تخمین ضرایب علیت در نقشه راهبردی سازمان به کمک آموزش نقشه شناختی فازی با الگوریتم جستجوی گرانشی
محورهای موضوعی : مهندسی برق و کامپیوترعلی جهان‌بیگی 1 , منصور شیخان 2 * , محسن روحانی 3
1 - دانشگاه آزاد اسلامی، واحد تهران جنوب
2 - دانشگاه آزاد اسلامی، واحد تهران جنوب
3 - دانشگاه آزاد اسلامی، واحد تهران جنوب
کلید واژه: الگوریتم جستجوی گرانشی روش کارت امتیازی متوازن نقشه راهبردی نقشه شناختی فازی,
چکیده مقاله :
بیش از دو دهه از معرفی روش کارت امتیازی متوازن جهت کنترل و پایش راهبردهای سازمان‌ها می‌گذرد. مهم‌ترین دستاورد این روش ترسیم نقشه راهبردی است. در این نقشه با ترسیم روابط علّی بین اهداف راهبردی، امکان تحلیل‌های گوناگون فراهم شده و در تصمیم‌گیری مدیران نقش به سزایی دارد. برای دست‌یابی به یک نقشه راهبردی دقیق لازم است شدت هر رابطه علّی به درستی تخمین زده شود. از این رو تخمین ضرایب این روابط در نقشه‌های راهبردی مورد توجه قرار گرفته است. از مهم‌ترین روش‌های موجود می‌توان روش‌های دیمتل و دلفی را نام برد که مبتنی بر نظرات کارشناسان می‌باشند. البته ممکن است نظرات کارشناسان در حوزه‌های پیچیده کسب و کار دقیق نباشند، لذا به کارگیری الگوریتم‌های هوش محاسباتی بر اساس داده‌های موجود برای تخمین دقیق‌تر ضرایب علّی مفید است. مورد مطالعه این تحقیق، نقشه راهبردی یک مؤسسه مالی بوده که روابط بین اهداف راهبردی و ضرایب آنها به روش دلفی- فازی از نظرات کارشناسان از قبل تعیین شده‌اند. هدف اصلی در این مقاله، تخمین دقیق‌تر ضرایب علّی به کمک داده‌های موجود و الگوریتم‌های هوش محاسباتی می‌باشد. بدین منظور، ابتدا نقشه راهبردی را به ازای هر هدف معلول موجود به چند نقشه شناختی فازی تجزیه کرده و سپس از الگوریتم جستجوی گرانشی برای آموزش هر نقشه شناختی فازی استفاده شده است. هدف از آموزش نقشه‌های شناختی، تعیین ضرایب علّی بهینه بر اساس دو تابع هدف می‌باشد. تابع هدف اول، سعی در کاهش خطای پیش‌بینی مقادیر درصد تحقق اهداف راهبردی را داشته و تابع هدف دوم، ضرایب علّی را در بازه تعیین‌شده توسط کارشناسان نگاه می‌دارد. نتایج به دست آمده از روش پیشنهادی، خطای مدل را نسبت به مدل کارشناسان کاهش داد. از مقایسه نتایج الگوریتم جستجوی گرانشی با سایر الگوریتم‌های بهینه‌یابی نیز مشاهده شد که الگوریتم جستجوی گرانشی در تعداد گام‌های کمتری در مقایسه با الگوریتم‌های بهینه‌یابی ازدحام ذرات و اجتماع مورچگان نقطه بهینه سراسری را می‌یابد.
More than two decades ago, the balanced scorecard method was proposed to control and monitor the strategy of organizations. The most important outcome of this method is the strategy map. The causal relations among strategic goals (SGs) are established in this map which can help managers in decision making process. To have a precise strategy map, it is necessary to estimate the strengths of each causal relation correctly. So, the estimation of causal coefficients has attracted research interest in forming strategy maps. In this way, DEMATEL and Delphi are two well-known methods that are based on the experts’ opinion. However, these opinions are not exact in the complex business fields; so, the computational intelligence (CI) algorithms have been employed for more precise estimation of causality coefficients. In this study, the relations among SGs and their coefficients have been provided by the experts of a banking institution as the input of the proposed method. The main purpose of this study is to improve the precision of causal coefficients using a CI-based algorithm. For this purpose, the strategy map is decomposed into multiple fuzzy cognitive maps (FCMs) and then, the gravitational search algorithm (GSA) is employed for FCM training. In this way, two objective functions are used for determining the optimal value of causality coefficients. The first objective function is employed for reducing error in the prediction of SG realization percentages. The second objective function keeps causal coefficients in the intervals determined by the experts. Experimental results show that the total error of proposed model is lower than the expert-based model. In addition, GSA performs better than the following algorithms in finding the global optimum point in this real-world case study: particle swarm optimization and ant colony optimization.
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