Makespan Reduction Research Articles

A multi-objective genetic algorithm based on two-stage reinforcement learning for green flexible shop scheduling problem considering machine speed

The consumption of energy and resources in the manufacturing industry has garnered significant attention due to the increasingly severe environmental issues. Green shop scheduling research is focused on optimizing economic and environmental indicators within the current manufacturing model. This paper specifically addresses the flexibility of job-shop scheduling problem considering machine speed (CMS-FJSP), as different machine speeds during the production process can impact energy and resource consumption. The objectives of this study include minimizing the makespan, total energy consumption, and tool wear. To tackle this problem, a multi-objective genetic algorithm that incorporates a two-stage reinforcement learning approach is proposed. In light of the characteristics of the problem, a three-layer encoding approach is suggested, which encompasses machine allocation, operation sequencing, and machine speed selection. Additionally, a decoding method that integrates energy-saving strategies is proposed to enhance the optimization process. To improve the quality of the population, three distinct initialization methods have been developed. Furthermore, a parameter adjustment strategy informed by two-stage reinforcement learning is introduced. This strategy incorporates a state set and action set tailored to the unique characteristics of two-stage reinforcement learning, alongside corresponding reward mechanisms. In 30 test cases, the proposed algorithm demonstrates superior uniformity and convergence compared to five classical algorithms. In a practical case within a hydraulic component production workshop conducted at a hydraulic component company, the proposed algorithm generates 26 scheduling schemes with different focuses, achieving a 14.39% reduction in makespan, a 2.13% decrease in energy consumption, and a 10.65% reduction in tool wear.

Modeling and scheduling a triply-constrained flow shop in biomanufacturing systems

The crude protein purification automated workstation has recently resolved the bottlenecks induced by manual operations, paving the way for high-throughput protein biomanufacturing. However, its three interacted constraints consisting of batch processing machines, limited buffer, and transportation present challenges for systematic scheduling. Here, we develop a triply-constrained flow shop model, enabling optimization in scheduling the crude protein purification automated workstation. A batching genetic algorithm is designed, where the flexible decoding resolves contradictions between the triple constraints, and the hybrid population initialization enhances performance by incorporating flow-shop heuristic and batching branch-and-bound. Computational experiments are conducted on 27 instances of varying problem scales ranging from small to large, demonstrating a notable 9.18 % reduction in makespan and enhanced stability when compared to three advanced meta-heuristics. Furthermore, the mechanism of how batching settings, including capacities and layouts, impact the makespan is revealed, offering managerial insights. This marks the first demonstration of modeling and scheduling crude protein purification automated workstations, signifying a significant advancement in biomanufacturing systems.

Integrated optimization of worker assignment, batch splitting and scheduling for a hybrid assembly line-seru production system

This paper studies an integrated optimization problem of worker assignment, batch splitting and scheduling for a hybrid assembly line-seru production system, which includes multiple serus and a short assembly line. The worker assignment involves assigning each multi-skilled worker to a seru or the assembly line, and assigning the worker tasks to perform. The batch splitting determines the number of subbatches split for each batch, the size of each subbatch, and in which seru the production of each subbatch is performed. The batch scheduling aims to optimize the process sequences of batches in serus and on the assembly line. For the integrated optimization problem, we propose a mixed-integer nonlinear programming model that minimizes the makespan. To solve large-scale instances of the problem, we develop a tailored hybrid variable neighborhood search algorithm with a four-layer solution coding scheme and four novel neighborhood structures. We validate the effectiveness of our model and solution algorithm on two sets of instances. The experimental results indicate that the proposed hybrid metaheuristic achieves a significant reduction in makespan, with an improvement of up to 20.94% compared to the solutions obtained by the Gurobi optimization solver within one hour. In addition, we demonstrate the advantage of optimized batch splitting by comparing it with no batch splitting and equal-size batch splitting strategies in the literature.

