求解最优潮流的稀疏拉格朗日牛顿算法

给出了用于电力系统最优潮流计算的稀疏拉格朗日牛顿算法,进行了实例计算,证明了本算法的有效性。     

适合柔性生产的调度技术

具有竞争力的生产组织要求精益化和柔性的数据处理方法，用于订单计划的制订以及订单的执行。现代生产调度技术严格遵循企业流程，并有效支持所有生产后勤工作部门的应用者。其结果是低成本、经改善的供货能力和员工更高的积极性。     

A genetic algorithm approach for production scheduling with mould maintenance consideration

The traditional approach for maintenance scheduling concerns single-resource (machine) maintenance during production which may not be sufficient to improve production system reliability as a whole. Besides, in the literature many researchers schedule maintenance activities periodically with fixed maintenance duration. However, in a real manufacturing system maintenance activities can be executed earlier and the maintenance duration will become shorter since less time and effort are required. A practical example is that in a plastic production system, the proportion of machine-related downtime is even lower than mould-related downtime. The planned production operations are usually interrupted seriously because of the mismatch among the maintenance periods between injection machine and mould. In this connection, this paper proposes to jointly schedule production and maintenance tasks of multi-resources in order to improve production system reliability by reducing the mismatch among various processes. To integrate machine and mould maintenance tasks in production, this paper attempts to model the production scheduling with mould scheduling (PS-MS) problem with time-dependent deteriorating maintenance schemes. The objective of this paper is to propose a genetic algorithm approach to schedule maintenance tasks jointly with production jobs for the PS-MS problem, so as to minimise the makespan of production jobs.     

Energy-aware feedback control for production scheduling and capacity control

In this paper, we propose an energy-aware feedback control model for production scheduling and capacity control. Specifically, we integrate functions of production scheduling and capacity control, taking into account the costs of energy consumption and machine maintenance varied by production capacity, and the penalty cost imposed by just-in-time production requirements. Continuous control variables are used to adjust the system and the resulting dynamics are modelled. Computational experiments show that interrelated dynamics among these three performance factors are well explained by the proposed controllers, and considerably better energy performance, about 20.0–40.0% improvement, in an energy-aware production compared to a conventional strategy.     

Reliability‐based Optimal Maintenance Scheduling by Considering Maintenance Effect to Reduce Cost

A new reliability‐based optimal maintenance scheduling method is presented that considers the effect of maintenance in reducing costs. An ordering list of element maintenance effects with various maintenance‐interval types is constructed. By means of this ordering list, reliability‐based optimal maintenance scheduling for simple reliability structures and composite reliability systems is then carried out. The properties of the proposed method, such as the evaluation of maintenance cost reduction, the simplicity of the proposed method by sacrificing system availability within the allowance method, the operation decision based on the optimal maintenance schedule, etc., are discussed. With simulations, the effectiveness of the proposed method is verified. Copyright © 2005 John Wiley & Sons, Ltd.     

Investigating the value of integrated operations planning: A case-based approach from automotive industry

During the last decade, many researchers have focused on joint consideration of various operations planning aspects like production scheduling, maintenance scheduling, inventory control, etc. Such joint considerations are becoming increasingly important from the point of view of current advancement in intelligent manufacturing, also known as Industry 4.0. Under the concept of Industry 4.0, advanced data analytics aim to remove human intervention in decision-making. Thus, the managerial level coordination of decisions taken independently by various departments will be out of trend. Therefore, developing an approach that optimises various operations planning decisions simultaneously is essential. Available literature on such joint considerations is more of the exploratory in nature and is limited to simplistic production environments. This necessitates the investigations of value of integrated operations planning for wide range of manufacturing scenarios. Present paper adopts a case-oriented approach to investigate the value of integrated operations planning. First, an integrated approach for simultaneously determining job sequencing, batch-sizing, inventory levels and preventive maintenance schedule is developed. The approach is tested in a complex production environment of an automotive plant and substantial economic improvement was realised. Second, a comprehensive evaluation is performed to study the robustness and implications of proposed approach for various production scenarios. Results of such pervasive performance investigations confirm the value of proposed approach over conventional approaches.     

考虑降低碳排放的废钢铁再制造等待时间受限生产调度研究

针对工件等待时间受限的废钢铁再制造生产调度问题,在碳排放约束下构建了等待时间受限的废钢铁再制造生产调度模型,并设计遗传算法进行了求解.最后结合算例,比较分析了碳排放对生产调度的影响,并得出:废钢铁再制造过程产生的碳排放量与设备空转时间呈显著的正相关关系;工件等待时间受限作为约束其主要作用在于避免重调度,对于减少makespan和碳排放量影响不大.     

