An efficient approach to cross-fab route planning for wafer manufacturing

This paper proposes an efficient approach to solve a cross-fab route planning problem for semiconductor wafer manufacturing. A semiconductor company usually adopts a dual-fab strategy. Two fab sites are built neighbor to each other to facilitate capacity-sharing. A product thus may be produced by a cross-fab route; that is, some operations of a product are manufactured in one fab and the other operations in the other fab. This leads to a cross-fab routing planning problem, which involves two decisions—determining the cut-off point of the cross-fab route and the route ratio for each product—in order to maximize the throughput subject a cycle time constraint. A prior study has proposed a method to solve the cross-fab route planning problem; yet it is computationally extensive in solving large scale cases. To alleviate this deficiency, we proposed three enhanced methods. Experiment results show that the best enhanced method could significantly reduce the computational efforts from about 13 h to 0.5 h, while obtaining a satisfactory solution.     

Real coded chemical reaction based optimization for short-term hydrothermal scheduling

This paper presents a real coded chemical reaction based (RCCRO) algorithm to solve the short-term hydrothermal scheduling (STHS) problem. Hydrothermal system is highly complex and related with every problem variables in a nonlinear way. The objective of the hydro thermal scheduling is to determine the optimal hourly schedule of power generation for different hydrothermal power system for certain intervals of time such that cost of power generation is minimum. Chemical reaction optimization mimics the interactions of molecules in term of chemical reaction to reach a low energy stable state. A real coded version of chemical reaction optimization, known as real-coded chemical reaction optimization (RCCRO) is considered here. To check the effectiveness of the RCCRO, 3 different test systems are considered and mathematical remodeling of the algorithm is done to make it suitable for solving short-term hydrothermal scheduling problem. Simulation results confirm that the proposed approach outperforms several other existing optimization techniques in terms quality of solution obtained and computational efficiency. Results also establish the robustness of the proposed methodology to solve STHS problems.     

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A steelmaking-continuous casting (SCC) scheduling problem is an example of complex hybrid flow shop scheduling problem (HFSSP) with a strong industrial background. This paper investigates the SCC scheduling problem that involves controllable processing times (CPT) with multiple objectives concerning the total waiting time, earliness/tardiness and adjusting cost. The SCC scheduling problem with CPT is seldom discussed in the existing literature. This study is motivated by the practical situation of a large integrated steel company in which the just-in-time (JIT) and cost-cutting production strategy have become a significant concern. To address this complex HFSSP, the scheduling problem is decomposed into two subproblems: a parallel machine scheduling problem (PMSP) in the last stage and an HFSSP in the upstream stages. First, a hybrid differential evolution (HDE) algorithm combined with a variable neighborhood decomposition search (VNDS) is proposed for the former subproblem. Second, an iterative backward list scheduling (IBLS) algorithm is presented to solve the latter subproblem. The effectiveness of this bi-layer optimization approach is verified by computational experiments on well-designed and real-world scheduling instances. This study provides a new perspective on modeling and solving practical SCC scheduling problems.     

Dynamic workforce allocation in a constrained flow shop with multi-agent system

The present work addresses the problem of real time workforce scheduling in assembly lines where the number of operators is less to the number of workstations.The problem is faced developing a two-steps procedure made of (i) a centralized scheduling based on a constraint optimization problem (COP) for initial operator scheduling, and (ii) a decentralized algorithm performed by a multiagent system (MAS) to manage workers in case of unforeseen events.In the proposed MAS architecture, Agents represent the operators trying to find local assignments for themselves. The system is validated with a simulation model and implemented with a hardware infrastructure in a real assembly line of electromechanical components. The main original contribution of the paper consists in proving – by means of both validation through a simulation model and test in a real assembly line of electromechanical components – that (1) multi-agent systems could be successfully adopted to solve a workforce scheduling problem, and (2) a combined approach consisting of centralized + distributed approach would provide better results compared with the application of one of the two approaches alone.     

