Supply chain-oriented two-stage assembly flowshops with sequence-dependent setup times

Supply chain-oriented scheduling problems have received recent recognition among production research scholars due to their ability in integrating production planning and control across manufacturing systems. This study contributes to the literature of the distributed scheduling problems developing an original Mixed-Integer Linear Programming (MILP) formulation to the Distributed Two-Stage Assembly Flowshop Scheduling Problem with Sequence-Dependent Setup Times (DTSAFSP-SDSTs). Besides, the Iterated Greedy algorithm is extended to effectively solve this relatively complex production scheduling situation considering the makespan as the optimization criterion. Extensive numerical tests and statistical analyses are conducted to evaluate the effectiveness of the developed solution algorithm. Results showed that the Improved Iterated Greedy (IIG) algorithm yields the best solution in nearly all of the large-scale instances. The statistical test of significance confirmed that IIG is superior to the current-best-performing algorithm. This study contributes to the transition from standalone optimization to integrated production planning and control of distributed manufacturing systems.     

基于离散人工蜂群算法的云任务调度优化

针对现今云计算任务调度只考虑单目标和云计算应用对虚拟资源的服务的质量要求高等问题,综合考虑了用户最短等待时间、资源负载均衡和经济原则,提出一种离散人工蜂群(ABC)算法的云任务调度优化策略。首先,从理论上建立了云任务调度的多目标数学模型;然后,结合偏好满意度策略并引入局部搜索算子和改变侦察蜂搜索方式,提出多目标离散型人工蜂群(MDABC)算法的优化策略。通过不同的云任务调度仿真实验,显示了改进离散人工蜂群算法相对于基础离散人工蜂群算法、遗传算法以及经典贪心算法,能够得到较高的综合满意度,表明了改进离散人工蜂群算法能够更好地改善虚拟资源中云任务调度系统的性能,具有一定的普适性。     

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.     

异构传感网中基于组合指派编码模型的节点调度算法

针对感知半径异构无线传感器网络(WSN)中的节点调度问题,提出了一种基于组合指派编码模型的分布式节点调度算法。首先确定最大可能的组个数;然后基于两跳簇概念进行分布式分簇;最后对每个簇中的节点采用组合指派编码模型分布式调度到不同的组中。理论分析与仿真实验表明,与已有基于随机方式与两跳簇方式的调度算法相比,所提算法能更有效地延长网络的生命周期,因此更加适合感知半径异构无线传感器网络环境。     

多路并行传输中数据调度算法的优化

针对异构无线网络环境中,基于流控制传输协议（SCTP）的多路并行传输协议（CMT-SCTP）存在接收缓存阻塞和路径负载失衡等问题,提出一种改进的轮询数据调度算法。该算法根据每条路径上的发送队列信息和拥塞状况对网络状况进行估计,并按照各路径上的网络状况分配相应的传输任务量,缩短数据包在接收端缓冲区的平均排队时延,减少接收端乱序数据包的数量。仿真结果表明,改进的轮询数据调度算法能有效提升CMT-SCTP在异构无线网络环境中的传输效率,有效缓解接收缓存的阻塞,且对不同的网络场景具有很好的适应性。     

Optimizing distributed no-wait flow shop scheduling problem with setup times and maintenance operations via iterated greedy algorithm

Nowadays, distributed scheduling problem is a reality in many companies. Over the last years, an increasingly attention has been given to the distributed flow shop scheduling problem and the addition of constraints to the problem. This article introduces the distributed no-wait flow shop scheduling problem with sequence-dependent setup times and maintenance operations to minimize makespan. A mixed-integer linear programming (MILP) is to mathematically describe the problem and heuristic procedures to incorporate maintenance operations to job scheduling are proposed. An Iterated Greedy with Variable Search Neighborhood (VNS), named IG_NM, is proposed to solve small and large instances with size of 4,800 and 13,200 problems, respectively. Computational experiments were conducted to evaluate the performance of IG_NM in comparison with MILP and the most recent methods of literature of distributed flow shop scheduling problems. Statistical results show that in the trade-off between effectiveness and efficiency the proposed IG_NM outperformed other metaheuristics of the literature.     

