Production engineering

About: Production engineering is a research topic. Over the lifetime, 2657 publications have been published within this topic receiving 37409 citations.

Decision Support Using Simulation for Customer-Driven Manufacturing System Design and Operations Planning

Abstract: Agile, fast and flexible production networks are a must in today’s global competition. The interrelations between manufacturing systems and processes are becoming more complex and the amount of data for decision making is growing. Manufacturing, engineering and production management decisions involve the consideration of multiple parameters. These often complex, interdependent factors and variables are too many for the human mind to cope with at one time. Agile production needs a management and evaluation tool for production changes, manufacturing system development, configuration and operations planning. A decision support system based on manufacturing simulation is one suitable solution. Discrete Event Simulation (DES) has mainly been used as a production system analysis tool to evaluate new production system concepts, layout and control logic. Recent development has enhanced DES models for use in the day-to-day operational production planning of manufacturing facilities. These "as built" models provide manufacturers with the ability to evaluate the capacity of the system for new orders, unforeseen events such as equipment downtime, and changes in operations. After a simulation model has been built, experiments are performed by changing the input parameters and predicting the response. Experimentation is normally carried out by asking "what-if" questions and using the model to predict the likely outcome. A simulation-based Decision Support System (DSS) can be used to augment the tasks of planners and schedulers to run production more efficiently (Figure 1). Some of the benefits of implementing an operational simulation scheduling system include: less effort required to plan day-to-day scheduling, customer order due date conformance, synchronisation of flow through the plant, minimisation of set-ups/changeovers, early warnings of potential problems, checks of critical resources and materials, and, naturally, “what-if” scenario analysis for capacity planning. Although dedicated software packages are currently available, there are limited examples of the use of simulation tools in the operational planning of manufacturing. This chapter also sheds light on development challenges and current development efforts to solve these challenges for this data and model-driven DSS. The major challenges are: 1) data integration, 2) automated simulation model creation and updates, and 3) visualisation of results for interactive and effective decision making.

Application of a knowledge engineering process to support engineering design application development

Abstract: The design, analysis and optimization process of complex products can be supported by automation of repetitive and non-creative engineering tasks The Design and Engineering Engine (DEE) is a useful concept to structure this automation Within the DEE, a product is parametrically defined using Knowledge Based Engineering (KBE) techniques To develop and successfully implement the concept of the DEE in industry, a Knowledge Engineering (KE) process is developed, integrating KBE techniques with Knowledge Management (KM) The KE process is applied to develop an application supporting the design and manufacturing of aircraft wiring harnesses, focussing on the assignment of electrical signals to connectors The resulting engineering design application reduces the recurring time of the assignment process by 80%

Process-Oriented Production Planning and Control: Factors That Influence System Design

Abstract: This research describes how five production planning and control functions are accomplished by successful information systems in process industries, identifies factors that influence the design of ...

Scanning the issue and beyond: Toward ITS knowledge automation

Abstract: S TARTING with the first issue of the bimonthly version of IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, I will begin each issue by scanning and summarizing each article in a format that is suitable for presentation at Weibo (Micro blogs in Chinese), Twitter, and Facebook. Please check @IEEE-TITS (http://www.weibo.com/u/ 3967923931) for Weibo, https://www.facebook.com/IEEEITS for Facebook, and @IEEEITS (https://twitter.com/IEEEITS) for Twitter. In addition, I will go beyond the papers published here and give my thought on issues that I consider interesting or important for current or future research and development in the area of intelligent transportation.