A state-of the-art survey of TOPSIS applications

Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors’ nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.     

An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank

Developing decision support system (DSS) can overcome the issues with personnel attributes and specifications. Personnel specifications have greatest impact on total efficiency. They can enhance total efficiency of critical personnel attributes. This study presents an intelligent integrated decision support system (DSS) for forecasting and optimization of complex personnel efficiency. DSS assesses the impact of personnel efficiency by data envelopment analysis (DEA), artificial neural network (ANN), rough set theory (RST), and K-Means clustering algorithm. DEA has two roles in this study. It provides data to ANN and finally it selects the best reduct through ANN results. Reduct is described as a minimum subset of features, completely discriminating all objects in a data set. The reduct selection is achieved by RST. ANN has two roles in the integrated algorithm. ANN results are basis for selecting the best reduct and it is used for forecasting total efficiency. Finally, K-Means algorithm is used to develop the DSS. A procedure is proposed to develop the DSS with stated tools and completed rule base. The DSS could help managers to forecast and optimize efficiencies by selected attributes and grouping inferred efficiency. Also, it is an ideal tool for careful forecasting and planning. The proposed DSS is applied to an actual banking system and its superiorities and advantages are discussed.     

Enhanced exact solution methods for the Team Orienteering Problem

The Team Orienteering Problem (TOP) is one of the most investigated problems in the family of vehicle routing problems with profits. In this paper, we propose a Branch-and-Price approach to find proven optimal solutions to TOP. The pricing sub-problem is solved by a bounded bidirectional dynamic programming algorithm with decremental state space relaxation featuring a two-phase dominance rule relaxation. The new method is able to close 17 previously unsolved benchmark instances. In addition, we propose a Branch-and-Cut-and-Price approach using subset-row inequalities and show the effectiveness of these cuts in solving TOP.     

CEDAR: Modeling impact of component error derating and read frequency on system-level vulnerability in high-performance processors

Reliability of the current microprocessor technology is seriously challenged by radiation-induced soft errors. Accurate Vulnerability Factor (VF) modeling of system components is crucial in designing cost-effective protection schemes in high-performance processors. Although Statistical Fault Injection (SFI) techniques can be used to provide relatively accurate VF estimations, they are often very time-consuming. Unlike SFI techniques, recently proposed analytical models can be used to compute VF in a timely fashion. However, VFs computed by such models are inaccurate as the system-level impact of soft errors is overlooked.     

Modification of poly(propylene) by grafted polyester‐amide‐based dendritic nanostructures with the aim of improving its dyeability

Fiber‐graded poly(propylene) was modified by polyester‐amide‐based dendritic nanostructures with the aim of improving its dyeability. Two different dendritic polymers were used and the dendritic nanostructures were formed in situ via reactive blending with maleic anhydride‐modified poly(propylene). Samples were chosen exploiting a 4‐component mixture design. Thermal, morphological, and rheological characterizations showed domains with different size and distribution were formed and primary properties of the dendritics determined the characteristics of the resulted domains. Morphological parameters were quantified by digital analysis of scanning electron microscope images. Thermal and rheological behavior also demonstrated good agreements with the inferred morphology of the formed dendritic domains. The modified samples were then dyed with dispersed dyestuffs. A variety of substantivities were obtained, and some of the modified samples showed a significant enhancement in dyeing properties. A predictive model was developed for K/S ratio, where K and S are absorption and scattering coefficients of the Kubelka‐Munk one constant theory, respectively. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Evaluation of Microstructure and Mechanical Properties in Dissimilar Austenitic/Super Duplex Stainless Steel Joint

To study the effect of chemical composition on microstructural features and mechanical properties of dissimilar joints between super duplex and austenitic stainless steels, welding was attempted by gas tungsten arc welding process with a super duplex (ER2594) and an austenitic (ER309LMo) stainless steel filler metal. While the austenitic weld metal had vermicular delta ferrite within austenitic matrix, super duplex stainless steel was mainly comprised of allotriomorphic grain boundary and Widmanstätten side plate austenite morphologies in the ferrite matrix. Also the heat-affected zone of austenitic base metal comprised of large austenite grains with little amounts of ferrite, whereas a coarse-grained ferritic region was observed in the heat-affected zone of super duplex base metal. Although both welded joints showed acceptable mechanical properties, the hardness and impact strength of the weld metal produced using super duplex filler metal were found to be better than that obtained by austenitic filler metal.     

Copper corrosion in sodium dodecyl sulphate solutions and carbon nanotube nanofluids: A modified Koutecky–Levich equation to model the agitation effect

Copper corrosion in sodium dodecyl sulphate (SDS) solutions and carbon nanotube (CNT) nanofluids were studied by potentiodynamic polarization. For the corrosion current densities calculations, Koutecky–Levich equation was modified to model the combined charge and mass transport. 0.005 M SDS reduced the copper corrosion current density by 81%. Higher SDS concentrations enhanced corrosion. Stirring SDS solutions increased the corrosion current density by ∼75%. By adding CNT to SDS solution, the corrosion current density first decreased and then remained constant. Stirring CNT nanofluids didn’t change the corrosion current density. An adsorbed CNT layer on copper controlled the corrosion process in CNT nanofluids.