Robotic Flexible Assembly Cells Research Articles

A new redundancy strategy for enabling graceful degradation in resilient robotic flexible assembly cells

The development of resilience in manufacturing systems has drawn more attention than ever. Using redundant components is one of the key strategies for building and enhancing the resilience of a manufacturing system. However, current redundancy strategies require duplicated machinery employed either in active or in standby status. This in turn causes extra costs in designing and achieving resilience. Achieving an efficient deployment of the redundant component in the face of failures is also challenging. In this paper, we introduce a novel redundancy strategy, called adaptive standby redundancy (ASR), to achieve resilient performance for discrete manufacturing systems while reducing the cost of employing the duplicated components that are typically used in traditional systems. This novel strategy permits achievement of high levels of utilisation of the system and graceful degradation in case of failure, keeping the system functional. The strategy is then validated in a developed robotic flexible assembly cell (RFAC), which is tested and results on its efficacy and performance enhancement are discussed.

Distributed multi-agent scheduling and control system for robotic flexible assembly cells

This paper deals with the development of a distributed multi-agent system (DMAS) for scheduling and controlling Robotic Flexible Assembly Cells (RFAC). In the proposed system, an approach for solving one of the most challenging decisional problems in RFAC is proposed and implemented. This problem is related to the products operations scheduling which requires their allocation and sequencing on the robots, while satisfying products and robots constraints under makespan minimization. The proposed DMAS addresses this challenge by using a cooperative approach supported by three kinds of autonomous control agents: supervisory agent, local agents, and remote agents. These agents interact by a negotiation protocol based on common dispatching rules for coordinating their individual decisions, satisfying their local objective and providing an optimized global solution. Moreover, because of the dynamic nature of the assembly systems, it is imperative to consider external disturbances on production scheduling and to solve the related issues. Consequently, DMAS has the ability to respond and manage some dynamic events that may occur in the cells such as unexpected robot breakdown or dynamic products arrivals. Computational results on benchmarks show the effectiveness and the robustness of the proposed system.

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

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.

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.

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 Fuzzy Logic to Multi-Objective Scheduling Problems in Robotic Flexible Assembly Cells

This paper is aimed at developing a methodology to solve a multi-objective problem in robotic flexible assembly cells. The proposed methodology is based on three main steps: (1) scheduling of the RFACs using different common rules, (2) normalisation of the scheduling outcomes, and (3) selection of the optimal scheduling rules, using a fuzzy inference system. In this paper, four rules, namely short processing time, long processing time, earlier due date and random, are examined. Four objectives are considered simultaneously: scheduling length, total transportation time, utilisation rate and workload rate. A realistic case study is provided for demonstrating applicability of the suggested methodology. The results show that the methodology is practical and works in RFACs settings.

A fuzzy decision support system for selecting the optimal scheduling rule in robotic flexible assembly cells

A fuzzy decision support system for selecting the optimal scheduling rule in robotic flexible assembly cells

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

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.

Efficient Scheduling Rule for Robotic Flexible Assembly Cells Based on Fuzzy Approach

This study aims to develop an efficient rule for scheduling robotic flexible assembly cells (RFACs). The proposed scheduling rule is called fuzzy sequencing rule (FSR) which is constructed by combining different input variables: processing time, due date, batch size and number of required assembly stations, using the fuzzy logic (FL) technique. Two independent performance measures are considered: makespan and maximum of tardiness. Simulation software named SIMPROCESS is used to examine the performance of FSR compared with existing scheduling rules. Simulation results show that the proposed rule outperforms the common scheduling rules.