A novel hybrid Artificial Gorilla Troops Optimizer with Honey Badger Algorithm for solving cloud scheduling problem

Cloud computing has revolutionized the way a variety of ubiquitous computing resources are provided to users with ease and on a pay-per-usage basis. Task scheduling problem is an important challenge, which involves assigning resources to users’ Bag-of-Tasks applications in a way that maximizes either system provider or user performance or both. With the increase in system size and the number of applications, the Bag-of-Tasks scheduling (BoTS) problem becomes more complex due to the expansion of search space. Such a problem falls in the category of NP-hard optimization challenges, which are often effectively tackled by metaheuristics. However, standalone metaheuristics generally suffer from certain deficiencies which affect their searching efficiency resulting in deteriorated final performance. This paper aims to introduce an optimal hybrid metaheuristic algorithm by leveraging the strengths of both the Artificial Gorilla Troops Optimizer (GTO) and the Honey Badger Algorithm (HBA) to find an approximate scheduling solution for the BoTS problem. While the original GTO has demonstrated effectiveness since its inception, it possesses limitations, particularly in addressing composite and high-dimensional problems. To address these limitations, this paper proposes a novel approach by introducing a new updating equation inspired by the HBA, specifically designed to enhance the exploitation phase of the algorithm. Through this integration, the goal is to overcome the drawbacks of the GTO and improve its performance in solving complex optimization problems. The initial performance of the GTOHBA algorithm tested on standard CEC2017 and CEC2022 benchmarks shows significant performance improvement over the baseline metaheuristics. Later on, we applied the proposed GTOHBA on the BoTS problem using standard parallel workloads (CEA-Curie and HPC2N) to optimize makespan and energy objectives. The obtained outcomes of the proposed GTOHBA are compared to the scheduling techniques based on well-known metaheuristics under the same experimental conditions using standard statistical measures and box plots. In the case of CEA-Curie workloads, the GTOHBA produced makespan and energy consumption reduction in the range of 8.12–22.76% and 6.2–18.00%, respectively over the compared metaheuristics. Whereas for the HPC2N workloads, GTOHBA achieved 8.46–30.97% makespan reduction and 8.51–33.41% energy consumption reduction against the tested metaheuristics. In conclusion, the proposed hybrid metaheuristic algorithm provides a promising solution to the BoTS problem, that can enhance the performance and efficiency of cloud computing systems.

Powder bed fusion factory productivity increases using discrete event simulation and genetic algorithm

AbstractPowder bed fusion is importance is growing with uses across industries in both polymer and metallic components, particularly in mass individualization. However, due to the relatively slow mass deposition speed compared to conventional methods, scheduling and production planning play a crucial role in scaling up additive manufacturing productivity to higher volumes. This paper introduces a framework combining discrete event simulation and a genetic algorithm showing makespan improvement opportunities for multiple powder bed fusion factories varying workers, jobs and available equipment. The results show that bottlenecks move among workstations based on worker and capital equipment availability, which depend on the size of the facility indicating a resource-driven constraint for makespan. A makespan reduction of 78% is achieved in the simulation. This shows the trade-off of worker and capital equipment to achieve makespan improvements. The addition of personnel or equipment increases production with further gains achieved by scheduling optimization. Two levels of job demands are analyzed showing productivity gains of 45% makespan improvement when adding the first worker and additional savings with scheduling optimization using a genetic algorithm up to 11%. Most research on additive manufacturing production has focused on the quality of produced parts and printing technology rather than factory level management. This is the first application of this methodology to varying sizes of these potential factories. The method developed here will help decision-makers to determine the appropriate number of resources to meet their customer demand on time, additionally, finding the optimal route for jobs before starting the production process.

Improvement in task allocation for VM and reduction of Makespan in IaaS model for cloud computing

Improvement in task allocation for VM and reduction of Makespan in IaaS model for cloud computing

