Showing papers on "Job shop scheduling published in 2020"

A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management

Abstract: This paper proposes a novel framework for home energy management (HEM) based on reinforcement learning in achieving efficient home-based demand response (DR). The concerned hour-ahead energy consumption scheduling problem is duly formulated as a finite Markov decision process (FMDP) with discrete time steps. To tackle this problem, a data-driven method based on neural network (NN) and ${Q}$ -learning algorithm is developed, which achieves superior performance on cost-effective schedules for HEM system. Specifically, real data of electricity price and solar photovoltaic (PV) generation are timely processed for uncertainty prediction by extreme learning machine (ELM) in the rolling time windows. The scheduling decisions of the household appliances and electric vehicles (EVs) can be subsequently obtained through the newly developed framework, of which the objective is dual, i.e., to minimize the electricity bill as well as the DR induced dissatisfaction. Simulations are performed on a residential house level with multiple home appliances, an EV and several PV panels. The test results demonstrate the effectiveness of the proposed data-driven based HEM framework.

Solving Fuzzy Job-Shop Scheduling Problem Using DE Algorithm Improved by a Selection Mechanism

Abstract: The emergence of fuzzy sets makes job-shop scheduling problem (JSSP) become better aligned with the reality. This article addresses the JSSP with fuzzy execution time and fuzzy completion time (FJSSP). We choose the classic differential evolution (DE) algorithm as the basic optimization framework. The advantage of the DE algorithm is that it uses a special evolutionary strategy of difference vector sets to carry out mutation operation. However, DE is not very effective in solving some instances of FJSSP. Therefore, we propose a novel selection mechanism augmenting the generic DE algorithm (NSODE) to achieve better optimization results. The proposed selection operator adopted in this article aims at a temporary retention of all children generated by the parent generation, and then selecting N better solutions as the new individuals from N parents and N children. Various examples of fuzzy shop scheduling problems are experimented with to test the performance of the improved DE algorithm. The NSODE algorithm is compared with a variety of existing algorithms such as ant colony optimization, particle swarm optimization, and cuckoo search. Experimental results show that the NSODE can obtain superior feasible solutions compared with solutions produced by several algorithms reported in the literature.

A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems

Abstract: Nowadays, with the development of cyber-physical systems (CPS), there are an increasing amount of applications deployed in the CPS to connect cyber space with physical world better and closer than ever. Furthermore, the cloud-based CPS bring massive computing and storage resource for CPS, which enables a wide range of applications. Meanwhile, due to the explosive expansion of applications deployed on the CPS, the energy consumption of the cloud-based CPS has received wide concern. To improve the energy efficiency in the cloud environment, the virtualized technology is employed to manage the resources, and the applications are generally hosted by virtual machines (VMs). However, it remains challenging to meet the Quality-of-Service (QoS) requirements. In view of this challenge, a QoS-aware VM scheduling method for energy conservation, named QVMS, in cloud-based CPS is designed. Technically, our scheduling problem is formalized as a standard multi-objective problem first. Then, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is adopted to search the optimal VM migration solutions. Besides, SAW (Simple Additive Weighting) and MCDM (Multiple Criteria Decision Making) are employed to select the most optimal scheduling strategy. Finally, simulations and experiments are conducted to verify the effectiveness of our proposed method.

Deep-Reinforcement-Learning-Based Offloading Scheduling for Vehicular Edge Computing

Abstract: Vehicular edge computing (VEC) is a new computing paradigm that has great potential to enhance the capability of vehicle terminals (VTs) to support resource-hungry in-vehicle applications with low latency and high energy efficiency. In this article, we investigate an important computation offloading scheduling problem in a typical VEC scenario, where a VT traveling along an expressway intends to schedule its tasks waiting in the queue to minimize the long-term cost in terms of a tradeoff between task latency and energy consumption. Due to diverse task characteristics, dynamic wireless environment, and frequent handover events caused by vehicle movements, an optimal solution should take into account both where to schedule (i.e., local computation or offloading) and when to schedule (i.e., the order and time for execution) each task. To solve such a complicated stochastic optimization problem, we model it by a carefully designed Markov decision process (MDP) and resort to deep reinforcement learning (DRL) to deal with the enormous state space. Our DRL implementation is designed based on the state-of-the-art proximal policy optimization (PPO) algorithm. A parameter-shared network architecture combined with a convolutional neural network (CNN) is utilized to approximate both policy and value function, which can effectively extract representative features. A series of adjustments to the state and reward representations are taken to further improve the training efficiency. Extensive simulation experiments and comprehensive comparisons with six known baseline algorithms and their heuristic combinations clearly demonstrate the advantages of the proposed DRL-based offloading scheduling method.

