基于目标规划的手术排程规则的比较

手术室是医院的重要医疗服务部门,其运营管理对医疗服务质量有重要影响。为减少加班时间、平衡医务人员工作负荷,本文以手术排程规则为研究对象,运用目标规划方法构建手术排程模型,利用天津市某心脑血管病医院导管室手术排程的实际数据和Simio软件进行仿真模拟,对常用的手术排程规则进行分析比较。结果表明,最短队列的排程方法能够更好地均衡各手术室的工作时间、减少加班次数、提高医务人员满意度。     

基于精益物流的单件离散型生产作业排程优化

通过实证研究,分析了单件离散型生产作业排程特征和设备对排程的重要性,提出了从设备使用和顾客需求角度研究作业排程优化的必要性.引入设备综合技术性能指数作为质量保证的考量指标,以精益物流为指导思想,综合质量保证指数、生产浪费指数和准时交货指数三项指标,以生产能力、工艺时序、随机不确定性因素为约束条件,建立了组合优化模型.为了有效地求解模型,设计了集成算法,并结合实例分别应用“SM&PR”(仿真&优先规则)和“SM&PR&GA”(仿真&优先规则&遗传算法)进行了仿真试验,检验了模型和算法的有效性.结果显示“SM&PR&GA”是最有效的方法,与传统方法相比,具有动态随机性、全局收敛性和智能性等特征,提高了作业排程的效率和科学性.     

手术持续时间不确定的手术排程鲁棒优化

针对病人手术持续时间有较大范围不确定性的手术排程问题,综合考虑医院成本和病人满意度,采用绝对鲁棒优化策略,构建了手术持续时间不确定的手术排程优化模型,并设计了将单亲遗传算法和内点法相结合的两层混合优化算法,外层的单亲遗传算法确定病人在不同手术台的手术顺序,内层的内点法确定在给定的手术顺序下实现最差性能的手术持续时间.通过对大量随机算例进行仿真实验,并与基于期望值的确定性优化策略进行对比,结果验证了所提绝对鲁棒优化策略的有效性.     

基于MRI设备差异的医疗检查任务分配与排程北大核心CSSCI

考虑多台MRI设备间等级、服务时间和病人类型差异的前提下,分配检查任务固定服务时间,设计队列池排队方式,寻求合理时间槽长度。仿真验证其有效性。依据设备型号与服务时间差异分配检查任务,可在控制病人等待时间的基础上,增加检查病人数量、提升设备利用率;队列池排队方式的高效性与时间槽长度和服务时间有关;合理设置时间槽长度方式优于预留时间槽的方式。     

考虑紧急病患随机到达的手术排程动态优化方法研究

科学合理的手术排程对医院和患者双方都具有重要意义。考虑手术室数量有限不确定因素造成手术时间波动及紧急病患随机到达的情况,以医院和患者双方利益最优为目标,建立多目标线性规划模型,利用蚁群算法对模型进行求解得到最优排程方案。并结合某医院手术数据进行仿真测试,实例验证了该排程方法的优越性及应对不确定因素干扰的动态调度优化效果,最后对模型中的相关因素进行灵敏度分析,证明提出的动态优化方法可应用于不同具体情境下的手术排程问题中。     

病人不守时下的CT检查预约仿真与优化

为解决医院CT部门病人等待时间过长以及检查室加班的问题,对我国某大型三甲综合医院放射科进行了实地调查和研究。根据实际检查流程并考虑病人不守时的情况,构建基于离散事件仿真的CT检查分时段预约模型,验证了模型的有效性,并得出了优化的预约规则。通过实际数据的仿真演算,验证了该预约规则使门诊病人和住院病人的平均等待时间分别缩短了44分钟和20分钟。     

面向核磁共振检查的预约调度仿真优化研究

对某三甲医院MRI检查进行实地调研,基于离散事件仿真方法建立MRI分时段检查预约模型,设计了三种新的预约调度规则,运用遗传算法优化了时间段长度和各时段的预约患者数量,结果表明三种新的规则均优于现行规则,缩短了患者等待、医生加班和设备空闲的时间。     

基于约束理论的造纸企业生产排程方法研究

针对造纸企业"集中制浆、多机台分散造纸"的生产模式,以浆纸平衡的抄纸系统加工方案为切入点,提出了一种生产排程模型.为了克服传统遗传算法早熟和收敛慢的特点,采用小生境遗传算法求解模型.实例表明,该方法求解造纸企业的排程问题效果好,有实用价值.     

