Performance Analyses of Graph Heuristics and Selected Trajectory Metaheuristics on Examination Timetable Problem

Examination timetabling problem is hard to solve due to its NP-hard nature, with a large number of constraints having to be accommodated. To deal with the problem effectually, frequently heuristics are used for constructing feasible examination timetable while meta-heuristics are applied for improving the solution quality. This paper presents the performances of graph heuristics and major trajectory metaheuristics or S-metaheuristics for addressing both capacitated and un-capacitated examination timetabling problem. For constructing the feasible solution, six graph heuristics are used. They are largest degree (LD), largest weighted degree (LWD), largest enrolment degree (LE), and three hybrid heuristic with saturation degree (SD) such as SD-LD, SD-LE, and SD-LWD. Five trajectory algorithms comprising of tabu search (TS), simulated annealing (SA), late acceptance hill climbing (LAHC), great deluge algorithm (GDA), and variable neighborhood search (VNS) are employed for improving the solution quality. Experiments have been tested on several instances of un-capacitated and capacitated benchmark datasets, which are Toronto and ITC2007 dataset respectively. Experimental results indicate that, in terms of construction of solution of datasets, hybridizing of SD produces the best initial solutions. The study also reveals that, during improvement, GDA, SA, and LAHC can produce better quality solutions compared to TS and VNS for solving both benchmark examination timetabling datasets

Solving Examination Timetabling Problem using Partial Exam Assignment with Great Deluge Algorithm

Constructing a quality solution for the examination timetable problem is a difficult task. This paper presents a partial exam assignment approach with great deluge algorithm as the improvement mechanism in order to generate good quality timetable. In this approach, exams are ordered based on graph heuristics and only selected exams (partial exams) are scheduled first and then improved using great deluge algorithm. The entire process continues until all of the exams have been scheduled. We implement the proposed technique on the Toronto benchmark datasets. Experimental results indicate that in all problem instances, this proposed method outperforms traditional great deluge algorithm and when comparing with the state-of-the-art approaches, our approach produces competitive solution for all instances, with some cases outperform other reported result

A Comparative Spectrophotometric Study Using Ferrozine and 1,10-Ortho-phenanthroline to Evaluate the Iron Redox Ratio (Fe2+/Sigma Fe) in Glass Prepared by Microwave Heating

In the present study, Fe-doped barium borosilicate glass has been melted at 1250 degrees C under microwave heating. The iron redox ratio (Fe2+/total Fe) in the glass is investigated by two spectrophotometric methods. A novel decomposition technique has been optimized to measure the ferrous oxidation state in glass. Ferrozine was chosen as a specific complexing reagent; it forms a deep violet color complex with Fe2+ and has a broad absorbance peak centered at 562 nm. 1,10-ortho-phenanthroline develops an orange color complex with Fe2+ (having an absorbance peak centered at similar to 510 nm) and has been used to determine ferrous ion in glass. Both the methods are compared and the estimated redox ratio was found to be higher in the ferrozine method. The error limit of measurement has been determined as 0.012 and 0.023 for the ferrozine and 1,10-ortho-phenanthroline methods, respectively

Antioxidant attributes of tea in North Bengal, India: Relation with its principal constituents and properties of soil

This study was performed in 18 tea gardens in North Bengal, India, from 2012 to 2017. The data were pooled to investigate the relationship with soil physico-chemical properties, phyto-constituents, antioxidant attributes and age of the tea bushes and principal component analysis (PCA). PCA and dendro-hit maps were also performed with each region. The 28 principal components were chosen based on their eigen values, explaining the total data variance for tea in Dooars, Terai and Darjeeling hill. In almost all cases, composite soil physico-chemical attributes were heavily loaded on the second principal component and clustered, as visual evidenced by the dendro-hit map. Different attributes were significantly correlated each other in case of Terai i.e. (value of “r’’ at P<0.01 level) clay fraction (0.778), electrical conductivity (0.618), N (0.777), S (0.748), P (0.514 ppm), flavour index (0.918), total polyphenol (0.687) DPPH (0.794), nitric oxide (0.913), anti-lipid peroxidation (0.717) and metal chelating (0.665). In Dooars region, attributes were significantly correlated with silt (0.718), pH (0.875), P (0.615 ), chloride (0.858), TP (0.776), flavonol (0.923), quinone (0.666), tannins (0.865), DPPH (0.536), superoxide (0.576), ABTS (0.520) and MC (0.777) and in the case of Darjeeling hills, attributes were highly correlated with clay (0.812), sand (0.818), silt fraction (0.974), K (0.932), S (0.999), MC of soil (0.671), TP (0.853), tannins (0.912), DPPH (0.624), ABTS (0.661) and MC (0.633) repectively

A comparative property investigation of lithium phosphate glass melted in microwave and conventional heating

The present study addresses the application of microwave (MW) energy for melting lithium phosphate glass. A comparative analysis of the properties is presented with glasses melted in conventional resistance heating adopting standard methods of characterization. The density of the glass was found less in MW heating. The glass transition temperature was recorded as 3–10◦C lower in MW prepared glass than in conventional glass. Micro-hardness is found to be improved in case of MW heating. Maximum forward power was recorded less than 2 kW with an average power ∼1kW during melting of 40g glass in MW furnace. MW forward and reflected power measured during melting in the MW cavity was elaborated. Total melting time was within 2h 30 min in MW heating, whereas it was 6–7 h in resistive heating. Total power consumed was ∼5kWh in MW heating and ∼14kWh in resistance heating