Sharif Digital Repository

This paper proposes an integrated integer nonlinear programming model for a concurrent cell formation and production planning problem in a dynamic cell-manufacturing system (DCMS) with limited resources to setup cells and to procure machines. The proposed model seeks to minimise the total costs associated with the production planning and the cell construction and formation under a dynamic system. To validate the model, it is first converted to a linear programming. Then, a numerical example is presented based on which the branch and bound method is used to solve it employing the Lingo 8 software. Besides, due to NP-hardness of the problem, two meta-heuristic algorithms namely a GA and a PSO... 

In this paper, the joint replenishment-location problem of some distribution centers (DCs) with a centralized decision maker who is responsible for ordering and dispatching shipments of a single product is modeled. The warehouse spaces of the DCs are limited and the product is sold under an incremental discount policy. The model seeks to minimize the total cost of the supply chain under the joint replenishment policy along with minimizing the cost of locating the DCs in potential sites as the first objective. The second objective is to minimize the warehouse space of all DCs using the revisable approach. As the proposed model is a bi-objective integer non-linear optimization problem... 

In this paper, the designing of a parallel channel queuing system is considered in which it is desirable to serve the customers immediately upon their arrival. To design such a service facility, the first and the second moments of the number of customers are obtained in an infinite channel server under the assumption that the arrival process has a general probability structure and the service times of customers are independent and identically distributed random variables. With the knowledge about the above two moments, one can obtain the approximate number of channels such that the queue length becomes zero. © Sharif University of Technology, January 2006  

In this study, we introduce a multivariate gamma distribution, then, by defining a new statistic, three control charts called the MG charts, are proposed for this distribution. The first control chart is based on the exact distribution of this statistic, the second control chart is based on the Satterthwaite approximation, and the last is based on the normal approximation. The efficiency of the proposed control charts is evaluated by the average run length (ARL) criterion. The results show that whenever the magnitude of the parameter shifts c<1, the control chart based on the exact distribution has smaller ARL1s, while for c>1, the control chart based on Satterthwaite approximation show... 

Monitoring the mortality associated with a surgical procedure leads to the proper decision making in a healthcare system. However, the surgical outcomes depend not only on the risk factors of each patient but also on other categorical influential covariates which cannot be easily measured. Ignoring the unmeasured covariates leads to the poor performance of monitoring procedures. To deal with this significant issue, a general Phase-I risk-adjusted cumulative sum control chart is proposed using a combination of accelerated failure time and frailty models to monitor surgical outcomes. Extensive simulation studies are conducted which reveal that the proposed frailty-based CUSUM chart outperforms... 

Direct marketing identifies customers who buy, more probable, a specific product to reduce the cost and increase the response rate of a marketing campaign. The advancement of technology in the current era makes the data collection process easy. Hence, a large number of customer data can be stored in companies where they can be employed to solve the direct marketing problem. In this paper, a novel Bayesian method titled correlation-augment naïve Bayes (CAN) is proposed to improve the conventional naïve Bayes (NB) classifier. The performance of the proposed method in terms of the response rate is evaluated and compared to several well-known Bayesian networks and other well-known classifiers... 

Lung cancer is one of the most common diseases for human beings everywhere throughout the world. Early identification of this disease is the main conceivable approach to enhance the possibility of patients’ survival. In this paper, a k-Nearest-Neighbors technique, for which a genetic algorithm is applied for the efficient feature selection to reduce the dataset dimensions and enhance the classifier pace, is employed for diagnosing the stage of patients’ disease. To improve the accuracy of the proposed algorithm, the best value for k is determined using an experimental procedure. The implementation of the proposed approach on a lung cancer database reveals 100% accuracy. This implies that one... 

Using control charts to monitor healthcare systems has gained particular attention. In this paper, a risk-adjusted cumulative sum control chart is designed to monitor surgery outputs. Before undergoing surgery, the patients have some unique risk factors which influence the surgery outputs. Thus, risk-adjustment is carried out with the purpose of taking these risks into account using an accelerated failure time model. But the technical implementation of the chart requires determining the design parameters which should be selected in an optimal way putting the desired statistical and economic considerations into service. To this end, a multi-objective model, considering multiple assignable... 

New technologies provide tremendous integration opportunities not only for the flow of goods and services but also for managing information and financial data. Flexibility and agility are important metrics in organisations. Cloud technology can have a significant impact on improving flexibility and agility in organisations through faster data transfer between supply chain components. In this study, we present an optimisation model for cloud-based supply chain network design (SCND) problem by optimising the costs and quality of service (QoS). We present a case study in the banking industry to show the applicability and exhibit the efficacy of the proposed optimisation model. Copyright © 2021... 

Statistical process control methods for monitoring processes with multivariate measurements in both the product quality variable space and process variable space are considered in this paper. Some processes, however, are better characterized by a profile or a function of quality variables. For each profile, we assume that a collection of data on the response variable along with the values of the corresponding quality variables is measured. While the linear function is the simplest, it occurs frequently that many of the nonlinear functions may be transferred to linear functions easily. This paper proposes a control chart based on the generalized linear test (GLT) to monitor coefficients of... 

