A robust programming approach towards design and optimization of microalgae-based biofuel supply chain. (October 2016)

• Record Type: Journal Article
• Authors: Mohseni, Shayan; Pishvaee, Mir Saman
• Is Part Of: Computers & industrial engineering. Volume 100(2016)
• Page Start: 58
• Page End: 71
• Publication Date: 2016-10
• Subjects: Microalgae -- Biomass supply chain -- Robust optimization -- Sustainable development -- Robust sensitivity analysis; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.08.003
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Developing a sustainable supply chain model for microalgae biofuel production. Presenting a robust mathematical model to determine the optimal strategic and tactical decisions. Suggesting strategies for cost reduction of biofuel production. Employment of the proposed model in a real case study in Iran. Abstract: Despite their inherent attractive characteristics, microalgae do not play a significant role in the green energy market because of high production cost. In order to propel this nascent industry one step further towards commercial viability, the present study expands the narrow outlook of previous studies which is restricted to a single location into a perspective with a nationwide network by proposing a sustainable supply chain model capable of revealing opportunities and limitations of algae industry's future. The proposed model is formulated based on batch and continuous production systems which completely cover the possible future configuration of microalgae-based biofuel supply chain and also offers managerial implications contributing to cost mitigation. Moreover, the uncertainty whose importance is accentuated in the case of algae production due to lack of real-world implementation is captured by the utilization of robust optimization under two norms of the uncertain data vector. A real case study is carried out to explore the applicability of the proposed model. Furthermore, robust sensitivity analysis technique is performed, allowing decision makers to analyze the effect of uncertain parameters on production cost based on their confidence levels.