PRODUCTION AND OPERATIONS MANAGEMENT
Year:2014 | Volume:5 | Issue:2 (9)|Papers Count:10

The aircraft industry is one of the most adventurous areas in investment due to its need for huge investments faced by various risks and uncertainties. The traditional project assessment methods in this industry suffer from a systematic approach in considering the risk and uncertainty and often ignoring the value of various options in project life. In this paper, we develop a seven step model based on Real Option Theory to evaluate project from both perspectives of risk and cost by using an extended binomial tree. At last, Monte Carlo simulations are done by considering the uncertainties as a distinct factor from the risks which in previous researches used to be examined jointly. While the traditional approaches don’t justify the project, our results of the proposed model approve doing the project by revealing its real values.

Nowadays in some manufacturing processes, the quality of a product or process is well expressed by both correlated attribute and variable quality characteristics. To best of our knowledge, there is no method for monitoring the covariance matrix of multivariate-attribute quality characteristics. In this paper, we propose a multi-layer perception artificial neural network to monitor multivariate-attribute processes as well as to diagnose the quality characteristic(s) responsible for out-of-control signals. The performance of the proposed method is evaluated through a numerical example from both detection and diagnosis perspectives. In addition, the performance of the proposed neural network in detecting shifts in the variance of quality characteristics is compared with two statistical methods first proposed for monitoring the variability of multivariate quality characteristics and developed in this paper for our problem. The results of numerical example show that the proposed artificial neural network outperforms the extended statistical methods in detecting different out-of-control shifts. The results also confirm that the performance of the proposed neural network in identifying the quality characteristic(s) responsible for out-of-control signal is satisfactory.

Production planning is one of the most important challenges in steel industry. Order planning is one major part of this aspect according to the fact that production is usually planned based on customer orders in this industry. Inventory planning is also vital for maximizing capacity utilization. The proposed model in this paper is capable of selecting order, assigning finish time of each order, and determining open orders in each process by considering order planning and inventory planning simultaneously. The model could have a key role in improving production planning of steel companies by on time delivering of the orders, reducing work-in-process, and balancing utility of capacities. Finally, a heuristic algorithm based on genetic algorithm is proposed and its efficiency evaluated in an empirical case study of Iran alloy steel enterprise.

This research studies resource constrained project scheduling problem in a part of a refinery construction project in real world. In real world, most of the activities are new and we have problems such as activity duration uncertainty. This problem causes a change in a project makespan. The RCPSP is NP-hard. Hence, we proposed an optimization method based on simulated annealing to solve the RCPS Problem. In this paper, Fuzzy sets theory is used to represent this activity duration uncertainty. Used schedule generation scheme, in the proposed simulated annealing algorithm, is a fuzzy parallel scheduling generation method. Proposed algorithm generates the minimum project makespan while considers renewable resource-constrained and activity precedence and also has ability to perform with fuzzy numbers for presenting project details such as start and final time of activities and project whole time with fuzzy numbers. Finally, the results of the algorithm will be evaluated and it will be represented that the proposed algorithm is very efficient and can be used by managers and scheduling programmers in real projects.

Optimal portfolio selection and how to invest in, is one of the key issues which is considered in the capital market and should be paid attention by investors. In this regard, investors studies in selecting optimal portfolio accords to risk and return level. What has been done in the field of financial calculations and portfolio selection should prioritize existing investments in terms of risk and return respectively, until investors be able to constitute their optimal portfolio according to the finance and their level of risk-taking. Hence, measuring risk is considered as a major issue in the investment of portfolio. So in this research which was done in a capital market of Iran, it is presented a multi objective single - period mathematical model for measuring the risk of portfolio which integrates measure of return and two measures of risk (semivariance, Cvar). This model enables investors to measure the risk of portfolio carefully under transactions cost constrains and invest in a portfolio with maximum rate of return and minimum rate of risk Results show that using integraed risk measure can increase the carefulness of investors in capital market for optimal portfolio selection.