On the Performance of Malleable APGAS Programs and Batch Job Schedulers

Malleability—the ability for applications to dynamically adjust their resource allocations at runtime—presents great potential to enhance the efficiency and resource utilization of modern supercomputers. However, applications are rarely capable of growing and shrinking their number of nodes at runtime, and batch job schedulers provide only rudimentary support for such features. While numerous approaches have been proposed to enable application malleability, these typically focus on iterative computations and require complex code modifications. This amplifies the challenges for programmers, who already wrestle with the complexity of traditional MPI inter-node programming. Asynchronous Many-Task (AMT) programming presents a promising alternative. In AMT, computations are split into many fine-grained tasks, which are processed by workers. This makes transparent task relocation via the AMT runtime system possible, thus offering great potential for enabling efficient malleability. In this work, we propose an extension to an existing AMT system, namely APGAS for Java. We provide easy-to-use malleability programming abstractions, requiring only minor application code additions from programmers. Runtime adjustments, such as process initialization and termination, are automatically managed by our malleability extension. We validate our malleability extension by adapting a load balancing library handling multiple benchmarks. We show that both shrinking and growing operations cost low execution time overhead. In addition, we demonstrate compatibility with potential batch job schedulers by developing a prototype batch job scheduler that supports malleable jobs. Through extensive real-world job batches execution on up to 32 nodes, involving rigid, moldable, and malleable programs, we evaluate the impact of deploying malleable APGAS applications on supercomputers. Exploiting scheduling algorithms, such as FCFS, Backfilling, Easy-Backfilling, and one exploiting malleable jobs, the experimental results highlight a significant improvement regarding several metrics for malleable jobs. We show a 13.09% makespan reduction (the time needed to schedule and execute all jobs), a 19.86% increase in node utilization, and a 3.61% decrease in job turnaround time (the time a job takes from its submission to completion) when using 100% malleable job in combination with our prototype batch job scheduler compared to the best-performing scheduling algorithm with 100% rigid jobs.

A self-learning framework combining association rules and mathematical models to solve production scheduling programs

ABSTRACT Data-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies.

Large-Scale Multi-Robot Coverage Path Planning via Local Search

We study graph-based Multi-Robot Coverage Path Planning (MCPP) that aims to compute coverage paths for multiple robots to cover all vertices of a given 2D grid terrain graph G. Existing graph-based MCPP algorithms first compute a tree cover on G---a forest of multiple trees that cover all vertices---and then employ the Spanning Tree Coverage (STC) paradigm to generate coverage paths on the decomposed graph D of the terrain graph G by circumnavigating the edges of the computed trees, aiming to optimize the makespan (i.e., the maximum coverage path cost among all robots). In this paper, we take a different approach by exploring how to systematically search for good coverage paths directly on D. We introduce a new algorithmic framework, called LS-MCPP, which leverages a local search to operate directly on D. We propose a novel standalone paradigm, Extended-STC (ESTC), that extends STC to achieve complete coverage for MCPP on any decomposed graph, even those resulting from incomplete terrain graphs. Furthermore, we demonstrate how to integrate ESTC with three novel types of neighborhood operators into our framework to effectively guide its search process. Our extensive experiments demonstrate the effectiveness of LS-MCPP, consistently improving the initial solution returned by two state-of-the-art baseline algorithms that compute suboptimal tree covers on G, with a notable reduction in makespan by up to 35.7% and 30.3%, respectively. Moreover, LS-MCPP consistently matches or surpasses the results of optimal tree cover computation, achieving these outcomes with orders of magnitude faster runtime, thereby showcasing its significant benefits for large-scale real-world coverage tasks.

Federated deep reinforcement learning for dynamic job scheduling in cloud-edge collaborative manufacturing systems

The cloud-edge collaborative manufacturing system (CCMS) connects distributed factories to a cloud centre through cloud-edge collaborative communication, introducing both opportunities and challenges to conventional dynamic job scheduling. Enhancing each factory's scheduling performance by sharing general scheduling knowledge among heterogeneous factories under the consideration of data privacy protection remains challenging. To this end, this paper proposes to solve the dynamic job scheduling in the context of CCMS with a novel federated deep reinforcement learning (FDRL) approach. Within each factory, the scheduling objective involves minimising the makespan and energy consumption, accounting for machine warm-up procedures and real-time dynamics. To handle heterogeneous policy structures, we aggregate their hidden parameters through FDRL, with states, actions, and rewards designed to facilitate the aggregation. The two-phase algorithm, comprising iterative local training and global aggregation, trains the scheduling policies. Constraint items are introduced to the loss functions to smooth local training, and the global aggregation considers production scales and obtained objectives. The proposed approach enhances the solution quality and generalisation of each factory's scheduling policy without exposing original production data. Numerical experiments conducted on sixty scheduling instances validate the superiority of the proposed approach compared to twelve dynamic scheduling methods. Compared to independently trained DRL-based approaches, the proposed FDRL-based approach achieves up to an 8.9% reduction in makespan and a 22.3% decrease in energy consumption through knowledge sharing.

