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Abstract

Abstract-The problem of computing a route for a mobile agent that incrementally fuses the data as it visits the nodes in a distributed sensor network is considered. The order of nodes visited along the route has a significant impact on the quality and cost of fused data, which, in turn, impacts the main objective of the sensor network, such as target classification or tracking. We present a simplified analytical model for a distributed sensor network and formulate the route computation problem in terms of maximizing an objective function, which is directly proportional to the received signal strength and inversely proportional to the path loss and energy consumption. We show this problem to be NP-complete and propose a genetic algorithm to compute an approximate solution by suitably employing a two-level encoding scheme and genetic operators tailored to the objective function. We present simulation results for networks with different node sizes and sensor distributions, which demonstrate the superior performance of our algorithm over two existing heuristics, namely, local closest first and global closest first methods.

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Purpose This paper aims to identify, analyze and categorize the major readiness factors for implementing Lean Six Sigma (LSS) in health-care organizations using total interpretive structural modelling technique. The readiness factors are identified would help the managers to recognize the areas that lack and provide importance to the successful implementation of LSS in those areas. The paper further intends to understand the hierarchical interrelationships among the readiness factors identified using dependence and driving power. Design/methodology/approach In total, 16 readiness factors are identified from the literature review and expert opinions are collected from hospitals. The scheduled interview is conducted based on a questionnaire survey in hospitals in the Indian context to identify the relevance of the relations among the readiness factors. The expert opinions are used in the initial reachability matrix and interpretative interaction matrix. Matrix impact cross multiplication applied to classification (MICMAC) analysis uses dependence and driving power to understand the hierarchical relationship among the readiness factors identified. Findings The result indicates that customer-oriented and goal management cultures are the key readiness factors for LSS. The execution technique and training are given according to the current demand of customers and goal change of organization. The manager needs to concentrate more on readiness factors to formulate the execution process of LSS for continuous improvement of the health-care organization. The readiness level helps the manager to identify the target area for LSS execution. Research limitations/implications This research focuses mainly on readiness factors for the implementation of LSS in the health-care industry. Practical implications This study would be useful for researchers and practitioners to understand the readiness factors before starting the implementation process of LSS. Originality/value Many research studies are being done on the success and failure rate of implementation of factors. The present study identifies the readiness factors related to LSS, especially for the health-care industry.