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Nonlinear Equations (NEs), which may usually have multiple roots, are ubiquitous in diverse fields. One of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run, however, it is a difficult and challenging task in numerical computation. In recent years, Intelligent Optimization Algorithms (IOAs) have shown to be particularly effective in solving NEs. This paper provides a comprehensive survey on IOAs that have been exploited to locate multiple roots of NEs. This paper first revisits the fundamental definition of NEs and reviews the most recent development of the transformation techniques. Then, solving NEs with IOAs is reviewed, followed by the benchmark functions and the performance comparison of several state-of-the-art algorithms. Finally, this paper points out the challenges and some possible open issues for solving NEs.

In this study, a comprehensive evaluation of management for pathogenic microbiology laboratories is performed based on a combination of Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Rank Sum Ratio (RSR) methods; in addition, the basis for improving laboratory management is provided. Using the laboratory evaluation tool developed by World Health Organization and a combination of TOPSIS and RSR methods, a system of evaluation indicators for the management of Chinese pathogenic microbiology laboratories is established for comprehensively evaluating the pathogenic microbiology laboratories of seven provincial Centers for Disease Control and Prevention. The evaluation system includes 12 primary indicators and 37 secondary indicators. In terms of laboratory management, the seven laboratories were ranked as D, G, E, C, F, B, and A in descending order. None of these laboratories were evaluated as “good” or “poor.” One of the laboratories was marked as “relatively poor” (A), two as “medium” (B and F), and four as “relatively good” (C, E, G, and D). In this study, a method for evaluating laboratory management using the TOPSIS and RSR methods is proposed, and a basis for comprehensively evaluating laboratory management for pathogenic microbiology laboratories is provided to reflect management practices.

Operation theatre is one of the most significant assets in a hospital as the greatest source of revenue as well as the largest cost unit. This paper focuses on surgery scheduling optimization, which is one of the most crucial tasks in operation theatre management. A combined scheduling policy composed of three simple scheduling rules is proposed to optimize the performance of scheduling operation theatre. Based on the real-life scenarios, a simulation-based model about surgery scheduling system is built. With two optimization objectives, the response surface method is adopted to search for the optimal weight of simple rules in a combined scheduling policy in the model. Moreover, the weights configuration can be revised to cope with dispatching dynamics according to real-time change at the operation theatre. Finally, performance comparison between the proposed combined scheduling policy and tabu search algorithm indicates that the combined scheduling policy is capable of sequencing surgery appointments more efficiently.