A method combining rules with genetic algorithm for minimizing makespan on a batch processing machine with preventive maintenance

This paper considers the problem of minimising makespan on a single batch processing machine with flexible periodic preventive maintenance. This problem combines two sub-problems, scheduling on a batch processing machine with jobs’ release dates considered and arranging the preventive maintenance activities on a batch processing machine. The preventive maintenance activities are flexible but the maximum continuous working time of the machine, which is allowed, is determined. A mathematical model for integrating flexible periodic preventive maintenance into batch processing machine problem is proposed, in which the grouping of jobs with incompatible job families, the starting time of batches and the preventive maintenance activities are optimised simultaneously. A method combining rules with the genetic algorithm is proposed to solve this model, in which a batching rule is proposed to group jobs with incompatible job families into batches and a modified genetic algorithm is proposed to schedule batches and arrange preventive maintenance activities. The computational results indicate the method is effective under practical problem sizes. In addition, the influences of jobs’ parameters on the performance of the method are analyzed, such as the number of jobs, the number of job families, jobs’ processing time and jobs’ release time.     

Integrating machine scheduling and self-healing maintenance by job-mix pull control

In general, machines degrade with use. But, for some manufacturing processes machine ageing can be reversed by processing alternative types of jobs. In the latter case, machines can run longer without breakdowns if job types are balanced and scheduling is optimised. However, when job arrivals are stochastic, even short-term fluctuations in job mixes can increase the risk of breakdown. This paper presents a proof of concept study on job-mix pull control to exploit the age-reversing, healing effect. Because adding pull control will impact the architecture of factory scheduling, three issues are addressed in the proof. First, it is shown that the new architecture would have a better performance than the existing dispatching approach. Second, a method of pulling jobs from upstream to reduce the probability of machine breakdown is developed. The condition of workload imbalance in which job mix control (JMC) should or should not be activated is analysed. Finally, the benefit of JMC is evaluated by using simulation to demonstrate potential improvements that can be achieved. Besides the proof of concept, this study produces an important finding. A lingering cumulative influence of the self-healing effect is discovered, pointing out a new direction for future maintenance scheduling research.     

A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.     

Preventive maintenance and optimal buffer inventory for products sold with warranty in an imperfect production system

This paper deals with an imperfect production system with allowable shortages due to regular preventive maintenance for products sold with free minimal repair warranty. Preventive maintenance is an essential factor of the just-in-time structure that results in a shutdown of the production process for a certain period of time. During such an interruption, a buffer stock is needed to adjust the market demand. The study includes the possibility of imperfect production and determines the optimum buffer level and production run time by trading off the holding cost, shortage cost, rework cost, repair cost for warranty, labour/energy costs, material cost and cost for maintenance so that the cost per unit product is minimised.     

Tolerancing for manufacturing: A three-dimensional model

This paper proposes a three-dimensional (3D) model on manufacturing tolerancing for mechanical parts. The work presented relies on research conducted at the LURPA on the computation of 3D tolerance chains for mechanisms. Starting from these works, the authors propose a formalization of the problem within the more specific context of manufacturing tolerances. Models of the workpiece, the set-ups and the machining operations are provided. The main originality is to model the machining set-up as a mechanism. The concept of the small displacements torsor (SDT) is used to model the process planning. It opens up the way for the 3D integration product/process because of the similarities between the concepts used in both points of view. The first part recalls the principle of the modelling of surface variations with SDT as well as its application to the modelling of mechanisms. The second part introduces the use of the concept in the case of manufacturing tolerancing. A third part shows the modelling of the problem with the help of an example. At last, a detailed computer implementation is described.     

Heuristic algorithms for scheduling heat-treatment furnaces of steel casting industries

This paper addresses a research problem of scheduling parallel, non-identical batch processors in the presence of dynamic job arrivals, incompatible job-families and non-identical job sizes. We were led to this problem through a real-world application involving the scheduling of heat-treatment operations of steel casting. The scheduling of furnaces for heat-treatment of castings is of considerable interest as a large proportion of the total production time is the processing times of these operations. In view of the computational intractability of this type of problem, a few heuristic algorithms have been designed for maximizing the utilization of heat-treatment furnaces of steel casting manufacturing. Extensive computational experiments were carried out to compare the performance of the heuristics with the estimated optimal value (using the Weibull technique) and for relative effectiveness among the heuristics. Further, the computational experiments show that the heuristic algorithms proposed in this paper are capable of obtaining near (statistically estimated) optimal utilization of heat-treatment furnaces and are also capable of solving any large size real-life problems with a relatively low computational effort.     