Hub location problem is further used in transportation and telecommunication networks (airlines, post delivery services, etc.) so origin-destination pairs, receive or send commodities via special facilities called Hub. Hub median problem with multiple allocation is an NP-hard problem which includes both locating hub facilities and allocating non-hub nodes to hubs as minimizes total transportation and location costs. In this paper, the hub median problem is considered in an environment which network flow varies during the time periods and the capacities of hubs and arcs are unlimited. Also opening and closing hubs in different periods of planning horizon are permitted. The model and the proposed algorithm for this problem were considered to Iran airlines network using real passenger flows data. Computational results state that the dynamic network compared with static model has lower cost and whatever the number of time periods in dynamic case increases, the cost will be reduced as well.

Several factors had led to increase competition among companies and then transferring this competition to supply chain. While the competition is getting more intense, the quick response to demand changing becomes increasingly important and agility will be a key strategy for the survival and persistence in competitive market. The concept of agile supply chain had developed as a means of achieving the capability of quick responding to demand changing in challenging competitive environment. The Success of operation in an agile supply chain relies heavily on choosing the most suitable partner in any condition. The aim of this research is to identify partner selection criteria and represent the relations between them. For these purposes we have used a tree-stage model. In the first stage, we extracted the criteria from literatures. Second stage was done to reduce the criteria to a lower number by interviewing with top managers of the company and using Fuzzy screening method. In the last stage interpretive structural modeling was used to identify the relationships between criteria and MICMAC analysis was used to classify them. After data analysis, variables were categorized in eight levels and then the ISM digraph was plotted. After the MICMAC analysis was done, the criteria was classified to independent, dependent and linkage variable and no criteria were identified as autonomous variables. Determination of the relationships between variables and types of variables can lead to a better perception of the issue and make appropriate decisions in the selection of partner.

In this study a linear programming model has been presented which its objective is to optimize revenue of hotel. In this model, a special planning horizon has been considered and there are several reservation periods. Each period of reservation may have different prices and the model has an ability to consider different kinds of rooms.Two situations of demand have been considered as certain and uncertain. We use scenario-based method for uncertain demand which is simple but near to reality method in stochastic programming. The unique characteristic of this study is considering cancellation and no-show which is a key factor in amount of acceptation of hotel guests. The model is validated by numeric examples and it is solved by linear programming software, finally.

Different methods for prioritization and selection of agility capabilities are presented. And most of them are compensatory multi-criteria decision making methods. With Compensation methods in some cases sufficiently accurate and precise results are not achieved. Because of the weakness of an agility, capabilities in one criteria will be offset by strengths in other criteria. In this paper, using the technique of data envelopment analysis an approach for prioritizing agility capabilities is offered. This method has higher accuracy compared with previous methods. This study was conducted in two successive stages. Agility drivers have been identified in the first stage, and in the second phase the required capabilities to meet the drivers, are prioritized. Firstly, based on three criteria of difficulties, the impact on the activities of the organization and intensity of change, organizational agility drivers ranked and key drivers are selected. The fuzzy TOPSIS and fractional programming techniques are used. Secondly, using the fuzzy DEA technique agility capabilities derived from the research literature base on their impact on key agility drivers are assessed and prioritized. Method presented in one piece construction of state-owned industries has been examined.

Today, one of the tools for Environmental sustainability, along with increasing manufacturers' performance is Green Supply Chain management (GSCM). In this way, the purpose of this paper is to study "Identifying&prioritizing the effective factors on Green Supply Chain Management in chemical and detergents manufacturers by using Path Analysis Approach". The empirical data used in this study consists of questionnaire responses from 36 managers chemical and detergents manufacturers. Statistical analysis of data was done in two stages; Kendall's Tau-b Correlation study and use of statistical method of Path Analysis. The results show the rejection of influence on internal drivers on Green Supply Chain Management practices (third hypothesis), while the influence of external drivers on internal drivers and GSCM practices have been approved. This indicates that external drivers can provide positive bias of internal drivers towards a GSCM practice and manufacturers have to do this in order to stay in this competitio