Optimizing capacity allocation in semiconductor manufacturing photolithography area – Case study: Robert Bosch

In this paper, we advance the state of the art for capacity allocation and scheduling models in a semiconductor manufacturing front-end fab (SMFF). In SMFF, a photolithography process is typically considered as a bottleneck resource. Since SMFF operational planning is highly complex (re-entrant flows, high number of jobs, etc.), there is only limited research on assignment and scheduling models and their effectiveness in a photolitography toolset. We address this gap by: (1) proposing a new mixed integer linear programming (MILP) model for capacity allocation problem in a photolithography area (CAPPA) with maximum machine loads minimized, subject to machine process capability, machine dedication and maximum reticles sharing constraints, (2) solving the model using CPLEX and proofing its complexity, and (3) presenting an improved genetic algorithm (GA) named improved reference group GA (IRGGA) biased to solve CAPPA efficiently by improving the generation of the initial population. We further provide different experiments using real data sets extracted from a Bosch fab in Germany to analyze both proposed algorithm efficiency and solution sensitivity against changes in different conditional parameters.     

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

This study presents a simulation optimization approach for a hybrid flow shop scheduling problem in a real-world semiconductor back-end assembly facility. The complexity of the problem is determined based on demand and supply characteristics. Demand varies with orders characterized by different quantities, product types, and release times. Supply varies with the number of flexible manufacturing routes but is constrained in a multi-line/multi-stage production system that contains certain types and numbers of identical and unrelated parallel machines. An order is typically split into separate jobs for parallel processing and subsequently merged for completion to reduce flow time. Split jobs that apply the same qualified machine type per order are compiled for quality and traceability. The objective is to achieve the feasible minimal flow time by determining the optimal assignment of the production line and machine type at each stage for each order. A simulation optimization approach is adopted due to the complex and stochastic nature of the problem. The approach includes a simulation model for performance evaluation, an optimization strategy with application of a genetic algorithm, and an acceleration technique via an optimal computing budget allocation. Furthermore, scenario analyses of the different levels of demand, product mix, and lot sizing are performed to reveal the advantage of simulation. This study demonstrates the value of the simulation optimization approach for practical applications and provides directions for future research on the stochastic hybrid flow shop scheduling problem.     

云环境下科学工作流的调度算法

提出一种云环境下科学工作流的调度算法.针对已有的调度算法和松弛时间资源分配策略均未考虑“或”控制结构的不足，给出了关键活动优先级（Critical Activity Priority，CAP）的概念；定义了活动的服务效益比（Service Benefit Ratio，SBR）；提出了活动松弛时间分配策略；并从流程定义和实例运行两个层次，给出了活动截止期限的分配算法.该项研究成果为解决科学工作流调度过程中的时间-成本优化问题提供了更合适的解决方案.     

无线传感器/执行器网络多目标任务调度策略略

针对多任务在多执行器节点的协作问题,提出一种多目标任务调度策略.该策略以执行任务的最大完成时间、能耗均衡指标和存储成本为目标,将任务调度建模成多目标优化问题,并运用理想点法解决不同目标量纲的差异性,进而转化为单目标优化问题求解,从而得到各任务在执行器节点上的局部最优执行方案.仿真结果表明,3个优化指标均得到一定程度的改善.     

Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling

The expanded job-shop scheduling problem (EJSSP) is a practical production scheduling problem with processing constraints that are more restrictive and a scheduling objective that is more general than those of the standard job-shop scheduling problem (JSSP). A hybrid approach involving neural networks and genetic algorithm (GA) is presented to solve the problem in this paper. The GA is used for optimization of sequence and a neural network (NN) is used for optimization of operation start times with a fixed sequence.

After detailed analysis of an expanded job shop, new types of neurons are defined to construct a constraint neural network (CNN). The neurons can represent processing restrictions and resolve constraint conflicts. CNN with a gradient search algorithm, gradient CNN in short, is applied to the optimization of operation start times with a fixed processing sequence. It is shown that CNN is a general framework representing scheduling problems and gradient CNN can work in parallel for optimization of operation start times of the expanded job shop.

Combining gradient CNN with a GA for sequence optimization, a hybrid approach is put forward. The approach has been tested by a large number of simulation cases and practical applications. It has been shown that the hybrid approach is powerful for complex EJSSP.     

Framework of agent-based intelligence system with two-stage decision-making process for distributed dynamic scheduling

The advent of multiagent systems, a branch of distributed artificial intelligence, introduced a new approach to problem solving through agents interacting in the problem solving process. In this paper, a collaborative framework of a distributed agent-based intelligence system is addressed to control and resolve dynamic scheduling problem of distributed projects for practical purposes. If any delay event occurs, the self-interested activity agent, the major agent for the problem solving of dynamic scheduling in the framework, can automatically cooperate with other agents in real time to solve the problem through a two-stage decision-making process: the fuzzy decision-making process and the compensatory negotiation process. The first stage determines which behavior strategy will be taken by agents while delay event occurs, and prepares to next negotiation process; then the compensatory negotiations among agents are opened related with determination of compensations for respective decisions and strategies, to solve dynamic scheduling problem in the second stage. A prototype system is also developed and simulated with a case to validate the problem solving of distributed dynamic scheduling in the framework.     