Solution of a Groundwater Control Problem with Implicit Filtering

In this paper we describe the application of a parallel implementation of the implicit filtering algorithm to a control problem from hydrology. We seek to control the temperature at a group of drinking water wells by placing barrier wells between the drinking water wells and a well that injects heated water from an industrial site.     

Joint optimization of preventive maintenance and flexible flowshop sequence-dependent group scheduling considering multiple setups

This study focuses on a joint optimization problem regarding preventive maintenance (PM) and non-permutation group scheduling for a flexible flowshop manufacturing cell in order to minimize makespan. A mixed-integer linear programming model for the investigated problem is developed, which features the consideration of multiple setups, the relaxation of group technology assumptions, and the integration of group scheduling and PM. Based on the model, a lower bounding technique is presented to evaluate the quality of solutions. Furthermore, a genetic algorithm (GA) is proposed to improve computational efficiency. In the GA, a threshold-oriented PM policy, a hybrid crossover and a group swap mutation operator are applied. Numerical experiments are conducted on 45 test problems with various scales. The results show that the proposed model can remarkably reduce makespan. Comparative experiments reveal that the GA outperforms CPLEX, particle swarm optimization and cuckoo search with respect to effectiveness and efficiency.     

Scheduling manufacturing systems with work-in-process inventory control: Reentrant systems

In this paper, we propose a procedure for production flow control in reentrant manufacturing systems. The system under study consists ofN machines and producesM product types simultaneously. Each part goes through the system following a predefined process and may visit a machine many times. All machines are subject to random failures and need random repair times. The scheduling objectives are to keep the production close to demand and to keep the WIP inventory level and cycle times at low values. The model is motivated by semiconductor fabrication production. A three-level hierarchical controller is constructed to regulate the production. At the top level of this hierarchy, we perform capacity planning by selecting the desirable buffer sizes and the target production level for each operation. A production flow rate controller is at the middle level which recalculates the production rates whenever a machine fails or is starved or blocked. The loading times for individual parts are determined at the bottom level of the hierarchy. Comparison with alternative control is made through simulation and it shows that the control policy performs well.     

Alternative control mechanisms for cyclical scheduling systems

This paper investigates the relative performance of alternative mechanisms for controlling the cycle length in a cyclical scheduling system. While all methods investigated are able to maintain a target long run average cycle length, some provide greater inventory efficiency than others, with more informed application of idle time. Simulation results demonstrate that a fixed idle time policy provides worst results, while an aggregate workload heuristic based on earlier dynamic policy characterisation provides superior performance. The importance of the control mechanism is shown to be greatest when the process is subject to high demand intensity, particularly when the demand process is sporadic.     

基于改进遗传算法的导向辊生产调度方法研究

目的 研究导向辊生产车间中的调度优化问题,有利于缩短工件的完工时间,提高产线生产效率。方法 以某导向辊生产车间为研究对象,以最小化最大完工时间为目标建立数学模型。针对该导向辊生产车间的实际工况,提出一种改进的遗传算法进行求解。通过对10种不同尺寸的导向辊进行生产调度,分别采用改进的遗传算法和传统遗传算法进行试验分析。结果 改进的遗传算法相比传统遗传算法寻优能力更高,工件的完工时间从139 min缩短为113 min,缩短了18.7%左右,生成了完工时间为113 min的生产调度甘特图。结论 与传统遗传算法相比,改进的遗传算法在导向辊生产调度优化中具有更高的全局优化能力和寻优精度。     

Value chain flexibility: A dichotomy of competence and capability

Increasing global competition, accelerating technological change and expanding customer expectations are creating a turbulent environment. Flexibility is a mechanism that enables firms to cope with this increasing uncertainty because it facilitates a quick response, which is strategically important as an order-winning criterion. To meet customer needs, flexibility must be across the value chain and it should include product development, manufacturing, logistics and spanning activities. The paper applies competence and capability theory to value chain flexibility and it explores the relationships among environmental uncertainty, value chain flexibility and competitive advantage. It provides a framework for understanding these relationships and builds a theoretical foundation for developing and testing relationships among these constructs. The research should help managers identify the flexible capabilities that impact relationships with their customers and the flexible competences that support these capabilities.