Showing papers on "Job shop scheduling published in 2021"

A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments

Abstract: Efficient task scheduling is considered as one of the main critical challenges in cloud computing. Task scheduling is an NP-complete problem, so finding the best solution is challenging, particularly for large task sizes. In the cloud computing environment, several tasks may need to be efficiently scheduled on various virtual machines by minimizing makespan and simultaneously maximizing resource utilization. We present a novel hybrid antlion optimization algorithm with elite-based differential evolution for solving multi-objective task scheduling problems in cloud computing environments. In the proposed method, which we refer to as MALO, the multi-objective nature of the problem derives from the need to simultaneously minimize makespan while maximizing resource utilization. The antlion optimization algorithm was enhanced by utilizing elite-based differential evolution as a local search technique to improve its exploitation ability and to avoid getting trapped in local optima. Two experimental series were conducted on synthetic and real trace datasets using the CloudSim tool kit. The results revealed that MALO outperformed other well-known optimization algorithms. MALO converged faster than the other approaches for larger search spaces, making it suitable for large scheduling problems. Finally, the results were analyzed using statistical t-tests, which showed that MALO obtained a significant improvement in the results.

A Self-Adaptive Differential Evolution Algorithm for Scheduling a Single Batch-Processing Machine With Arbitrary Job Sizes and Release Times

Abstract: Batch-processing machines (BPMs) can process a number of jobs at a time, which can be found in many industrial systems. This article considers a single BPM scheduling problem with unequal release times and job sizes. The goal is to assign jobs into batches without breaking the machine capacity constraint and then sort the batches to minimize the makespan. A self-adaptive differential evolution algorithm is developed for addressing the problem. In our proposed algorithm, mutation operators are adaptively chosen based on their historical performances. Also, control parameter values are adaptively determined based on their historical performances. Our proposed algorithm is compared to CPLEX, existing metaheuristics for this problem and conventional differential evolution algorithms through comprehensive experiments. The experimental results demonstrate that our proposed self-adaptive algorithm is more effective than other algorithms for this scheduling problem.

Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Abstract: Group scheduling problems have attracted much attention owing to their many practical applications. This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time, release time, and due time. It is originated from an important industrial process, i.e., wire rod and bar rolling process in steel production systems. Two objective functions, i.e., the number of late jobs and total setup time, are minimized. A mixed integer linear program is established to describe the problem. To obtain its Pareto solutions, we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods, i.e., an insertion-based local search and an iterated greedy algorithm. The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers. Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

Abstract: This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

A Two-Stage Cooperative Evolutionary Algorithm With Problem-Specific Knowledge for Energy-Efficient Scheduling of No-Wait Flow-Shop Problem

Abstract: Green scheduling in the manufacturing industry has attracted increasing attention in academic research and industrial applications with a focus on energy saving. As a typical scheduling problem, the no-wait flow-shop scheduling has been extensively studied due to its wide industrial applications. However, energy consumption is usually ignored in the study of typical scheduling problems. In this article, a two-stage cooperative evolutionary algorithm with problem-specific knowledge called TS-CEA is proposed to address energy-efficient scheduling of the no-wait flow-shop problem (EENWFSP) with the criteria of minimizing both makespan and total energy consumption. In TS-CEA, two constructive heuristics are designed to generate a desirable initial solution after analyzing the properties of the problem. In the first stage of TS-CEA, an iterative local search strategy (ILS) is employed to explore potential extreme solutions. Moreover, a hybrid neighborhood structure is designed to improve the quality of the solution. In the second stage of TS-CEA, a mutation strategy based on critical path knowledge is proposed to extend the extreme solutions to the Pareto front. Moreover, a co-evolutionary closed-loop system is generated with ILS and mutation strategies in the iteration process. Numerical results demonstrate the effectiveness and efficiency of TS-CEA in solving the EENWFSP.

Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-Based Fog Computing Applications

Abstract: To improve the quality of service (QoS) needed by several applications areas, the Internet of Things (IoT) tasks are offloaded into the fog computing instead of the cloud. However, the availability of ongoing energy heads for fog computing servers is one of the constraints for IoT applications because transmitting the huge quantity of the data generated using IoT devices will produce network bandwidth overhead and slow down the responsive time of the statements analyzed. In this article, an energy-aware model basis on the marine predators algorithm (MPA) is proposed for tackling the task scheduling in fog computing (TSFC) to improve the QoSs required by users. In addition to the standard MPA, we proposed the other two versions. The first version is called modified MPA (MMPA), which will modify MPA to improve their exploitation capability by using the last updated positions instead of the last best one. The second one will improve MMPA by the ranking strategy based reinitialization and mutation toward the best, in addition to reinitializing, the half population randomly after a predefined number of iterations to get rid of local optima and mutated the last half toward the best-so-far solution. Accordingly, MPA is proposed to solve the continuous one, whereas the TSFC is considered a discrete one, so the normalization and scaling phase will be used to convert the standard MPA into a discrete one. The three versions are proposed with some other metaheuristic algorithms and genetic algorithms based on various performance metrics such as energy consumption, makespan, flow time, and carbon dioxide emission rate. The improved MMPA could outperform all the other algorithms and the other two versions.

Robust Optimal Energy Management of a Residential Microgrid Under Uncertainties on Demand and Renewable Power Generation

Abstract: Smart microgrids are experiencing an increasing growth due to their economic, social, and environmental benefits. However, the inherent intermittency of renewable energy sources (RESs) and users’ behavior lead to significant uncertainty, which implies important challenges on the system design. Facing this issue, this article proposes a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each owning individual RESs, noncontrollable loads (NCLs), energy- and comfort-based CLs, and individual plug-in electric vehicles (PEVs). Moreover, users share a number of RESs and an energy storage system (ESS). We assume that the microgrid can buy/sell energy from/to the grid subject to quadratic/linear dynamic pricing functions. The objective of scheduling is minimizing the expected energy cost while satisfying device/comfort/contractual constraints, including feasibility constraints on energy transfer between users and the grid under RES generation and users’ demand uncertainties. To this aim, first, we formulate a min–max robust problem to obtain the optimal CLs scheduling and charging/discharging strategies of the ESS and PEVs. Then, based on the duality theory for multi-objective optimization, we transform the min–max problem into a mixed-integer quadratic programming problem to solve the equivalent robust counterpart of the scheduling problem effectively. We deal with the conservativeness of the proposed approach for different scenarios and quantify the effects of the budget of uncertainty on the cost saving, the peak-to-average ratio, and the constraints’ violation rate. We validate the effectiveness of the method on a simulated case study and we compare the results with a related robust approach. Note to Practitioners —This article is motivated by the emerging need for intelligent demand-side management (DSM) approaches in smart microgrids in the presence of both power generation and demand uncertainties. The proposed robust energy scheduling strategy allows the decision maker (i.e., the energy manager of the microgrid) to make a satisfactory tradeoff between the users’ payment and constraints’ violation rate considering the energy cost saving, the system technical limitations and the users’ comfort by adjusting the values of the budget of uncertainty. The proposed framework is generic and flexible as it can be applied to different structures of microgrids considering various types of uncertainties in energy generation or demand.

Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends

Abstract: Cloud computing is a recently looming-evoked paradigm, the aim of which is to provide on-demand, pay-as-you-go, internet-based access to shared computing resources (hardware and software) in a metered, self-service, dynamically scalable fashion. A related hot topic at the moment is task scheduling, which is well known for delivering critical cloud service performance. However, the dilemmas of resources being underutilized (underloaded) and overutilized (overloaded) may arise as a result of improper scheduling, which in turn leads to either wastage of cloud resources or degradation in service performance, respectively. Thus, the idea of incorporating meta-heuristic algorithms into task scheduling emerged in order to efficiently distribute complex and diverse incoming tasks (cloudlets) across available limited resources, within a reasonable time. Meta-heuristic techniques have proven very capable of solving scheduling problems, which is fulfilled herein from a cloud perspective by first providing a brief on traditional and heuristic scheduling methods before diving deeply into the most popular meta-heuristics for cloud task scheduling followed by a detailed systematic review featuring a novel taxonomy of those techniques, along with their advantages and limitations. More specifically, in this study, the basic concepts of cloud task scheduling are addressed smoothly, as well as diverse swarm, evolutionary, physical, emerging, and hybrid meta-heuristic scheduling techniques are categorized as per the nature of the scheduling problem (i.e., single- or multi-objective), the primary objective of scheduling, task-resource mapping scheme, and scheduling constraint. Armed with these methods, some of the most recent relevant literature are surveyed, and insights into the identification of existing challenges are presented, along with a trail to potential solutions. Furthermore, guidelines to future research directions drawn from recently emerging trends are outlined, which should definitely contribute to assisting current researchers and practitioners as well as pave the way for newbies excited about cloud task scheduling to pursue their own glory in the field.

Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling

Abstract: Dynamic flexible job-shop scheduling (DFJSS) is a challenging combinational optimization problem that takes the dynamic environment into account. Genetic programming hyperheuristics (GPHH) have been widely used to evolve scheduling heuristics for job-shop scheduling. A proper selection of the terminal set is a critical factor for the success of GPHH. However, there is a wide range of features that can capture different characteristics of the job-shop state. Moreover, the importance of a feature is unclear from one scenario to another. The irrelevant and redundant features may lead to performance limitations. Feature selection is an important task to select relevant and complementary features. However, little work has considered feature selection in GPHH for DFJSS. In this article, a novel two-stage GPHH framework with feature selection is designed to evolve scheduling heuristics only with the selected features for DFJSS automatically. Meanwhile, individual adaptation strategies are proposed to utilize the information of both the selected features and the investigated individuals during the feature selection process. The results show that the proposed algorithm can successfully achieve more interpretable scheduling heuristics with fewer unique features and smaller sizes. In addition, the proposed algorithm can reach comparable scheduling heuristic quality with much shorter training time.

Reinforcement Learning for Real-Time Pricing and Scheduling Control in EV Charging Stations

Abstract: This article proposes a reinforcement-learning (RL) approach for optimizing charging scheduling and pricing strategies that maximize the system objective of a public electric vehicle (EV) charging station. The proposed algorithm is “online” in the sense that the charging and pricing decisions made at each time depend only on the observation of past events, and is “model-free” in the sense that the algorithm does not rely on any assumed stochastic models of uncertain events. To cope with the challenge arising from the time-varying continuous state and action spaces in the RL problem, we first show that it suffices to optimize the total charging rates to fulfill the charging requests before departure times. Then, we propose a feature-based linear function approximator for the state–value function to further enhance the efficiency and generalization ability of the proposed algorithm. Through numerical simulations with real-world data, we show that the proposed RL algorithm achieves on average 138.5% higher charging-station profit than representative benchmark algorithms.

Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors

Abstract: In today's competitive environment, it is essential to design a flexible-responsive manufacturing system with automatic material handling systems. In this study, a fuzzy Mixed Integer Linear Programming (MILP) model is designed for Cell Formation Problem (CFP) including the scheduling of parts within cells in a Cellular Manufacturing System (CMS) where several Automated Guided Vehicles (AGVs) are in charge of transferring the exceptional parts. Notably, using these AGVs in CMS can be challenging from the perspective of mathematical modeling due to consideration of AGVs’ collision as well as parts pickup/delivery. This paper tries to investigate the role of AGVs and human factors as indispensable components of automation systems in the cell formation and scheduling of parts under fuzzy processing time. The proposed objective function includes minimizing the makespan and inter-cellular movements of parts. Due to the NP-hardness of the problem, a hybrid Genetic Algorithm (GA/heuristic) and a Whale Optimization Algorithm (WOA) are developed. The experimental results reveal that our proposed algorithms have a high performance compared to CPLEX and other two well-known algorithms, i.e., Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), in terms of computational efficiency and accuracy. Finally, WOA stands out as the best algorithm to solve the problem.

