Scientia Iranica
Volume:28 Issue: 5, Sep-Oct 2021

In the cellular industry, the components of products are increasingly being manufactured by multiple companies, which are distributed across different regions resulting in increased production costs. Here, a cooperative cellular manufacturing system is introduced to decrease these costs. A mathematical programming model has been proposed, which evaluates the production cost when companies work independently and the model is then extended to consider coalitional conditions in which the companies cooperate as an integrated cell formation system. A key question that arises in this scenario is how to arrange the cells and machines of multiple companies when their cell formation systems are designed cooperatively. Through a realistic case study of three high-tech suppliers of the Mega Motor Company, we show that these companies can reduce the costs through a cooperative cellular manufacturing system. We then compute the cost saving of each coalition of companies obtained from cooperation to get a fair allocation of the cost savings among the cooperating firms. Four cooperative game theory methods including Shapley value, τ -value, core-center, and least core are proposed to examine fair sharing of cost saving. A comprehensive analysis of the case study reveals important managerial insights.

With increase in the inventory of stored items and in the number of orders received, the picking process and the response time gain greater importance. It should be noted that, in order to enhance the efficiency of warehouse management system, effective correlation and coordination between order batching and order picking process is of crucial role. In this paper, novel mixed integer nonlinear programming for on-line order batching is proposed for improving performance of the warehouse which in turn results in reducing the response time and idle times. The proposed method is based on a blocked warehouse using a zoning system, which is called Online Order Batching in Blocked Warehouse with One Picker for each Block (OOBBWOPB). The mentioned model is solved by using two algorithm of artificial bee colony (ABC) and Ant-colony (ACO). For proving the analyses and claims, two numerical examples as cases 1 and 2 are defined and analyzed by this algorithms in MATLAB environment. Based on the results, the proposed warehouse shows better performance with a substantial reduction in the average response time of a set of customer orders compare to zhang et al. (2017) results. It’s noteworthy that the ACO yields better results than ABC.

A portfolio selection model is developed in this study, using a new risk measure. The proposed risk measure is based on the fundamental value of stocks. For this purpose, a mathematical model is developed and transformed into an integer linear programming. In order to analyze the model's efficiency, the actual data of the Tehran Stock Exchange market are used in 12 scenarios to solve the proposed model. In order to evaluate the scenarios, data mining approaches are employed. Data mining methods which are used in this paper include ANFIS, decision tree, random forest, ADF, and GEP. The best method for scenario evaluation is GEP based on numerical results. Hence, the market values are evaluated by this algorithm. Software packages like MATLAB, GEP xpero tools, and LINGO are used to solve the model. Different trends of market value and fundamental value volatility in the optimum stock portfolio are determined. It is possible to examine the optimum portfolio profitability in different scenarios. By using real-world data, trends are extracted and analyzed. Results show that the developed model can be effectively applied in bubble situations.

Normal intuitionistic fuzzy number (NIFN), which is introduced based on intuitionistic fuzzy sets and normal fuzzy numbers, is a useful tool for presenting uncertain information under complicated situations. This study focuses on the development of an effective method by combining NIFNs with the power average and harmonic mean operators to address multi-criteria group decision-making (MCGDM) problems, wherein weight information is completely unknown. First, an effective ranking method for NIFNs is provided in view of defects of the existing comparison method of NIFNs. Subsequently, three normal intuitionistic generalized power harmonic aggregation operators are proposed based on the operations of NIFNs. Next, a new MCGDM method is developed. Finally, a numerical example concerning coal mine safety evaluation is provided for demonstration. The feasibility and validity of the proposed method are further verified by sensitivity analysis and comparison with other existing methods.

In this study, we propose optional randomized response technique (RRT) models in binary response situation. The utility of proposed optional RRT models under stratification are also explored. Gupta et al.cite{Singh} introduced an ingenious idea of optional RRT model, that a question may be sensitive for one respondent but may not be sensitive for another. This study focus on estimating $ pi $, the prevalence of sensitive attribute, $ omega $, the sensitivity level of the underlying sensitive question when the proportion of unrelated innocuous attribute $ pi_{{x}} $ is unknown. A new multi-question approach are proposed and used for estimation of parameters $ (pi,omega) $. A comparison between proposed optional RRT models and corresponding full RRT models are carried out numerically under simple and stratified random sampling.

In the process of hazardous material transportation, the risk is a significant factor that should be considered due to the potential severe consequence of an incident. Regardless of risks, time is a paramount concern that should be considered in hazardous material transportation. In this way, this paper introduces a bi-objective model for a vehicle routing and scheduling problem of hazardous material distribution problems under the fuzzy condition to minimize both total distribution time and risks. In the proposed model, the fuzzy inference system and fuzzy failure mode and effects analysis are applied to identify and calculate the high-level risks instead of the previous simple methods for the first time. Moreover, Jimenez method and fuzzy goal programming are respectively utilized to convert the fuzzy bi-objective model into the same crisp and single-objective one. Besides, to cope with the NP-hardness of the large-sized problems, two meta-heuristic algorithms namely invasive weeds optimization and genetic algorithm is used, and several sensitivity analyses are performed to prove the efficiency of the proposed approach. The performance of the proposed algorithms is also assessed through a comparative study. Finally, the proposed model is implemented to a real case study to prove the validity of the model.

