Mitigating Inventory Overstocking: Optimal Order‐up‐to Level to Achieve a Target Fill Rate over a Finite Horizon

Service level agreements (SLAs) are widely adopted performance‐based contracts in operations management practice, and fill rate is the most common performance metric among all the measurements in SLAs. Traditional procedures characterizing the order‐up‐to level satisfying a specified fill rate implicitly assume an infinite performance review horizon. However, in practice, inventory managers are liable to maintain and report fill rates over a finite performance review horizon. This horizon discrepancy leads to deviation between the target fill rate and actual achieved fill rate. In this study, we first examine the behavior of the fill rate distribution over a finite horizon with positive lead time. We analytically prove that the expected fill rate assuming an infinite performance review horizon exceeds the expected fill rate assuming a finite performance review horizon, implying that there exists some inventory “waste” (i.e., overstocking) when the traditional procedure is used. Based on this observation and the complexity of the problem, we propose a simulation‐based algorithm to reduce excess inventory while maintaining the contractual target fill rate. When the lead time is significant relative to the length of the contract horizon, we show that the improvement in the inventory system can be over 5%. Further, we extend our basic setting to incorporate the penalty for failing to meet a target, and show how one can solve large‐scale problems via stochastic approximation. The primary managerial implication of our study is that ignoring the performance review horizon in an SLA will cause overstocking, especially when the lead time is large.     

不确定需求下考虑大数据投资决策的供应链鲁棒优化及协调模型

针对由数据公司、制造商和零售商组成的三级供应链系统,在仅知需求均值和方差信息下,建立了集中和分散两种决策情况下的供应链鲁棒优化模型。集中决策下,全体供应链成员共同决定产品零售价和产品生产数量;分散决策下,数据公司作为主方,决定需求信息零售价,制造商和零售商作为从方,分别决定产品批发价和产品订货数量。针对两种决策下的供应链鲁棒优化模型,给出了相应的最优决策,分析了大数据投资成本、消费者异质性需求满足程度等系统参数对供应链决策的影响。进一步,设计了能实现三级供应链完美协调的利润共享契约。最后,通过数值算例验证了所建模型和契约协调的有效性。研究表明,在集中和分散决策下,大数据投资成本只有在满足一定条件下,投资大数据才有利于供应链成员及系统利润的改善。特别地,与无协调时的情况相比,虽然协调机制下相关系统参数对供应链成员决策及利润绩效的作用机制存在差异,但文中提出的利润共享契约在一定条件下能够显著改进供应链成员利润且实现供应链完美协调。     

Reliability analysis of man–machine systems using fuzzy cognitive mapping with genetic tuning

This article offers a method for analyzing the reliability of a man–machine system (MMS) and ranking of influencing factors based on a fuzzy cognitive map (FCM). The ranking of influencing factors is analogous to the ranking of system elements the probabilistic theory of reliability. To approximate the dependence of “influencing factors—reliability,” the relationship of variable increments is used, which ensures the sensitivity of the reliability level to variations in the levels of influencing factors. The novelty of the method lies in the fact that the expert values of the weights of the FCM graph edges (arcs) are adjusted based on the results of observations using a genetic algorithm. The algorithm's chromosomes are generated from the intervals of acceptable values of edge weights, and the selection criterion is the sum of squares of deviations of the reliability simulation results from observations. The method is illustrated by the example of a multifactor analysis of the reliability of the “driver–car–road” system. It is shown that the FCM adjustment reduces the discrepancy between the reliability forecast and observations almost in half. Possible applications of the method can be complex systems with vaguely defined structures whose reliability depends very much on interrelated factors measured expertly.     

上市公司董事会业绩评价模型

阐述了开发上市公司董事会业绩评价模型的意义,建立了业绩评价指标体系,并介绍了业绩评价模型实现的思想,描述了模糊综合评价系统的综合评价机理,最后给出了应用实例。应用实例表明,该模糊综合评价系统计算速度快、结果表达清楚、显示方式直观灵活。     

随机需求条件下产品退货政策协调与优化

本文研究了随机需求条件下基于退货的单周期单一产品逆向供应链模型.首先,研究了集中决策条件下集成厂商的利润函数,并讨论了实现利润最大化的最优性条件及其最优订货量;其次,在分散决策条件下,分别研究了制造商和销售商的利润函数,并讨论了销售商的最优性条件及其最优订货量;最后,在集中决策和分散决策模型的基础上,重点研究了供应链的协调与优化,得出了实现供应链协调的基本条件和两者之间的利润分配机制.通过制定批发价格和退货价格来实现两者之间的增加利润的任意分配.     

