Design of an intelligent support system for fabric quality inspection

Efficient quality management in production process is the key factor for firm’s permanence and prosperity. The rapped globalization development and shortage of resources leading to enhance the efforts toward good raw material exploitation. The present work aims to develop a Decision Support System (DSS) that may provide and facilitate one of the most difficult multi-decision problems that quality managers, in textile manufacturing firm face. In addition, the DSS is developed for a textile manufacturer and it will automate a variety of tasks to improve rolls quality; control the defect distribution on rolls by inventing anew cutting scenarios with respect to inspection results, rolls length, and number of assemblies. The DSS tool, applied for more than 100 fabric lots, will be demonstrated through a short selection of practical case studies

Decision Support System for Production Scheduling (DSSPS)

AbstractIn this paper, we study scheduling problems that have batching considerations, and with sequence-dependent setup times for single machine and job shop flow planning. An important focus is the explicit treatment of setup times (costs), missed due dates (tardiness) and wastage as important cost components that impacts the direct and indirect cost. Indirect cost reflects the true cost of an urgent order that caused the machine setup to be re-arranged that can influence business decision on how to priced such order and how on a recurring basis might affect the bottom line profit and lost.[Allahverdi et al., 2006] found that the majority of the earlier papers assumed that the setup time (cost) is negligible or part of the job processing time (cost). This assumption simplifies the analysis however it adversely affects the solution quality. However, there is an increased interest in scheduling problems involving setup times as many recognised that there are tremendous savings when setup times/costs are explicitly incorporated in scheduling decision in various real world industrial environments