A combined dispatching criteria approach to scheduling semiconductor manufacturing systems

The scheduling problem of semiconductor manufacturing systems has multiple responses of interest. The objective is to simultaneously optimize these different responses or to find the best-compromised solution. Most previous research in the area of semiconductor manufacturing systems has focused on optimizing a single performance measure. Dabbas and Fowler proposed a modified dispatching approach that combines multiple dispatching criteria into a single rule with the objective of simultaneously optimizing multiple objectives. In this paper, we validate their proposed approach using two different fab models at different levels of complexity: a hypothetical six stage-five machines Mini-Fab model and a full scale wafer fab model adapted from an actual Motorola wafer fab. We also discuss the actual implementation of the proposed dispatching algorithm into a scheduler for daily operation at a Motorola wafer fabrication facility. Results show an average 20% improvement for all responses when using the proposed dispatching approach.     

A bi-level evolutionary optimization approach for integrated production and transportation scheduling

This paper investigates an integrated production and transportation scheduling (IPTS) problem which is formulated as a bi-level mixed integer nonlinear program. This problem considers distinct realistic features widely existing in make-to-order supply chains, namely unrelated parallel-machine production environment and product batch-based delivery. An evolution-strategy-based bi-level evolutionary optimization approach is developed to handle the IPTS problem by integrating a memetic algorithm and heuristic rules. The efficiency and effectiveness of the proposed approach is evaluated by numerical experiments based on industrial data and industrial-size problems. Experimental results demonstrate that the proposed approach can effectively solve the problem investigated.     

求解柔性机器人车间调度问题的混合蚁群算法

在柔性作业车间调度问题的基础上,考虑多台搬运机器人执行不同工序在不同机床之间的搬运,形成柔性机器人作业车间调度问题,提出混合蚁群算法。用改进析取图对问题进行描述,使用混合选择策略、自适应伪随机比例规则和改进信息素更新规则优化蚁群算法,结合遗传算子完成机床选择和工序排序。使用一种多机器人排序算法完成搬运机器人分配和搬运工序排序。通过多组算例仿真测试并与其他算法进行比较,验证了算法的有效性和可靠性。     

A simulation-based two-stage scheduling methodology for controlling semiconductor wafer fabs

In this paper, we propose an efficient simulation-based two-stage scheduling methodology by integrating vector ordinal optimization (VOO) and response surface methodology (RSM) to make a good scheduling policy for fab operation. The suggested method combines local and global rules into a single rule, with the objective of simultaneously optimizing multiple performance indices. Our approach consists of 2 stages: rule combination selection, and parameter optimization. In the first stage, we apply the VOO techniques to effectively selecting good rule combination. Results show that 1 orders of computation time reduction over traditional simulations can be achieved. In the second stage, we adopt RSM and desirability function to tune the parameters associated to scheduling algorithm. The proposed approach is validated by means of a comparison with other scheduling policies. The results show that the proposed scheduling method is effective.     

基于滚动策略的集装箱码头连续泊位与桥吊集成调度

针对连续泊位与桥吊集成调度大规模求解困难的问题,提出一种基于滚动策略的优化方法。首先,建立了最小化船舶偏离偏好泊位的成本以及延迟靠泊、延迟离港的惩罚成本的基本的多目标优化模型;然后,采用滚动调度方法根据动态抵泊的船舶抵达顺序将调度过程分成连续的调度窗口,并设计窗口的平移策略、当前窗口对下一窗口的参数更新方式;对每个窗口内船舶进行调度优化,根据每个窗口内的优化结果,更新下一个窗口中数学模型的输入参数;通过选取以船舶数量表示的滚动计划窗口和冻结船舶的数量,持续滚动获得每个窗口的最优解,叠加后获得对所有船舶的靠泊计划。通过算例分析表明,滚动调度能够解决较大规模的调度问题,其效率受滚动窗口大小、冻结船舶数量及滚动次数影响     

Flow-shop path planning for multi-automated guided vehicles in intelligent textile spinning cyber-physical production systems dynamic environment