A learning and evolution-based intelligence algorithm for multi-objective heterogeneous cloud scheduling optimization

The multi-objective directed acyclic graph scheduling problem (MDAGSP) is prevalent in cloud scheduling systems, involving the selection, assignment, and execution of multiple tasks/jobs with complex coupling interdependencies. High-quality solutions can yield substantial economic benefits. However, prevailing methods face challenges in obtaining a set of superior solutions for MDAGSP, due to the multifaceted nature of its variables, objectives, constraints, and heterogeneity. Firstly, this paper formulates a three-objective MDAGSP that includes makespan, energy costs and revenue, to model cloud scheduling systems. Subsequently, we propose a composite algorithm consisting of a selection phase and an assignment phase to automatically generate an efficient scheduling policy for this model. During the selection phase, a graph convolutional neural network learns high-level features to extract complex dependencies between tasks. During the assignment phase, an adaptive evolutionary algorithm assigns tasks to the appropriate executors. Finally, a series of experiments are conducted to validate the model’s accuracy and assess the algorithm’s efficiency. Compared to heuristic approaches, the algorithm achieves at least a 20.1% makespan reduction and 3.17% revenue increase. Remarkably, the algorithm obtains at least an 8.86% reduction in energy costs over the eleven baselines. In conclusion, the proposed algorithm provides decision-makers with a global view of scheduling plan.

Energy hardware and workload aware job scheduling towards interconnected HPC environments

New HPC machines are getting close to the exascale. Power consumption for those machines has been increasing, and researchers are studying ways to reduce it. A second trend is HPC machines' growing complexity, with increasing heterogeneous hardware components and different clusters architectures cooperating in the same machine. We refer to these environments with the term heterogeneous multi-cluster environments. With the aim of optimizing performance and energy consumption in these environments, this paper proposes an Energy-Aware-Multi-Cluster (EAMC) job scheduling policy. EAMC-policy is able to optimize the scheduling and placement of jobs by predicting performance and energy consumption of arriving jobs for different hardware architectures and processor frequencies, reducing workload's energy consumption, makespan, and response time. The policy assigns a different priority to each job-resource combination so that the most efficient ones are favored, while less efficient ones are still considered on a variable degree, reducing response time and increasing cluster utilization. We implemented EAMC-policy in Slurm, and we evaluated a scenario in which two CPU clusters collaborate in the same machine. Simulations of workloads running applications modeled from real-world show a reduction of response time and makespan by up to 25% and 6% while saving up to 20% of total energy consumed when compared to policies minimizing runtime, and by 49%, 26%, and 6% compared to policies minimizing energy.

Improved Black Widow Optimization: An investigation into enhancing cloud task scheduling efficiency

The Black Widow Optimization (BWO) algorithm has garnered significant attention within the realm of metaheuristic algorithms due to its potential to address diverse problems across various domains. However, a noteworthy weakness of BWO is its utilization of a random selection technique, which can lead to reduced diversity, expedited convergence, and potential entrapment in local optima. This research introduces a novel approach to augment the BWO algorithm by integrating alternative selection schemes, thereby surpassing the limitations of the current selection methodology. To assess the effectiveness of these proposed variants, we employ the CEC 2019 benchmark functions as the standard evaluation metric. Subsequently, we utilize the best-performing BWO version, PIBWO, to address cloud scheduling challenges. In a series of comparative experiments, PIBWO demonstrates superior performance compared to existing algorithms, showcasing remarkable enhancements in makespan reduction, energy consumption minimization, and cost efficiency. These findings underscore PIBWO’s potential as a robust solution for addressing cloud task scheduling challenges, offering promising avenues for developing more sustainable and cost-effective cloud computing systems.

