Managing disruption in an imperfect production–inventory system

In this paper, a disruption recovery model is developed for an imperfect single-stage production–inventory system. For it, the system may unexpectedly face either a single disruption or a mix of multiple dependent and/or independent disruptions. The system is usually run according to a user defined production–inventory policy. We have formulated a mathematical model for rescheduling the production plan, after the occurrence of a single disruption, which maximizes the total profit during the recovery time window. The model thereby generates a revised plan after the occurrence of the disruption. The mathematical model, developed for a single disruption, is solved by using both a pattern search and a genetic algorithm, and the results are compared using a good number of randomly generated disruption test problems. We also consider multiple disruptions, that occur one after another as a series, for which a new occurrence may or may not affect the revised plan of earlier occurrences. We have developed a new dynamic solution approach that is capable of dealing with multiple disruptions on a real-time basis. Some numerical examples and a set of sensitivity analysis are presented to explain the usefulness and benefits of the developed model. The proposed quantitative approach helps decision makers to make prompt and accurate decisions for managing disruption.     

Improving Airline Network Robustness and Operational Reliability by Sequential Optimisation Algorithms

A sequential optimisation algorithm is developed to improve the operational reliability of airline schedules. Simulation results show that departure delays are reduced by 30% after optimisation by using extra 260 min buffer times in the schedule. This also increases the network-wide schedule reliability from 37 to 52% and an estimated delay cost saving of $20 million dollars per annum for a small airline network. The advantage of sequential optimisation is that it considers the delay/punctuality propagation in airline networks, so to prevent airlines from planning excessive buffer times to individual flights by considering aircraft rotation as a whole process.     

Flight operations recovery: New approaches considering passenger recovery

The sources of disruption to airline schedules are many, including crew absences, mechanical failures, and bad weather. When these unexpected events occur, airlines recover by replanning their operations. In this paper, we present airline schedule recovery models and algorithms that simultaneously develop recovery plans for aircraft, crews, and passengers by determining which flight leg departures to postpone and which to cancel. The objective is to minimize jointly airline operating costs and estimated passenger delay and disruption costs. This objective works to balance these costs, potentially increasing customer retention and loyalty, and improving airline profitability. Using an Airline Operations Control simulator that we have developed, we simulate several days of operations, using passenger and flight information from a major US airline. We demonstrate that our decision models can be applied in a real-time decision-making environment, and that decisions from our models can potentially reduce passenger arrival delays noticeably, without increasing operating costs.     

设备故障条件下柔性作业车间重调度方法

设备故障是生产实践中最为常见的一类不确定事件,它易对正常的生产计划造成影响。为了有效应对生产中的设备随机故障干扰,对设备随机故障条件下的柔性作业车间调度问题进行了研究,提出了一种基于组合策略的重调度方法。在重调度方法中,对重调度成本进行了系统分析并建立了重调度成本函数,设计了两种重调度策略,并结合免疫算法对遗传算法进行了改进,用于模型的求解计算。通过算例分析,验证了方法的可行性和有效性。实验结果表明,所提出方法能够更好地处理多种情况下的设备故障扰动。     

Determination of replenishment dates for restricted-storage,static demand,cyclic replenishment schedule

This study focuses on scheduling replenishment lots of multiple products in a warehouse with restricted storage space where the replenishment cycle time of each product is given and is an integer multiple of some basic period. Assuming that the warehouse replenishes at the beginning of some basic period, this study proposes a new heuristic that utilizes two simple procedures to generate a replenishment schedule that minimizes the maximum warehouse-space requirement. By including a re-optimization mechanism and a Boltzmann function, the proposed heuristic obtains solutions very close to the global optimum within a reasonable run time. Using randomly generated instances, this study shows that the proposed heuristic significantly outperforms a previously published heuristic.     

