Journal of Industrial and Systems Engineering
Year:2007 | Volume:1 | Issue:3|Papers Count:6

As more and more container terminals open up all over the world, competition for business is becoming very intense for container terminal operators. They are finding out that even to keep their existing Sea Line customers, they have to make them happy by offering higher quality service. The quality of service they can provide depends on their operating policies and the design of the terminal layout. Existing layouts based on designs prepared a long time ago pose inherent limitations. We summarize some of these problems, and report on newer operating policies and designs which can help improve performance.

In this paper we introduce the optimal solution for a simple and yet practical inventory policy with the important characteristic which eliminates the uncertainty in demand for suppliers. In this new policy which is different from the classical inventory policies, the time interval between any two consecutive orders is fixed and the quantity of each order is one. Assuming the fixed ordering costs are negligible, lead times are constant, and demand forms a Poisson process, we use queuing theory concepts to derive the long-run average total inventory costs, consisting of holding and shortage costs in terms of the average inventory. We show that the total cost rate has the important property of being entirely free of the lead time. We prove that the average total cost rate is a convex function and thus has a unique solution. We, then derive the relation for the optimal value of the time interval between any two consecutive orders. Finally we present a numerical example to compare the performance of this new policy with the classical one-for-one ordering policy. The provided example intends to re-examine the optimality of (s, S) policy in continuous review inventory models as well to establish the fact that even for the case where demand forms a Poisson process the optimality does not hold.

The quality loss function developed by Genichi Taguchi considers three cases, nominal-the best, smaller-the-better, and larger-the-better. The methodology used to deal with the larger-the better case is slightly different than the other two cases. This research employs a term called target-mean ratio to propose a common formula for all three cases to bring about similarity among them. The target-mean ratio can take different values representing all three cases to bring consistency and simplicity in the model. In addition, it eliminates the assumption of target performance at an infinite level and brings the model closer to reality. Characteristics such as efficiency, coefficient of performance, and percent non-defective are presently not larger-the better characteristics due to the assumption of target performance at infinity and the subsequent necessary derivation of the formulae. These characteristics can also be brought under the category of the larger-the-better characteristics. An example of the efficiency of prime movers is discussed to illustrate that the efficiency can also be considered as a larger-the-better characteristic. A second example is presented to suggest the subtle differences between both methodologies.

In this research, we consider an application of the Bayesian Inferences in machine replacement problem. The application is concerned with the time to replace two machines producing a specific product; each machine doing a special operation on the product when there are manufacturing defects because of failures. A common practice for this kind of problem is to fit a single distribution to the combined defect data, usually a distribution with an increasing hazard rate. While this may be convenient, it does not adequately capture the fact that there are two different underlying causes of failures. A better approach is to view the defect as arising from a mixture population: one due to the first machine failures and the other due to the second one. This allows one to estimate the various parameters of interest including the mixture proportion and the distribution of time between productions of defective products for each machine, separately. To do this, first we briefly introduce the data augmentation method for Bayesian inferences in the context of the finite mixture models. Then, we discuss the analysis of time-to-failure data and propose an optimal decision-making procedure for machine replacement strategy. In order to demonstrate the application of the proposed method we provide a numerical example.

The Mahalanobis Taguchi System is a diagnosis and forecasting method for multivariate data. Mahalanobis distance is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. The Mahalanobis Taguchi System is of interest because of its reported accuracy in forecasting small, correlated data sets. This is the type of data that is encountered with consumer vehicle ratings. MTS enables a reduction in dimensionality and the ability to develop a scale based on MD values. MTS identifies a set of useful variables from the complete data set with equivalent correlation and considerably less time and data. This paper presents the application of the Mahalanobis-Taguchi System and its application to identify a reduced set of useful variables in multidimensional systems.

This paper introduces an analytical method for approximating the performance of a two-class priority M/M/1 system. The system is fully non-preemptive. More specifically, the prioritized class-1 jobs are real-time and served with the non-preemptive earliest-deadline-first (EDF) policy, but despite their priority cannot preempt any non real-time class-2 job. The waiting class-2 jobs can only be served from the time instant that no class-1 job is in the system. The service discipline of the class-2 jobs is FCFS. The required mean service times may depend on the class of the jobs. The real-time jobs have exponentially distributed relative deadlines until the end of service. The system is approximated by a Markovian model in the long run, which can be solved numerically using standard Markovian solution techniques. The performance measures of the system are the loss probability of the class-1 jobs and the mean sojourn (waiting) time of the class-2 jobs. Comparing the numerical and simulation results, we find that the existing errors are relatively small.