A multi-objective wholesaler–retailers inventory-distribution model with controllable lead-time based on probabilistic fuzzy set and triangular fuzzy number

This paper develops a single wholesaler and multi retailers mixture inventory distribution model for a single item involving controllable lead-time with backorder and lost sales. The retailers purchase their items from the wholesaler in lots at some intervals throughout the year to meet the customers’ demand. Not to loose the demands, the retailers offer a price discount to the customers on the stock-out items. Here, it is assumed that the lead-time demands of retailers are uncertain in both stochastic and fuzzy sense, i.e., these are simultaneously random and imprecise. To implement this behavior of the lead-time demands, at first, these demands are assumed to be random, say following a normal distribution. With these random demands, the expected total cost for each retailer is obtained. Now, the mean lead-time demands (which are crisp ones) of the retailers are fuzzified. This fuzzy nature of the lead-time demands implies that the annual average demands of the retailers must be fuzzy numbers, suppose these are triangular fuzzy numbers. Using signed distance technique for defuzzification, the estimate of total costs for each retailer is derived. Therefore, the problem is reduced to optimize the crisp annual costs of wholesaler and retailers separately. The multi-objective model is solved using Global Criteria method. Numerical illustrations have been made with the help of an example taking two retailers into consideration. Mathematical analyses have been made for global pareto-optimal solutions of the multi-objective optimization problem. Sensitivity analyses have been made on backorder ratio and pareto-optimal solutions for wholesaler and different retailers are compared graphically.     

不同质量调整法在消费者价格指数(CPI)中的应用

传统的消费者价格指数(CPI)存在着一定的质量偏差,不能真实地反映实际价格的变化.直接比较法、交叠法、生产成本法、链接法等质量调整法的应用均有一定的局限性,且缺陷较多,均指插补调整借助于缺失值插补的原理,以同一组内商品价格的平均变化率作为质量调整系数,能有效地降低价格指数的质量偏差。Hedonic质量调整法是以实际资料建立的回归方程为基础,运用数学的手段科学的将质量变化程度加以量化,因此与传统的质量调整法相比含有较少的主观因素,为CPI指数的准确度量提供了科学的质量调整依据.     

计算机辅助装调与传统基准传递技术相结合实现三镜消像散系统的装调

为满足复杂航天光学系统对精度的要求,克服传统基准传递技术与计算机辅助装调技术对多于3片反射镜的复杂光学系统进行装调时存在的局限性,提出了两种技术相结合的装调方法。采用提出的方法对三镜消像散(TMA)空间相机进行了装调,结果显示:三镜在Y向和Z向的失调量分别由18.651和9.879 mm降低到1.036和0.102 mm,系统波前差达到全视场平均值1/14λ(RMS)。结果证明:此方法能有效缩短装调时间并达到系统要求的精度指标,对于多镜复杂光学系统装调具有指导和参考价值。     

量子纠缠消相干对确定型远程制备的影响

研究了两种典型的量子纠缠消相干现象对确定型量子态远程制备方案的影响.首先对该确定型远程制备方案进行了分析,得到该方案确定性和比特消耗情况; 然后通过分析制备过程中纠缠消相干现象对系统的影响得出: 在极化消相干过程中,该系统保真度与目标量子比特在Bloch球上的经度选择无关,仅与目标比特的纬度和消相干的大小有关;在相位消相干中,该系统的保真度不会受到消相干的影响,仅与目标量子态的纬度相关. 关键词： 远程制备 纠缠消相干 通信消耗 保真度     

The opportunity cost of mean–variance choice under estimation risk

Mean–variance portfolio choice is often criticized as sub-optimal in the more general expected utility framework. It is argued that the expected utility framework takes into consideration higher moments ignored by mean variance analysis. A body of research suggests that mean–variance choice, though arguably sub-optimal, provides very close-to-expected utility maximizing portfolios and their expected utilities, basing its evaluation on in-sample analysis where mean–variance choice is sub-optimal by definition. In order to clarify this existing research, this study provides a framework that allows comparing in-sample and out-of-sample performance of the mean variance portfolios against expected utility maximizing portfolios. Our in-sample results confirm the results of earlier studies. On the other hand, our out-of-sample results show that the expected utility model performs worse. The out-of-sample inferiority of the expected utility model is more pronounced for preferences and constraints under which in-sample mean variance approximations are weakest. We argue that, in addition to its elegance and simplicity, the mean–variance model extracts more information from sample data because it uses the covariance matrix of returns. The expected utility model may reach its optimal solution without using information from the covariance matrix.     