Explicit Evolutionary Multitasking for Combinatorial Optimization: A Case Study on Capacitated Vehicle Routing Problem

Abstract: Recently, evolutionary multitasking (EMT) has been proposed in the field of evolutionary computation as a new search paradigm, for solving multiple optimization tasks simultaneously. By sharing useful traits found along the evolutionary search process across different optimization tasks, the optimization performance on each task could be enhanced. The autoencoding-based EMT is a recently proposed EMT algorithm. In contrast to most existing EMT algorithms, which conduct knowledge transfer across tasks implicitly via crossover, it intends to perform knowledge transfer explicitly among tasks in the form of task solutions, which enables the employment of task-specific search mechanisms for different optimization tasks in EMT. However, the autoencoding-based explicit EMT can only work on continuous optimization problems. It will fail on combinatorial optimization problems, which widely exist in real-world applications, such as scheduling problem, routing problem, and assignment problem. To the best of our knowledge, there is no existing effort working on explicit EMT for combinatorial optimization problems. Taking this cue, in this article, we thus embark on a study toward explicit EMT for combinatorial optimization. In particular, by using vehicle routing as an illustrative combinatorial optimization problem, the proposed explicit EMT algorithm (EEMTA) mainly contains a weighted $l_{1}$ -norm-regularized learning process for capturing the transfer mapping, and a solution-based knowledge transfer process across vehicle routing problems (VRPs). To evaluate the efficacy of the proposed EEMTA, comprehensive empirical studies have been conducted with the commonly used vehicle routing benchmarks in multitasking environment, against both the state-of-the-art EMT algorithm and the traditional single-task evolutionary solvers. Finally, a real-world combinatorial optimization application, that is, the package delivery problem (PDP), is also presented to further confirm the efficacy of the proposed algorithm.

Resource Scheduling in Edge Computing: A Survey

Abstract: With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the surging demand for data communications and computing calls for the emerging edge computing paradigm. By moving the services and functions located in the cloud to the proximity of users, edge computing can provide powerful communication, storage, networking, and communication capacity. The resource scheduling in edge computing, which is the key to the success of edge computing systems, has attracted increasing research interests. In this paper, we survey the state-of-the-art research findings to know the research progress in this field. Specifically, we present the architecture of edge computing, under which different collaborative manners for resource scheduling are discussed. Particularly, we introduce a unified model before summarizing the current works on resource scheduling from three research issues, including computation offloading, resource allocation, and resource provisioning. Based on two modes of operation, i.e., centralized and distributed modes, different techniques for resource scheduling are discussed and compared. Also, we summarize the main performance indicators based on the surveyed literature. To shed light on the significance of resource scheduling in real-world scenarios, we discuss several typical application scenarios involved in the research of resource scheduling in edge computing. Finally, we highlight some open research challenges yet to be addressed and outline several open issues as the future research direction.

Improved Artificial Immune System Algorithm for Type-2 Fuzzy Flexible Job Shop Scheduling Problem

Abstract: In practical applications, particularly in flexible manufacturing systems, there is a high level of uncertainty. A type-2 fuzzy logic system (T2FS) has several parameters and an enhanced ability to handle high levels of uncertainty. This article proposes an improved artificial immune system (IAIS) algorithm to solve a special case of the flexible job shop scheduling problem (FJSP), where the processing time of each job is a nonsymmetric triangular interval T2FS (IT2FS) value. First, a novel affinity calculation method considering the IT2FS values is developed. Then, four problem-specific initialization heuristics are designed to enhance both quality and diversity. To enhance the exploitation abilities, six local search approaches are conducted for the routing and scheduling vectors, respectively. Next, a simulated annealing method is embedded to accept antibodies with low affinity, which can enhance the exploration abilities of the algorithm. Moreover, a novel population diversity heuristic is presented to eliminate antibodies with high crowding values. Five efficient algorithms are selected for a detailed comparison, and the simulation results demonstrate that the proposed IAIS algorithm is effective for IT2FS FJSPs.

Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in SAGIN

Abstract: In this article, we investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the considered scenario, an unmanned aerial vehicle (UAV) collects computing tasks from IoT devices and then makes online offloading decisions, in which the tasks can be processed at the UAV or offloaded to the nearby base station or the remote satellite. Our objective is to design a task scheduling policy that minimizes offloading and computing delay of all tasks given the UAV energy capacity constraint. To this end, we first formulate the online scheduling problem as an energy-constrained Markov decision process (MDP). Then, considering the task arrival dynamics, we develop a novel deep risk-sensitive reinforcement learning algorithm. Specifically, the algorithm evaluates the risk, which measures the energy consumption that exceeds the constraint, for each state and searches the optimal parameter weighing the minimization of delay and risk while learning the optimal policy. Extensive simulation results demonstrate that the proposed algorithm can reduce the task processing delay by up to 30% compared to probabilistic configuration methods while satisfying the UAV energy capacity constraint.

