Optimal production scheduling for smart manufacturers with application to food production planning. (June 2020)

• Record Type: Journal Article
• Authors: Angizeh, Farhad; Montero, Hector; Vedpathak, Abhinay; Parvania, Masood
• Is Part Of: Computers & electrical engineering. Volume 84(2020)
• Publication Date: 2020-06
• Subjects: Smart manufacturing -- Mixed-integer linear programming -- Energy cost reduction -- Energy flexibility -- Demand response -- Production planning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106609
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: This paper aims at implementing an optimization technique in smart manufacturing applications for co-optimizing the operation of multiple production lines to supply the desired demand for multiple products. The proposed model, which is formulated as a mixed-integer linear programming (MILP) problem, minimizes the total manufacturing cost by co-optimizing the operation schedule of production lines. The proposed model implements several operating constraints of the production lines that include their energy consumption and associated costs, productivity, scrap rates, changeover duration, and labor costs. The proposed model is implemented on a sample manufacturer, where seven production lines with different operational constraints are optimized to manufacture six product types. In addition, the model is utilized to optimize the operation of a real-world food production plant with five lines and twelve different product types. The simulation results show that the proposed optimal scheduling model allows the manufacturers to enhance production efficiency and save on operation costs by co-optimizing the operation schedule of production lines. Sensitivity analyses are conducted to demonstrate the performance of the model in different demand levels, where the manufacturers are enabled to optimally schedule the production lines at minimum cost given the production demand.