Iranian Journal Of Operations Research
Volume:13 Issue: 2, Summer and Autumn 2022

The most convenient models of Solid Transportation (ST) problems have been justly considered a kind of uncertainty in their parameters such as fuzzy, grey, stochastic, etc. and usually, they suggest solving the main problems by solving some crisp equivalent model/models based on their proposed approach such as using ranking functions, embedding problems etc. Furthermore, there exist some shortcomings in formulating the main model for the realistic situations, since it omitted the flexibility conditions in their studies. Hence, to overcome these shortages, we formulate these conditions for the mentioned these problems with fuzzy flexible constraints, where there are no exact predictions for the values of the resources. In particluar, numerical investigation shows that each increasing for the values of the supply and demand is not effective for improving the objective function.  The value of the objective function is sensitive when supply and demand change, so we conduct a new study to diversify the value of the objective function, due to changes in resource and demand levels simultaneously.

In this paper, a new method is presented using a combination of deep learning method, specifically recursive neural network, and Markov chain. The aim is to obtain more realistic results with lower cost in predicting COVID-19 patients. For this purpose, the BestFirst algorithm is used for the search section, and the Cfssubseteval algorithm is implemented for evaluating the features in the data preprocessing section. The proposed method is simulated using the real data of COVID-19 patients who were hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020. The obtained results were compared with three valid advanced methods. The results showed that the proposed method significantly reduces the amount of memory resource usage and CPU usage time compared to similar methods, and at the same time, the accuracy also increases significantly.

Tone [29] proposed a method of super-efficiency slack-based measures (SBM) for ranking efficient decision-making units (DMUs), so that this model would rank efficient DMUs. The established model was able to measure radially. It calculates and measuring the efficiency of inefficient DMUs and the amount of super-efficiency of efficient DMUs. Du et al. [11] developed the Charens et al. [6] model in to the additive DEA model, as well as the additive super performance model. Turn et al. [32] used a linear SBM and S-SBM integrated model that had the properties of both models and reduced the time factor compared to previous models. In order to be able to calculate the amount of additive super efficiency; First we identify the efficient DMUs and then apply the additive super-efficiency model to the efficient DMUs. In this paper, the proposed model obtains the additive efficiency value of inefficient DMUs and the additive super efficiency value of efficient DMUs with less computation time. The amount of DMUs calculated from the integrated model in this article can be compared to the Guo et al. [15] article in comparison with the time table of the text of the article.

Due to the importance of vehicle routing for delivering a large number of orders with different restrictions in the world, various optimization methods have been studied in past researches. In this article, a number of researches of recent years have been discussed, then the proposed model is described in 3 phases with the penalty index. This model has the ability to assign orders, route vehicles and determine the number of active vehicles dynamically with the aim of minimizing the total cost of distribution. By examining valid metaheuristic models and using their strengths and weaknesses, and considering multiple limitations, a new model of "dynamic 3-phase optimization" has been designed. The main application of the proposed model is for vehicle routing problems with capacity constraints of fleet number and capacity constraints (maximum and minimum number of orders). Finally, with simulation, the outputs of the model have been analyzed in different conditions . Although the limitation of maximum and minimum capacity is added to the problem, by dynamically considering the number of vehicles and using star clustering (initiative of this research), three social, environmental and economic dimensions were improved. The time for orders to reach customers decreased by 19.3%, fuel consumption and air pollution by 14.9%, and logistics costs by 8.7%. To calculate the final value of system stability, a unique 3D fuzzy model has been used. With the sensitivity analysis, we came to the conclusion that the 3-phase dynamic optimization model has led to a 14.58% improvement in system stability.

Data Envelopment Analysis is one of the most appropriate methods in Evaluation of decision-making units in the real world. That is why researchers have always tried to improve and develop existing methods and approaches in this field. Network Data Envelopment Analysis is used to evaluate the efficiency of network systems by considering processes within divisions. In the evaluation of network systems, one of the challenges is the presence of undesirable and non-discretionary data in the system. Not many conducted have been done about the simultaneous presence of these factors in general two-stage network systems. For this reason, by extending CCR model and combining some methods in this study, we presented a model that is able to evaluate two-stage systems with the mentioned conditions. One of the strengths of the proposed model in this study is the achievement of the efficiency of the system and divisions simultaneously. At the end of the article, we analyzed the results with a numerical example. The results show the ability of the presented model in evaluating the systems under investigation.

Demand planning based on demand data in the supply chain includes the most significant steps in production planning. Therefore, the supply chain's correct demand forecasting may reduce this effect, known as the bullwhip effect or uncertainty concerning customer demand, thus reducing companies' and organizations' costs and surplus activities. Therefore, this article examined the statistical population characteristics to test the hypotheses through the path analysis drawn using descriptive statistics and FCM(fuzzy cognitive map)  method. Then, the model strength was investigated using structural equation modeling (SEM) in AMOS software, and structural equations were presented. This article selected the Aftab oil factory as a case study. The findings of this study emphasized that demand management performance is highly essential for industries. Companies can design the sector independently as a demand management sector for evaluating customer demands at different levels of the supply chain. According to the fit of the main model, CFI and NFI indices are equal to 0.99 and 0.97, respectively, which are close to the optimal fit threshold. RMSEA and SRMR indices are equal to 0.01 and 0.01, respectively, both showing a relatively good fit of the model.

A look at the world production and consumption indicates that production systems resiliency and sustainability is highly regarded by businessmen and the general users for long surviving of human being race and ecological endurance. By conducting theoretical studies and reviewing the literature, and searching previous studies to identify the resilience factors important to manufacturing industries, a list of effective strategies was determined. The most important strategies of resilience considered in this study are: capacity management, multi sourcing, demand management, information sharing, additional inventory holding, contracting with backups, risk management and disaster recovery, dropping market feeding strategy, enlightenment of business flow complexity, and suppliers/facilities reinforcement. In this article, DEMATEL approach is used to demonstrate how production resilience factors can impacts on each other and what the interrelationships among these factors are. After that, a questionnaire was designed for pairwise comparisons of resilience strategies of capacity scaling, multi sourcing, contracts, inventory management, risk management, and production level. Then, a system dynamics approach is used to model the interrelations among the resilience factors by taking feedback loops into consideration managing to trace their impacts on production and inventory levels. A production system with its main processes of: production order rate, planned work, work in process (WIP), production rate, inventory level, desired shipment rate, backlogs, rejected rate, rework rate, required capacity, and capacity scaling are designed for this study. This model presents a production system with circular resilience’s strategies impacts on production scaling and hence their impacts on sustainability indicators of job creation, and salary (social pillar), profit and investment (economic pillar), and ecosystem destruction (environment pillar). System dynamics approach helped us in presenting the long trends of sustainability indicators as shown by a number of figures in the body of this article. Five scenarios are developed and the results were presented to the team of our experts presenting them by wi=0, wp=0 (case 1), wi=0, wp=0.5 (case 2), wi=1, wp=0 (case 3), wi=0, wp=1 (case 4), and wi=0.36, wp=0.47 (case 5). Experts’ opinions were gathered and then use TOPSIS approach for determining the best case the among cases discussed above. The results indicates that the data generated by Vensim computer software for five cases, case 5 with wi=0.36 and wp=0.47 is the best case among all cases.