OptiDJS+: A Next-Generation Enhanced Dynamic Johnson Sequencing Algorithm for Efficient Resource Scheduling in Distributed Overloading within Cloud Computing Environment

The continuously evolving world of cloud computing presents new challenges in resource allocation as dispersed systems struggle with overloaded conditions. In this regard, we introduce OptiDJS+, a cutting-edge enhanced dynamic Johnson sequencing algorithm made to successfully handle resource scheduling challenges in cloud computing settings. With a solid foundation in the dynamic Johnson sequencing algorithm, OptiDJS+ builds upon it to suit the demands of modern cloud infrastructures. OptiDJS+ makes use of sophisticated optimization algorithms, heuristic approaches, and adaptive mechanisms to improve resource allocation, workload distribution, and task scheduling. To obtain the best performance, this strategy uses historical data, dynamic resource reconfiguration, and adaptation to changing workloads. It accomplishes this by utilizing real-time monitoring and machine learning. It takes factors like load balance and make-up into account. We outline the design philosophies, implementation specifics, and empirical assessments of OptiDJS+ in this work. Through rigorous testing and benchmarking against cutting-edge scheduling algorithms, we show the better performance and resilience of OptiDJS+ in terms of reaction times, resource utilization, and scalability. The outcomes underline its success in reducing resource contention and raising service quality generally in cloud computing environments. In contexts where there is distributed overloading, OptiDJS+ offers a significant advancement in the search for effective resource scheduling solutions. Its versatility, optimization skills, and improved decision-making procedures make it a viable tool for tackling the resource allocation issues that cloud service providers and consumers encounter daily. We think that OptiDJS+ opens the way for more dependable and effective cloud computing ecosystems, assisting in the full realization of cloud technologies’ promises across a range of application areas. In order to use the OptiDJS+ Johnson sequencing algorithm for cloud computing task scheduling, we provide a two-step procedure. After examining the links between the jobs, we generate a Gantt chart. The Gantt chart graph is then changed into a two-machine OptiDJS+ Johnson sequencing problem by assigning tasks to servers. The OptiDJS+ dynamic Johnson sequencing approach is then used to minimize the time span and find the best sequence of operations on each server. Through extensive simulations and testing, we evaluate the performance of our proposed OptiDJS+ dynamic Johnson sequencing approach with two servers to that of current scheduling techniques. The results demonstrate that our technique greatly improves performance in terms of makespan reduction and resource utilization. The recommended approach also demonstrates its ability to scale and is effective at resolving challenging work scheduling problems in cloud computing environments.

Reduction of makespan through flexible production and remanufacturing to maintain the multi-stage automated complex production system

Multi-stage production systems have been increasingly popular with a smart production system that has automated and flexible production rates. However, due to the complex structure of such a production system, most studies approach this as a single-stage production system or multi-stage by only incorporating a minimum number of real features. Thus motivated, this study presents the importance of automation and flexibility to reduce the makespan for multi-stage work-in-process inventory under a complex production system. This is a pioneer study in reducing the makespan for flexible multi-stage production systems since each stage of production produces defective items and the former stage’s production is required to finish before running the latter stage. We consider the automated multi-stage production system and aim to reduce makespan duration by optimally controlling the amount of production lot, production, and backorder rate. In this system, finished products are obtained in the last stage, and in each stage, there are defective items. To mitigate defective items, the production system has a remanufacturing process in each stage, in parallel with production, such that the makespan does not increase and defective products of that stage are transferred to perfect items. This study characterizes the optimal lot size, production rate, and backorder rate at each stage along with the optimal selling price to maximize the total profit of the system. In analyzing the production model, we use a classical optimization method. We also conducted numerical experiments to validate the model and performed a sensitivity analysis of key parameters to derive several managerial insights on the impacts of optimized backorder rate and lot sizing on the profit. The results show that a significant reduction of defective items as waste reduction can be achieved by the reduction of makespan that utilizes a flexible production rate and remanufacturing rate. Our results also show that the benefit of flexible production instead of traditional production is 11.48% and the benefits of variable backorder for multi-stage instead of fixed and no-backorder are 93.62% and 99.55%, respectively.

