Mohammad Mehdi Sepehri

Multi-objective portfolio optimization considering the dependence structure of asset returns

Portfolio optimization context has shed only a little light on the dependence structure among the financial returns along with the fat-tailed distribution associated with them. This study tries to find a remedy for this shortcoming by exploiting stable distributions as the marginal distributions together with the dependence structure based on copula function. We formulate the portfolio optimization problem as a multi-objective mixed integer programming. Value-at-Risk (VaR) is specified as the risk measure due to its intuitive appeal and importance in financial regulations. In order to enhance the models applicability, we take into account cardinality and quantity constraints in the model. Imposing such practical constraints has resulted in a non-continuous feasible region. Hence, we propose two variants of multi-objective particle swarm optimization (MOPSO) algorithms to tackle this issue. Finally, a comparative study among the proposed MOPSOs, NSGAII and SPEA2 algorithms is made to demonstrate which algorithm is outperformed. The empirical results reveal that one of the proposed MOPSOs is superior over the other salient algorithms in terms of performance metrics.

Operating Room Scheduling in Teaching Hospitals

Operating room scheduling is an important operational problem in most hospitals. In this paper, a novel mixed integer programming (MIP) model is presented for minimizing Cmax and operating room idle times in hospitals. Using this model, we can determine the allocation of resources including operating rooms, surgeons, and assistant surgeons to surgeries, moreover the sequence of surgeries within operating rooms and the start time of them. The main features of the model will include the chronologic curriculum plan for training residents and the real-life constraints to be observed in teaching hospitals. The proposed model is evaluated against some real-life problems, by comparing the schedule obtained from the model and the one currently developed by the hospital staff. Numerical results indicate the efficiency of the proposed model compared to the real-life hospital scheduling, and the gap evaluations for the instances show that the results are generally satisfactory.

Resource planning in the emergency departments: A simulation-based metamodeling approach

Abstract Patient’s congestion and their long waiting times in Emergency Departments (EDs) are the most common problems in hospitals. This paper extends application domain of metamodels into decision-making in the EDs by developing a discrete event simulation (DES) model combined with suitable metamodels. This is used as a novel decision support system to improve the patients flow and relieve congestion by changing the number of ED resources (i.e., the number of receptionists, nurses, residents, and beds). This new tool could be used for decision-making in operational, tactical, and strategic levels. In the first step, we develop a simulation routine of the ED in order to evaluate the system performance measure (total average waiting times of patients) for each configuration of resources. In the next step, we use different metamodel techniques and choose one with the maximum efficiency through a cross validation technique to replace the computationally expensive DES model with an accurate and efficient metamodel. Then the proposed model is used to minimize the total average waiting times of patients subject to both budget and capacity constraints. We implement our proposed model in an emergency department in Iran. Experimental results with the current ED budget verify that after using the resource allocation obtained from the proposed model, the total waiting time of patients is reduced by about 48%. Furthermore, to evaluate the efficiency of the selected metamodel, we compare the respective results with those obtained through OptQuest in terms of both the accuracy and the time needed to perform the optimization process.

Intelligent Sales Prediction for Pharmaceutical Distribution Companies: A Data Mining Based Approach

One of the problems of pharmaceutical distribution companies (PDCs) is how to control inventory levels in order to prevent costs of excessive inventory and to prevent losing customers due to drug shortage. Consequently, the purpose of this study is to propose a novel method to forecast sales of PDCs. The presented method is a combination of network analysis tools and time series forecasting methods. Due to lack of enough past sales records of each drug, an explorative network based analysis is conducted to find clique sets and group members and to use comembers’ sales data in their sales prediction. Afterwards, time series sales forecasting models were built with three different approaches including ARIMA methodology, neural network, and an advanced hybrid neural network approach. The offered hybrid method by applying each drug and its comembers past records facilitates capturing both linear and nonlinear patterns of sales accurately. The performance of the proposed method was evaluated by a real dataset provided by one of the leading PDCs in Iran. The results indicated that the proposed method is able to cope with low number of past records while it forecasts medicines sales accurately.

Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs

Clustering of retail stores in a distribution network with specific geographical limits plays an important and effective role in distribution and transportation costs reduction. In this paper, the relevant data and information for an established automotive spare-parts distribution and after-sales services company (ISACO) for a 3-year period have been analyzed. With respect to the diversity and lot size of the available information such as stores location, order, goods, transportation vehicles and road and traffic information, three effecting factors with specific weights have been defined for the similarity function: 1. Euclidean distance, 2. Lot size 3. Order concurrency. Based on these three factors, the similarity function has been examined through 5 steps using the Association Rules principles, where the clustering of the stores is performed using k-means algorithm and similar stores are allocated to the clusters. These steps include: 1. Similarity function based on the Euclidean distances, 2. Similarity function based on the order concurrency, 3. Similarity function based on the combination of the order concurrency and lot size, 4. Similarity function based on the combination of these three factors and 5. Improved similarity function. The above mentioned clustering operation for each 5 cases addressed in data mining have been carried out using R software and the improved combinational function has been chosen as the optimal clustering function. Then, trend of each retail store have been analyzed using the improved combinational function and along with determining the priority of the depot center establishment for every cluster, the appropriate distribution policies have been formulated for every cluster. The obtained results of this study indicate a significant cost reduction (32%) in automotive spare-parts distribution and transportation costs.

