On-line dispatching rules for vehicle-based internal transport systems

On-line vehicle dispatching rules are widely used in many facilities such as warehouses and manufacturing facilities to control vehicles’ movements. Single-attribute dispatching rules, which dispatch vehicles based on only one parameter, are usually used. However, multi-attribute dispatching rules prove to be better in general. In this paper, we study the impact of reassigning moving vehicles on some good dispatching rules, both single- and multi-attribute, in the literature. Results suggest that reassigning moving-to-park vehicles has a significant positive effect on reducing the average load waiting time. We evaluate the dispatching rules’ performance using the experimental design of a real-life case study. The performance criteria are: minimizing the average load waiting time, keeping the maximum load waiting time as short as possible and utilizing better vehicles. The results show that the combined dispatching rules which integrates multi-attribute dispatching and vehicle reassignment yields the best performance overall.     

Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems

The goal of the current study is to identify appropriate application domains of Ant Colony Optimisation (ACO) in the area of dynamic job shop scheduling problem. The algorithm is tested in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems. The steady-state performances of ACO in terms of mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest processing time, and minimum slack time. Two series of experiments are carried out to identify the best ACO strategy and the best performing dispatching rule. Those two approaches are thereafter compared with different variations of processing times. The experimental results show that ACO outperforms other approaches when the machine utilisation or the variation of processing times is not high.     

基于聚类状态隶属度的动态调度Q-学习

提出了一种利用Q-学习解决动态单机调度环境下的自适应调度规则选择的方法.该方法针对动态调度环境中系统状态空间大,Q-学习不易收敛的特点,首先提取系统状态特征,对系统状态进行合理聚类,有效地降低系统状态空间维数,然后在学习过程中令设备Agent根据瞬时状态向量对各聚类状态的隶属度做出综合判断,选择合适规则,并在每次迭代后根据隶属度将动作奖惩分配给各聚类状态的动作值函数.仿真结果表明,所提Q-学习算法较之传统Q-学习具有更快的收敛速度,提高了设备Agent的动态调度规则选择能力.     

Electroplating production scheduling by cyclogram unfolding in dynamic hoist scheduling problem

This article presents a scheduling problem that exists in electroplating lines. An electroplating line is an automated manufacturing system which covers machine parts with a coat of metal. It consists of a set of tanks that chemically process the items and hoists that transport the items between workstations. Scheduling the movements of these hoists is commonly called a hoist scheduling problem. The most common approaches to the problem are cyclic hoist scheduling problem and dynamic hoist scheduling problem (DHSP). This article presents a DHSP solution method. The method divides the problem into real time and non-real time. Special schedules, called cyclograms, allow minimisation of the length of non-real time calculations. A notion of the problem is introduced, an outline of a scheduling system is presented, as well as the heuristic algorithm itself. The results of the described method, referred to as a cyclogram unfolding method, are compared to several cases available in the literature.     

Mitigating supply and production uncertainties with dynamic scheduling using real-time transport information

Supply and production uncertainties can affect the scheduling and inventory performance of final production systems. Facing such uncertainties, production managers normally choose to maintain the original production schedule, or follow the first-in-first-out policy. This paper develops a new, dynamic algorithm policy that considers scheduling and inventory problems, by taking advantage of real-time shipping information enabled by today’s advanced technology. Simulation models based on the industrial example of a chemical company and the Taguchi’s method are used to test these three policies under 81 experiments with varying supply and production lead times and uncertainties. Simulation results show that the proposed dynamic algorithm outperforms the other two policies for supply chain cost. Results from Taguchi’s method show that companies should focus their long-term effort on the reduction of supply lead times, which positively affects the mitigation of supply uncertainty.     

Effective vehicle dispatching method minimising the blocking and delivery times in automatic material handling systems of 300 mm semiconductor fabrication

This paper proposes an efficient vehicle dispatching rule which minimises the vehicle blocking and delivery times in automatic material handling systems of 300 mm semiconductor manufacturing. In order to evaluate the performance of the proposed dispatching method, discrete event simulation models were developed. The results show that the proposed method has a significant impact on average delivery time, throughput and vehicle utilisation. In particular, it reduces the variance of the delivery time remarkably.     

Self-adaptive dynamic scheduling of virtual production systems

To enhance the agility of virtual production systems (VPSs) under today's dynamic and changing manufacturing environment, a self-adaptive dynamic scheduling method based on event-driven is proposed for VPSs in this paper. This method is composed of the mechanisms and algorithm of self-adaptive dynamic scheduling. In the mechanisms, the dynamic events faced by VPSs are determined through users’ inputs or supervisory controllers’ detections, the local effects made on the schedule are analysed according to the dynamic events, and the self-adaptive measures and rules are specified correspondingly. To implement the dynamic scheduling of VPSs under the guidance of self-adaptive rules, a modified heuristic rescheduling algorithm is proposed for affected operations. A case study illustrates that the proposed method can well accomplish the dynamic scheduling of VPSs in a self-adaptive manner.     

Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming

At present, a lot of references use discrete event simulation to evaluate the fitness of evolved rules, but which simulation configuration can achieve better evolutionary rules in a limited time has not been fully studied. This study proposes three types of hyper-heuristic methods for coevolution of the machine assignment rules (MAR) and job sequencing rules (JSR) to solve the DFJSP, including the cooperative coevolution genetic programming with two sub-populations (CCGP), the genetic programming with two sub-trees (TTGP) and the genetic expression programming with two sub-chromosomes (GEP). After careful parameter tuning, a surrogate simulation model is used to evaluate the fitness of evolved scheduling policies (SP). Computational simulations and comparisons demonstrate that the proposed surrogate-assisted CCGP method (CCGP-SM) shows competitive performance with other evolutionary approaches using the same computation time. Furthermore, the learning process of the proposed methods demonstrates that the surrogate-assisted GP methods help accelerating the evolutionary process and improving the quality of the evolved SPs without a signi?cant increase in the length of SP. In addition, the evolved SPs generated by the CCGP-SM show superior performance as compared with existing rules in the literature. These results demonstrate the effectiveness and robustness of the proposed method.     

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.     

Flexible job-shop scheduling/rescheduling in dynamic environment: a hybrid MAS/ACO approach

In real-world manufacturing, disruptions are often encountered during the execution of a predetermined schedule, leading to the degradation of its optimality and feasibility. This study presents a hybrid approach for flexible job-shop scheduling/rescheduling problems under dynamic environment. The approach, coined as ‘HMA’ is a combination of multi-agent system (MAS) negotiation and ant colony optimisation (ACO). A fully distributed MAS structure has been constructed to support the solution-finding process by negotiation among the agents. The features of ACO are introduced into the negotiation mechanism in order to improve the performance of the schedule. Experimental studies have been carried out to evaluate the performance of the approach for scheduling and rescheduling under different types of disruptions. Different rescheduling policies are compared and discussed. The results have shown that the proposed approach is a competitive method for flexible job-shop scheduling/rescheduling for both schedule optimality and computation efficiency.     

Hybrid approach for the integrated scheduling of production and transport processes along supply chains

The rise of new information and communication technologies leads to enhanced information transparency in supply chains. In order to utilise the resulting potentials, novel scheduling approaches that are capable of processing large amounts of data and coping with dynamic disturbances of manufacturing and transport stages have to be developed. For this purpose, the paper at hand proposes a hybrid approach for the integrated scheduling of production and transport processes along supply chains. The procedure combines mixed integer linear programming, discrete event simulation and a genetic algorithm. Obtained results show a significant reduction in the number of late orders, substantiating that proper scheduling approaches combined with information visibility allow for operational improvements in manufacturing supply chains.     

An investigation of cooperative dispatching for minimising mean flowtime in a finite-buffer-capacity dynamic flowshop

Scheduling in a dynamic flowshop that receives jobs at random and unforeseen points in time has traditionally been done by using dispatching rules. This study compares the performances of leading dispatching rules with a cooperative dispatching approach, for the objective of minimising mean flowtime in a flowshop, in which the buffers that hold in-process jobs between machines have finite capacities. Cooperative dispatching employs a consultative and consensus-seeking methodology for deciding which job to dispatch next on a machine. Computational experiments using randomly generated test problems for three different utilisation (congestion) levels are carried out for 5- and 10-machine flowshops, under a wide range of buffer capacities. The results highlight the sensitivity of some of the popular dispatching rules to buffer size. In contrast, cooperative dispatching emerges as a robust method that performs consistently well across the range of buffer sizes and machine utilisations tested. The reductions in mean flowtime obtained by cooperative dispatching, in comparison to the other dispatching rules, are particularly large in flowshops that operate with very tight buffer capacities and elevated levels of congestion     

A two-layer dynamic scheduling method for minimising the earliness and tardiness of a re-entrant production line

This study focuses on the dynamic scheduling problem of a re-entrant production line, in which all of the parts are assumed to have the same processing routes and be processed on every machine. A two-layer dynamic scheduling method is proposed for the dynamic scheduling of the re-entrant line with the objective of minimising total earliness and tardiness. This method consists of two layers. The top layer is to select the appropriate scheduling policy, and the bottom layer is to generate the scheduling by using the policy selected in the top layer. In the top layer, three different rules are constructed for selecting scheduling policies, namely the lateness comparison rule, the lateness variation comparison rule, and the equal interval switching rule. In the bottom layer, three different scheduling policies are proposed to generate the real-time scheduling for manufacturing, namely the FCFS (first come first service) scheduling policy, the PI (proportional-integral) scheduling policy, and the fuzzy PI scheduling policy. Considering that the real-time status of manufacturing changes constantly, it is necessary to switch among different scheduling policies to adapt to this change. Numerical experiments are performed in the situations with and without urgent jobs. The results show that the proposed two-layer dynamic scheduling method outperforms any single scheduling policy (e.g., the FCFS policy, the PI policy and the fuzzy PI policy) for the dynamic scheduling of a re-entrant production line.     

