Using fahp-vikor for operation selection in the flexible job-shop scheduling problem: A case study in textile industry

Scheduling of Flexible Job Shop Systems is a combinatorial problem which has been addressed by several heuristics and meta-heuristics. Nevertheless, the operation selection rules of both methods are limited to an ordered variant wherein priority-dispatching rules are not simultaneously deemed in the reported literature. Therefore, this paper presents the application of dispatching algorithm with operation selection based on Fuzzy Analytic Hierarchy Process (FAHP) and VIKOR methods while considering setup times and transfer batches. Dispatching, FAHP, and VIKOR algorithms are first defined. Second, a multi-criteria decision-making model is designed for operation prioritization. Then, FAHP is applied to calculate the criteria weights and overcome the uncertainty of human judgments. Afterwards, VIKOR is used to select the operation with the highest priority. A case study in the textile industry is shown to validate this approach. The results evidenced, compared to the company solution, a reduction of 61.05% in average delay

A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach

E-commerce start-ups have ventured into emerging economies and are growing at a significantly faster pace. Big data has acted like a catalyst in their growth story. Big data analytics (BDA) has attracted e-commerce firms to invest in the tools and gain cutting edge over their competitors. The process of adoption of these BDA tools by e-commerce start-ups has been an area of interest as successful adoption would lead to better results. The present study aims to develop an interpretive structural model (ISM) which would act as a framework for efficient implementation of BDA. The study uses hybrid multi criteria decision making processes to develop the framework and test the same using a real-life case study. Systematic review of literature and discussion with experts resulted in exploring 11 enablers of adoption of BDA tools. Primary data collection was done from industry experts to develop an ISM framework and fuzzy MICMAC analysis is used to categorize the enablers of the adoption process. The framework is then tested by using a case study. Thematic clustering is performed to develop a simple ISM framework followed by fuzzy analytical network process (ANP) to discuss the association and ranking of enablers. The results indicate that access to relevant data forms the base of the framework and would act as the strongest enabler in the adoption process while the company rates technical skillset of employees as the most important enabler. It was also found that there is a positive correlation between the ranking of enablers emerging out of ISM and ANP. The framework helps in simplifying the strategies any e-commerce company would follow to adopt BDA in future. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature