JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL
Year:2006 | Volume:2 | Issue:3|Papers Count:6

In quality control charts, the problem of determining the optimum process mean arises when the deviation of a quality characteristic in one direction is more harmful than in the opposite direction. The failure mode in these two directions is usually different. A great majority of researches in this area have considered asymmetric cost function for processes with single quality characteristics. In this paper, we consider processes in which there are more than one quality characteristics to monitor. The quality characteristics themselves may or may not be independent. Based upon the specification limits and the costs associated with the deviations we derive a formula to determine the optimum process mean. To illustrate the proposed formula and to estimate the costs associated with the optimum process mean we present four numerical examples by simulation. The results of the simulation studies show that considerable amount of savings can be obtained by applying the proposed process means.

This paper deals with the single machine multi-product lot scheduling problem in which defective items are produced in any production run of each product. We have adopted the common cycle scheduling policy and assumed that the setup times for production of each product can be non-zero. Further, we have assumed that defective items will be reworked and the inspection costs during the normal production and rework processing times are different. For this system we obtained the optimal batch sizes for each product such that the total cost per unit time is minimized.

This study shows the usefulness of Artificial Neural Network (ANN) in maintenance planning and management. An ANN model based on the multi-layer perceptron having three hidden layers and four processing elements per layer was built to predict the expected downtime resulting from a breakdown or a maintenance activity. The model achieved an accuracy of over 70% in predicting the expected downtime.

In this study, a cross-docking system is designed at strategic and tactical levels. For making the strategic decisions, a multi-objective nonlinear location allocation model for cross-docks is presented based on a distribution location allocation model by Andreas Klose and Andreas Drexl. The model is further developed to include the whole supply chain members and the objective functions are weighted by implementing AHP. A genetic algorithm solution is designed for sample cross-dock location allocation problems. In the tactical stage, model was further simulated under two different distribution strategies to decide on the tactical parameters. As an example, the performance of the model is verified.

Scheduling for job shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. The combination of several optimization criteria induces additional complexity and new problems. In this paper, we propose a Pareto approach to solve multi-objective job shop scheduling. The objective considered is to minimize the overall completion time (makespan) and total weighted tardiness (TWT). An effective simulated annealing algorithm based on proposed approach is presented to solve multi-objective job shop scheduling problems. An external memory of non-dominated solutions is considered to save and update the non-dominated solutions during the problem solving process. The parameters in the proposed algorithm are determined after conducting a pilot study. Numerical examples are used to evaluate and study the performance of the proposed algorithm.

Industrial organizations are complex systems' where the interactions among the various functions such as Sales, Distribution, Manufacturing, Materials, Finance, Human Resources and Maintenance have to be managed towards a common purpose of delivering the customers satisfaction. However, since most of these organizations have a `Functional Structure', each function or department works towards their own goals and objectives, rather than the organizational goals. This is further aggravated because information flow is restricted by functions, and even when other functions want to take a holistic view, they do not have the information to do so. These are the issues addressed by Enterprise Resource Planning solutions providing a common, consistent system to capture data organization wide, with minimum redundancy. ERP integrates the resources such as information across functions, and provides a set of tools for planning and monitoring the various functions and processes and ensuring progress towards a common purpose in direction of goals and strategies. In this paper, we introduce a model for performance measurement of an organization, which they are working in competitive, monopoly or attractive business environment, and have total system or use ERP solution. We developed a Performance Measurement system that based on organization's strategic and goals. In the following sections, we describe a brief survey of performance models. Then, we focused on performance measurement process, developing performance indicators and metrics. Our approach is to develop a process and a model for performance measurement by assigning priorities to all goals with respect to organization strategies in specific business condition. The observation of using this model shows that it works very close to real world behaviors and can help senior managers for intelligent decision-making.