Fuzzy Inventory Model with Variable Production and Selling Price Dependent Demand under Inflation for Deteriorating Items

The main purpose of this paper is to develop an inventory model under fuzzy approach by considering the effect of inflation and time value of money, to determine the optimal time period for inventory cycle and minimum total average costs. The model is integrated production inventory model developed where;the Demand has a direct linear impact on production rate. The model can be divided into four stages. In the first two stages with original production rate and subsequent change in production rate, inventory level rises. Third stage is time after the accumulation of inventory and before the deterioration starts, where demand which selling price dependent is depreciating the inventory level, while in the fourth stage deterioration occurs, which is considered to follow two parameter Weibull distribution. The back-order is not considered. Hexagonal fuzzy numbers are used to derive optimum solution and defuzzification by graded mean integration representation method. A numerical example is given to demonstrate the applicability of the purposed model and sensitivity analysis is carried out to reveal the impact of change in parameter values.

An EPQ model with variable production,probabilistic deterioration and partial backlogging under inflation

This paper develops an economic production quantity(EPQ)model under the effect of inflation and time value of money.The rate of replenishment is considered to be a variable and the generalized unit production cost function is formulated by incorporating several factors,such as raw material,labour,replenishment rate,advertisements and other factors of the manufacturing system.The selling price of a unit is determined by a mark-up over the production cost.We have considered three types of continuous probabilistic deterioration function,and also considered that the holding cost of the item per unit time is assumed to be an increasing linear function of time spent in storage.In addition,shortages are allowed and partially backlogged.This model aids in minimizing the total inventory cost by finding the optimal cycle length and the optimal production quantity.The optimal solution of the model is illustrated with the help of numerical examples.

Development of process energy intensity formula under different state variables

In a production process, the actual energy consumption is greatly affected by the production state. Certain processing operations are classified into six states, including normal production, abnormal production, planned overhaul, unplanned overhaul, transitional period from unplanned overhaul to normal production (referred for short as unplanned transition) and transitional period from planned overhaul to normal production (referred for short as planned transition). The article takes the analysis of relationship between different states of a certain processing operation and corresponding energy consumptions as a startup point to develop a process energy intensity formula with variables of operating rate, yielding rate and operating frequency, etc. This process energy intensity formula can be used to analyze effectively the pattern of impact exerted by different state variables on energy consumption.

Assessment of intraseasonal variabilities in China Ocean Reanalysis(CORA)

A regional reanalysis product-China Ocean Reanalysis（CORA）-has been developed for the China＇s seas and the adjacent areas. In this study, the intraseasonal variabilities（ISVs） in CORA are assessed by comparing with observations and two other reanalysis products（ECCO2 and SODA）. CORA shows a better performance in capturing the intraseasonal sea surface temperatures（SSTs） and the intraseasonal sea surface heights（SSHs） than ECCO2 and SODA do, probably due to its high resolution, stronger response to the intraseasonal forcing in the atmosphere（especially the Madden-Julian Oscillation）, and more available regional data for assimilation. But at the subsurface, the ISVs in CORA are likely to be weaker than reality, which is probably attributed to rare observational data for assimilation and weak diapycnal eddy diffusivity in the CORA model. According to the comparison results, CORA is a good choice for the study related to variabilities at the surface, but cares have to be taken for the study focusing on the subsurface processes.

Responses of Grassland Net Primary Productivity to Environmental Variables in Northern China

Various environmental factors affect net primary productivity （NPP） of grassland ecosystem. Extensive reports on the effects of environmental variables on NPP can be found in literature. However, the agreement on the relative importance of various factors in shaping the spatial pattern of grassland NPP has not yet been reached. Here a grassland in situ NPP database comprising 602 samples in northern China for 1980-1999 was developed based on a literature review of published biomass and forage yield field measurements. Correlation analyses and dominance analysis were used to quantify the separate and combined effects of environmental variables （climate topography and soil） on spatial variation in NPP separately. Grassland NPP ranged from 4.76 g C m-2a-1 to 975.94gCm-2a-1, showing significant variations in space. NPP increased with annual precipitation and declined with annual mean temperature significantly. Specifically, precipitation had the greatest impact on deserts, followed by steppes and meadows. Grassland NPP decreased with increasing altitude because of water limitation, and positively correlated with slope, but weakly correlated with aspect. Soil quality showed positive effects on NPP. Annual precipitation was the dominant factor affecting the spatial variability of net primary productivity, followed by elevation.