Stochastic Modelling of Times to Temperature for Furnaces Supplying Titanium Blooms to a Rolling Mill at TIMET

In conjunction with TIMET at Waunarlwydd (Swansea, UK) a model has been developed that will optimise the scheduling of various blooms to their eight furnaces so as to minimise the time taken to roll these blooms into the finished mill products. This production scheduling model requires reliable data on times taken for the various furnaces that heat the slabs and blooms to reach the temperatures required for rolling. These times to temperature are stochastic in nature and this paper identifies the distributional form for these times using the generalised F distribution as a modelling framework. The times to temperature were found to be similarly distributed over all furnaces. The identified distributional forms were incorporated into the scheduling model to optimise a particular campaign that was run at TIMET Swansea. Amongst other conclusion it was found that, compared to the actual campaign, the model produced a schedule that reduced the makespan by some 35%.     

考虑交通时间与设备转换时间的跨区域就医调度优化问题

在资源共享时代背景下,跨区域就医可以很好地解决患者日益增长的就医需求与医疗资源紧张的矛盾。本论文以医疗联盟为研究对象,在关键医疗资源共享的前提下,通过患者跨区域就医实现就医诊断延误最小化,以满足患者就医需求。本研究同时考虑了患者跨区域交通时间与基于患者诊断类型的设备转换时间,以最小化患者就医总延迟为目标,分配患者就诊医院及优化患者就诊/检查顺序。针对该问题,论文首次提出以最早交货期原则（EDD rule）为基础,以患者再分配为主导的EDD-ReAss1和EDD-ReAss2启发式算法,结合局部搜索算法以进一步提高就医调度方案的质量,缩短患者诊断/检查等待时间。实验结果表明,新启发式算法EDD-ReAss1和EDD-ReAss2算法性能显著好于EDD,SPT和LPT等调度规则;在较短运算时间内Swap局部搜索算法性能最优。     

基于杂合遗传算法的工艺路线可变Job Shop调度研究

提出一种将遗传算法与启发式规则、模拟退火法等搜索方法结合在一起的杂合遗传算法,用于求解工艺路线可变的JobShop调度问题。通过对某双极型集成电路封装企业的JobShop调度仿真,结果表明算法是有效和可行的。     

多项目人力资源调度实证研究

针对某公寓大修项目,建立有关人力资源约束下的多项目进度管理问题混合整数规划模型。其中考虑了多种约束,如项目对人员能力、水平的不同要求,而人员又具有多种能力及水平;目标为满足约束的条件下总成本最小化,其中包含按时间计费的工资,和福利等的固定费用。为了简化计算,采用列生成法把复杂的多项目模型分解为一个主问题和多个子问题并协调主问题和子问题求解。同时由于子问题的复杂性难以精确求解,采用启发式算法求解:首先由基于优先原则的启发式方法给出问题的初始解,再由遗传算法寻优。最后通过该实际案例的应用,表明此方法能够快速有效的解决实际问题,给决策者提供信息,帮助指导实践。     

Bi-objective multi-resource scheduling problem for emergency relief operations

Abstract

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the completion times in emergency relief operations. Despite the importance of such decisions, only a few mathematical models of emergency relief operations have been studied. This article presents a bi-objective mixed integer programming (MIP) that helps to minimize both the total weighted time of completion of the demand points and the makespan of the total emergency relief operation. A two-phase method is developed to solve the bi-objective MIP problem. Additionally, a case study of hospital network in the Melbourne metropolitan area is used to evaluate the model. The results indicate that the model can successfully support the decisions required in the optimal resource scheduling of emergency relief operations.     

炼钢连铸生产调度问题的两阶段遗传算法

将炼钢连铸生产过程抽象为混合流水车间,建立了0-1型混合整数线性规划调度模型。模型将严格连续浇注作为等式约束,并通过分段惩罚来平衡炉次的驻留时间。在对模型进行Benders分解的基础上,提出了将GA与LP结合的两阶段遗传算法。在算法设计中,提出了一种新的染色体编码来表示炉次设备指派与排序方案,给出了相应的遗传操作方法。算法的第一阶段通过最小化设备析取冲突来寻找高质量的种群,第二阶段通过求解线性规划模型来指导遗传算法的迭代过程。基于生产实际数据的仿真实验表明,该算法能够有效求解炼钢连铸生产调度问题。     

A technique for uncertainty reduction based on order commonality

The use of commonality is widely diffused as a criterion to reduce uncertainty in demand forecasts for the master production schedule (MPS). Nevertheless, studies have mostly focused on exploiting component commonality in make to stock and assemble to order manufacturing. This paper refers to planning environments with two specific characteristics. First, the degree of certainty of the demand is extremely low, due to product complexity, with poor modularization and standardization, and to the presence of few customers of large dimensions. Second, the delivery lead time is less than the total lead time. In this situation, demand for MPS planning units is extremely uncertain and sporadic. It is therefore necessary to formulate in advance forecasts of customer orders with a redundant configuration. In this paper a technique for the reduction of demand uncertainty is introduced, based on the exploitation of order commonalities. In particular, relations between order commonality and uncertainty reduction in a planning environment with such complex features are illustrated. Then, guidelines for the implementation of the technique in order to reduce over-planning in the master production schedule are provided. Finally, the performances of the technique are empirically analysed by means of both a simulation model and experimental application in a telecommunication systems manufacturer     

基于鲁棒优化模型的项目调度策略遗传算法

针对任务工期不确定的资源受限项目调度问题,提出了求解该问题的鲁棒优化数学模型。在随机规划基础上,针对该鲁棒优化模型设计了遗传算法。算法通过多种任务优先规则生成初始种群以保证种群多样性。应用该遗传算法对项目实例进行了求解,分析表明所求得的项目调度策略能够有效应对任务工期不确定性导致的随机差异,具有较强的鲁棒性。参数分析表明决策者可以通过调节模型权重系数有效平衡解的可行性与最优性,有助于决策者根据风险偏好进行选择。     

三台平行机排序的一个复合半在线问题的算法

本文讨论一个三台平行机半在线排序问题.对预先知道工件的总加工时间和最大的工件的加工时间的复合半在线模型,我们证明了不存在半在线算法,其竞争比为4/3,并给出了一个竞争比为7/5的半在线算法,两者的差距小于0.067.     

带工期指派的产品服务系统订单随机调度问题研究

针对由一个制造工厂和多个区域服务中心组成的服务型制造企业,研究了考虑生产时间和服务时间均具有随机性且工期可指派的产品服务系统（PSS）订单调度问题。首先以最小化订单提前、误工和工期指派费用的期望总额为目标构建问题的优化模型,然后分析目标函数近似值的最优性条件,据此提出加权最短平均生产时间排序规则,并结合该规则与插入邻域局部搜索设计了启发式算法对问题进行求解,最后通过数值仿真验证算法的可行性和有效性。研究表明,提前费用偏差对PSS订单调度与工期指派决策的影响很小,因此企业管理者无需准确估计库存费用也能制定出比较有效的PSS订单调度策略;而工期指派费用偏差对决策结果的影响非常大,因此企业管理者在决策时必须谨慎估计该项费用。