نیاز به یکپارچه‌سازی از مدت‌ها پیش موردتوجه پژوهشگران و مدیران صنایع بوده است. ازاین‌رو برخی از پژوهشگران به دنبال ایجاد هماهنگی و یکپارچه‌سازی تولید و نگهداری و تعمیرات بودند، مباحث یکپارچه‌سازی برنامه ریزی ادغامی تولید، نگهداری و تعمیرات و کنترل کیفیت با دیگر بخش‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌های سیستم های تولیدی و صنعتی باعث کاهش هزینه ها و افزایش سودآوری سازمان های تولیدی می شود. بر این اساس، در این پژوهش به ادغام‌‌سازی و پیوستگی در برنامه ریزی هم زمان حوزه های تولیدی و پشتیبانی در سازمانهای تولیدی پرداخته شده‌است. این حوزه‌ها شامل برنامه زمانبندی تولید، نگه‌داری و تعمیرات پیشگیرانه و منابع انسانی می‌باشد. در اینرو ارائه‌ مدل بهینه سازی ریاضی با هدف برنامه ریزی نیروی انسانی و افزایش حجم تولیدات شرکت با در نظر گرفتن محدودیت منابع انسانی ارائه‌شده‌است. در این مدل مهارت پرسنل، نرخ استفاده از تجهیزات و نرخ از کارافتادگی تجهیزات در حالت عدم قطعیت با روش فازی در پارامترهای مدل استفاده شده است. هدف مدل پیشنهادی حداقل سازی کسر نیروی کار برکارایی تولید است. نتایج کسب شده از پیاده‌‌سازی در یک سازمان تولیدی از مقایسه حل غیر قطعی و فرا ابتکاری نشان از بهبود در تولیدات شرکت با حداقل زمان معقول و منطقی، جواب‌هایی با حداقل خطای ممکن ارائه می دهد. با انجام تحلیل حساسیت مشاهده می شود که نرخ از کارافتادگی تجهیزات در بازه های قبل و بعد از فرایند نگهداری و تعمیرات پیشگیرانه، باعث تاثیر بسیار زیاد بر مقدار تابع هدف مدل ریاضی دارد. پرونده مقاله

In this research, a tri-objective mathematical model is proposed for the Transportation-Location-Routing problem. The model considers a three-echelon supply chain and aims to minimize total costs, maximize the minimum reliability of the traveled routes and establish a well-balanced set of routes. In order to solve the proposed model, four metaheuristic algorithms, including Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Water Cycle Algorithm (MOWCA), Multi-objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) are developed. The performance of the algorithms is evaluated by solving various test problems in small, medium, and large scale. Four performance measures, including Diversity, Hypervolume, Number of Non-dominated Solutions, and CPU-Time, are considered to evaluate the effectiveness of the algorithms. In the end, the superior algorithm is determined by Technique for Order of Preference by Similarity to Ideal Solution method. پرونده مقاله

Multi-Attribute Decision Making (MADM) process is the most well-known branch of decision making and it is one of the most important tasks that have received a lot of attention in many areas. In solving MADM issues, the parameters of decision-making are often faced with problems, such as imprecise, vague, uncertain, or incomplete information which lead to inaccurate decision-making. To cope with these problems, the researchers apply fuzzy set theory as the best-developed approach. Among different fuzzy methods, the fuzzy rule-based system (FRBS) due to its flexibility, simplicity, and experts' knowledge modeling is an adequate technique for solving MADM problems. The main objective of this study is to apply experts' opinions by Z-numbers in MADM issues to enhance the accuracy of the decision-making process. The fundamental issue in solving MADM problems is that inadequate information in the experts' opinions leads to some degree of uncertainty in decisions. Indeed, in FRBS research to ranking, the reliability level (Z-numbers) in experts' opinions within the decision-making process has not been taken into account. Whereas, the Z-numbers play a key role in the decision-making process to reach more precise decisions affecting the final ranking results. In the proposed approach (Z-FRBS), by considering experts' opinions in the form of Z-numbers to deal with inadequate information and modeling experts' knowledge through FRBS, the process of making a decision is performed without using conventional techniques which resulted in a more accurate solving MADM problems. The effectiveness and validity of the proposed method was approved with an illustrative example, sensitivity analysis, and comparison with three other validated method. پرونده مقاله

The problem of allocation of financial resources in projects is one of the most important problems of mathematical optimization. Incorrect allocation of financial resources can lead to project failure, increased costs, and reduced profitability. The importance of this issue has led to the modeling of a financial resource allocation problem for sustainable projects under uncertainty in this article. A fuzzy programming method was used to control model parameters and GSSA, GA, and SSA algorithms were used to solve the model. In the mathematical model, the goal was to optimize the objective function consisting of predicted return, investment risk, and project sustainability. Mathematical calculation results showed that meta-heuristic algorithms have high efficiency in achieving optimal solutions in a short time. so that the average time to solve them was less than 10 seconds. Also, the calculation results showed that increasing the uncertainty rate leads to increasing the value of the objective function and creating a distance from the optimal point. This is due to increasing costs and decreasing profits in sustainable projects. Finally, usage the TOPSIS method, the ranking of solving algorithms was done, and the GSSA algorithm was the most efficient algorithm among other algorithms with a desirability weight of 0.846. پرونده مقاله