Reconfigurable manufacturing system: a systematic bibliometric analysis and future research agenda

PurposeThe purpose of this paper is to conduct a systematic bibliometric analysis of reconfigurable manufacturing system (RMS) articles using VOSviewer to identify their research themes and future research trends and investigate their interconnectivity. This paper also aims to identify prominent authors, publishers, organizations, countries and their collaborations in the RMS domain.Design/methodology/approachIn this study, the Scopus database is used to retrieve 454 RMS articles published between 1988 and 2020. These articles are then investigated using VOSviewer to determine their interconnectedness, clusters and citations, as well as to generate a map based on text data. The network visualization diagrams and clusters obtained for documents, authors, sources, organizations and countries are explored to determine the current state and future trends in RMS research.FindingsA bibliometric analysis of selected articles is performed, and current research hotspots in this domain are identified. This work also investigates the current status and future research trends in this domain. The work presented also identifies top researchers, journals, countries and documents in RMS.Practical implicationsThis paper can provide academics, researchers and practitioners with additional research insights. At the same time, the research trends identified here can help to direct research and benefit researchers.Originality/valueThe study is the first attempt to review selected documents in the RMS domain using bibliometric analysis tools, and it presents a method for collecting articles, organizing them and analyzing the data.

Process Planning, Scheduling, and Layout Optimization for Multi-Unit Mass-Customized Products in Sustainable Reconfigurable Manufacturing System

Currently, manufacturers seek to provide customized and sustainable products, requiring flexible manufacturing systems and advanced production management to cope with customization complexity and improve environmental performance. The reconfigurable manufacturing system (RMS) is expected to provide cost-effective customization in high responsiveness. However, reconfiguration optimization to produce sustainable mass-customized products in RMS is a complex problem requiring multi-criteria decision making. It is related to three problems, process planning, scheduling, and layout optimization, which should be integrated to optimize the RMS performance. This paper aims at integrating the above three problems and developing an effective approach to solving them concurrently. It formulates a multi-objective mathematical model simultaneously optimizing process planning, job-shop scheduling, and open-field layout problem to improve RMS sustainability. The penalty for product tardiness, the total manufacturing cost, the hazardous waste, and the greenhouse gases emissions are minimized. Economic and environmental indicators are defined to modify the Pareto efficiency when searching the Pareto-optimal solutions. Exact Pareto-optimal solutions are obtained by brute-force search and compared with those of the non-environmental indicator model. NSGA-III is adopted to obtain the approximate Pareto-optimal solutions in high effectiveness and efficiency. A small numerical example is applied to validate the mathematical model and resolution methods.

Modeling Of Multi-Objective Process Plan, Its Optimization using Linear Modeling Technique

The Process planning is the procedure to opt for and schedule manufacturing procedure so as to attain one or more organizational goals and suit with a set of constraints. More specifically a Process planning in the reconfigurable manufacturing setup engages a sequence of all activities from raw material storage into the finished manufactured yield. In the current study a manufacturing setup of automotive industry is discussed. At the outset of papers, a basic process plan is modeled that includes design requirements after that it is mathematically modeled. Mathematically modeled process plan is then optimized in order to find optimal solution. Research then search the potential of linear programming optimization technique in handling optimization of process plan.