Iranian Journal Of Operations Research
Volume:11 Issue: 2, Summer and Autumn 2020

New concepts of -feasibility and -efficiency of solutions for fuzzy mathematical programming problems are used, where  is a vector of distinct satisfaction degrees. Recently, a special kind of fuzzy mathematical programming entitled Fuzzy Flexible Linear programming (FFLP) is attracted many interests. Using the mentioned concepts, we propose a two-phase approach to solve FFLP. In the first phase, the original FFLP problem converts it to a Multi-Parametric Linear Programing (MPLP) problem, and then in phase II using the convenient optimal solution with the higher feasibility degree is concluded. Using this concept, we have solved the problem of the animal diet. In the process of milk production, the highest cost relates to animal feed. Based on reports provided by the experts, around seventy percent of dairy livestock costs included feed costs. In order to minimize the total price of livestock feed, according to the limits of feed sources in each region or season, and also the transportation and maintenance costs and ultimately milk price reduction, optimization of the livestock nutrition program is an essential issue. Because of the uncertainty and lack of precision in the optimal food ration done with existing methods based on linear programming, there is a need to use appropriate methods to meet this purpose. Therefore, in this study formulation of completely mixed nutrient diets of dairy cows is done by using a fuzzy linear programming in early lactation. Application of fuzzy optimization method and floating price make it possible to formulate and change the completely mixed diets with adequate safety margins. Therefore, applications of fuzzy methods in feed rations of dairy cattle are recommended to optimize the diets. Obviously, it would be useful to design suitable software, which provides the possibility of using floating prices to set feed rations by the use of fuzzy optimization method.

The present study aims at designing a cold multi-cycle supply chain based on a multi cross-dock system taking into account uncertainty. In the first step, we identified the factors and variables of the model. In the second, by selecting the study period through designing data collection forms and using the documents reviewing methodologies, the raw data required to measure the final indicators were collected and processed in the project model. Then, they were analyzed considering the research topic and using the techniques of genetic algorithm and particle swarm optimization. The primary objective function is minimizing the cost of transportation and warehousing throughout the supply chain, the second minimizing the total operation time and the number of vehicles within the supply chain, and the third maximizing the product freshness time. Also meta-heuristic optimization methods (strongly adjustable) were adopted to deal with the travel time of suburban vehicles. We also provide an example of the performance of optimization models for a small-sized sample. The computational results showed that longer travel time and further distance do not necessarily increase costs. In fact, it is possible to distribute the products with the right number of trucks at an optimal cost at the right time.

In this study, an alternative method is proposed based on recursive deep learning with limited steps and prepossessing, in which the data is divided into A unit classes in order to change a long short term memory and solve the existing challenges. The goal is to obtain predictive results that are closer to real world in COVID-19 patients. To achieve this goal, four existing challenges including the heterogeneous data, the imbalanced data distribution in predicted classes, the low allocation rate of data to a class and the existence of many features in a process have been resolved. The proposed method is simulated using the real data of COVID-19 patients hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020, which has led to recovery or death. The obtained results are compared against three valid advanced methods, and are showed that the amount of memory resources usage and CPU usage time are slightly increased compared to similar methods  and the accuracy is increased by an average of 12%.

Hubs are facilities that can decrease the cost of many-to-many distribution systems by acting as an interconnector between the demand and supply nodes. This type of facility can reduce the number of direct links needed in a logistics network. Hub location problems (HLP) have been discussed by many authors for more than four decades, and different approaches have been developed for modeling and solving this problem. We propose a fuzzy type I and II programming approach for a new model presented in the literature, i.e., the single allocation ordered median problem. The level of flow among the nodes will be considered as a fuzzy parameter. In the fuzzy type I approach, a linear programming problem with fuzzy parameters is used, while for the fuzzy type II approach, the rules of interval arithmetic are developed to simplify the problem to the fuzzy type I case. Finally, we apply our method on Kalleh Dairy Co. data of transportation as a case study and compare crisp and fuzzy situations. We show that the results of the fuzzy approach could be 2% better than the crisp approach and also discuss the pros and cons of fuzzy type I and type II approaches.

Today, new technologies have changed the global financial panorama and communities. Due to the advancement of new technologies and the competitiveness of the company, the nature of innovation has changed. In order to take full advantage of the potential of technological transformation, companies must incorporate platforms into their operations. The purpose of this have a look at became to designing and explaining the model of technological platforms capabilities within the cosmetics enterprise. The prevailing take a look at is carried out in terms of motive and descriptive in phrases of the way to acquire information. The information evaluation method locations this study within the discipline of qualitative research of interpretive type. The study population in the present study consists of all university professors in the field of business management and information technology management. In order to design the model of the present study, interviews were conducted based on purposive and theoretical sampling methods to the extent of theoretical saturation. In this study, in order to evaluate the validity of the interview from the approach of credibility or credibility criteria including the use of negative case strategies, triangulation, rich explanation and reliability approach including the use of third parties and also repetition of the coding process based on the validity model. Qualitative research by Lincoln and Guba (1982) was used. In evaluating the reliability of the interview, two strategies of using third party as well as repetition of coding were used (Lincoln and Guba, 1985). The analysis of the interview data using the data-based method was based on the systematic approach of Strauss and Corbin (1998), based on three stages of open, axial and selective coding. Data coding showed the extraction of 16 selected codes, 60 axial codes and 248 open codes which were classified based on causal conditions, central phenomenon, interfering factors, contextual factors, strategies and consequences.

Selecting resilient-sustainable suppliers can improve sustainability status and reduce supply chain disruption. This study aims to design a model for selecting resilient-sustainable suppliers in the supply chain of the Shahid Ghandi Corporation Complex. For this purpose, after reviewing the theoretical literature, 76 and 50 indicators were identified for evaluating sustainable suppliers and resilient suppliers, respectively. These indicators were investigated by supply chain experts in Shahid Ghandi Corporation Complex and, then, 15 indicators were determined to be suitable for each of the sustainable and resilient suppliers. A questionnaire was distributed among the supply chain experts of Shahid Ghandi Corporation Complex and the resilient-sustainable supplier selection model was confirmed using confirmatory factor analysis (CFA) based on the 136 questionnaires gathered from the participants. Sustainability indicators were classified into three economic, social, and environmental dimensions, and resilience indicators were divided into three categories of absorptive capacity, adaptive capacity, and restorative capacity. The results showed that the economic dimension had the first rank, the environmental dimension the second rank, and the indices of adaptation capacity, restorative capacity, social capacity and absorption capacity in choosing the sustainable-resilient supplier model were the next priorities, respectively.