Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.
Supplier selection is one of the most critical activities of purchasing management in supply chain. Supplier selection is a complex problem involving qualitative and quantitative multi-criteria. A trade-off between these tangible and intangible factors is essential in selecting the best supplier. The work incorporates AHP in choosing the best suppliers. The results suggest that AHP process makes it possible to introduce the optimum order quantities among the selected suppliers so that the Total Value of Purchasing (TVP) becomes maximum. In this work, an AHP-based supplier selection model is formulated and then applied to a real case study for a steel manufacturing company in Malaysia. The use of the proposed model indicates that it can be applied to improve and assist decision making to resolve the supplier selection problem in choosing the optimal supplier combination. The work represents the systematic identification of the important criteria for supplier selection process. In addition, the results exhibit the application of development of a multi-criteria decision model for evaluation and selection of suppliers with proposed AHP model, which by scoring the performance of suppliers is able to reduce the time taken to select a vendor.
In today's highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evaluations prior to suppliers' ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker's assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including ''extremely preferred'', ''moderately preferred'', and ''weakly preferred''. In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.
Robots play an important role in performing operations such as welding, drilling and screwing parts in manufacturing. Optimizing the robot arm movement time between different points is an important task which will minimize the make-span and maximize the production rate. But robot programming is a complex task whereby the user needs to teach and control the robot in order to perform a desired action. In order to address the above problem, an integrated 3-dimensional (3D) simulation software and virtual reality (VR) system is developed to simplify and speed up tasks and therefore enhance the quality of manufacturing processes. This system has the capability to communicate, transfer, optimize and test the data obtained from the VR and 3D environment to the real robot in a fast and efficient manner. In addition, this system eliminates the need for robot programming, and thus it is easily implemented by users with limited engineering knowledge. The optimization model is tested on a test case, in which the data are extracted from the VR system. The results show an increase in production rate and a decrease in cycle time when the make-span is minimized. The virtual reality robotic teaching system (VRRTS) offers several benefits to users, and will therefore surpass complex and time-intensive conventional robot programming methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.