minimization of Machine Idle Time and Penalty Cost in Flexible Manufacturing System Scheduling

2019 ◽  
Vol 8 (6) ◽  
pp. 483-487
Keyword(s):  
Objective Function ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Cuckoo Search ◽  
Scheduling Problem ◽  
Jaya Algorithm ◽  
Penalty Cost ◽  
Paper Address ◽  
Better Than

This paper address the application of Jaya algorithm to solve Multi objective scheduling problem in Flexible Manufacturing System(FMS) to Minimize the Combined Objective Function(COF) Value. The effectiveness of this algorithm is tested on the problem of 43 jobs processed on 16 machines taken from literature. The MATLAB code is written to find best sequence and Combined Objective Function value by implementing Jaya Algorithm. Results obtained by Jaya Algorithm are compared with different algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Shortest Processing Time (SPT), Cuckoo Search (CS) and Modified Cuckoos Search (MCS) for the problem considered. It is observed from the results that COF value for the sequence obtained by Jaya Algorithm is better than other algorithms. It is concluded that the Jaya algorithm is best suitable for solving the Scheduling problem considered in Flexible Manufacturing System.

Scheduling Research of AGV with Double Buffers Based Genetic Algorithm in Flexible Manufacturing System

2011 ◽  
Vol 121-126 ◽  
pp. 1630-1635
Author(s):  
Nai Fei Ren ◽  
Yan Zhao ◽  
Jun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
System A ◽  
Fms Scheduling ◽  
Moving Path ◽  
Made In

Aiming at solving scheduling problem of flexible manufacturing system, this paper puts forward a FMS scheduling problem where single AGV with two buffers system is to be considered. Such an AGV with two buffers system was replaced with double-buffer AGV system in the next content. To solve FMS scheduling problem with double-buffer AGV system, a mathematical model which integrated double-buffer AGV and jobs was designed. And an improved genetic algorithm is proposed to sequence processing of jobs and the moving path of double-buffer AGV. The experiments made in simulation FMS production line laboratory realized scheduling integration of jobs and AGV, meanwhile, experiments gained processing sequence of jobs on each machine and moving path of AGV. Contrasting results of double-buffer and no-buffer AGV system verified double-buffer AGV system has higher feasibility and effectiveness.

Optimization of Linear Layout Problem in Flexible Manufacturing System Using Evolutionary Algorithms - Case Study

2015 ◽  
Vol 766-767 ◽  
pp. 896-901
Author(s):  
M. Saravanan ◽  
S. Ganesh Kumar ◽  
V. Srinivasa Raman
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Transportation Cost ◽  
Sheep Flock ◽  
Layout Problem ◽  
Proposed Model ◽  
Better Than

Linear layout is the commonly major preferred arrangement of the flexible manufacturing systems (FMS). The proposed enhanced sheep flock heredity algorithm to solving the unequal area linear layout problem through the real case study problem. The proposed model is to minimize the transportation cost with non-overlapping. Computational results show that proposed sheep flock heredity algorithm (SFHA) can obtain better than particle swarm optimization (PSO) and existing method.

An APIm Algorithm to Solve the Scheduling Problem in an FMS with Presence of Breakdowns

2012 ◽  
Vol 232 ◽  
pp. 532-536 ◽  
Author(s):  
Houbad Yamina ◽  
Souier Mehdi ◽  
Hassam Ahmed ◽  
Sari Zaki ◽  
Belkaid Fayçal
Keyword(s):  
Optimization Algorithm ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Behavior Model ◽  
Scheduling Problem ◽  
Ant Colonies ◽  
Ant Colony Algorithms ◽  
Alternative Routings ◽  
Pachycondyla Apicalis

Ant colony algorithms are computational methods inspired from the behavior of real ant colonies. In this paper our interest is focused on the adaptation of an optimization algorithm called API based on the foraging behavior model of primitive ants’ population called Pachycondyla apicalis to solve real time alternative routings selection problem in a Flexible Manufacturing System (FMS) with and without presence of breakdowns. The FMS consists of seven machining centers, a loading and an unloading stations, and six different part types. Owing to the presence of identical machining centers, the parts have alternative routings. The scheduling decisions has been established in terms of how the parts are routed through various machines in the system.

A review of machine learning in dynamic scheduling of flexible manufacturing systems

2001 ◽  
Vol 15 (3) ◽  
pp. 251-263 ◽  
Author(s):  
PAOLO PRIORE ◽  
DAVID DE LA FUENTE ◽  
ALBERTO GOMEZ ◽  
JAVIER PUENTE
Keyword(s):  
Machine Learning ◽  
Flexible Manufacturing ◽  
Flexible Manufacturing Systems ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Dynamic Scheduling ◽  
Dispatching Rule ◽  
Training Examples ◽  
Better Than

A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no single rule exists that is better than the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a review of the main machine learning-based scheduling approaches described in the literature is presented.

scholarly journals A bio-inspired scheduling approach for machines and automated guided vehicles in flexible manufacturing system using hormone secretion principle

2020 ◽  
Vol 12 (2) ◽  
pp. 168781402090778
Author(s):  
Wenbin Gu ◽  
Yuxin Li ◽  
Kun Zheng ◽  
Minghai Yuan
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Dynamic Scheduling ◽  
Hormone Secretion ◽  
Automated Guided Vehicles ◽  
Optimization Approach ◽  
Scheduling Problem ◽  
Scheduling Optimization ◽  
Material Transportation

The product quality and production efficiency of a flexible manufacturing system have improved effectively by introducing the computer management and the material transportation system. The flexible manufacturing system performance greatly depends on the performance of the material transportation system. As a mobile robot controlled by a central controller, an automated guided vehicle has a strong ability for material transportation. This article studies a dynamic scheduling problem in a shop floor, where machines and automated guided vehicles run at a specified speed and specifies a mathematical model for the dynamic scheduling problem with the goal of makespan minimization. Meanwhile, inspired by the hormone secretion principle of the endocrine system, a bio-inspired scheduling optimization approach is developed to solve the dynamic scheduling problem in the flexible manufacturing system. To verify its practical application, the bio-inspired scheduling optimization approach and other scheduling approaches are tested, and the results illustrate that the bio-inspired scheduling optimization approach has better scheduling performance as well as optimizes the quality of integrated and real-time scheduling of machines and automated guided vehicles.

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