考虑工人疲劳的工作排程研究

为提高企业整体生产力,降低工人累积性疲劳对生产过程的影响,在综合考虑工人疲劳与作业负荷的基础上,以单人多任务排程为研究对象,工人的最大可接受工作时间为休息与否的判定依据,构建了单人工作-休息排程的数学模型。从不同的工作休息组合中选出最佳的排程组合,以优化最小化任务的最大完工时间和因工人工作时间超过最大可接受工作时间而产生的额外能量消耗双目标函数。应用线性加权和法将多目标化为单目标用遗传算法求解,通过算例分析得出模型的满意解,从而得到任务的排序以及工人的休息时间和休息次数的排程组合方案。模型存在多个满意解,为管理者提供多种备选方案,管理者根据实际情况选择实施最优化的组合方案。     

基于Drools规则引擎的柔性化钣金产线优化排程

针对柔性化钣金产线加工订单呈现离散化特征所导致的人工排程不能满足需求的问题,在订单按时交货的基础上以最小化综合成本及最低综合设备损耗为目标,基于Drools规则约束引擎,研究适用于柔性化钣金产线的快速寻优排程方法.以钣金生产为例,分析排程规划问题的求解过程,通过分析多目标求解的问题提出结合Drools规则引擎的求解方法...     

A fuzzy logic modelling of dynamic scheduling in FMS

This paper is concerned with scheduling in flexible manufacturing systems (FMSs) using a fuzzy logic (FL) approach. Four fuzzy input variables: machine allocated processing time, machine priority, due date priority and setup time priority are defined. The job priority is the output fuzzy variable, showing the priority status of a job to be selected for next operation on a machine. The model will first select the machines and then assign operations based on a multi-criteria scheduling scheme. The performance of the approach is compared against established methods reported in the literature. The performance measures considered average machine utilisation, meeting due dates, setup times, work in process and mean flow times. The test results demonstrate the superiority of the fuzzy logic approach in most performance measures.     

生产调度问题的系统研究

对生产调度问题进行了系统的描述,总结了调度问题的研究方法和策略,在此基础上讨论了现行生产调度问题研究中存在的问题与发展趋势.     

A tabu search algorithm for simultaneous machine/AGV scheduling problem

Machines and automated guided vehicles (AGVs) scheduling problems are two essential issues that need to be addressed for the efficiency of the overall production system. The purpose of this paper is to study the simultaneous scheduling problem of machines and AGVs in a flexible manufacturing system (FMS) since the global optimum cannot be reached by considering each of them individually. In this paper, a mixed integer linear programming (MILP) model is developed with the objective of makespan minimisation. The MILP model consists of the following two constraint sets: machines and AGVs scheduling sub-problems. As both sub-problems are known to be NP-hard, a heuristic algorithm based on tabu search (TS) is proposed to get optimal or near to optimal solution for large-size problems within reasonable computation time. The proposed algorithm includes a novel two-dimensional solution representation and the generation of two neighbour solutions, which are alternately and iteratively applied to improve solutions. Moreover, an improved lower bound calculation method is introduced for the large-size problems. Computational results show the superior performance of the TS algorithm for the simultaneous scheduling problem.     

Distributed project scheduling with information sharing in supply chains: part II—theoretical analysis and computational study

The first part of this paper (Lau, J.S.K., Huang, G.Q. and Mak, K.L., Distributed project scheduling with information sharing in supply chains: part I—agent-based negotiation algorithm. Int. J. Prod. Res., 2005, 43, 4813–4838) has outlined the background of this research. The paper proposes and demonstrates a new negotiation-based algorithm (NEG) for solving distributed project scheduling problems (DPSP). This new algorithm not only acknowledges and accommodates the autonomy and independence of individual enterprises in making decisions in the entire supply chain, but also takes advantage of limited information shared among them to improve the quality and efficiency. This second paper will conduct a thorough theoretical analysis and computational study on the proposed algorithm. The theoretical analysis shows that the algorithm converges such that schedules of project and contractor agents do not have conflicts. The computational study reveals that NEG performs better than a centralized heuristic and contract net protocols in terms of the solution quality (e.g. total operating cost). Computational efficiency of NEG is also reasonably comparable and competitive to the extent that good quality solutions can be obtained within an affordable time limit.     

Batch versus cyclic scheduling of flexible flow shops by mixed-integer programming

New mixed-integer programming models are proposed for deterministic batch or cyclic scheduling in flow shops with parallel machines and finite in-process buffers. Models for scheduling with all machines continuously available for processing throughout the entire scheduling horizon as well as for scheduling with an arbitrary pattern of machine availability due to pre-scheduled downtime events are provided. Numerical examples modelled after real-world flexible flow shop scheduling in electronics manufacturing are presented, and to compare the batch and cyclic schedules with continuous or with limited machine availability, results of computational experiments are reported.     