Device Scheduling with Fast Convergence for Wireless Federated Learning

Abstract: Owing to the increasing need for massive data analysis and model training at the network edge, as well as the rising concerns about the data privacy, a new distributed training framework called federated learning (FL) has emerged. In each iteration of FL (called round), the edge devices update local models based on their own data and contribute to the global training by uploading the model updates via wireless channels. Due to the limited spectrum resources, only a portion of the devices can be scheduled in each round. While most of the existing work on scheduling focuses on the convergence of FL w.r.t. rounds, the convergence performance under a total training time budget is not yet explored. In this paper, a joint bandwidth allocation and scheduling problem is formulated to capture the long-term convergence performance of FL, and is solved by being decoupled into two sub-problems. For the bandwidth allocation sub-problem, the derived optimal solution suggests to allocate more bandwidth to the devices with worse channel conditions or weaker computation capabilities. For the device scheduling sub-problem, by revealing the trade-off between the number of rounds required to attain a certain model accuracy and the latency per round, a greedy policy is inspired, that continuously selects the device that consumes the least time in model updating until achieving a good trade-off between the learning efficiency and latency per round. The experiments show that the proposed policy outperforms other state-of-the-art scheduling policies, with the best achievable model accuracy under training time budgets.

Dynamic Group Learning Distributed Particle Swarm Optimization for Large-Scale Optimization and Its Application in Cloud Workflow Scheduling

Abstract: Cloud workflow scheduling is a significant topic in both commercial and industrial applications. However, the growing scale of workflow has made such a scheduling problem increasingly challenging. Many current algorithms often deal with small- or medium-scale problems (e.g., less than 1000 tasks) and face difficulties in providing satisfactory solutions when dealing with the large-scale problems, due to the curse of dimensionality. To this aim, this article proposes a dynamic group learning distributed particle swarm optimization (DGLDPSO) for large-scale optimization and extends it for the large-scale cloud workflow scheduling. DGLDPSO is efficient for large-scale optimization due to its following two advantages. First, the entire population is divided into many groups, and these groups are coevolved by using the master-slave multigroup distributed model, forming a distributed PSO (DPSO) to enhance the algorithm diversity. Second, a dynamic group learning (DGL) strategy is adopted for DPSO to balance diversity and convergence. When applied DGLDPSO into the large-scale cloud workflow scheduling, an adaptive renumber strategy (ARS) is further developed to make solutions relate to the resource characteristic and to make the searching behavior meaningful rather than aimless. Experiments are conducted on the large-scale benchmark functions set and the large-scale cloud workflow scheduling instances to further investigate the performance of DGLDPSO. The comparison results show that DGLDPSO is better than or at least comparable to other state-of-the-art large-scale optimization algorithms and workflow scheduling algorithms.

An Ensemble Discrete Differential Evolution for the Distributed Blocking Flowshop Scheduling with Minimizing Makespan Criterion

Abstract: The distributed blocking flowshop scheduling problem (DBFSP) plays an essential role in the manufacturing industry and has been proven to be as a strongly NP-hard problem In this paper, an ensemble discrete differential evolution (EDE) algorithm is proposed to solve the blocking flowshop scheduling problem with the minimization of the makespan in the distributed manufacturing environment In the EDE algorithm, the candidates are represented as discrete job permutations Two heuristics method and one random strategy are integrated to provide a set of desirable initial solution for the distributed environment The front delay, blocking time and idle time are considered in these heuristics methods The mutation, crossover and selection operators are redesigned to assist the EDE algorithm to execute in the discrete domain Meanwhile, an elitist retain strategy is introduced into the framework of EDE algorithm to balance the exploitation and exploration ability of the EDE algorithm The parameters of the EDE algorithm are calibrated by the design of experiments (DOE) method The computational results and comparisons demonstrated the efficiency and effectiveness of the EDE algorithm for the distributed blocking flowshop scheduling problem

A Knowledge-Based Cooperative Algorithm for Energy-Efficient Scheduling of Distributed Flow-Shop

