Determining how a new production cell will function is problematic and can lead to disastrous results if done incorrectly. Discrete-event simulation can provide information on how a line will function before, during, and after the line is in operation. A simulation model can also provide a visual animation of the line to see how product will flow through the line. This paper discusses the development and analysis of a simulation model of a new manufacturing line. The manufacturing cell is a new motor assembly cell. An analysis of the capability of the line for varying demand levels was conducted for the two main motor types produced on the line. An ARENA® simulation model was developed, verified, and validated to determine the daily production and potential problem areas for the various demand levels. The results show that at all but one demand level, the line is capable of producing to within one unit of customer demand if the required number of workers is present. At the highest demand level, the simulation results suggest that the line is not capable of meeting demand. Additional analysis indicates that multiple workstations could prove problematic with minor fluctuations in demand. Problematic workstations were identified for each assembly area and for the line as a whole.

Glacial acetic acid as a protic acid was employed as a catalyst in a solvent free condition for facile preparation of di(indolyl)methanes (DIMs) via one-pot condensation of indole with aryl or heteroaryl aldehydes. Various aryl and heteroaryl aldehydes were efficiently converted to the corresponding di(indolyl)methanes (1a-p) in high yields. The described novel synthetic method proposes several advantages of safety, mild condition, short reaction times, high yields, simplicity and the inexpensively glacial acetic acid compared to other catalysts.

Highway accident data analysis provides probability of occurrence of road accidents by associating different accident factors using data mining algorithms. Analysis can be improved by using advanced data mining algorithms that compute relationships with minimum processing time. As accident datasets are very heterogeneous in nature, it is difficult to identify the relationship between critical factors responsible for road accidents without data mining algorithms. In this study, K-modes for clustering and frequent pattern growth algorithms to extract relationships between critical accident factors have been used. The accomplished result concludes better relationships with better accuracy than earlier implemented data mining algorithms and has found meaningful hidden situations that would be beneficial for future work in decreasing the number of highway accidents.

This paper presents an empirical investigation to determine whether or there is any difference between the returns of two value and growth portfolios, sorted by price-to-earnings (P/E) and price-to-book value (P/BV), in terms of the ratios of market sensitivity to index (β), firm size and market liquidity in listed firms in Tehran Stock Exchange (TSE) over the period 2001-2008. The selected firms were collected from those with existing two-consecutive positive P/E and P/BV ratios and by excluding financial and holding firms. There were five independent variables for the proposed study of this paper including P/E, P/B, market size, market sensitivity beta (β) and market liquidity. In each year, we first sort firms in non-decreasing order and setup four set of portfolios with equal firms. Therefore, the first portfolio with the lowest P/E ratio is called value portfolio and the last one with the highest P/E ratio is called growth portfolio. This process was repeated based on P/BV ratio to determine value and growth portfolios, accordingly. The study investigated the characteristics of two portfolios based on firm size, β and liquidity. The study has implemented t-student and Levin’s test to examine different hypotheses and the results have indicated mix effects of market sensitivity, firm size and market liquidity on returns of the firms in various periods.

In this paper, we presents an inventory problem where initially, a retailer purchases Q(=P+R) units and after fulfilling the backlogged quantities, there is a P unit of the on-hand inventory. It continuously declines to meet the customer’s demand, which depends on the on-hand inventory level up to the time t = t1. After that the inventory level declines by constant demand up to t = t2. Thereafter, shortage occurs and it accumulates at the rate ?(T-t) till t = T when the next batch arrives. This whole cycle repeats itself after the cycle length T. The proposed model of this paper is investigated under various conditions and the implementation of the proposed model is presented through some numerical examples.

Business process evaluation is a common norm in small-medium-large industries globally and information obtained during such evaluation have been used in simulating future performance of most industries using mathematical models such as Autoregressive Integrated Moving Average (ARIMA) and artificial neural network (ANN). This study explored the possibility of predicting port productivity and berth effectiveness of seaport using ANN and ARIMA. A comparative analysis of multi-layer perceptron (MLP) back propagation algorithm and ARIMA performance was carried out based on ships days at port, days at berth and tonnage which serves as model inputs, while port productivity and berth effectiveness were the model outputs. The MLP-ANN and ARIMA (1, 0, 4- port productivity) and (1, 0, 4-berth effectiveness) results were compared based on their coefficient of correlation and mean square error. The coefficient of correlation for port productivity prediction using MLP-ANN was 0.998. This value outperformed that of ARIMA (0.9862) for port productivity. The coefficient of correlation of 0.9956 and 0.9928 were obtained for berth effectiveness using MLR and ARIMA, respectively.

This paper considers the dynamic variant of single processor scheduling problem of minimizing total tardiness. In practice, it occurs when minimizing tardiness penalty. The problem is NP-hard; thus two heuristics were proposed. The utility of the proposed models was demonstrated through computational experiments and comparative analyses against existing solution methods and the Branch and Bound (BB) method. The results show that the proposed models yield effi-cient solutions and in most cases perform effectively better than the existing heuristics in the lit-erature.

A highly sensitive, accurate and simple spectrophotometric method was established for determination of naproxen (NAP). The method involved ion-pair complex formation between naproxen and bromophenol blue (BPB). The colored product was measured at 432 nm. The reaction conditions were optimized. The absorbance was found to increase linearly with increase in concentration of NAP which was corroborated by correlation coefficient value. Beer’s law was obeyed in the concentration range of 1-110 µg mL-1 with molar absorptivity of 9.756×103 L mol-1 cm-1. The limits of detection (LOD) and quantitation (LOQ) for the proposed method are 0.292 and 0.973 µg mL-1, respectively. Recovery of the method was carried out by standard addition method. Recovery studies and statistical data proved the accuracy, reproducibility and the precision of the proposed method. The common excipients did not interfere in this analysis. Hence the method is useful for routine estimation of naproxen in pharmaceutical formulation and human serum samples.

Reliability and quality assurance have become major considerations in the design and manufacture of today’s parts and products. Survivability of green compact using powder metallurgy technology is considered as one of the major quality attributes in manufacturing systems today. During powder metallurgy (PM) production, the compaction conditions and behavior of the metal powder dictate the stress and density distribution in the green compact prior to sintering. These parameters greatly influence the mechanical properties and overall strength of the final component. In order to improve these properties, higher compaction pressures are usually employed, which make unloading and ejection of green compacts more challenging, especially for the powder-compacted parts with relatively complicated shapes. This study looked at a mathematical survivability model concerning green compact characteristics in PM technology and the stress-strength failure model in reliability engineering. This model depicts the relationship between mechanical loads (stress) during ejection, experimentally determined green strength and survivability of green compact. The resulting survivability is the probability that a green compact survives during and after ejection. This survivability model can be used as an efficient tool for selecting the appropriate parameters for the process planning stage in PM technology. A case study is presented here in order to demonstrate the application of the proposed survivability model.

Food industries as one of the most important industries in country, needs appropriate planning at both macro and micro levels. One of the important parts, which need to be discussed is the supply chain management in this industry. In this study, the importance of supply chain in food industries is studied. The proposed study determines different suppliers and using various criteria, the study applies Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank different alternatives. The preliminary results indicate that the proposed model of this paper is capable of determining appropriate suppliers in food industry.