Maintenance costs and makespan minimization for assembly permutation flow shop scheduling by considering preventive and corrective maintenance

The joint optimization of production scheduling and maintenance planning has a significant influence on production continuity and machine reliability. However, limited research considers preventive maintenance (PM) and corrective maintenance (CM) in assembly permutation flow shop scheduling. This paper addresses the bi-objective joint optimization of both PM and CM costs in assembly permutation flow shop scheduling. We also propose a new mixed integer linear programming model for the minimization of the makespan and maintenance costs. Two lemmas are inferred to relax the expected number of failures and CM cost to make the model linear. A restarted iterated Pareto greedy (RIPG) algorithm is applied to solve the problem by including a new evaluation of the solutions, based on a PM strategy. The RIPG algorithm makes use of novel bi-objective-oriented greedy and referenced local search phases to find non-dominated solutions. Three types of experiments are conducted to evaluate the proposed MILP model and the performance of the RIPG algorithm. In the first experiment, the MILP model is solved with an epsilon-constraint method, showing the effectiveness of the MILP model in small-scale instances. In the remaining two experiments, the RIPG algorithm shows its superiority for all the instances with respect to four well-known multi-objective metaheuristics.     

An innovative approach for resource sharing and scheduling in a sustainable distributed manufacturing system

Secure, transparent, and sustainable distributed manufacturing system (DMS) is a pressing need for current Industry 4.0. In this paper, exchange of highly sensitive information in a more transparent and secure way and to avoid the misunderstandings and trust issues between the enterprises a smart contract based on blockchain technology has been proposed in case of a distributed manufacturing environment. Here, we used a public-permission less Ethereum platform to execute the smart contracts in the Blockchain to process the customer orders and to identify the right enterprise. Later, a multi-objective mixed-integer linear programming (MILP) model is formulated for optimal resource sharing and scheduling in a considered sustainable DMS. The objectives of the proposed model consist of simultaneously improvement of the performance measures such as makespan, machine utilization, energy consumption, and reliability. To solve this MILP model, a new Multi-objective-based Hybridized Moth Flame Evolutionary Optimization Algorithm (HMFEO) is developed and then the effectiveness of the proposed algorithm is validated with the Non-dominated Sorting Genetic Algorithm (NSGA-III). The results obtained from implementing the model using experimental data along with different cases show the efficiency and the validity of the proposed model and solution approach. Moreover, several performance indicators like hyper volume are increased by nearly 15–20 % that shows the superiority of the proposed algorithm with the NSGA-III.     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.     

面向机器学习的分布式并行计算关键技术及应用

当前机器学习等算法的计算、迭代过程日趋复杂, 充足的算力是保障人工智能应用落地效果的关键。本文首先提出一种适应倾斜数据的分布式异构环境下的任务时空调度算法,有效提升机器学习模型训练等任务的平均效率;其次,提出分布式异构环境下高效的资源管理系统与节能调度算法,实现分布式异构环境下基于动态预测的跨域计算资源迁移及电压/频率的动态调节,节省了系统的整体能耗;然后构建了适应于机器学习/深度学习算法迭代的分布式异构优化环境,提出了面向机器学习/图迭代算法的分布式并行优化基本方法。最后,本文研发了面向领域应用的智能分析系统,并在制造、交通、教育、医疗等领域推广应用,解决了在高效数据采集、存储、清洗、融合与智能分析等过程中普遍存在的性能瓶颈问题。     

A Case Study on Integrated Production Planning and Scheduling in a Three-Stage Manufacturing System

