Luminomagnetic bifunctionality of Mn2+-bonded graphene oxide/reduced graphene oxide two dimensional nanosheets

Herein, we report the luminomagnetic bifunctional properties of two-dimensional (2D) Mn2+ bonded graphene oxide (GO)/reduced graphene oxide (RGO) nanosheets synthesized using a facile route of oxidation followed by a solvothermal reduction method. Photoluminescence (PL) studies (excited by different wavelengths) revealed that the resonant energy transfer between Mn2+ and sp(3)/sp(2) clusters of GO/RGO is responsible for the enhancement of emissions. Moreover, pH-sensitive PL behaviors have also been investigated in detail. The ferromagnetic behavior is believed to arise due to defects in Mn2+ bonded GO composites. Thus, present reduction method provides a direct route to tune and enhance the optical properties of GO and RGO nanosheets bonded with Mn2+ ions, which creates an opportunity for various technological applications

A Bi-index continuous time MILP model for short-term scheduling of single-stage multi-product batch plants with parallel units

This paper presents a mixed integer linear programming formulation for the short-term scheduling of single-stage multi-product batch plants with parallel non-identical production units. This scheduling problem is highly combinatorial in nature especially because of the sequence-dependent changeover constraints. To formulate this type of problem, tri-index discrete decision variables, i.e. (order, order, unit), are commonly applied to represent the order assignments. This approach requires a large number of discrete decision variables that consequently make the model very time consuming to solve. To overcome this problem, the proposed formulation instead applies bi-index discrete variables (order, order). This greatly reduces the overall number of discrete decision variables while still keeping the generality of the model. For handling large-scale problems, pre-ordering heuristics were imposed to further reduce the solution time. Examples with various numbers of units and orders illustrate the effectiveness of the formulation both with and without the pre-ordering constraints. © 2000 Elsevier B.V. All rights reserved

A novel MILP formulation for short-term scheduling of multistage multi-product batch plants

This study presents a continuous-time mixed-integer linear programming model for short-term scheduling of multistage multi-product batch plants. The model determines the optimal sequencing and the allocation of customer orders to non-identical processing units by minimizing the earliness and tardiness of order completion. This is a highly combinatorial problem, especially when sequence-dependent relations considered to be are such as the setup time between consecutive orders. A common approach to this scheduling problem relies on the application of tetra-index binary variables, i.e. (order, order, stage, unit) to represent all the combinations of order sequences and assignments to units in the various stages. This generates a huge number of binary variables and, as a consequence, much time is required for solutions. This study proposes a novel formulation that replaces the tetra-index binary variables by one set of tri-index binary variables (order, order, stage) without losing the model's generality. By the elimination of the unit index, the new formulation requires considerably fewer binary variables, thus significantly shortening the solution time. (C) 2000 Elsevier Science Ltd. All rights reserved