سال:1387 | دوره: | شماره: |تعداد مقالات:8

In this paper, a special class of redundancy optimization problem with fuzzy random variables is presented. In this model, fuzzy random lifetimes are considered as basic parameters and the Er-expected of system lifetime is used as a major type of system performance. Then a redundancy optimization problem is formulated as a binary integer programming model. Furthermore, illustrative numerical examples are also given to clarify the methods discussed in this paper.

One of the existing problems of multi-attribute process monitoring is the occurrence of high number of false alarms (Type I error). Another problem is an increase in the probability of not detecting defects when the process is monitored by a set of independent uni-attribute control charts. In this paper, we address both of these problems and consider monitoring correlated multi-attributes processes following multi-binomial distributions using two artificial neural network based models. In these processes, out-of-control observations are due to assignable causes coming from some shifts on the mean vector of the proportion nonconforming of the attributes. Model one, which is designed for positively correlated attributes, consists of three neural networks. The first network not only detects whether the process is out-of-control, but also determines the direction of shifts in the attribute means. In this situation, the second and the third networks diagnose the process attribute/s that has/have caused the out-of-control signal due to increase or decrease in proportion nonconforming, respectively. Model two is designed for negatively correlated attributes and consists of two neural networks. The first network is designed to detect whether the process is out-of-control and the second one diagnoses the attribute/s that make/s the signal. The results of five simulation studies on the performance of the proposed methodology are encouraging.

This paper presents an exact model for the resource investment problem with generalized precedence relations in which the minimum or maximum time lags between a pair of activities may vary depending on the chosen modes. All resources considered are renewable. The objective is to determine a mode and a start time for each activity so that all constraints are obeyed and the resource investment cost is minimized. Project scheduling of this type occurs in many fields for instance, construction industries. The proposed model has been inspired by the packing problems. In spite of the fact that it needs a feasible solution to start for conventional models, the new model has no need for a feasible solution to startup with. Computational results with a set of 60 test problems have been reported and the efficiency of the proposed model has been analyzed.

This paper presents a multi-objective geometric programming model which determines the product's selling price in two markets. We assume demand is a function of price and marketing expenditure in two markets. The cost of production is also assumed to be a function of demands in both markets. Our model is a posynomial function which is solved using Geometric Programming (GP). In our GP implementation, we use a transformed dual problem to change the model into an optimization of an unconstraint problem with a single variable solved using a simple line search. In order to study the behavior of the model we analyze the solution in different cases and a numerical example is used to demonstrate the implementation for each case.

This paper presents a new mixed-integer goal programming (MIGP) model for a parallel machine scheduling problem with sequence-dependent setup times and release dates. Two objectives are considered in the model to minimize the total weighted flow time and the total weighted tardiness simultaneously. Due to the complexity of the above model and uncertainty involved in real-world scheduling problems, it is sometimes unrealistic or even impossible to acquire exact input data. Hence, we consider the parallel-machine scheduling problem with sequence-dependent set-up times under the hypothesis of fuzzy processing time's knowledge and two fuzzy objectives as the MIGP model. In addition, a quite effective and applicable methodology for solving the above fuzzy model is presented. At the end, the effectiveness of the proposed model and the denoted methodology is demonstrated through some test problems.

We present several heuristic algorithms based on tabu search for solving the multi-stage sequencedependent group scheduling (SDGS) problem by considering minimization of makespan as the criterion. As the problem is recognized to be strongly NP-hard, several meta (tabu) search-based solution algorithms are developed to efficiently solve industry-size problem instances. Also, two different initial solution generators are developed to aid in the application of the tabu search-based algorithms. A lower bounding technique based on relaxing the mathematical model for the original SDGS problem is applied to estimate the quality of the heuristic algorithms. To find the best heuristic algorithm, random test problems, ranging in size from small, medium, to large are created and solved by the heuristic algorithms. A detailed statistical experiment, based on nested split-plot design, is performed to find the best heuristic algorithm and the best initial solution generator. The results of the experiment show that the tabu search-based algorithms can provide high quality solutions for the problems with an average percentage error of only 1.00%.

The economic production quantity (EPQ) is a commonly used inventory model. An assumption in the EPQ model is that all units produced are perfect. Some researchers have studied the effects after relaxing this assumption on the inventory models. The objective of this paper is to determine the economic production quantity with reduced pricing, rework and reject situations in a single-stage system in which rework takes place in each cycle after processing to minimize total system costs. The assumption entertained in this paper is that processing leads to different products classified in the four groups of perfect products, imperfect products, defective but reworkable products, and, finally, non-reworkable defective products. The percentage of each type is assumed to be constant and deterministic. A mathematical model is developed and numerical examples are presented to illustrate the usefulness of this model compared to previous ones.

In this paper, we used the fuzzy set theory for modeling flexible constraints and uncertain data in nurse scheduling problems and proposed a fuzzy linear model for nurse rostering problems. The developed model can produce rosters that satisfy hospital objectives, ward requirements and staff preferences by satisfying their requests as much as possible. Fuzzy sets are used for modeling demands of personnel in each shift. The objective is to identify the optimum roster for nurses in order to complete the weekly roster with fuzzy constraints.This model is implemented for the data collected from Namazi Hospital (NH) of Shiraz, which is the largest hospital in south of Iran. After modeling, this problem is solved by using Lingo software. Finally we compare the result of fuzzy rosters with goal programming rosters that we have previously modeled and with manual rosters that are produced by the head nurses of NH.