Performance evaluation of small-batch container delivery systems used in lean manufacturing – Part 2: number of Kanban and workstation starvation

In lean manufacturing, milk run (MR) systems represent route-based, cyclic material handling systems that are used widely to enable frequent and consistent deliveries of containerised parts on an as-needed basis from a central storage area (the ‘supermarket’) to multiple line-side deposit points on the factory floor. In the first part of this two-part paper, a basic, single-tugger MR system is described, and stability conditions as well as the probability of exceeding either the physical capacity of the tugger or the prescribed cycle time are derived. Given the stability conditions, and the distribution of the number of containers requested per MR, in the second part of the paper, the number of Kanban required in the MR system is examined, and analytical approximations are derived both for the number of Kanban required and for predicting workstation starvation. The latter is a key concern when designing a MR system that will support workstations in a manufacturing plant. The performance of the analytic approximation is evaluated by simulating various MR systems. Our results suggest that, in a stable MR system, the number of Kanban and the physical capacity of the tugger have a bigger impact on workstation starvation than the prescribed cycle time.     

An optimization based decision support system for integrated planning and scheduling on dedicated freight corridors

Empty wagon redistribution, train formation, routing and scheduling are complex problems for large railways, many of which currently have or are planning dedicated freight railway corridors (DFC). DFC operations due to their unique characteristics require research and new models for better operations planning. The rolling-stock, being expensive assets, need to be utilised in an optimal manner while meeting service quality levels. Motivated by Indian DFC, we present an integer programming formulation of the dynamic problem of empty distribution and train scheduling in DFC and discuss associated modelling issues. By unifying the separate problems into a single and dynamic model, we have developed a framework for more effective rolling stock utilisation. Based on this optimization model, an interactive decision support system is proposed for better decision-making on rolling-stock allocation and train scheduling. Extensive experiments and systematic analyses for a case of Indian DFC highlight the potentialities and effectiveness of the proposed DSS for DFC operations planning and management.     

A deep learning approach for the dynamic dispatching of unreliable machines in re-entrant production systems

This research combines deep neural network (DNN) and Markov decision processes (MDP) for the dynamic dispatching of re-entrant production systems. In re-entrant production systems, jobs enter the same workstation multiple times and dynamic dispatching oftentimes aims to dynamically assign different priorities to various job groups to minimise weighted cycle time or maximise throughput. MDP is an effective tool for dynamic production control, but it suffers from two major challenges in dynamic control problems. First, the curse of dimensionality limits the computational performance of solving large MDP problems. Second, a different model should be built and solved after system configuration is changed. DNN is used to overcome both challenges by learning directly from optimal dispatching policies generated by MDP. Results suggest that a properly trained DNN model can instantly generate near-optimal dynamic control policies for large problems. The quality of the DNN solution is compared with the optimal dynamic control policies through the standard K-fold cross-validation test and discrete event simulation. On average, the performance of the DNN policy is within 2% of optimal in both tests. The proposed artificial intelligence algorithm illustrates the potential of machine learning methods in manufacturing applications.     

基于层次模糊推理的施工机群动态调度方法研究

以Zadah的模糊数学理论为基础,结合决策融合技术提出了一种基于层次模糊推理的施工机群动态调度方法。与传统的单层模糊调度控制方法相比,该方法具有建模简单且可扩展性好的优点,同时避免了单层推理的维数灾难问题。将该方法应用于施工机群的动态调度系统中,按照生产情况对施工系统进行了建模仿真,仿真结果和调度实例证明了该方法的有效性和合理性。该方法为机群施工以及与之相似的生产施工的调度提供了技术框架。     

对称型内平动齿轮系统的非线性动力学分析

摘要：基于拉格朗日方程,建立了含有两个呈对称布置的平动齿轮的内平动齿轮传动机构的动力学模型,通过啮合相对位移函数分析及无量纲化处理,得到系统的无量纲6自由度运动微分方程。通过对系统可能存在的不对称因素(平动齿轮支撑轴承不对称、啮合间隙不对称以及平动齿轮受载不对称)对系统动力学特性的影响进行分析,表明三种不对称因素均会引起系统的分岔,且混沌区域随非对称因素的不同表现出不同的分布规律,并且使得周期解呈现出不同的特性。