Scheduling with few changes

In this work we consider scheduling problems where a sequence of assignments from products to machines – or from tasks to operators, or from workers to resources – has to be determined, with the goal of minimizing the costs (=money, manpower, and/or time) that are incurred by the interplay between those assignments. To account for the different practical requirements (e.g. few changes between different products/tasks on the same machine/operator, few production disruptions, or few changes of the same worker between different resources), we employ different objective functions that are all based on elementary combinatorial properties of the schedule matrix. We propose simple and efficient algorithms to solve the corresponding optimization problems, and provide hardness results where such algorithms most likely do not exist.     

On the core of cost-revenue games: Minimum cost spanning tree games with revenues

In this paper, we analyze cost sharing problems arising from a general service by explicitly taking into account the generated revenues. To this cost-revenue sharing problem, we associate a cooperative game with transferable utility, called cost-revenue game. By considering cooperation among the agents using the general service, the value of a coalition is defined as the maximum net revenues that the coalition may obtain by means of cooperation. As a result, a coalition may profit from not allowing all its members to get the service that generates the revenues. We focus on the study of the core of cost-revenue games. Under the assumption that cooperation among the members of the grand coalition grants the use of the service under consideration to all its members, it is shown that a cost-revenue game has a nonempty core for any vector of revenues if, and only if, the dual game of the cost game has a large core. Using this result, we investigate minimum cost spanning tree games with revenues. We show that if every connection cost can take only two values (low or high cost), then, the corresponding minimum cost spanning tree game with revenues has a nonempty core. Furthermore, we provide an example of a minimum cost spanning tree game with revenues with an empty core where every connection cost can take only one of three values (low, medium, or high cost).     

Competition through capacity investment under asymmetric existing capacities and costs

This paper discusses the way that different operational characteristics including existing capacity, scale economies, and production policy have an important influence on the capacity outcomes when firms compete in the market place. We formulate a game-theoretical model where each firm has an existing capacity and faces both fixed and variable costs in purchasing additional capacity. Specifically, the firms simultaneously (or sequentially) make their expansion decisions, and then simultaneously decide their production decisions with these outputs being capacity constrained. We also compare our results with cases where production has to match capacity. By characterizing the firms’ capacity and production choices in equilibrium, our analysis shows that the operational factors play a crucial role in determining what happens. The modeling and analysis in the paper gives insight into the way that the ability to use less production capacity than has been built will undermine the commitment value of existing capacity. If a commitment to full production is not possible, sinking operational costs can enable a firm to keep some preemptive advantage. We also show that the existence of fixed costs can introduce cases where there are either no pure strategy equilibrium or multiple equilibria. The managerial implications of our analysis are noted in the discussion. Our central contribution in this paper is the innovative integration of the strategic analysis of capacity expansion and well-known (s,S)$(s\text{,}S)$ policy in operations and supply chain theory.     

Polynomial cases of the economic lot sizing problem with cost discounts

In this paper we study the economic lot sizing problem with cost discounts. In the economic lot sizing problem a facility faces known demands over a discrete finite horizon. At each period, the ordering cost function and the holding cost function are given and they can be different from period to period. There are no constraints on the quantity ordered in each period and backlogging is not allowed. The objective is to decide when and how much to order so as to minimize the total ordering and holding costs over the finite horizon without any shortages. We study two different cost discount functions. The modified all-unit discount cost function alternates increasing and flat sections, starting with a flat section that indicates a minimum charge for small quantities. While in general the economic lot sizing problem with modified all-unit discount cost function is known to be NP-hard, we assume that the cost functions do not vary from period to period and identify a polynomial case. Then we study the incremental discount cost function which is an increasing piecewise linear function with no flat sections. The efficiency of the solution algorithms follows from properties of the optimal solution. We computationally test the polynomial algorithms against the use of CPLEX.     

特高含水期井震结合注采系统挖潜调整研究

进入特高含水期后,开展注采系统调整挖潜剩余油是油田持续稳产的关键.为了提高特高含水阶段油田采收率,提出井震结合方法精细标定构造层位、认识储层特征,构建地质模型并数值模拟剩余油分布,针对剩余油应用井震结合解释成果设计注采系统调整方案并优化相应注水强度.实验区块研究结果表明:特高含水期,采用井震结合注采系统调整方法最终产油量和采收率分别提高31.21×10~4t和1.54%,能够达到有效挖潜剩余油的目的;而改变注水强度对最终开发效果影响显著,随着注水强度增加,采收率先增大后减小,存在最佳的注水强度为9m~3/d·m.