不定需求情形下确定生产批量的方法

引入模糊技术,在只考虑产品的生产成本、潜损成本、折价成本、存储成本情况下,建立了具有单生产周期、需求不确定情况下的最佳生产批量模型。并以成本最小化为目标,运用模糊数的隶属函数、λ-截集的相关理论,采用模糊数的积分值指标排序法,推导出了模型的最优解。即:成本最小的最佳生产批量。该方法特别适合那些缺乏历史数据和统计数据的情况,具有广泛的实用性。

集装箱货运需求不定的最优船槽数量转换方法

在集装箱货物运输和调度管理过程中,针对需求随机产生、不确定情形下的干货槽与冷藏槽转换数量问题,通过蒙特卡罗仿真模拟方法计算获取船舶的最优化调度顺序,提出了4类动态联动的集装箱延迟或拒绝策略;依据此顺序对船上2类船槽的数量加以转化,并设计了一种启发式迭代算法、在最优调度顺序基础上确定了能够使得集装箱数量总延迟最小以及公司收益最大的船槽数量分布。以法国海运集团运营的1条航线为具体算例进行深入剖析和讨论,进一步实验结果表明,运用该算法优化后,公司的平均收益值总体能从初始的5 251 493.88美元提高至5 290 486.25美元,有效提升了7.43‰。

Discrete logistics network design model under interval hierarchical OD demand based on interval genetic algorithm

Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of the system profit,the uncertain demand of logistics network is measured by interval variables and interval parameters,and an interval planning model of discrete logistics network is established.The risk coefficient and maximum constrained deviation are defined to realize the certain transformation of the model.By integrating interval algorithm and genetic algorithm,an interval hierarchical optimal genetic algorithm is proposed to solve the model.It is shown by a tested example that in the same scenario condition an interval solution[3275.3,3 603.7]can be obtained by the model and algorithm which is obviously better than the single precise optimal solution by stochastic or fuzzy algorithm,so it can be reflected that the model and algorithm have more stronger operability and the solution result has superiority to scenario decision.

A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

Abstract Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model-under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve.the hybrid programming model, along with an error≤0.5% when the deviatiorgmean≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor （0.1-0.2） can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.3% and 0.4% enhancement, respectavely.

A Method for Structuring Service Failure Factors to Realize Highly Reliable Services

As the society matures, customer requirements have become more varied. Services have been attracting increasing attention from industry and academic field as an effective mean to satisfy such varied customer requirements. In order to make a profit, it is important for companies to build and maintain long-term relationships with customers. Therefore, service providers should maintain their service quality and always satisfy their customers. To realize highly reliable product or services, in general, it is an effective approach to prevent failures from occurring in the use phase. Therefore, it is necessary that analysts identify the factors that could cause service failure and take appropriate measures against the target failure factor in advance. However, service failure factors are varied compared to physical products because service failures occur due to uncertainty elements such as human factors. In this study, we aim to enable service analysts to identify the critical failure factor from a number of failure factors. To achieve this, we identify complex failure factors and relationships among them from the viewpoint of the field where the service provided. This paper proposes a method for structuring the causal sequence between service failure factors by using a method of system modeling.

Oil Refining Planning under Petroleum Products Demand Uncertainties： Case of Algeria

This paper aims to analyze, with a linear dynamic programming, the Algerian refining industry development by 2030 in the presence of uncertainties, both on the domestic demand and the exportation of the petroleum products. Currently, the Algerian refining industry has to be adapted to meet demand progress both in terms of volume and also in terms of specifications, in a general context marked by a strong volatility of the oil markets. Commonly, refining operations planning models are based on a deterministic linear programming. However, because of the demand fluctuation, and other conditions for the market, many parameters should be considered as uncertain such as the demand and the exportation. The impact of such uncertainties on the development＇s pattern of refining capacities is analyzed with a stochastic model. Finally, the results of both deterministic and stochastic models are compared.

DYNAMIC PORTFOLIO CHOICE UNDER THE TIME-VARYING,JUMPS,AND KNIGHT UNCERTAINTY OF ASSET RETURN PROCESS

By introducing a stochastic element to the double-jump diffusion framework to measure the Knight uncertainty of asset return process, this paper builds the model of dynamic portfolio choice, which maximizes the expected utility of terminal portfolio wealth. Through specifying the state function of uncertainty-aversion, it utilizes the max-min method to derive the analytical solution of the model to study the effect of the time-varying, jumps, and Knight uncertainty of asset return process on dynamic portfolio choice and their interactions. Results of comparative analysis show： The time-varying results in positive or negative intertemporal hedging demand of portfolio, which depends on the coefficient of investor＇s risk aversion and the correlation coefficient between return shift and volatility shift; the jumps in asset return overall reduce investor＇s demand for the risky asset, which can be enhanced or weakened by the jumps in volatility; due to the existing of the Knight uncertainty, the investor avoids taking large position on risky asset, and the resulting is the improving of portfolio＇s steady and immunity. At last, an empirical study is done based on the samples of Shanghai Exchange Composite Index monthly return data from January 1997 to December 2009, which not only tests the theoretical analysis but also demonstrates that the proposed method in the paper is useful from the aspect of portfotio＇s equivalent utility.

FUZZY-BASED NETWORK BANDWIDTH DESIGN UNDER DEMAND UNCERTAINTY

In communication networks （CNs）, the uncertainty is caused by the dynamic nature of the traffic demands. Therefore there is a need to incorporate the uncertainty into the network bandwidth capacity design. For this purpose, this paper developed a fuzzy methodology for network bandwidth design under demand uncertainty. This methodology is usually used for offiine traffic engineering optimization, which takes a centralized view of bandwidth design, resource utilization, and performance evaluation. In this proposed methodology, uncertain traffic demands are first handled into a fuzzy number via a fuzzification method. Then a fuzzy optimization model for the network bandwidth allocation problem is formulated with the consideration of the trade-off between resource utilization and network performance. Accordingly, the optimal network bandwidth capacity can be obtained by maximizing network revenue in CNs. Finally, an illustrative numerical example is presented for the purpose of verification.