PurposeGiven the importance of estimating the demand for relief items in earthquake disaster, this research studies the complex nature of demand uncertainty in a vehicle routing problem in order to distribute first aid relief items in the post disaster phase, where routes are subject to disruption.Design/methodology/approachTo cope with such kind of uncertainty, the demand rate of relief items is considered as a random fuzzy variable and a robust scenario-based possibilistic-stochastic programming model is elaborated. The results are presented and reported on a real case study of earthquake, along with sensitivity analysis through some important parameters.FindingsThe results show that the demand satisfaction level in the proposed model is significantly higher than the traditional scenario-based stochastic programming model.Originality/valueIn reality, in the occurrence of a disaster, demand rate has a mixture nature of objective and subjective and should be represented through possibility and probability theories simultaneously. But so far, in studies related to this domain, demand parameter is not considered in hybrid uncertainty. The worth of considering hybrid uncertainty in this study is clarified by supplementing the contribution with presenting a robust possibilistic programming approach and disruption assumption on roads.
This paper develops an inventory model for items with uncertain deterioration rate, time-dependent demand rate with nonincreasing function, and allowable shortage under fuzzy inflationary situation. The goods are not deteriorating upon reception, but the deteriorating starts after elapsing a specified time. The lead time and inflation rate are both uncertain in the model. The resultant effect of inflation and time value of money is assumed to be fuzzy in nature and also we consider lead time as a fuzzy function of order quantity. Furthermore the following different deterioration rates have been considered: for the first case we consider fuzzy deterioration rate and for the second case we assume that the deterioration rate is time dependent and follows Weibull distribution with three known parameters. Since the inflation rate, deterioration rate, and the lead time are fuzzy numbers, the objective function becomes fuzzy. Therefore the estimate of total costs for each case is derived using signed distance technique for defuzzification. The optimal replenishment policy for the model is to minimize the total present value of inventory system costs, derived for both the above mentioned policies. Numerical examples are then presented to illustrate how the proposed model is applied.
The literature review on the inflationary inventory systems shows that a lot of researches have been made with considering the inflation as: (1) deterministic and constant; (2) deterministic and variable (time varying); (3) stochastic or (4) fuzzy. However, no attempt has been made to address the issue of how to deal with incomplete, imprecise and missing (ignorance) information in inflation, which is essentially inherent and sometimes inevitable in human being's subjective judgments. The purpose of this paper is to develop a new method, on the basis of the evidential reasoning (ER) approach in order to handle various types of possible uncertainties that may occur in the determining of the inflation rate in the inventory decision making. It is capable of modeling various types of uncertainties using a unified belief structure in a pragmatic, rigorous, reliable, systematic, transparent and repeatable way. The evidential reasoning approach uses a systematic way to accumulate the incomplete data about inflation, which have been gathered from different decision makers. This approach causes interval inflation by accumulating information of all decision makers. Representing the inflation by an interval number and using the interval arithmetic, the objective function for cost is changed to corresponding multi objective functions. These functions are minimized and solved by NSGA-II approach of Multi-objective Genetic Algorithm. The algorithm parameters are tuned by Taguchi method and the mentioned parametertuned algorithm has been validated using several numerical examples by comparison with the optimal solution. The results show that the proposed GA takes less time than the classical model in solving the problem. This difference of times is more significant when we want to do a sensitivity analysis in a wide range of parameters.
Earned Value Management (EVM) is a project management technique used to measure project progress by integrating e cient management of the most important three elements in a project i.e. cost, schedule, and scope. This paper presents an Evidential Reasoning (ER) based model for estimating the Earned Value (EV) of the projects activities with uncertainties in progress data. Since the subjective nature of EV measurement can incorporate errors and uncertainties which cause biased judgments, and as the uncertainty is inherent in real-life activities, the developed ER based model is very useful to evaluate the EV of a project wherein uncertainty arises. A case study is provided to illustrate how the new model will be used and can be implemented in reality.
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