Abstract: Facing increasingly serious ecological problems, sustainable development and green manufacturing have attracted much attention. Meanwhile, with the development of globalization, distributed manufacturing is becoming widespread. This paper addresses an energy-efficient scheduling of the distributed permutation flow-shop (EEDPFSP) with the criteria of minimizing both makespan and total energy consumption. Considering the distributed and multiobjective optimization complexity, a knowledge-based cooperative algorithm (KCA) is proposed to solve the EEDPFSP. First, a cooperative initialization scheme is presented with both extended energy-efficient Nawaz–Enscore–Ham heuristic and slowest allowable speed rule that are specially designed to produce good initial solutions with certain diversity. Second, several properties of the nondominated solutions are investigated based on the characteristics of the bi-objective problem, which are used to develop the knowledge-based search operators. Third, a cooperative search strategy of multiple operators is designed for the solutions with different characteristics to tradeoff two objectives. Fourth, a knowledge-based local intensification is used for exploiting better nondominated solutions sufficiently. Moreover, an energy saving method based on the critical path is used to further improve the performance. The effect of parameter setting on the KCA is investigated with the Taguchi method of design-of-experiment. Extensive computational tests and comparisons are carried out, which verify the effectiveness of the special designs of the KCA in solving the EEDPFSP.

Dependency-Aware Task Scheduling in Vehicular Edge Computing

Abstract: Vehicular edge computing (VEC) offers a new paradigm to improve vehicular services and augment the capabilities of vehicles. In this article, we study the problem of task scheduling in VEC, where multiple computation-intensive vehicular applications can be offloaded to roadside units (RSUs) and each application can be further divided into multiple tasks with task dependency. The tasks can be scheduled to different mobile-edge computing servers on RSUs for execution to minimize the average completion time of multiple applications. Considering the completion time constraint of each application and the processing dependency of multiple tasks belonging to the same application, we formulate the multiple tasks scheduling problem as an optimization problem that is NP-hard. To solve the optimization problem, we develop an efficient task scheduling algorithm. The basic idea is to prioritize multiple applications and prioritize multiple tasks so as to guarantee the completion time constraints of applications and the processing dependency requirements of tasks. The numerical results demonstrate that our proposed algorithm can significantly reduce the average completion time of multiple applications compared with benchmark algorithms.

Online scheduling via learned weights

Abstract: Online algorithms are a hallmark of worst case optimization under uncertainty. On the other hand, in practice, the input is often far from worst case, and has some predictable characteristics. A recent line of work has shown how to use machine learned predictions to circumvent strong lower bounds on competitive ratios in classic online problems such as ski rental and caching. We study how predictive techniques can be used to break through worst case barriers in online scheduling. The makespan minimization problem with restricted assignments is a classic problem in online scheduling theory. Worst case analysis of this problem gives Ω(log m) lower bounds on the competitive ratio in the online setting. We identify a robust quantity that can be predicted and then used to guide online algorithms to achieve better performance. Our predictions are compact in size, having dimension linear in the number of machines, and can be learned using standard off the shelf methods. The performance guarantees of our algorithms depend on the accuracy of the predictions, given predictions with error η, we show how to construct O(log η) competitive fractional assignments. We then give an online algorithm that rounds any fractional assignment into an integral schedule. Our algorithm is O((log log m)3)-competitive and we give a nearly matching Ω(log log m) lower bound for online rounding algorithms.1 Altogether, we give algorithms that, equipped with predictions with error η, achieve O(log η (log log m)3) competitive ratios, breaking the Ω(log m) lower bound even for moderately accurate predictions.

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Abstract: Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this article, for the first time, we apply the latest metaheuristics whale optimization algorithm (WOA) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called I mproved W OA for C loud task scheduling (IWC) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks.

Hybrid Artificial Bee Colony Algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs

Abstract: In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.

Scheduling Dual-Objective Stochastic Hybrid Flow Shop With Deteriorating Jobs via Bi-Population Evolutionary Algorithm

Abstract: Hybrid flow shop scheduling problems have gained an increasing attention in recent years because of its wide applications in real-world production systems. Most of the prior studies assume that the processing time of jobs is deterministic and constant. In practice, jobs’ processing time is usually difficult to be exactly known in advance and can be influenced by many factors, e.g., machines’ abrasion and jobs’ feature, thereby leading to their uncertain and variable processing time. In this paper, a dual-objective stochastic hybrid flow shop deteriorating scheduling problem is presented with the goal to minimize makespan and total tardiness. In the formulated problem, the normal processing time of jobs follows a known stochastic distribution, and their actual processing time is a linear function of their start time. In order to solve it effectively, this paper develops a hybrid multiobjective optimization algorithm that maintains two populations executing the global search in the whole solution space and the local search in promising regions, respectively. An information sharing mechanism and resource allocating method are designed to enhance its exploration and exploitation ability. The simulation experiments are carried out on a set of instances, and several classical algorithms are chosen as its peers for comparison. The results demonstrate that the proposed algorithm has a great advantage in dealing with the investigated problem.