Experimental study for makespan reduction in enterprise application integration processes using bio-inspired algorithms

Experimental study for makespan reduction in enterprise application integration processes using bio-inspired algorithms

Experimental study for makespan reduction in enterprise application integration processes using bio-inspired algorithms

Experimental study for makespan reduction in enterprise application integration processes using bio-inspired algorithms

An efficient list‐based task scheduling algorithm for heterogeneous distributed computing environment

AbstractThe task scheduling in heterogeneous distributed computing systems plays a crucial role in reducing the makespan and maximizing resource utilization. The diverse nature of the devices in heterogeneous distributed computing systems intensifies the complexity of scheduling the tasks. To overcome this problem, a new list‐based static task scheduling algorithm namely Deadline‐Aware‐Longest‐Path‐of‐all‐Predecessors (DA‐LPP) is being proposed in this article. In the prioritization phase of the DA‐LPP algorithm, the path length of the current task from all its predecessors at each level is computed and among them, the longest path length value is assigned as the rank of the task. This strategy emphasizes the tasks in the critical path. This well‐optimized prioritization phase leads to an observable minimization in the makespan of the applications. In the processor selection phase, the DA‐LPP algorithm implements the improved insertion‐based policy which effectively utilizes the unoccupied leftover free time slots of the processors which improve resource utilization, further least computation cost allocation approach is followed to minimize the overall computation cost of the processors and parental prioritization policy is incorporated to further reduce the scheduling length. To demonstrate the robustness of the proposed algorithm, a synthetic graph generator is used in this experiment to generate a huge variety of graphs. Apart from the synthetic graphs, real‐world application graphs like Montage, LIGO, Cybershake, and Epigenomic are also considered to grade the performance of the DA‐LPP algorithm. Experimental results of the DA‐LPP algorithm show improvement in performance in terms of scheduling length ratio, makespan reduction rate , and resource reduction rate when compared with other algorithms like DQWS, DUCO, DCO and EPRD. The results reveal that for 1000 task set with deadline equals to two times of the critical path, the scheduling length ratio of the DA‐LPP algorithm is better than DQWS by 35%, DUCO by 23%, DCO by 26 %, and EPRD by 17%.

Opposition based genetic optimization algorithm with Cauchy mutation for job shop scheduling problem

In manufacturing industry, job shop scheduling (JSS) is predominant to improve productivity. The major goal of proposed work is to shorten the make span time. The performance of four optimizations techniques namely genetic algorithm optimization (GA), particle swarm optimization (PSO), opposition based particle swarm optimization with Cauchy distribution (OPSO CD) and opposition based genetic optimization algorithm with Cauchy distribution (OGA CD) are analysed to find the best make span reduction method. To determine the best effective optimization approach for solving JSSP, a comparison analysis of different optimization techniques was done. In comparison to other JSSP algorithms, the results demonstrate that OGA CD has the shortest make span time.

Development of scheduling methodology in a multi-machine flexible manufacturing system without tool delay employing flower pollination algorithm

This paper addresses machines, automated guided vehicles (AGVs), tool transporter (TT), and tools concurrent scheduling in a multi-machine flexible manufacturing system (FMS) for makespan minimization. The fewest number of copies of each tool type is employed to prevent tool delays, and job and tool shift times between machines are taken into account. The tools are placed in a central tool magazine (CTM), which shares and serves them to many machines to cut down the price of duplicating the tools in each machine. This simultaneous scheduling problem is challenging to solve because it entails determining the fewest tool copies of each tool kind without tool delay, assigning AGVs and tool copies to job-operations (jb-ons), ordering jb-ons on machines, and related trip operations such as deadheading and loaded flight times for both TT and AGVs. This paper uses a mixed-integer nonlinear programming (MINLP) framework to present the problem, and a flower pollination algorithm (FPA) is employed to solve it. For verification, a manufacturing company’s industrial problem is employed. The results show that employing two copies each for two tool types and one copy each for the remaining tool types causes no tool delay, reduction in makespan and cost, and the FPA outperforms the Jaya algorithm.