Aiming at the path planning and decision-making problem, multi-automated guided vehicles (AGVs) have played an increasingly important role in the multi-stage industries, e.g., textile spinning. We recast a framework to investigate the improved genetic algorithm (GA) on multi-AGV path optimization within spinning drawing frames to solve the complex multi-AGV maneuvering scheduling decision and path planning problem. The study reported in this paper simplifies the scheduling model to meet the drawing workshop's real-time application requirements. According to the characteristics of decision variables, the model divides into two decision variables: time-independent variables and time-dependent variables. The first step is to use a GA to solve the AGV resource allocation problem based on the AGV resource pool strategy and specify the sliver can's transportation task. The second step is to determine the AGV transportation scheduling problem based on the sliver can-AGV matching information obtained in the first step. One significant advantage of the presented approach is that the fitness function is calculated based on the machine selection strategy, AGV resource pool strategy, and the process constraints, determining the scheduling sequence of the AGVs to deliver can. Moreover, it discovered that double-path decision-making constraints minimize the total path distance of all AGVs, and minimizing single-path distances of each AGVs exerted. By using the improved GA, simulation results show that the total path distance was shortened.     

泛集群环境中计算密集型任务流调度策略

针对计算节点较多的泛集群环境下难以快速、合理地制定计算密集型任务流调度方案的问题,提出一种基于多目标连续竞买博弈的任务调度策略.建立多目标优化调度模型,降低多目标优化函数维度,并采用线性加权和法将其转化为总和目标函数,以保证最优解的合理性.为提高最优解搜索速度,引入ETC矩阵作为最优解表达形式,设计连续竞买博弈算法.模拟真实场景并通过与同类算法的对比,表明了调度策略在泛集群环境下的响应速度、资源性价比和总成本支出等方面具有明显优势.     

Analysis of different approaches to cross-dock truck scheduling with truck arrival time uncertainty

This paper studies scheduling of inbound trucks at the inbound doors of a cross-dock facility under truck arrival time uncertainty. Arrival time of an inbound truck is considered to be unknown. In particular, the cross-dock operator only acknowledges the arrival time window of each truck, i.e., the lower and upper bounds of any inbound truck’s arrival time. In absence of any additional information, the cross-dock operator may use three approaches to determine a scheduling strategy: deterministic approach (which assumes expected truck arrival times are equal to their mid-arrival time windows), pessimistic approach (which assumes the worst truck arrivals will be realized), and optimistic approach (which assumes the best truck arrivals will be realized). In this paper, a bi-level optimization problem is formulated for pessimistic and optimistic approaches. We discuss a Genetic Algorithm (GA) to solve the truck-to-door assignments for given truck arrival times, which solves the deterministic approach. Then the GA is modified to solve the bi-level formulations of the pessimistic and the optimistic approaches. Our numerical studies show that an hybrid approach regarding the pessimistic and the optimistic approaches may outperform all of the three approaches in certain cases.     

Setup Scheduling of Manufacturing Systems as the Art of Juggling

The problem of detailed scheduling of complex flexible manufacturing systems is addressed by optimal flow control. A model problem of scheduling parallel machines is considered to obtain necessary setup conditions. Studying the conditions results in a new solution approach that takes advantage of a juggling analogy of the production/setup scheduling. This analogy is used in the paper to direct construction of a solution method. The method searches for a globally optimal schedule by means of both a juggling strategy and a method of global optimization. The results obtained for a model problem are then generalized to systems with complex production and setup operations. Computational examples demonstrate the validity of the approach.     

Digital twin-based job shop anomaly detection and dynamic scheduling

Scheduling scheme is one of the critical factors affecting the production efficiency. In the actual production, anomalies will lead to scheduling deviation and influence scheme execution, which makes the traditional job shop scheduling methods are not sufficient to meet the needs of real-time and accuracy. By introducing digital twin (DT), further convergence between physical and virtual space can be achieved, which enormously reinforces real-time performance of job shop scheduling. For flexible job shop, an anomaly detection and dynamic scheduling framework based on DT is proposed in this paper. Previously, a multi-level production process monitoring model is proposed to detect anomaly. Then, a real-time optimization strategy of scheduling scheme based on rolling window mechanism is explored to enforce dynamic scheduling optimization. Finally, the improved grey wolf optimization algorithm is introduced to solve the scheduling problem. Under this framework, it is possible to monitor the deviation between the actual processing state and the planned processing state in real time and effectively reduce the deviation. An equipment manufacturing job shop is taken as a case study to illustrate the effectiveness and advantages of the proposed framework.