Two-agent scheduling with linear resource-dependent processing times

This paper considers a two-agent scheduling problem with linear resource-dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs. To meet the due date requirements of the jobs of the two agents, additional amounts of a common resource, which may be in discrete or continuous quantities, can be allocated to the processing of the jobs to compress their processing durations. The actual processing time of a job is a linear function of the amount of the resource allocated to it. The objective is to determine the optimal job sequence and resource allocation strategy so as to minimize the weighted number of tardy jobs of one agent, while keeping the weighted number of tardy jobs of the other agent, and the total resource consumption cost within their respective predetermined limits. It is shown that the problem is -hard in the ordinary sense, and there does not exist a polynomial-time approximation algorithm with performance ratio unless ; however it admits a relaxed fully polynomial time approximation scheme. A proximal bundle algorithm based on Lagrangian relaxation is also presented to solve the problem approximately. To speed up convergence and produce sharp bounds, enhancement strategies including the design of a Tabu search algorithm and integration of a Lagrangian recovery heuristic into the algorithm are devised. Extensive numerical studies are conducted to assess the effectiveness and efficiency of the proposed algorithms.     

并联系统可靠性分配的模糊层次分析法

综合考虑影响系统可靠性分配的定性因素和定量因素、影响因素的模糊性以及系统的结构组成.利用层次分析法可集定性与定量分析于一体,从而确定出各影响因素对可靠性分配的权重向量;模糊数学理论进而确定出分系统之间可靠性水平的相对比重;进行模糊变换最终进行并联系统的可靠性分配.结合实例分析,对并联系统运用模糊理论和层次分析法相结合的方法进行可靠性分配科学、合理,便于工程应用.     

Spatial pricing and product allocation in online retailing

Spatial pricing means a retailer price discriminates its customers based on their geographic locations. In this article, we study how an online retailer should jointly allocate multiple products and facilitate spatial price discrimination to maximize profits. When deciding between a centralized product allocation ((i.e., different products are allocated to the same fulfillment center) and decentralized product allocation (ie, different products are allocated to different fulfillment centers), the retailer faces the tradeoff between shipment pooling (ie, shipping multiple products in one package), and demand localization (ie, stocking products to satisfy local demand) based on its understanding of customers' product valuations. In our basic model, we consider two widely used spatial pricing policies: free on board (FOB) pricing that charges each customer the exact amount of shipping cost, and uniform delivered (UD) pricing that provides free shipping. We propose a stylized model and find that centralized product allocation is preferred when demand localization effect is relatively low or shipment pooling benefit is relatively high under both spatial pricing policies. Moreover, centralized product allocation is more preferred under the FOB pricing which encourages the purchase of virtual bundles of multiple products. Furthermore, we respectively extend the UD and FOB pricing policies to flat rate shipping (ie, the firm charges a constant shipping fee for each purchase), and linear rate shipping (ie, the firm sets the shipping fee as a fixed proportion of firm's actual fulfillment costs). While similar observations from the basic model still hold, we find the firm can improve its profit by sharing the fulfillment cost with its customers via the flat rate or linear rate shipping fee structure.     

Pricing and capacity allocation strategies: Implications for manufacturers with product sharing

Emerging sharing modes, like the consumer-to-consumer (C2C) sharing of Uber and the business-to-consumer (B2C) sharing of GoFun, have considerably affected the retailing markets of traditional manufacturers, who are motivated to consider product sharing when making pricing and capacity decisions, particularly electric car manufacturers with limited capacity. In this paper, we examine the equilibrium pricing for a capacity-constrained manufacturer under various sharing modes and further analyze the impact of capacity constraint on the manufacturer's sharing mode selection as well as equilibrium outcomes. We find that manufacturers with low-cost products prefer B2C sharing while those with high-cost products prefer C2C sharing except when the sharing price is moderate. However, limited capacity motivates manufacturers to enter into the B2C sharing under a relatively low sharing price, and raise the total usage level by sharing high-cost products. We also show that the equilibrium capacity allocated to the sharing market with low-cost products first increases and then decreases. Finally, we find that sharing low-cost products with a high limited capacity leads to a lower retail price under B2C sharing, which creates a win-win situation for both the manufacturer and consumers. However, sharing high-cost products with a low limited capacity creates a win-lose situation for them.     

大学自习教室优化设置研究

针对目前大学自习教室资源浪费普遍存在的现象,将学生上自习与否视为独立同分布随机变量,从自习教室的管理与维护成本、电费成本以及学生的满意度3个方面出发,建立了双目标随机规划模型,并在模型基础上提出了大学自习教室设置的一般方法。通过实例分析,对模型的正确性和有效性进行了论证。结果表明:在各种情况下,该模型都能很好地解决自习教室设置这一问题,并且能在学生满意度较高的条件下,节约能源13%以上。该模型可以有效地对自习教室进行优化设置,不仅提高了资源利用水平,而且节约了能源。     

双站无源定位最佳配置分析

为了提高双站无源定位精度,在全局坐标系下分析了双站纯方位定位系统的最佳配置形式。首先研究了最小GDOP(Geometric Dilution of Precision)意义下的方位角约束关系及最优夹角,得到双站的最佳配置形式是目标与两传感器间呈一确定夹角的等腰三角形;在此基础上,讨论了夹角对GDOP的影响进而提出有效定位区域的概念,指出只有当目标位于该区域内时双站才能获得较好的定位精度。仿真结果验证了上述关于最佳配置形式及有效定位区域的分析,指出该结论可以应用到基于传感器管理的多站无源定位算法中。     

战时弹药供应协同调运模型研究

弹药协同调运是战时弹药保障工作中的重要环节,其协同调运的合理性将直接影响到弹药保障工作的顺利进行.针对弹药的调运问题,从战时技术实施与应用角度研究弹药的调运问题,以到达需求点的运输时间、弹药输送车数量以及弹药损失量为优化目标,建立一种多目标决策模型,为缩短运输时间、减少弹药输送车数量、提高安全到达需求点的弹药量提供一种实用的方法.     

软件可靠性分配的遗传模型研究

对软件可靠性分配的遗传模型进行了研究,结合相关知识缩小了基本遗传算法的搜索区域,比较灵活地处理了非线性约束条件,提高了搜索效率,避免了早熟收敛现象.此外,在基本遗传算法中增加了保优操作,从而能够在理论上保证实现分配的最优化.算例表明,该算法十分有效.     

多操纵面战斗机动态控制分配策略研究(英文)

现代先进飞机配置多个操纵面且又冗余,这有利于实现非常规飞行,提高飞机敏捷性,使飞机在机动和故障时有更多的选择,但是同时也导致了飞机控制设计复杂度的增加.为了达到更好的效果,就必须利用控制分配器来充分利用多操纵面的不同带宽.从频率域角度出发,研究了动态控制分配方法,并将其应用于某型先进战斗机的控制分配器设计中,利用鸭翼的频率特性抵消府舱过载,经仿真验证,采用动态控制分配策略时,可以有效地减小座舱过载,同时还可以减小配平阻力,取得了良好的控制效果.     

一种基于最小生成树的无线多跳网络信道分配算法

为提高无线多跳网络的吞吐量和传输可靠性,提出一种信道分配算法。该算法优先考虑最小生成树上的可用信道,为每个节点分配信道资源;然后考虑利用生成树外其他可用链路,为节点提供信道资源,以提高吞吐量。算法通过考虑每个用户的通信需求,可以充分利用空闲信道资源。仿真结果显示,相比于不考虑最小生成树外链路时,有效地提高了网络整体吞吐量。