-
حرية الوصول المقاله
1 - تخمین جواب مدل برنامهریزی غیرخطی روش بهترین-بدترین با استفاده از حل مدلهای برنامهریزی خطی مختلط
محمدرضا دهقانی مهدی عباسیروش بهترین - بدترین یکی از روشهای جدید در مسائل تصمیمگیری چند شاخصه میباشد. روش مزبور با تشکیل و حل یک مدل برنامهریزی غیرخطی، جواب بهینه مسأله را تعیین میکند. با توجه به مشکلات حل مدل برنامهریزی غیرخطی مربوطه، تلاشهایی جهت ارائه مدلهای برنامهریزی خطی یا مدلهای أکثرروش بهترین - بدترین یکی از روشهای جدید در مسائل تصمیمگیری چند شاخصه میباشد. روش مزبور با تشکیل و حل یک مدل برنامهریزی غیرخطی، جواب بهینه مسأله را تعیین میکند. با توجه به مشکلات حل مدل برنامهریزی غیرخطی مربوطه، تلاشهایی جهت ارائه مدلهای برنامهریزی خطی یا مدلهای برنامهریزی خطی مختلط معادل صورت پذیرفته است. اما بر هر یک از مدلهای ارائه شده، ایراداتی وارد است. در این مقاله با رفع ایرادات مزبور، الگوریتمی جهت تخمین جواب مدل برنامهریزی غیرخطی روش مزبور با میزان خطای قابل قبول با استفاده از مدلسازی و حل مسائل برنامهریزی خطی مختلط پیشنهاد شده است. در الگوریتم پیشنهادی ابتدا مدل برنامهریزی غیرخطی معادل مدل اصلی تشکیل میشود. سپس با تقریب تکهای خطی جملات غیرخطی مدل توسط روش SOS2، اولین مدل برنامهریزی خطی مختلط متناظر تشکیل و حل میشود. اگر خطای جواب حاصله قابل قبول نباشد، بهبود تقریب تکهای خطی جملات غیرخطی و همچنین تشکیل و حل مدلهای جدید برنامهریزی خطی مختلط تا حصول جواب با میزان خطای قابل قبول ادامه مییابد. به منظور بررسی اعتبار الگوریتم، روشی جهت تولید نمونههای پوشش دهنده حالتهای مختلف یک مسأله پیشنهاد شد. سپس با استفاده از روش مزبور، تعداد 128 نمونهی سه و پنج شاخصه تولید شد. نتایج حاصل از پیادهسازی الگوریتم پیشنهادی برای حل نمونههای تولید شده، عملکرد مناسب الگوریتم پیشنهادی را نشان میدهد. در این خصوص با حل حداکثر سه مدل برنامهریزی خطی مختلط جهت حل نمونهها، تخمین جواب با حداکثر 1% خطا به دست میآید. تفاصيل المقالة -
حرية الوصول المقاله
2 - Alternative mixed integer linear programming model for finding the most efficient decision making unit in data envelopment analysis
Masomeh Abbasi Abbas GhomashiFinding the Most Efficient Decision-Making Unit (DMU) provides more information about efficient DMUs in data envelopment analysis (DEA). Hence, in recent years, many mixed-integer linear programming (MILP) models based on a common set of weights have been proposed to de أکثرFinding the Most Efficient Decision-Making Unit (DMU) provides more information about efficient DMUs in data envelopment analysis (DEA). Hence, in recent years, many mixed-integer linear programming (MILP) models based on a common set of weights have been proposed to determine the most efficient DMU. This paper introduces another MILP model to find the most efficient DMU. In this model, we use a numerical parameter to increase the discrimination power of the proposed model. To illustrate the various potential applications of the proposed model, we compare the performance of our model with the other three models using two real numerical examples. تفاصيل المقالة -
حرية الوصول المقاله
3 - Planning for Medical Emergency Transportation Vehicles during Natural Disasters
Hesam Adrang Ali Bozorgi-Amiri Kaveh Khalili-Damghani Reza Tavakkoli-MoghaddamOne of the main critical steps that should be taken during natural disasters is the assignment and distribution of resources among affected people. In such situations, this can save many lives. Determining the demands for critical items (i.e., the number of injured peop أکثرOne of the main critical steps that should be taken during natural disasters is the assignment and distribution of resources among affected people. In such situations, this can save many lives. Determining the demands for critical items (i.e., the number of injured people) is very important. Accordingly, a number of casualties and injured people have to be known during a disaster. Obtaining an acceptable estimation of the number of casualties adds to the complexity of the problem. In this paper, a location-routing problem is discussed for urgent therapeutic services during disasters. The problem is formulated as a bi-objective Mixed-Integer Linear Programming (MILP) model. The objectives are to concurrently minimize the time of offering relief items to the affected people and minimize the total costs. The costs include those related to locations and transportation means (e.g., ambulances and helicopters) that are used to carry medical personnel and patients. To address the bi-objectiveness and verify the efficiency and applicability of the proposed model, the ε-constraint method is employed to solve several randomly-generated problems with CLEPX solver in GAMS. The obtained results include the objective functions, the number of the required facility, and the trade-offs between objectives. Then, the parameter of demands (i.e., number of casualties), which has the most important role, is examined using a sensitivity analysis and the managerial insights are discussed. تفاصيل المقالة -
حرية الوصول المقاله
4 - A TOPSIS-Based Improved Weighting Approach With Evolutionary Computation
Mithat Zeydan Murat Güngör Burak UrazelAlthough optimization of weighted objectives is ubiquitous in production scheduling, the literature concerning the determination of weights used in these objectives is scarce. Authors usually suppose that weights are given in advance, and focus on the solution methods f أکثرAlthough optimization of weighted objectives is ubiquitous in production scheduling, the literature concerning the determination of weights used in these objectives is scarce. Authors usually suppose that weights are given in advance, and focus on the solution methods for the specific problem at hand. However, weights directly settle the class of optimal solutions, and are of utmost importance in any practical scheduling problem. In this study, we propose a new weighting approach for single machine scheduling problems. First, factor weights to be used in customer evaluation are found by solving a nonlinear optimization problem using the covariance matrix adaptation evolutionary strategy (CMAES) under fuzzy environment that takes a pairwise comparison matrix as input. Next, customers are sorted using the technique for order of preference by similarity to ideal solution (TOPSIS) by means of which job weights are obtained. Finally, taking these weights as an input, a total weighted tardiness minimization problem is solved by using mixed-integer linear programming to find the best job sequence. This combined methodology may help companies make robust schedules not based purely on subjective judgment, find the best compromise between customer satisfaction and business needs, and thereby ensure profitability in the long run. تفاصيل المقالة