What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

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Learning To Teach In Higher Education

コンピュータ・サイエンス : ACM computing surveys

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Over the last three decades, a large number of evolutionary algorithms have been developed for solving multi-objective optimization problems. However, there lacks an upto-date and comprehensive software platform for researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve their real-world problems. The demand of such a common tool becomes even more urgent, when the source code of many proposed algorithms has not been made publicly available. To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multiobjective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files. More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators. Source code of PlatEMO is now available at: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.

PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]

In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.This article1 provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

/pdf/search-based-software-engineering-trends-techniques-and-3b0bwaagfi.pdf

Search-based software engineering: Trends, techniques and applications

https://bura.brunel.ac.uk/bitstream/2438/13305/1/Fulltext.pdf

The relationship between green supply chain management and performance: A meta-analysis of empirical evidences in Asian emerging economies

This paper is concerned with the Multi-Objective Next Release Problem (MONRP), a problem in search-based requirements engineering Previous work has considered only single objective formulations In the multi-objective formulation, there are at least two (possibly conflicting) objectives that the software engineer wishes to optimize It is argued that the multi-objective formulation is more realistic, since requirements engineering is characterised by the presence of many complex and conflicting demands, for which the software engineer must find a suitable balance The paper presents the results of an empirical study into the suitability of weighted and Pareto optimal genetic algorithms, together with the NSGA-II algorithm, presenting evidence to support the claim that NSGA-II is well suited to the MONRP The paper also provides benchmark data to indicate the size above which the MONRP becomes non--trivial

/pdf/the-multi-objective-next-release-problem-5b2ouemlyj.pdf

The multi-objective next release problem

https://bura.brunel.ac.uk/bitstream/2438/11837/1/Fulltext.pdf

S. Afshin Mansouri

Papers

Search-based software engineering: Trends, techniques and applications

The relationship between green supply chain management and performance: A meta-analysis of empirical evidences in Asian emerging economies

The multi-objective next release problem

Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption

A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines