Scientia Iranica
Volume:26 Issue: 4, Jul-Agust 2019

Despite the significance of full coordination of N-echelon supply chains in real-world decision-making situations, the relevant literature has rarely addressed this issue. Furthermore, there is a scarcity of mathematical models in the supply chain management literature for partially coordinated cases. To address these shortcomings, this study concerns both the full and partial coordination in serial N-echelon supply chains facing stochastic demand. In particular, three general cases including decentralized (no coordination), sub-supply chain coordination (partial coordination) and centralized (full coordination) cases are examined to support decisions on ordering and pricing. In addition, this study adds to the literature by investigating how to coordinate a serial N-echelon supply chain through a new spanning profit sharing contract, which can coordinate the entire supply chain through only one contract. Furthermore, we analytically prove the occurrence of externality benefit in partially coordinated cases, which is a paradoxical phenomenon suggesting that small coalitions are unstable. Two numerical examples extracted from the literature are given to verify the effectiveness and validity of the proposed contracts and models. The results show that the proposed contracts can be applied in a rather simple and convenient way and is reliable enough for use in real-world applications.

Aiming at the problem of small sample modelling of oil price and exchange rate with time-delayed causality, a grey multivariate time lag model and its solution are proposed against the new economic background of economic development, structural optimization and power conversion. Considering the difficulty of solving q-order differential equations analytically, we obtain a numerical solution. On the basis of this solution, the validity of the model is proved. The numerical results show that the model can describe and predict the operating rules of oil price and exchange rate economic systems with time delay, and it is concluded that the development of oil price and exchange rate is not coordinated under the new state of the economy.  The relationship of oil prices and the exchange rate has changed in this state, oil prices have a positive effect on the rise of the exchange rate.

In this study, we develop some new estimators for estimating the population variance by utilizing the information on midrange and inter decile range of an auxiliary variable. A general class of estimators is also suggested. The derivations of the bias and the mean squared error are presented. Conditions are determined to verify the efficiency of the proposed estimators over existing estimators considered in this study. An Empirical study is also provided for illustration and verification.  Moreover, a robust study is also carried out to evaluate the performance of proposed estimators as compared to existing estimators in case of extreme values. From the theoretical and empirical study, it is found that the suggested estimators perform more efficiently as compared to the existing estimators considered in this study.

Understanding complex biological networks enable us to better understand the systems’ diseases such as cancers and heart attacks, and to produce drug targets which is one of the major research questions under the personalized medicine. But the description of these complexities is challenging since the associated data are very sparse, high dimensional and seriously correlated. The copula Gaussian graphical model (CGGM), which depends on the representation of the multivariate normal distribution via marginals and a copula term, is one of the successful modelling approaches to present such type of datasets. In this study, we apply CGGM in modelling steady-state activations of biological networks and make inference of model parameters under Bayesian settings. We suggest the reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to estimate plausible interactions between the systems’ elements which are proteins or genes. We also generate the open-source R codes of RJMCMC for CGGM under different dimensional networks. In the application, we use real datasets and evaluate the accuracy of estimates via F1-score. From the results, we observe that CGGM with RJMCMC is successful in the presentation of real and complex systems with higher accuracy and can be a promising approach to understand biological networks and diseases.

The design structure matrix (DSM) is a potent tool in the management of product design processes. Although the compactness and ability to represent design cycles are the main advantages of DSMs over existing traditional tools, the intact whole DSM is not always an understandable piece of information. To overcome this shortcoming, certain analyses have been proposed for a better understanding of the matrix in which partitioning and tearing have significant importance. There are several algorithms for these two analyses that mainly focus on a few rules of thumb. Although partitioning and tearing were originally developed for binary DSMs, they can be extended to numerical variants in which the work transformation matrix (WTM) is of the highest fame and application. In this paper, the authors have proposed an algorithm inspired by the formation of sugar crystals in saturated syrup for reordering the activities in a coupled block of activities (CBAs) based on their level of coupling. To implement this approach, a code was developed to achieve pseudo-optimum solutions. By using a discrete-time simulation, which was applied to an aerospace case study, it was demonstrated that the method produces restructured schemes of the WTM that are comparable/superior to the classical methods.

The purpose of the current study is to select suppliers and determine their order allocation in a way that the performance of the sustainability of the supply process gets optimized on the whole. In this research, after reviewing the literature and investigating the supply chain of the case study (Iran Khodro’s supply chain) through Delphi method, a set of evaluation criteria related to the performance of the suppliers in economical, social and environmental terms was identified. In the next stage, by using the identified criteria, the multi-objective mathematical integer programming was presented to solve the problems of suppliers’ selection and order allocation. The suggested mathematical programming in this research is designed to be multi-product, single-period and multiple sourcing. Fuzzy TOPSIS method is applied to calculate the qualitative parameters that are used in the suggested mathematical programming. Ultimately, the mathematical model suggested in the research will be solved by two methods, i.e. Epsilon Constraint Method and Weighted Sum Method. Moreover, the total value of the sustainable purchasing (TVSP) will be calculated for both cases. Comparing these two methods indicates that in this research the results of weighted sum method are better than those epsilon constraint method.

The present study aimed to design a bi-objective bi-level mathematical model for multi-dimensional cellular manufacturing system. Minimizing the total number of voids and balancing the assigned workloads to cells are regarded as two objectives of the upper level of the model. However, the lower level attempts to maximize the workers' interest to work together in a special cell. To this aim, two nested bi-level metaheuristics including particle swarm optimization (NBL-PSO) and a population-based simulated annealing algorithm (NBL-PBSA) were implemented to solve the model. In addition, the goal programming approach was utilized in the upper level procedure of these algorithms. Further, nine numerical examples were applied to verify the suggested framework and the TOPSIS method was used to find the better algorithm. Furthermore, the best weights for upper level objectives were tuned by using a weight sensitivity analysis. Based on computational results, all three objectives were different from their ideal goals when decisions about inter and intra-cell layouts, and cell formation to balance the assigned workloads by considering voids and workers' interest were simultaneously madeby considering a wide assumption-made problem closer to the real world. Finally, NBL-PBSA could perform better than NBL-PSO, which confirmed the efficiency of the proposed framework.

In real-world manufacturing systems, encountering with imperfect raw materials and generation of defective finished products are inevitable. In order to cope with these practical problems, this paper studies a manufacturer which orders raw materials from external source (supplier), and then produces a finished product. The raw materials contain imperfect quality items and, in addition, the production process is defective. The imperfect raw materials are sold after screening process, while the defective finished products go under a further rework process. It is also assumed that defective rate of machine is a random variable, resulting three possible cases regarding occurrence of backordering shortage. The aim is to determine economic order/production lot sizes for each case in such a way that the total cost of system is minimized. The optimal closed form solution is derived for each case separately. Moreover the applicability of the proposed manufacturing model is illustrated via a numerical example.

In this paper with respect to the importance of risks in real-world projects and ability of interval type-2 fuzzy sets (IT2FSs) to tackle the uncertainty, a new approach is introduced to consider risks and the correlation among risk factors by subjective judgments of experts on the probability and impact under IT2FSs. Furthermore, a new impact function for considering the correlation among the risk factors are extended under an IT2F environment. Moreover, a new subtraction operator is introduced for the critical path analysis. The node-weighted rooted tree (NWRT) method is modified based on the proposed new operator to avoid producing negative number for characteristics of each activity. Also, in order to cope with the uncertainty of the projects, NWRT method is developed under the IT2FSs. Eventually, to illustrate the validity and capability of the proposed method, two examples from the literature are solved and compared.

In this study, to select suppliers of raw materials in Saipa Automotive Corporation as one of the largest factories in Iran, environmental criteria, flexibility and agility criteria are considered and some sub-criteria are also considered for each criterion. The sub-criteria include green design, clean technology, environmental performance, agility in operational systems, market agility, logistics agility, product flexibility, flexibility in transportation, resource flexibility. It should be mentioned that the said criteria may need modification and revision due to opinions of experts during the research implementation. Therefore, main variables for the issue of identification of criteria affecting selection of suppliers are studied with regard to environmental factors within the organization. In order to rank suppliers and to select the best option based on a best-worst multi-criteria decision-making method (BWM) and VIKOR-based approach was used. According to the calculations based on the proposed process in this study and the information about the desired criteria, 7 suppliers were selected as the best options. As the approach presented in this study has combined two worst-best method to determine weights and VIKOR method for final ranking of options, this approach can be also used in other studies.