We present a novel method for the development of a micro lenslets hexagonal array. We use gradient index (GRIN) micro lenses where the variation of the refraction index is achieved with a structure of nanorods made of 2 types of glasses. To develop the GRIN micro lens array, we used a modified stack-and-draw technology which was originally applied for the fabrication of photonic crystal fibers. This approach results in a completely flat element that is easy to integrate with other optical components and can be effectively used in high refractive index medium as liquids. As a proof-of-concept of the method we present a hexagonal array of 469 GRIN micro lenses with a diameter of 20 µm each and 100% fill factor. The GRIN lens array is further used to build a Shack-Hartmann detector for measuring wavefront distortion. A 50 lens/mm sampling density is achieved.

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This study develops an operational policy for a three-echelon supply chain. A mathematical programming model is proposed to maximize the combined profit of the suppliers, manufacturers, and distributors. The model is developed under the supply chain management paradigm, which highlights the cooperation of all the nodes along the supply chain to improve performance criteria like total profit. We take into account the deteriorating nature of raw materials and final products. The proposed model was then applied to a real-world case of an Iranian supply chain active in the detergent production industry using its historical data for parameters. The model was solved using GAMS. Results indicate that the integrated approach was able to increase the supply chain’s total profit by 15%. Sensitivity analysis also showed that variable costs of final products are high, leaving a shallow margin. Other managerial insights are also drawn from results.

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.