CPS-Based Self-Adaptive Collaborative Control for Smart Production-Logistics Systems

Abstract: Discrete manufacturing systems are characterized by dynamics and uncertainty of operations and behavior due to exceptions in production-logistics synchronization. To deal with this problem, a self-adaptive collaborative control (SCC) mode is proposed for smart production-logistics systems to enhance the capability of intelligence, flexibility, and resilience. By leveraging cyber-physical systems (CPSs) and industrial Internet of Things (IIoT), real-time status data are collected and processed to perform decision making and optimization. Hybrid automata is used to model the dynamic behavior of physical manufacturing resources, such as machines and vehicles in shop floors. Three levels of collaborative control granularity, including nodal SCC, local SCC, and global SCC, are introduced to address different degrees of exceptions. Collaborative optimization problems are solved using analytical target cascading (ATC). A proof of concept simulation based on a Chinese aero-engine manufacturer validates the applicability and efficiency of the proposed method, showing reductions in waiting time, makespan, and energy consumption with reasonable computational time. This article potentially enables manufacturers to implement CPS and IIoT in manufacturing environments and build up smart, flexible, and resilient production-logistics systems.

Iterated Greedy Algorithms for Flow-Shop Scheduling Problems: A Tutorial

Abstract: An iterated greedy algorithm (IGA) is a simple and powerful heuristic algorithm. It is widely used to solve flow-shop scheduling problems (FSPs), an important branch of production scheduling problems. IGA was first developed to solve an FSP in 2007. Since then, various FSPs have been tackled by using IGA-based methods, including basic IGA, its variants, and hybrid algorithms with IGA integrated. Up until now, over 100 articles related to this field have been published. However, to the best of our knowledge, there is no existing tutorial or review paper of IGA. Thus, we focus on FSPs and provide a tutorial and comprehensive literature review of IGA-based methods. First, we introduce a framework of basic IGA and give an example to clearly show its procedure. To help researchers and engineers learn and apply IGA to their FSPs, we provide an open platform to collect and share related materials. Then, we make classifications of the solved FSPs according to their scheduling scenarios, objective functions, and constraints. Next, we classify and introduce the specific methods and strategies used in each phase of IGA for FSPs. Besides, we summarize IGA variants and hybrid algorithms with IGA integrated, respectively. Finally, we discuss the current IGA-based methods and already-solved FSP instances, as well as some important future research directions according to their deficiency and open issues.

Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling

Abstract: Dynamic flexible job shop scheduling (JSS) is an important combinatorial optimization problem with complex routing and sequencing decisions under dynamic environments. Genetic programming (GP), as a hyperheuristic approach, has been successfully applied to evolve scheduling heuristics for JSS. However, its training process is time consuming, and it faces the retraining problem once the characteristics of job shop scenarios vary. It is known that multitask learning is a promising paradigm for solving multiple tasks simultaneously by sharing knowledge among the tasks. To improve the training efficiency and effectiveness, this article proposes a novel surrogate-assisted evolutionary multitask algorithm via GP to share useful knowledge between different scheduling tasks. Specifically, we employ the phenotypic characterization for measuring the behaviors of scheduling rules and building a surrogate for each task accordingly. The built surrogates are used not only to improve the efficiency of solving each single task but also for knowledge transfer in multitask learning with a large number of promising individuals. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for all scenarios. In addition, the proposed algorithm manages to solve multiple tasks collaboratively in terms of the evolved scheduling heuristics for different tasks in a multitask scenario.

A distributed heterogeneous permutation flowshop scheduling problem with lot-streaming and carryover sequence-dependent setup time

Abstract: We solve a distributed permutation flowshop scheduling problem (DPFSP) with non-identical factories, which is also referred to as the distributed heterogeneous permutation flowshop scheduling problem (DHPFSP). Stemming from the practical demands, we introduce the lot-streaming and carryover sequence-dependent setup time into our problem, both of which have not been previously studied under the DPFSP environment. The objective is to minimize the makespan among factories. To address this problem, we first present a mixed integer linear model. Next, we develop five constructive heuristics to find high-quality solutions in a timely manner. Moreover, we propose an enhanced artificial bee colony algorithm (NEABC). In the NEABC, an initialization method with strong intensification is proposed to generate promising swarm. Then, a collaboration mechanism among individuals is presented in the onlooker bee stage to improve the search capability. Besides, a restart strategy is designed in the scout bee stage with the consideration of the special onlooker bee stage of the algorithm. Comprehensive experiments based on instances with a wide range of scales are carried out and the results prove the validity of presented algorithms for the problem under study.