This paper presents an integrated optimization model of production planning and scheduling for a three-stage manufacturing system, which is composed of a forward chain of three kinds of workshops: a job shop, a parallel flow shop consisting of parallel production lines, and a single machine shop. As the products at the second stage are assembled from the parts produced in its upstream workshop, a complicated production process is involved. On the basis of the analysis of the batch production, a dynamic batch splitting and amalgamating algorithm is proposed. Then, a heuristic algorithm based on a genetic algorithm (known as the integrated optimization algorithm) is proposed for solving the problem. Note to Practitioners-This paper presents a method for integrated production planning and scheduling in a three-stage manufacturing system consisting of a forward chain of three kinds of workshops, which is common in such enterprises as producers of automobiles and household electric appliances, as in the case of an autobody plant usually with the stamping workshop, the welding and assembling workshop, and the painting workshop. Herein, the production planning and scheduling problems are simultaneously addressed in the way that a feasible production plan can be obtained and the inventory reduced. A batch splitting and amalgamating algorithm is proposed for balancing the production time of the production lines. And a case study of the integrated planning and scheduling problem in a real autobody plant verifies the effectiveness of our method     

离散人工蜂群算法求解资源时变的项目调度问题

针对资源量随时间变动的项目调度问题提出了一种新的离散人工蜂群求解算法。算法食物源的位置采用基于任务排列的编码方法,并提出一种可以保持解的离散性和可行性的候选食物源生成方法。仿真结果表明,该算法能有效地求解资源时变的受限项目调度问题,研究发现在保持资源总量不变甚至减少的情况下,通过调整资源配置能够显著缩短项目工期,可见资源配置优化在项目管理中的重要作用。     

An active learning genetic algorithm for integrated process planning and scheduling

In traditional approaches, process planning and scheduling are carried out sequentially, where scheduling is done separately after the process plan has been generated. However, the functions of these two systems are usually complementary. The traditional approach has become an obstacle to improve the productivity and responsiveness of the manufacturing system. If the two systems can be integrated more tightly, greater performance and higher productivity of a manufacturing system can be achieved. Therefore, the research on the integrated process planning and scheduling (IPPS) problem is necessary. In this paper, a new active learning genetic algorithm based method has been developed to facilitate the integration and optimization of these two systems. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of the IPPS problem.     

Network modeling and evolutionary optimization for scheduling in manufacturing

Scheduling is one of the most important fields in Advanced Planning and Scheduling or a manufacturing optimization. In this paper, we propose a network modeling technique to formulate the complex scheduling problems in manufacturing, and focus on how to model the scheduling problems to mathematical formulation. We propose a multi-section evolutionary algorithm for the scheduling models formulated by network modeling. Through a combination of the network modeling and this multi-section evolutionary algorithm, we can implement the auto-scheduling in the manufacturing system. The effectiveness and efficiency of proposed approach are investigated with various scales of scheduling problems by comparing with recent related researches. Lastly, we introduced service-oriented evolutionary computation architecture software. It help improved the evolutionary computation??s availability in the variable practical scheduling in manufacturing.     

面向柔性作业分布式车间的分层调度模型研究

针对多车间分布式制造系统调度优化问题,结合车间实际生产情况,提出一种基于目标级联法和遗传算法的层次调度模型。模型将生产调度过程划分为生产计划层、车间调度层和零件规划层,并将整体时间最短的优化目标划分到各个层次,通过层层优化达到时间最优后反馈至上层,以实现整体调度时间最优。以3个制造车间协调调度问题为例,验证了该模型在零件分配和零件的工艺路线选择上的合理性和有效性。     

Deep reinforcement learning for solving resource constrained project scheduling problems with resource disruptions

The resource-constrained project scheduling problem (RCPSP) is encountered in many fields, including manufacturing, supply chain, and construction. Nowadays, with the rapidly changing external environment and the emergence of new models such as smart manufacturing, it is more and more necessary to study RCPSP considering resource disruptions. A framework based on reinforcement learning (RL) and graph neural network (GNN) is proposed to solve RCPSP and further solve the RCPSP with resource disruptions (RCPSP-RD) on this basis. The scheduling process is formulated as sequential decision-making problems. Based on that, Markov decision process (MDP) models are developed for RL to learn scheduling policies. A GNN-based structure is proposed to extract features from problems and map them to action probability distributions by policy network. To optimize the scheduling policy, proximal policy optimization (PPO) is applied to train the model end-to-end. Computational results on benchmark instances show that the RL-GNN algorithm achieves competitive performance compared with some widely used methods.     