In classical inventory control problems, it is usually assumed that all of items are of perfect quality, and the inspection process works perfectly well. However, in practice, the order lots often contain imperfect quality items, and the inspection process, for recognition of these items, is not necessarily error-free. In this article, we extend the economic order quantity model under imperfect quality items where the inspection process involves type I and II errors. The type I error can lead to recognition of perfect quality items as defective, while the type II error can lead to delivery of imperfect quality items to customers even for several consecutive times. We present two cases depending on the length of special inspection process and determine optimal order sizes, analytically, for maximizing total profit per unit time for both cases. A numerical example is provided to compare two cases and a sensitivity analysis is discussed to assess the effect of main parameters on the total profit per unit time.

Linguistic Z-numbers (LZNs), as a more rational extension of linguistic description, not only consider the fuzzy restriction of assessment information but also take the reliability of the information into account. Maclaurin symmetric mean (MSM) operator has the advantage which can take account of interrelationship of different attributes and there are a lot of research results on it. However, it has not been used to handle multi-attribute decision-making (MADM) problems expressed by LZNs. To sum up the advantages of LZNs and MSM, in this paper, we present the linguistic Z-Numbers MSM (LZMSM) and linguistic Z-Numbers weight MSM (LZWMSM) operators, respectively, and several characters and several special cases of them are explored. Moreover, we propose an approach to handle some MADM problems by using LZWMSM operator. In the end, an example is given to illustrate the effectiveness and superiority of this new presented approach by comparing with several existing approaches.

One of the important topics in Data Envelopment Analysis is congestion. Many scholars research in this field and represent their methods. In most of the represented methods, we have to solve lots of models or its used for a special aim like negative data, integer data, different Production Possibility Set and etc. Here we represent our method that measures the congestion without solving a model. It can be used for different Production Possibility Set (different technology) like T_{New} and FDH; different data like negative data and integer data. Also, we can distinguish strongly or weakly congestion of Decision Making Unit. Furthermore, each DMU has congestion, efficient and inefficient, we can measure it by this method. Finally, we represent some numerical example of our purpose method and compare our method with other methods then show the results in tables.

This paper aimed to design a sustainable agile retail supply chain using multi-objective optimization methods. To this end, a mathematical model was presented for the sustainable agile supply chain with five objectives, including "minimizing costs", "minimizing unanswered demand", "maximizing the quality of goods purchased from suppliers," "maximizing social responsibility or social benefits", and "minimizing environmental impacts". The NSGA-II, PESA and SPEA-II algorithms were used to solve the proposed model, which were run in MATLAB software. After collecting data from the SAIPA Company’s supply chain, the model was solved using the three algorithms. The results indicate that the SPEA-II algorithm produces more high quality responses, compared to the other two algorithms. Furthermore, the SPEA-II algorithm was found to be among the Pareto Front responses. A decrease of environmental impacts had no effect on the problem responses due to the lack of a specific structure in the current system.

One of the most important factors in a humanitarian supply chain during a disaster is to respond quickly and efficiently. Delivering emergency commodities to the affected areas is critical in reducing consequences. Moreover, transferring the injured people through the fastest and the shortest time by using all available resources is vitally important. To this aim, a multi-echelon, multi-objective forward and backward relief network is proposed that considers the location of hospitals, local warehouses and hybrid centers, which are hospital-warehouse centers in the pre-disaster phase. In the post-disaster phase, the routing of relief commodities is considered in the forward route. In the backward route some vehicles that can transfer injured people after delivering commodities; hybrid transportation facilities; will take injured to hospitals and hybrid centers. According to the degree of hardness, a hybrid non-dominated sorting genetic algorithm (NSGA-II) with simulated annealing (SA) and variable neighborhood search (VNS) algorithms is proposed to solve the given problems. The results of this hybrid algorithm are compared with NSGA-II and multi-objective SA-VNS using five metrics (i.e., a number of Pareto, mean ideal distance, spacing, diversity and time) in order to emphasize that the proposed hybrid algorithm outperforms the two foregoing algorithms

In today’s competitive and high technology world, companies are forced to differentiate themselves with continuous improvement. They need creative, well-educated and self-confident human resource more than ever. Hiring the right person to the right job plays a significant role on firm’s growth. The goal of this paper is to propose a systematic approach for personnel selection problem (PSP) of a textile company in Turkey by considering various performance requirements and criteria. The proposed framework consists of three phases. Initially, Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method is used for weighting social criteria. Then, weights of technical requirements are calculated by applying Fuzzy Quality Function Deployment (QFD) method allowing to evaluate the interrelationships and correlation of social and technical criteria. Finally, Fuzzy Grey Relational Analysis (GRA) method has been applied to rank the alternatives by considering criteria scores acquired in the previous phase. The method has been illustrated by a case study and compared to the current approach used in the company. The results indicate that this proposed approach can deal with the PSP effectively and help companies to establish a systematic and unbiased way for the problem.