Intensity-modulated radiation therapy is a well-known technique for treating cancer patients worldwide. A treatment plan in this technique requires decision-making for three main problems: selection of beam angles, intensity map calculation, and leaf sequencing. Previous works investigated these problems sequentially. We present a new integrated framework for simultaneous decision-making of directions, intensities, and aperture shape, called direct angle and aperture optimization, and develop a mixed-integer nonlinear mathematical model for the problem. Due to the nonlinearity and the dimension of the problem, three efficient metaheuristics based on differential evolution (DE) called classic... 

This research addresses the location problem of congested facilities, assuming service interruptions and customer withdrawals. Service interruptions can occur as a result of events such as machine failures, power outages, and communication system disconnections. As long as no interruption occurs, each facility functions as a M/M/1 queuing system. Upon an interruption, the server stops working, and customers receiving service or waiting in line leave the queue before being served. Moreover, customers who visit the facility during the repair avoid entering the facility. The problem is first formulated as a mixed-integer nonlinear programming (MINLP) model, for which two piecewise mixed-integer... 

Integrated treatment planning for cancer patients has high importance in intensity modulated radiation therapy (IMRT). Direct aperture optimization (DAO) is one of the prominent approaches used in recent years to attain this goal. Considering a set of beam directions, DAO is an integrated approach to optimize the intensity and leaf position of apertures in each direction. In this paper, first, a mixed integer-nonlinear mathematical formulation for the DAO problem in IMRT treatment planning is presented. Regarding the complexity of the problem, two well-known metaheuristic algorithms, particle swarm optimization (PSO) and differential evolution (DE), are utilized to solve the model. The... 

In this study, a multi-item economic order quantity model with shortage under vendor managed inventory policy in a single vendor single buyer supply chain is developed. This model explicitly includes warehouse capacity and delivery constraints, bounds order quantity, and limits the number of pallets. Not only the demands are considered imprecise, but also resources such as available storage and total order quantity of all items can be vaguely defined in different ways. An ant colony optimization is employed to find a near-optimum solution of the fuzzy nonlinear integer-programming problem with the objective of minimizing the total cost of the supply chain. Since no benchmark is available in... 

A hybrid algorithm of a particle swarm optimization and a fuzzy rule-based classification system is proposed in this study to diagnose breast cancer. Two orthogonal and triangular types of fuzzy sets are applied to represent the input variables. In additional, different input membership functions are considered to increase the classification accuracy. The performance of the proposed hybrid algorithm is studied using a classification accuracy measure on the Wisconsin breast cancer dataset. The results of the comparison using different training data sets show the higher performance of the proposed methodology  

The difference between the signaling time and the real change point of a process is an important monitoring issue. If the exact time at which the change manifests itself into the process is known, then process engineers can identify and eliminate the root causes of process disturbance efficiently and quickly, resulting in considerable amount of time and cost savings. Multistage count processes that are often observed in production environments must be monitored to assure quality products. In this study, multistage Poisson count processes are first introduced. Then, the process is modeled using a first-order integer-valued autoregressive time series (INAR(1)). For out-of-control signals... 

Multistage process surveillance is considered to effectively improve the product reliability in manufacturing or service operations. To this end, the process output is commonly inspected under specific conditions and the values of the reliability-related quality characteristic are measured. However, in some cases, the observations from the process output are autocorrelated. This brings about the situation where the use of existing monitoring schemes is futile. Therefore, a class of survival analysis regression models called the proportional hazards (PH) model has been modified to justify the effect of cascade property in line with the autocorrelation issue. Subsequently, three monitoring... 

Product quality problem is a critical issue for multistage manufacturing processes, especially in continuous production lines whereby quality characteristics are measured at the end of the line. Therefore, it is important to reduce process variation by identifying its sources and eliminating its causes. In this regard, a novel approach, to identify the source of variation in multistage manufacturing processes through integration of the Fisher's linear discriminant analysis and the stream of variation methodology, is proposed. Linear discriminant analysis is used to separate the variation of quality characteristics through the different stages of the manufacturing processes while the stream... 

The purpose of this paper is to develop a multi-item economic order quantity (EOQ) model with shortage for a single-buyer single-supplier supply chain under green vendor managed inventory (VMI) policy. This model explicitly includes the VMI contractual agreement between the vendor and the buyer such as warehouse capacity and delivery constraints, bounds for each order, and limits on the number of pallets. To create a kind of green supply chain, tax cost of green house gas (GHG) emissions and limitation on total emissions of all items are considered in the model. A hybrid genetic and imperialist competitive algorithm (HGA) is employed to find a near-optimum solution of a nonlinear... 

There are two main assumptions in multiperiodic inventory control problems. The first is the continuous review, where, depending on the inventory level, orders can happen at any time, and the other is the periodic review, where orders can only happen at the beginning of each period. In this paper, these assumptions are relaxed, and the periods between two replenishments are assumed independent and identically distributed random variables. Furthermore, the decision variables are assumed integer-type and that there are two kinds of space and budget constraints. The incremental discounts to purchase products are considered, and a combination of backorder and lost sales are taken into account...