航班计划优化模型及其应用研究

以利润最大化为目标函数,构造了航空公司航班计划优化模型。采用禁忌搜索算法对其进行求解,通过对航班数据的预处理和技巧,加快了算法的处理效率,对航空公司编制航班计划具有实际意义。以某航空公司特定机型的航班计划数据进行实证,验证了该模型和算法的可行性。     

一种实时任务可调度性预测研究

对于嵌入式系统来说,通过预测一个任务能否在绝对时限之前运行完成而决定是否调度执行是很有意义的。在ARMLinux上,为了对新任务的运行结束时间进行预测,对它的内核作了修改,按优先级排序就绪队列,每次时钟中断判断是否有优先级比当前任务更高的任务就绪,以决定是否调度,去掉了SCHED_RR调度策略,这样新任务的运行结束时间可以得到准确的计算,并根据任务的绝对时限来判断对新任务的接受和拒绝,对修改后的内核进行了试验验证。     

A multi-agent architecture for dynamic scheduling of steel hot rolling

Steel production is a complex process and finding coherent and effective schedules for the wide variety of production steps, in a dynamic environment, is a challenging task. In this paper, we propose a multi-agent architecture for integrated dynamic scheduling of the hot strip mill (HSM) and the continuous caster. The scheduling systems of these processes have very different objectives and constraints, and operate in an environment where there is a substantial quantity of real-time information concerning production failures and customer requests. Each process is assigned to an agent which independently, seeks an optimal dynamic schedule at a local level taking into account local objectives, real-time information and information received from other agents. Each agent can react to real-time events in order to fix any problems that occur. We focus here, particularly, on the HSM agent which uses a tabu search heuristic to create good predictive–reactive schedules quickly. The other agents simulate the production of the coil orders and the real-time events, which occur during the scheduling process. When real-time events occur on the HSM, the HSM agent might decide whether to repair the current schedule or reschedule from scratch. To address this problem, a range of schedule repair and complete rescheduling strategies are investigated and their performance is assessed with respect to measures of utility, stability and robustness, using an experimental simulation framework.     

On the variable-depth-search heuristic for the linear-cost generalized assignment problem

The classical assignment problem seeks to determine a mapping between a set of assignees and a set of assignments. The linear cost assignment problem (LCGAP), as a generalized model, incorporates the relative workloads between assignees and assignments. Although LCGAP is computationally intractable, it has been extensively studied because of its practical implications in many real world situations. Variable-depth-search heuristic (VDSH) method is one of the solution methods that have been developed to produce quality near-optimal solutions to LCGAP. The main structure of VDSH consists of two basic operations: reassign and swap. In this paper, we make further observations on this effective heuristic method through a series of computational experiments. The numerical results statistically evince that different combina-tions of these two operations will result in solutions of different quality levels. These findings are expected to have similar implications to search heuristics for other optimization problems.     

光突发交换网络中的重调度算法

介绍了光突发交换（OBS）网络,在现有算法LAUC_VF的基础上提出了一种重调度算法,即LAUC_VF_RESCHEDULE算法,它的主要思想是：对新到达的突发运用LAUC_VF算法调度不成功时,将已经调度成功的突发从原有信道Ⅰ重新调度到另一数据信道J上,并保持该突发的到达时刻和结束时刻不变,从而将新到达的突发调度到数据信道Ⅰ上。仿真结果表明在大多数情况下该重调度算法相对于LAUC_VF算法对网络性能的改善是比较大的。     

Rescheduling rail networks with maintenance disruptions using Problem Space Search

The creation of train timetables for long-haul single track networks is a challenging process. This task is more difficult if track maintenance disruptions are to be taken into account. This paper describes how the Problem Space Search (PSS) meta-heuristic can be used for large scale problems to create quality timetables in which both train movements and scheduled track maintenance are simultaneously considered. We show that the PSS meta-heuristic can rapidly generate a large number of alternative train timetables and then describe how the technique is generalized to construct an integrated timetable which includes track maintenance. We show how the technique can also be used as an operational tool where a revised schedule can be quickly generated to take into account the new state of a disrupted system. A case study for a single track rail network in Queensland Australia, which spans a distance of 480 km, has 57 crossing loops and typically carries over 50 trains per day is discussed.     

