“限时服务规则”下的复线船闸多目标调度优化

针对现有船舶过闸排队规则的欠缺,基于“限时服务规则”,构建复线船闸多目标双层优化调度模型:上层模型用于获得两个闸室安全区域的船舶排布可行方案;下层模型用于获得不同船舶排布可行方案的优化闸次数。下层模型分两个阶段完成:对符合“限时服务规则”的船舶,构建以闸次最少为目标的0-1规划模型,获得此类船舶安排的闸次;对其余船舶按照“先到先服务规则”,构建以闸次最少、闸室利用率最大为目标的多目标决策模型,获得不同船舶排布可行方案应该安排的频次。以位于江苏省干线航道上的某复线船闸某日24小时内过闸船舶的数据为例,计算结果表明:采用本文优化模型获得的优化方案与“经验编排方式”相比,两座船闸各节约2个闸次,两个船闸的平均闸室利用率分别提高了3.66和4.72个百分点。     

基于改进DPSO算法的并行测试任务优化调度研究

并行测试以减少测试时间和降低测试成本的强大优势,已成为当前自动测试系统发展的方向。针对并行自动测试过程中,测试任务调度复杂,难以优化的问题,以PSO算法为基础,通过对问题空间编码的重新定义,并运用交叉、变异算子给出了新的粒子位置的更新公式,提出了一种改进后的DPSO算法。依据并行测试完成时间极限定理,给出了并行测试任务调度的目标函数与约束条件。以某雷达电子装备并行测试系统中三块电路板并行测试为例,对改进的DPSO算法进行了仿真验证,得到了最优调度测试序列。结果表明：与遗传算法相比,改进后的DPSO算法迭代次数更少,寻优性能更好,适用于工程应用。     

“仓店一体化”模式下订单拣选与配送集成调度优化

针对“仓店一体化”模式下提供限时送达服务的新零售仓店,研究多拣货员、多客户、配送方式为带限时送达约束的路径优化分批配送情形下的订单拣选与配送集成调度问题。以最小化订单最大履行时间和配送成本为目标,构建了混合整数非线性规划模型,并设计两阶段启发式算法(H-2)进行求解,最后通过数值实验对算法进行验证与分析。数值实验结果表明,H-2具有较高的求解质量;相较于传统调度算法(TS)在效率提升、资源节约以及客户满意度提高方面具有更优的表现,进而为新零售仓店管理者提供决策支持。     

Scheduling elective surgery under uncertainty and downstream capacity constraints

The objective of this study is to generate an optimal surgery schedule of elective surgery patients with uncertain surgery operations, which includes uncertainty in surgery durations and the availability of downstream resources such as surgical intensive care unit (SICU) over multi-periods. The stochastic optimization is adapted and the sample average approximation (SAA) method is proposed for obtaining an optimal surgery schedule with respect to minimizing the total cost of patient costs and overtime costs. A computational experiment is presented to evaluate the performance of the proposed method.     

Heuristics for the multi-depot petrol station replenishment problem with time windows

In the multi-depot petrol station replenishment problem with time windows (MPSRPTW), the delivery of petroleum products stored in a number of different petroleum depots to a set of petrol distribution stations has to be optimized. Each depot has its own fleet of heterogeneous and compartmented tank trucks. Stations specify their demand by indicating the minimum and maximum quantities to be delivered for each ordered product and require the delivery within a predetermined time window. Several inter-related decisions must be made simultaneously in order to solve the problem. For this problem, the set of feasible routes to deliver all the demands, the departure depot for each route, the quantities of each product to be delivered, the assignment of these routes to trucks, the time schedule for each trip, and the loading of the ordered products to different tanks of the trucks used need to be determined. In this paper, we propose a mathematical model that selects, among a set of feasible trips, the subset that allows the delivery of all the demands while maximizing the overall daily net revenue. If this model is provided with all possible feasible trips, it determines the optimal solution for the corresponding MPSRPTW. However, since the number of such trips is often huge, we developed a procedure to generate a restricted set of promising feasible trips. Using this restricted set, the model produces a good but not necessarily optimal solution. Thus the proposed solution process can be seen as a heuristic. We report the results of the extensive numerical tests carried out to assess the performance of the proposed heuristic. In addition, we show that, for the special case of only one depot, the proposed heuristic outperforms a previously published solution method.     

Analysis,modeling and solution of the concrete delivery problem

This paper describes a specific local search approach to solve a problem arising in logistics which we prove to be NP-hard. The problem is a complex scheduling or vehicle routing problem where we have to schedule the tours of concrete mixer vehicles over a working day from concrete-producing depots to concrete-demanding customers and vice versa. We give a general mixed integer programming model which is too hard to solve for state of the art mixed integer programming optimizers in the case of the usually huge problem instances coming from practice. Therefore we present a certain local search approach to be able to handle huge practical problem instances.     

A parallel multiple reference point approach for multi-objective optimization

This paper presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approximating the Pareto Frontier, multiple reference points can be used instead of traditional techniques. These multiple reference points can easily be implemented in a parallel algorithmic framework. The reference points can be uniformly distributed within a region that covers the Pareto Frontier. An evolutionary algorithm is based on an achievement scalarizing function that does not impose any restrictions with respect to the location of the reference points in the objective space. Computational experiments are performed on a bi-objective flow-shop scheduling problem. Results, quality measures as well as a statistical analysis are reported in the paper.     

有关单机两代理排序问题的两个结果

研究了两个单机两代理排序问题. 在第一个两代理排序问题中, 代理A的目标函数为极小化所有工件的加权完工时间总和, 代理B的目标函数为极小化最大工件费用. 在第二个两代理排序问题中, 代理A的目标函数为极小化所有工件的加权完工时间总和, 代理B的目标函数为极小化所有工件的最大完工时间. 证明了第一个问题是强NP-难的, 改进了已有的一般意义NP-难的结果; 对第二个问题给出了一个与现有的动态规划算法不同的动态规划算法.     

转包且具有不同费用时间段的排序问题

本文研究制造商可以将工件转包给承包商加工的排序模型,承包商仅有一台机器,转包费用由分配给转包工件的不同时间段费用确定.本文分别研究制造商有一台单机及两台自由作业机器环境情形,需要确定被转包工件集及全部工件的加工顺序,使得工件最大完工时间与转包费用和最小.本文利用归约方法对制造商每个机器环境,证明问题NP困难性,并提出动态规划算法.     

软件项目支付进度问题研究

软件项目的复杂性和特点使得其支付进度问题较具特殊性。针对软件质量的差异性,对软件项目支付进度问题进行研究。以承包商净现值最大化为优化目标,构建了具有惩罚结构的支付进度优化模型,该模型反映了不同软件质量对承包商收益的影响;针对该问题的特点设计了启发式求解算法;最后通过一个算例对模型进行了验证。可为承包商在签订外包合同及外包实施中的策略提供决策依据。