An effective multi-start iterated greedy algorithm to minimize makespan for the distributed permutation flowshop scheduling problem with preventive maintenance

Abstract: In recent years, distributed scheduling problems have been well studied for their close connection with multi-factory production networks. However, the maintenance operations that are commonly carried out on a system to restore it to a specific state are seldom taken into consideration. In this paper, we study a distributed permutation flowshop scheduling problem with preventive maintenance operation (PM/DPFSP). A multi-start iterated greedy (MSIG) algorithm is proposed to minimize the makespan. An improved heuristic is presented for the initialization and re-initialization by adding a dropout operation to NEH2 to generate solutions with a high level of quality and disperstiveness. A destruction phase with the tournament selection and a construction phase with an enhanced strategy are introduced to avoid local optima. A local search based on three effective operators is integrated into the MSIG to reinforce local neighborhood solution exploitation. In addition, a restart strategy is adpoted if a solution has not been improved in a certain number of consecutive iterations. We conducted extensive experiments to test the performance of the presented MSIG. The computational results indicate that the presented MSIG has many promising advantages in solving the PM/DPFSP under consideration.

An Effective Cooperative Co-Evolutionary Algorithm for Distributed Flowshop Group Scheduling Problems.

Abstract: This article addresses a novel scheduling problem, a distributed flowshop group scheduling problem, which has important applications in modern manufacturing systems. The problem considers how to arrange a variety of jobs subject to group constraints at a number of identical manufacturing cellulars, each one with a flowshop structure, with the objective of minimizing makespan. We explore the problem-specific knowledge and present a mixed-integer linear programming model, a counterintuitive paradox, and two suites of accelerations to save computational efforts. Due to the complexity of the problem, we consider a decomposition strategy and propose a cooperative co-evolutionary algorithm (CCEA) with a novel collaboration model and a reinitialization scheme. A comprehensive and thorough computational and statistical campaign is carried out. The results show that the proposed collaboration model and reinitialization scheme are very effective. The proposed CCEA outperforms a number of metaheuristics adapted from closely related scheduling problems in the literature by a significantly considerable margin.

Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints

Abstract: The rise of multi-cloud systems has been spurred. For safety-critical missions, it is important to guarantee their security and reliability. To address trust constraints in a heterogeneous multi-cloud environment, this work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints. The method is divided into two phases for task scheduling. The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance, security, and reliability in a multi-cloud environment; the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time. The proposed algorithm, the modified cuckoo search (MCS), hybrid chaotic particle search (HCPS), modified artificial bee colony (MABC), max-min, and min-min algorithms are implemented in CloudSim to create simulations. The simulations and experimental results show that our proposed method achieves shorter makespan, lower cost, higher resource utilization, and better trade-off between time and economic cost. It is more stable and efficient.

Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing

Abstract: Edge computing (EC) has recently emerged as a promising paradigm that supports resource-hungry Internet of Things (IoT) applications with low latency services at the network edge. However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD). The problem is formulated as a Markov decision process (MDP) for which the state, action, state transition, and reward are designed. We leverage deep reinforcement learning (DRL) to solve both time scheduling (i.e., the task execution order) and resource allocation (i.e., which VM the task is assigned to), considering the diversity of the tasks and the heterogeneity of available resources. A policy-based REINFORCE algorithm is proposed for the task scheduling problem, and a fully-connected neural network (FCN) is utilized to extract the features. Simulation results show that the proposed DRL-based task scheduling algorithm outperforms the existing methods in the literature in terms of the average task satisfaction degree and success ratio.

A discrete artificial bee colony algorithm for distributed hybrid flowshop scheduling problem with sequence-dependent setup times

Abstract: With the development of global and decentralised economies, distributed production emerges in large manufacturing firms. A distributed production model exists with hybrid flowshops. As an extension...