Khalid Abd

Simulation modelling and analysis of scheduling in robotic flexible assembly cells using Taguchi method

Due to increasing competition in the developing global economy, today’s companies are facing greater challenges than ever to employ flexible manufacturing systems (FMS) capable of dealing with unexpected events and meeting customers’ requirements. One such system is robotic flexible assembly cells (RFACs). There has been relatively little work on the scheduling of RFACs, even though overall scheduling problems of FMS have attracted significant attention. This paper presents Taguchi optimisation method in conjunction with simulation modelling in a new application for dynamic scheduling problems in RFACs, in order to minimise total tardiness and number of tardy jobs (NT). This is the first study to address these particular problems. In this study, Taguchi method has been used to reduce the minimum number of experiments required for scheduling RFACs. These experiments are based on an L9 orthogonal array with each trial implemented under different levels of scheduling factors. Four factors are considered simultaneously: sequencing rule, dispatching rule, cell utilisation and due date tightness. The experimental results are analysed using an analysis of mean to find the best combination of scheduling factors and an analysis of variance to determine the most significant factors that influence the system’s performance. The resulting analysis shows that this proposed methodology enhances the system’s scheduling policy.

DEVELOPMENT OF A FUZZY-SIMULATION MODEL OF SCHEDULING ROBOTIC FLEXIBLE ASSEMBLY CELLS

Due to the complexity of scheduling flexible manufacturing systems, the generation of production schedules requires an intelligent technique. Many artificial intelligence techniques such as fuzzy logic, genetic algorithms and neural networks have been successfully applied to the scheduling of advanced manufacturing systems. One such system is Robotic Flexible Assembly Cells (RFACs). Few studies have addressed the problem of scheduling RFACs. The major limitation is that these studies are limited to the assembly of only one product type. The objective of this study is to propose a new intelligent model of scheduling RFACs in a multi-product assembly environment, using fuzzy logic.

A Fuzzy Decision Support System for Selecting the Optimal Scheduling Rule in Robotic Flexible Assembly Cells

Abstract The aim of this paper is to present a proposed methodology to select optimal scheduling rule for robotic flexible assembly cells based fuzzy decision support system. Six common scheduling rules are considered, and three independent objectives, namely, the minimising of makespan (Cmax), total tardiness (TD) and percentage of tardy jobs (%T) are measured. A fuzzy decision support system is implemented using the Matlab fuzzy toolbox. The final results demonstrate the effectiveness of the proposed methodology to select optimal scheduling rule in a multi-product assembly environment.

Multi-objective optimisation of dynamic scheduling in robotic flexible assembly cells via fuzzy-based Taguchi approach

To our knowledge, this is first study that addresses dynamic scheduling in RFACs.Cell utilisation and due date tightness are the significant scheduling factors.Percentage of prediction error (9%) confirms the efficient of proposed methodology.The results of confirmation run improved by 11% compared to initial experiments. This paper presents Taguchi method coupled with fuzzy logic for dealing with multi-objective optimisation problems for dynamic scheduling in robotic flexible assembly cells (RFACs). This is the first study to address these particular problems. In this study, Taguchi optimisation method has been applied to reduce the number of experiments required for scheduling RFACs. The experiments are implemented with four different scheduling factors, namely sequencing rule, dispatching rule, cell utilisation and due date tightness. These factors are difficult to optimise considering the objectives of multiple functions instead of a single objective. Therefore, a multiple performance characteristics index (MPCI) based fuzzy logic approach has been developed to derive the optimal solution. The predicted results of MPCIs have been verified via a confirmation test. Results of the confirmation test show significant improvement in MPCI using the optimal levels of the scheduling factors.

APPLICATION OF A FUZZY-SIMULATION MODEL OF SCHEDULING ROBOTIC FLEXIBLE ASSEMBLY CELLS

This study describes the application of the develop ed model to the scheduling of the Robotic Flexible Assembly Cells (RFACs). The present study is a continuation of the paper titled “Development of a Fuzz ySimulation Model of Scheduling Robotic Flexible Assembly Cells”. This new model for multi-objective scheduling problems in RFACs was based on combining a fuzzy-based mathematical model with simulation software tools. In this study, a hypothetical case study of RFACs is presented to demonstrate the effectiveness of the developed model and then compare the results that are obtained by the new methodology with the common scheduling rules. The simulation results show that the performance of the proposed methodology outperforms the most popular scheduling rules from previous research.

Application of a fuzzy multi-criteria decision-making approach for dynamic scheduling in robotic flexible assembly cells

This paper is devoted to the application of the developed approach presented in Part I, to demonstrate its capability in tackling real-world MCDM problems. In this paper, a hypothetical case study of robotic flexible assembly cells (RFACs) is considered, to solve multi-objective optimization problems for dynamic scheduling. In order to find the optimal solution, a fuzzy decision support system (FDSS) is applied and built using the fuzzy logic toolbox in MATLAB software. The FDSS combines multi-objective functions in one performance measure named a multiple performance characteristics index (MPCI). The analysis results show that the developed approach is practical, works in RFACs setting, and deal with imprecise and uncertain information.

Background and Research Scope

With the rapidly developing global economy, today’s companies face greater challenges than ever to employ manufacturing systems capable of dealing with unexpected events and meeting customers’ specific requirements. In overcoming these challenges, flexibility is the key concept in the development of manufacturing systems. The issue of flexibility in manufacturing systems is not new and has attracted significant attention by researchers since the development of flexible manufacturing systems (FMS) four decades ago.

Case Study 1: Application of the Developed Methodology Using Fuzzy Logic and Simulation

The research work in Chap. 3 was to develop a methodology for scheduling RFAC in multi-product assembly environments by combining a fuzzy-based mathematical model with simulation modelling. In this chapter, the proposed methodology will be examined using a realistic case study to prove the effectiveness of the methodology in generating the schedule for assembling multi-products.

Case Study 2: Application of Hybrid Fuzzy MCDM Approach to Optimize Dynamic Scheduling in RFAC

The research work described in Chap. 5 was to propose a methodology for scheduling RFAC in a dynamic environment by combining Taguchi experimental design method with simulation modelling. This work was restricted to solving single-objective optimization problems. Chapter 6 was devoted to achieving multi-objective optimisation of dynamic scheduling in RFAC using a new Fuzzy Multi-Criteria Decision-Making (Fuzzy MCDM) approach. The proposed approach will be examined, verified and validated in this chapter, using a realistic case study.

Literature Review and Research Objectives

The concept of robotic flexible assembly cell (RFAC) has the potential to introduce significant improvements in system performance, as highlighted in Chap. 1. To achieve these improvements, the problems of RFAC at scheduling level need to be critically addressed. To date, researchers have paid little attention to handling the scheduling problems relevant to RFAC.