A heuristic algorithm for the distributed and flexible job-shop scheduling problem

The distributed manufacturing takes place in a multi-factory environment including several factories, which may be geographically distributed in different locations, or in a multi-cell environment including several independent manufacturing cells located in the same plant. Each factory/cell is capable of manufacturing a variety of product types. An important issue in dealing with the production in this decentralized manner is the scheduling of manufacturing operations of products (jobs) in the distributed manufacturing system. In this paper, we study the distributed and flexible job-shop scheduling problem (DFJSP) which involves the scheduling of jobs (products) in a distributed manufacturing environment, under the assumption that the shop floor of each factory/cell is configured as a flexible job shop. A fast heuristic algorithm based on a constructive procedure is developed to obtain good quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic is computationally efficient and promising for practical problems.     

A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem

In this paper, a discrete artificial bee colony (DABC) algorithm is proposed to solve the lot-streaming flow shop scheduling problem with the criterion of total weighted earliness and tardiness penalties under both the idling and no-idling cases. Unlike the original ABC algorithm, the proposed DABC algorithm represents a food source as a discrete job permutation and applies discrete operators to generate new neighboring food sources for the employed bees, onlookers and scouts. An efficient initialization scheme, which is based on the earliest due date (EDD), the smallest slack time on the last machine (LSL) and the smallest overall slack time (OSL) rules, is presented to construct the initial population with certain quality and diversity. In addition, a self adaptive strategy for generating neighboring food sources based on insert and swap operators is developed to enable the DABC algorithm to work on discrete/combinatorial spaces. Furthermore, a simple but effective local search approach is embedded in the proposed DABC algorithm to enhance the local intensification capability. Through the analysis of experimental results, the highly effective performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature.     

Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling

Process planning and scheduling are two key sub-functions in the manufacturing system. Traditionally, process planning and scheduling were regarded as the separate tasks to perform sequentially. Recently, a significant trend is to integrate process planning and scheduling more tightly to achieve greater performance and higher productivity of the manufacturing system. Because of the complementarity of process planning and scheduling, and the multiple objectives requirement from the real-world production, this research focuses on the multi-objective integrated process planning and scheduling (IPPS) problem. In this research, the Nash equilibrium in game theory based approach has been used to deal with the multiple objectives. And a hybrid algorithm has been developed to optimize the IPPS problem. Experimental studies have been used to test the performance of the proposed approach. The results show that the developed approach is a promising and very effective method on the research of the multi-objective IPPS problem.     

作业车间环境下的采购-生产-配送联合调度模型

针对生产-配送联合调度（IPDS）模型较少考虑复杂生产环境以及采购环节的问题,建立了在作业车间环境下,以最小化订单完成时间为目标的采购-生产-配送联合调度（IPPDS）模型,并采用改进的动态人工蜂群（DABC）算法进行求解。根据IPPDS问题的特征,首先,采用二维实数矩阵的编码方式,实现任务（加工与运输）与资源（设备与车辆）的匹配关系;其次,采用基于工艺过程的解码方式,并在解码过程中针对不同任务设计了满足约束条件的方法,来保证解码方案的可行性;最后,在算法过程中设计了引领蜂与跟随蜂的动态协调机制和局部启发式信息。通过实验给出DABC适当的参数区间,对比实验结果表明,IPPDS策略相较于分段调度和IPDS策略,调度时间分别缩短了35.59%和30.95%;DABC相较于人工蜂群（ABC）算法求解效果平均提升了2.54%,相对于改进的遗传算法（AGA）求解效果平均提升了6.99%。因此,IPPDS策略能更快速地满足客户需求,而DABC算法既减少需设置的参数,又具有良好的探索和开发能力。