Design and Analysis of a Health Care Supply Chain Management

Supply chain management in healthcare is evaluated with a particular focus on the distribution of medicines from a wholesaler to clinics. Currently, there are issues with service levels to clinics that need addressing. The value of the paper arises from providing a detailed analysis of a healthcare supply chain in the developing world and Diagnosis the parameter involved in inventory.

Critical WIP loops: a mechanism for material flow control in flow lines

Critical WIP loops (CWIPL) is a proposed material flow control mechanism for a balanced flow line environment aiming at improving throughput and lead time. The mechanism establishes critical loops which their WIP identifies the time of releasing raw material to the line. So, through control of WIP level of critical loops the material flow is managed. The proposed mechanism releases the raw material to the line if the ‘total WIP of the line’ or ‘the WIP of the last machine’ is less than the limit. Besides the aforementioned condition, the necessary condition for releasing the raw material to the line is ‘idleness of the first machine’. Simulation is used to compare the performance of the CWIPL, CONWIP and G-MaxWIP. Different line characteristics such as number of machines, processing time distributions and the maximum WIP level of the line are considered in numerical examples. The results show that CWIPL improves both throughput and lead time compared with CONWIP, while CWIPL has better results than G-MaxWIP with respect to both throughput and lead time in the flow line that has less than nine machines.

Implementation of Predictive Data Mining Techniques for Identifying Risk Factors of Early AVF Failure in Hemodialysis Patients

Arteriovenous fistula (AVF) is an important vascular access for hemodialysis (HD) treatment but has 20–60% rate of early failure. Detecting association between patients parameters and early AVF failure is important for reducing its prevalence and relevant costs. Also predicting incidence of this complication in new patients is a beneficial controlling procedure. Patient safety and preservation of early AVF failure is the ultimate goal. Our research society is Hasheminejad Kidney Center (HKC) of Tehran, which is one of Irans largest renal hospitals. We analyzed data of 193 HD patients using supervised techniques of data mining approach. There were 137 male (70.98%) and 56 female (29.02%) patients introduced into this study. The average of age for all the patients was 53.87 ± 17.47 years. Twenty eight patients had smoked and the number of diabetic patients and nondiabetics was 87 and 106, respectively. A significant relationship was found between “diabetes mellitus,” “smoking,” and “hypertension” with early AVF failure in this study. We have found that these mentioned risk factors have important roles in outcome of vascular surgery, versus other parameters such as “age.” Then we predicted this complication in future AVF surgeries and evaluated our designed prediction methods with accuracy rates of 61.66%–75.13%.

Antecedents of Strategic Information Systems Alignment in Iran

ABSTRACT Factors affecting business-information technology strategic alignment have been investigated in numerous research studies, mostly in developed countries including Western Europe and North America. Given that the Western world has a distinct business culture, there is reason to investigate the boundaries of the generalizability of these research studies. This article, therefore, investigates strategic alignment in a non-Western, developing-country context, specifically Iran. For this purpose, the antecedents of alignment in the existing literature were investigated. After categorizing them and developing a research model, the model was tested using structural equation modeling. Our main aim is to find out whether the antecedents identified in prior studies are also relevant in Iran. The results provide preliminary evidence of differences between the factors affecting strategic alignment in each region.

Analysis of Occupational Accident Fatalities and Injuries Among Male Group in Iran Between 2008 and 2012

Background: Because of occupational accidents, permanent disabilities and deaths occur and economic and workday losses emerge. Objectives: The purpose of the present study was to investigate the factors responsible for occupational accidents occurred in Iran. Patients and Methods: The current study analyzed 1464 occupational accidents recorded by the Ministry of Labor and Social Affairs’ offices in Iran during 2008 - 2012. At first, general understanding of accidents was obtained using descriptive statistics. Afterwards, the chi-square test and Cramer’s V statistic (Vc) were used to determine the association between factors influencing the type of injury as occupational accident outcomes. Results: There was no significant association between marital status and time of day with the type of injury. However, activity sector, cause of accident, victim’s education, age of victim and victim’s experience were significantly associated with the type of injury. Conclusions: Successful accident prevention relies largely on knowledge about the causes of accidents. In any accident control activity, particularly in occupational accidents, correctly identifying high-risk groups and factors influencing accidents is the key to successful interventions. Results of this study can cause to increase accident awareness and enable workplace’s management to select and prioritize problem areas and safety system weakness in workplaces.