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

Abstract: Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance. In this paper, we propose to automatically learn PDRs via an end-to-end deep reinforcement learning agent. We exploit the disjunctive graph representation of JSSP, and propose a Graph Neural Network based scheme to embed the states encountered during solving. The resulting policy network is size-agnostic, effectively enabling generalization on large-scale instances. Experiments show that the agent can learn high-quality PDRs from scratch with elementary raw features, and demonstrates strong performance against the best existing PDRs. The learned policies also perform well on much larger instances that are unseen in training.

Dynamic Resource Provisioning With Fault Tolerance for Data-Intensive Meteorological Workflows in Cloud

Abstract: Cloud computing is a formidable paradigm to provide resources for handling the services from Industrial Internet of Things (IIoT), such as meteorological industry. Generally, the meteorological services, with complex interdependent logics, are modeled as workflows. When any of the computing nodes for hosting the meteorological workflows fail, all sorts of consequences (e.g., data loss, makespan enlargement, performance degradation, etc.) could arise. Thus recovering the failed tasks as well as optimizing the makespan and the load balance of the computing nodes is still a critical challenge. To address this challenge, a dynamic resource provisioning method (DRPM) with fault tolerance for the data-intensive meteorological workflows is proposed in this article. Technically, the Virtual Layer 2 (VL2) network topology is exploited to build meteorological cloud infrastructure. Then, the nondominated sorting genetic algorithm II (NSGA-II) is employed to minimize the makespan and improve the load balance. Finally, comprehensive experimental analysis of DRPM are proceeded.

GRP-HEFT: A Budget-Constrained Resource Provisioning Scheme for Workflow Scheduling in IaaS Clouds

Abstract: In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (Multi-Objective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.

Actor-critic deep reinforcement learning for solving job shop scheduling problems

Abstract: In the past decades, many optimization methods have been devised and applied to job shop scheduling problem (JSSP) to find the optimal solution. Many methods assumed that the scheduling results were applied to static environments, but the whole environments in the real world are always dynamic. Moreover, many unexpected events such as machine breakdowns and material problems may be present to adversely affect the initial job scheduling. This work views JSSP as a sequential decision making problem and proposes to use deep reinforcement learning to cope with this problem. The combination of deep learning and reinforcement learning avoids handcraft features as used in traditional reinforcement learning, and it is expected that the combination will make the whole learning phase more efficient. Our proposed model comprises actor network and critic network, both including convolution layers and fully connected layer. Actor network agent learns how to behave in different situations, while critic network helps agent evaluate the value of statement then return to actor network. This work proposes a parallel training method, combining asynchronous update as well as deep deterministic policy gradient (DDPG), to train the model. The whole network is trained with parallel training on a multi-agent environment and different simple dispatching rules are considered as actions. We evaluate our proposed model on more than ten instances that are present in a famous benchmark problem library - OR library. The evaluation results indicate that our method is comparative in static JSSP benchmark problems, and achieves a good balance between makespan and execution time in dynamic environments. Scheduling score of our method is 91.12% in static JSSP benchmark problems, and 80.78% in dynamic environments.

A Three-Stage Multiobjective Approach Based on Decomposition for an Energy-Efficient Hybrid Flow Shop Scheduling Problem

Abstract: This paper investigates an energy-efficient hybrid flowshop scheduling problem with the consideration of machines with different energy usage ratios, sequence-dependent setups, and machine-to-machine transportation operations. To minimize the makespan and total energy consumption simultaneously, a mixed-integer linear programming (MILP) model is developed. To solve this problem, a three-stage multiobjective approach based on decomposition (TMOA/D) is suggested, in which each solution is bound with a main weight vector and a set of its neighbors. Accordingly, a variable direction strategy is developed to ensure each solution along its main direction is thoroughly exploited and can jump to the neighboring directions using a proximity principle. To ensure an active schedule of arranging jobs to machines, a two-level solution representation is employed. In the first phase, each solution attempts to improve itself along its current weight vector through a developed neighborhood-based local search. In the second phase, the promising solutions are selected through the technique for order preference by similarity to an ideal solution. Then, they attempt to update themselves with a proposed global replacement strategy via incorporation with their closing solutions. In the third phase, a solution conducts a large perturbation when it goes through all its assigned weight vectors. Extensive experiments are conducted to test the performance of TMOA/D, and the results demonstrate that TMOA/D has a very competitive performance.