基于动态约束满足的炼钢连铸重调度算法

针对转炉出钢延迟的炼钢连铸重调度问题,以开工时间、加工时间以及加工机器的差异度和同一炉次相邻设备间的等待时间的差异化最小为目标建立了动态约束满足模型,提出了基于约束满足和断浇修复的重调度算法。算法通过变量和值选择规则依次对变量赋值,利用冲突识别与解消规则识别赋值过程中产生的冲突并予以解消冲突;在形成的准可行调度中,利用断浇修复启发式规则修复连铸机的断浇现象。仿真实验模拟了3组均匀分布随机产生的延迟时间量,所得目标值分别为0.15,0.28和0.51。结果表明延迟时间量的大小对目标函数值有一定影响,所提算法能够最大限度地满足生产的实时性和稳定性的需求。     

Robust airline schedule design in a dynamic scheduling environment

In the past decade, major airlines in the US have moved from banked hub-and-spoke operations to de-banked hub-and-spoke operations in order to lower operating costs. In Jiang and Barnhart (2009) [1], it is shown that dynamic airline scheduling, an approach that makes minor adjustments to flight schedules in the booking period by re-fleeting and re-timing flight legs, can significantly improve utilization of capacity and hence increase profit. In this paper, we develop robust schedule design models and algorithms to generate schedules that facilitate the application of dynamic scheduling in de-banked hub-and-spoke operations. Such schedule design approaches are robust in the sense that the schedules produced can more easily be manipulated in response to demand variability when embedded in a dynamic scheduling environment. In our robust schedule design model, we maximize the number of potentially connecting itineraries weighted by their respective revenues. We provide two equivalent formulations of the robust schedule design model and develop a decomposition-based solution approach involving a variable reduction technique and a variant of column generation. We demonstrate, through experiments using data from a major U.S. airline that the schedule generated can improve profitability when dynamic scheduling is applied. It is also observed that the greater the demand variability, the more profit our robust schedules achieve when compared to existing ones.     

An integrated scenario-based approach for robust aircraft routing,crew pairing and re-timing

For reasons of tractability, the airline scheduling problem has traditionally been sequentially decomposed into various stages (e.g. schedule generation, fleet assignment, aircraft routing, and crew pairing), with the decisions from one stage imposed upon the decision-making process in subsequent stages. Whilst this approach greatly simplifies the solution process, it unfortunately fails to capture many dependencies between the various stages, most notably between those of aircraft routing and crew pairing, and how these dependencies affect the propagation of delays through the flight network. In Dunbar et al. (2012) [9] we introduced a new algorithm to accurately calculate and minimize the cost of propagated delay, in a framework that integrates aircraft routing and crew pairing. In this paper we extend the approach of Dunbar et al. (2012) [9] by proposing two new algorithms that achieve further improvements in delay propagation reduction via the incorporation of stochastic delay information. We additionally propose a heuristic, used in conjunction with these two approaches, capable of re-timing an incumbent aircraft and crew schedule to further minimize the cost of delay propagation. These algorithms provide promising results when applied to a real-world airline network and motivate our final integrated aircraft routing, crew pairing and re-timing approach which provides a substantially significant reduction in delay propagation.     

The rescheduling arc routing problem

In this paper, the rescheduling arc routing problem is introduced. This is a dynamic routing and scheduling problem that considers adjustments to an initial routing itinerary when one or more vehicle failures occur during the execution stage and the original plan must be modified. We minimize the operational and schedule disruption costs. Formulations based on mixed‐integer programming are presented to compare different policies in the rerouting phase. A solution strategy is developed when both costs are evaluated and it is necessary to find a solution quickly. Computational tests on a large set of instances compare the different decision‐maker policies.     

基于启发式规则的混合遗传算法及其在生产计划优化中的应用

以最优或近似最优的作业顺序编制满足关键资源约束的生产计划优化问题一直是企业生产管理中重要的研究课题之一。文章提出了一种基于传统启发式规则的混合遗传算法。该算法将染色体分为两段,前段表示资源安排策略,后段表示为优先分配规则序列,并设计了一种新的交叉算子。最后,介绍了根据此算法编制的一个制造企业生产控制的软件系统。