Minimising makespan heuristics for scheduling a single batch machine processing machine with non-identical job sizes

In this paper, the problem of minimising maximum completion time on a single batch processing machine is studied. A batch processing is performed on a machine which can simultaneously process several jobs as a batch. The processing time of a batch is determined by the longest processing time of jobs in the batch. The batch processing machine problem is encountered in many manufacturing systems such as burn-in operations in the semiconductor industry and heat treatment operations in the metalworking industries. Heuristics are developed by iterative decomposition of a mixed integer programming model, modified from the successive knapsack problem by Ghazvini and Dupont (1998 Ghazvini, F.J. and Dupont, L. 1998. Minimising mean flow times criteria on a single batch processing machine with non-identical jobs sizes. International Journal of Production Economics, 55: 273–280. [Crossref], [Web of Science ®] , [Google Scholar], Minimising mean flow times criteria on a single batch processing machine with non-identical jobs sizes. International Journal of Production Economics 55: 273–280) and the waste of batch clustering algorithm by Chen, Du, and Huang (2011 Chen, H., Du, B. and Huang, G.Q. 2011. Scheduling a batch processing machine with non-identical job sizes: a clustering perspective. International Journal of Production Research, 49(19): 5755–5778. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar], Scheduling a batch processing machine with non-identical job sizes: a clustering perspective. International Journal of Production Research 49 (19): 5755–5778). Experimental results show that the suggested heuristics produce high-quality solutions comparable to those of previous heuristics in a reasonable computation time.     

Maintenance,shutdown and production scheduling in semiconductor robotic cells

Our approach is the first to study simultaneous scheduling of preventive maintenance, shutdowns and production for robotic cells in semiconductor manufacturing. It hereby exploits the frequent periods of overcapacity in semiconductor manufacturing to reduce wear and tear. In contrast to existing approaches, our scheduling approach is able to deal with different preventive-maintenance types. We borrow the Resource Task Network representation from the process-industry domain to represent our problem and facilitate its formulation as a mathematical model. In addition, we develop efficiency-improving constraints based on the characteristics of the preventive-maintenance activities. In numerical tests based on industry data, we show that the model generates high-quality schedules even without applying the inequalities, although the optimality gap is reduced only when including inequalities. We furthermore assess the trade-off between shutdowns and batch lead times. We compare our model’s schedule quality to (i) the simple industry practice of shutting down chambers permanently to reduce wear and tear and (ii) an approach that schedules maintenance and production sequentially. The numerical tests yield the following managerial insights. First, integrating maintenance and production scheduling has substantial advantages. Second, the practice of shutting equipment down permanently diminishes scheduling flexibility and solution quality. Third, shutdowns scheduling must also consider the impact on batch waiting times.     

Integrated maintenance planning and production scheduling with Markovian deteriorating machine conditions

In many industries, production capacity diminishes as machine conditions deteriorate. Maintenance operations improve machine conditions, but also occupy potential production time, possibly delaying the customer orders. Therefore, one challenge is to determine the joint maintenance and production schedule to minimize the combined costs of maintenance and lost production over the long term. In this paper, we address the problem of integrated maintenance and production scheduling in a deteriorating multi-machine production system over multiple periods. Assuming that at the beginning of each period the demand becomes known and machine conditions are observable, we formulate a Markov decision process model to determine the maintenance plan and develop sufficient conditions guaranteeing its monotonicity in both machine condition and demand. We then formulate an integer programming model to find the maintenance and the production schedule in each period. Our computational results show that exploiting online condition monitoring information in maintenance and production decisions leads to 21% cost savings on average compared to a greedy heuristic and that the benefit of incorporating long-term information in making short-term decisions is highest in industries with medium failure rates.     

Distributed project scheduling with information sharing in supply chains: part I—an agent-based negotiation model

Distributed project scheduling problem (DPSP) in supply chains is concerned with configuration and scheduling of multiple projects in a network of independent and autonomous enterprises. Individual enterprises must collaborate with each other during two main stages: the configuration of a project—selection of contractors for performing project operations and the project scheduling—determining when the operations start. However, the collaboration is especially difficult because none of these enterprises holds the global information about the entire supply chain and all constituent enterprises. Instead, they have to capitalize whatever information is shared between them in order to solve their own local problems in a distributed and autonomous fashion. It is essential for the solution process to strike an overall balance between effectiveness and efficiency. The research, reported in this two-part paper, is aimed at proposing a negotiation-based algorithm for solving DPSP. Its emphasis is how to improve the convergence and quality of the solution by taking advantage of inter-enterprise information sharing especially the sharing of schedule flexibility information (SFI). The first part of this paper describes a new agent-based approach to DPSPs in supply chains while the second part will present detailed discussion on the theoretical and experimental analysis.     

An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems that have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real-life industries a machine can be unavailable for many reasons, such as unanticipated breakdowns (stochastic unavailability), or due to scheduled preventive maintenance where the periods of unavailability are known in advance (deterministic unavailability). This paper deals with hybrid flow shop scheduling problems in which there are sequence-dependent setup times (SDSTs), and machines suffer stochastic breakdowns, to optimise objectives based on the expected makespan. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. An immune algorithm (IA) can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. In this research, a computational method based on a clonal selection principle and an affinity maturation mechanism of the immune response is used. This paper describes how we can incorporate simulation into an immune algorithm for the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdowns. The results obtained are analysed using a Taguchi experimental design.