Service Innovation and Service Innovation Performance: A Study of Banking Services

This study empirically examines the relationship between service innovation and service innovation performance. Typology of service innovation (SI) based on new service offering/product (NSO), new service process (NSP) and new service business model (NSBM)is tested for their likely effect on service innovation performance (SIP) of banks from a developing country context in the face of business environment (BE) characterized by dynamism and competitiveness. It uses quantitative data gathered through cross-sectional self-administered survey questionnaire on a 5 -point Likert-type scale from a sample of 220 managers from the banking organizations to predict the impact of service innovation on service innovation performance. Data are analyzed through SPSS-19 and Amos-18 by means of bivariate correlation and regression. Results indicate a strong impact of multi-dimensional service innovation on service innovation performance. Each dimension of service innovation significantly predicts service innovation performance. Business environment theorized in terms of competition and uncertainty fails to moderate the relationship between service innovation and service innovation performance. In this way, this study offers many valuable insights in the field of service innovation and performance management areas which can be valuable to several stakeholders such as researchers, practitioners and policy makers in developing and implementing optimum service innovation strategies to augment and synergize performance of their services

A Preliminary Study on Effectiveness of a Standardized Multi-Robot Therapy for Improvement in Collaborative Multi-Human Interaction of Children with ASD

This research article presents a preliminary longitudinal study to check the improvement in multi-human communication of children with Autism Spectrum Disorder (ASD) using a standardized multirobot therapy. The research is based on a 3 step framework: 1) Human-Human Interaction, Stage-1 (HHIS1), 2) Human-Robot Interaction, Stage-2 (HRI-S2), and 3) Human-Human Interaction, Stage-3 (HHI-S3). All three stages of the therapy consist of two command sets: 1) Controls commands and 2) Evaluation commands (auditory commands, visual commands, and combination of both). The concept of multiple robots is introduced to help multi-human communication and discourage isolation in ASD children. The joint attention of an ASD child is improved by the robotic therapy in stage 2 considering it as a key parameter for a multi-human communication scenario. The improvement in joint attention results in better command following in a triad multi-human communication scenario in stage 3 as compared to stage 1. The proposed intervention has been tested on 8 ASD subjects with 10 sessions over a period of two and a half months (10 weeks). Each session of human-human interaction (stage 1 and 3) consisted of 14 cues whereas 18 cues were presented by each robot for human-robot interaction (stage 2). The results indicate an overall 86improvement in the social communication skills of ASD children in case of a multi-human scenario. Validation of results and effectiveness of the therapy has been further accomplished through the use of the Childhood Autism Rating Scale (CARS) score

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions

Multi-Hole Drilling Tool Path Planning and Cost Management through Hybrid SFLA-ACO Algorithm for Composites and Hybrid Materials

In the process of drilling multiple holes in composites and hybrid materials, almost 70% of the time is consumed in tool traveling and tool changing. Recently, researchers have focused on this consumption of time for optimization of the tool path. A literature review revealed the following research gap: little work has been performed on the hybridization of metaheuristics. In the present study, the hybridization of SFLA and ACO metaheuristic algorithms is carried out, which is based on this research gap. The hybridization of SFLA and ACO metaheuristic algorithms provides originality and novelty in this study. The main objective of this study is to minimize the tool path in drilling problems. The proposed algorithm was applied to five benchmark multi-hole drilling problems and one industrial problem from the literature. The outcome of this work is evaluated with the results of dynamic programming (DP), ACO, an immune-based evolutionary approach (IA), and a modified SFLA for five benchmark problems. The accuracy of the results was improved by 2.27% using the proposed hybrid algorithm, indicating that the proposed algorithm is superior to DP, ACO, IA, and the modified SFLA. Additionally, the results of the proposed hybrid algorithm for an example industrial problem from the literature were compared with those of the SFLA and modified SFLA. The proposed algorithm reduced the total cost by 6.17% and 3.76% compared with the SFLA and modified SFLA, respectively. Thus, the efficacy of the proposed hybrid algorithm was confirmed, along with its applicability

Application of hybrid SFLA-ACO algorithm and CAM softwares for optimization of drilling tool path problems

Abstract In drilling process almost seventy percent time is spent in tool switching and moving the spindle from one hole to the other. This time travel is non productive as it does not take part in actual drilling process. Therefore, this non productive time needs to be optimized. Different metaheuristic algorithms have been applied to minimize this non productive tool travel time. In this study, two metaheuristic approaches, shuffled frog leaping algorithm (SFLA) and ant colony optimization (ACO) have been hybridized. In industry, the CAM softwares are employed for minimization of non productive tool travel time and it is considered that the path obtained by using the CAM softwares is the optimized path. However this is not the case in all problems. In order to show the contribution of the SFLA-ACO algorithm and to prove that results achieved through CAM softwares are not always optimized, hybrid SFLA-ACO algorithm has been applied to two drilling problems as case studies with the main objective of minimization of non productive tool travel time. The drilling problems which are taken from the manufacturing industry include ventilator manifold problem and lift axle mounting bracket problem. The results of hybrid SFLA-ACO algorithm have been compared with the results of commercially available computer aided manufacturing (CAM) software. For comparison purpose, the CAM softwares used are Creo 6.0, Pro E, Siemens NX and Solidworks. The comparison shows that the results of proposed hybrid SFLA-ACO algorithm are better than commercially available CAM softwares in both real world manufacturing problems. Article highlights Different optimization techniques are being used for optimization of drilling tool path problems. In this paper two techniques SFLA and ACO has been combined to form a hybrid SFLA-ACO algorithm and has been applied to the real world industrial problems. Two real world problems have been taken from the local manufacturing industries. In both the problems the objective is to optimize the tool traveling time through hybrid SFLA-ACO and compare it with CAM software. Four CAM softwares have been used for comparison purpose. The problems undertaken are solved through these CAM software and compared with the results of hybrid SFLA-ACO results. As result of comparison it is found that in both the problems the performance of hybrid SFLA-ACO algorithm remains outclass. This signifies that results of CAM software in case of optimization of drilling tool path are not always optimal and these can be improved by using different optimization techniques

Intelligent Machine Vision Based Modeling and Positioning System in Sand Casting Process

Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location