Fuzzy quality-team formation for value added auditing: A case study

Value-added (quality) auditing is emerging as one of the most powerful tools for continuous quality improvement, with the introduction of the ISO 9001:2000 and ISO 19011 standards that focus team-based audits, proper auditor skills, process auditing, and effectiveness, etc. Formation of an effective quality audit team (QAT) based on the required auditor skills is therefore the initial stage of the value added auditing. QATs audit organizations at different locations with varying auditing requirements in order to evaluate an organization's own quality system (first party part audits according to IEC9001 Clause 8.2.2). The QAT consists of a lead auditor and one or more auditors that have the required skills in varying levels for the execution of an audit. For a successful audit, the formation of a QAT is vital since each audit team must at least fulfill the minimum requirements and skills needed for a specific audit. In this case study, a fuzzy mathematical-programming model and a solution algorithm based on “simulated annealing” is proposed for the formation of QATs. This is one of the first attempts in the literature to form this kind of teams analytically. Example problems are also solved in the paper to present the application of the proposed approach.     

摩擦市场中允许卖空的最优投资组合选择

资本市场的一个重要特征就是存在不确定因素,为了衡量其导致的风险,本文提出基于绝对偏差的新型函数,在一个存在摩擦的资本市场中构造均值-绝对离差(mean-absolute deviation,MAD)投资组合选择模型.该模型结构特殊性使其转化为线性规划问题,从而可利用单纯型法求解.实证分析比较了不同风险函数下投资组合的有效前沿,并验证了该方法的有效性.     

改进粒子群优化算法在电源规划中的应用

电源规划是一类复杂、非线性组合优化问题.传统的方法随着规划期的延长,考虑因素的增多,难以有效的进行优化,在实际应用中作用有限.首先,对电源规划优化问题进行了建模.然后,对于粒子群(PSO)的迭代策略进行改进,在此基础上,运用遗传粒子群(GPHA)混合优化算法进行了优化尝试.考虑到电源规划中相关参数众多,在优化过程中引入了虚拟变量对电源规划中的问题进行了简化描述;GHPA算法的适应度评价函数设计中,运用了罚函数的思想,以提高算法优化的效果.最后本文使用某省实际负荷预测和系统负荷实际数据,进行了电源规划方案优化,得到了优化后的电源规划方案,并与普通的遗传算法、粒子群算法以及传统的动态规划算法得到的结果进行了比较.比较的结果显示出了本文提出的算法在优化结果和速度方面具有明显效果.     

Portfolio optimization: New capabilities and future methods

Summary  This paper addresses the problem of portfolio selection in finance. In many cases, currently available software to compute the efficient frontier runs into difficulty in problems with more than about 600 securities. To proceed beyond this size, it is often necessary to modify the problem in which case there is typically a loss of information. In this paper, we discuss a computer capability that can exactly compute mean-variance efficient frontiers of problems with up to 2,000 securities in very reasonable time (even if a problem’s covariance matrix is 100% dense). The paper also discusses an augmentation to the theory of portfolio selection that allows multiple objectives (such as dividends, liquidity, social responsibility, amount invested in R&D, and so forth) to be incorporated into the portfolio selection process. In such problems, the efficient set is no longer a “frontier,” but is now best described as a “surface” with the interesting property that it is composed of platelets (like on the back of a turtle). Moreover, the computer capability that can compute the exact efficient frontier of a mean-variance problem with up to 2,000 securities also has, after additional coding, the ability to compute exactly all platelets of the efficient surface of a tri-criterion portfolio problem with up to 400 securities.

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Zusammenfassung  In dieser Arbeit stellen wir einen leistungsf?higen Rechenalgorithmus vor, um den effizienten Rand (die nichtdominierten Alternativen) von Portfolio-Auswahlproblemen in der Finanzierung zu bestimmen. Wir bezeichnen den Berechnungsalgorithmus, der in Java programmiert ist, mit MPQ (multi-parametric quadratic programming). MPQ weist gegenüber bisherigen Berechnungsverfahren eine Reihe von Vorteilen auf: Es kann für umfangreiche Anwendungsf?lle genutzt werden, ist durch passable Rechenzeiten charakterisiert und kann die Menge effizienter Alternativen in einem Bruchteil bisher üblicher Rechenzeiten bestimmen.

     

Coverage by directional sensors in randomly deployed wireless sensor networks

We study a novel “coverage by directional sensors” problem with tunable orientations on a set of discrete targets. We propose a Maximum Coverage with Minimum Sensors (MCMS) problem in which coverage in terms of the number of targets to be covered is maximized whereas the number of sensors to be activated is minimized. We present its exact Integer Linear Programming (ILP) formulation and an approximate (but computationally efficient) centralized greedy algorithm (CGA) solution. These centralized solutions are used as baselines for comparison. Then we provide a distributed greedy algorithm (DGA) solution. By incorporating a measure of the sensors residual energy into DGA, we further develop a Sensing Neighborhood Cooperative Sleeping (SNCS) protocol which performs adaptive scheduling on a larger time scale. Finally, we evaluate the properties of the proposed solutions and protocols in terms of providing coverage and maximizing network lifetime through extensive simulations. Moreover, for the case of circular coverage, we compare against the best known existing coverage algorithm.