Multi-objective Scheduling Algorithm Research Articles

A multi-objective energy-efficient scheduling algorithm for solving hybrid flow workshops with parallel heterospeed machines

Abstract. In hybrid flow assembly workshops, minimizing completion time and reducing energy consumption are two key indicators of efficient workshop management, which are often inversely related. This study investigates how parallel heterospeed machines with adjustable times can reduce energy costs and assembly times at the production system level. To address these issues, we propose the MO-HFSP-Batch algorithm based on the enhanced NSGA-II algorithm, aimed at improving the algorithm's optimization capability. Given the trade-offs between optimization objectives and the high computational complexity of the proposed multi-objective mixed integer programming, a three-tier chromosome encoding structure was introduced during the algorithm design phase. This structure meets the triple requirements of batching assembly tasks, matching operations with parallel machines, and sequencing operations in actual hybrid flow assembly workshops. Extensive data analysis proves that our proposed algorithm effectively solves the scheduling issues in hybrid flow workshops and performs better in solving completion time indicators and stability of non-dominated solution sets than other algorithms.

Evaluating Scheduling Efficiency for Complex Scientific Workflows in Cloud Infrastructures

Scientific workflow scheduling in Cloud computing is critical for efficiently managing data-exhaustive and compute-rigorous applications. With the rising demand of distributed computing, the interest in feasible assignment planning calculations has developed primarily. Revised text: This paper presents a comprehensive survey of advanced scheduling techniques focusing on minimizing energy consumption and improving resource utilization. Various scheduling algorithms, including heuristic-based, meta-heuristic-based, and reinforcement learning-based methods, are analyzed and compared. Additionally, the paper addresses the challenges of scheduling workflows with complex dependencies, offering a novel multi-objective workflow scheduling algorithm using reinforcement learning. The algorithm outperforms current methods in terms of makespan and energy consumption. Finally, the paper highlights open research issues and future directions in scientific workflow scheduling for distributed computing. Through our experiments, we have achieved significant improvements in scheduling efficiency, demonstrated by various performance metrics illustrated in our graphs.

A Q-learning-based multi-population algorithm for multi-objective distributed heterogeneous assembly no-idle flowshop scheduling with batch delivery

Distributed heterogeneous factory environments have become the mainstream in real-world manufacturing enterprises. Scheduling the assembly no-idle flowshops in such distributed heterogeneous environments is significant for practitioners. This problem takes into account the heterogeneity between different factories and the batch delivery process. To minimize inventory and tardiness costs of this new problem, a novel Q-learning-based multi-population multi-objective evolutionary algorithm is proposed. The algorithm incorporates five problem-specific properties to design the solution representation and calculate objectives. To obtain high-quality initial dual populations, a collaborative initialization technique combining four NEH heuristics and a completely random approach is proposed. To enhance algorithm’s search efficiency, a Q-learning-based selection method is developed to coordinate four job-related and four product-related operators. The dual-cooperation strategy is introduced to improve the global search capability. Experimental results demonstrate the effectiveness of all the designed components, indicating that the proposed algorithm outperforms five existing algorithms. It is well-suited for addressing real-world distributed heterogeneous scheduling scenarios by the proposed algorithm.

A clustering-aided multi-agent deep reinforcement learning for multi-objective parallel batch processing machines scheduling in semiconductor manufacturing

Batch processing machines are often the bottleneck in semiconductor manufacturing and their scheduling plays a key role in production management. Pioneer researches on multi-objective batch machines scheduling mainly focus on evolutionary algorithms, failing to meet the online scheduling demand. To deal with the challenges confronted by incompatible job families, dynamic job arrivals, capacitated machines and multiple objectives, we propose a clustering-aided multi-agent deep reinforcement learning approach (CA-MADRL) for the scheduling problem. Specifically, to achieve diverse nondominated solutions, an offline multi-objective scheduling algorithm named Multi-Subpopulation fast elitist Non-Dominated Sorting Genetic Algorithm (MS-NSGA-II) is firstly developed to obtain the Pareto Fronts, and a clustering algorithm based on cosine distance is employed to analyze the distribution of Pareto frontier solution, which would be used to guide reward functions design in multi-agent deep reinforcement learning. To realize multi-objective optimization, several reinforcement learning base models are trained for different optimization directions, each of which composed of batch forming agent and batch scheduling agent. To alleviate time complexity of model training, a parameter sharing strategy is introduced between different reinforcement learning base model. By validating the proposed approach with 16 instances designed based on actual production data from a semiconductor manufacturing company, it has been demonstrated that the approach not only meets the high-frequency scheduling requirements of manufacturing systems for parallel batch processing machines but also effectively reduces the total job tardiness and machine energy consumption.

Hybrid Prairie Dog and Beluga Whale optimization algorithm for multi-objective load balanced-task scheduling in cloud computing environments

Hybrid Prairie Dog and Beluga Whale optimization algorithm for multi-objective load balanced-task scheduling in cloud computing environments

A reinforcement learning enhanced memetic algorithm for multi-objective flexible job shop scheduling toward Industry 5.0

Flexible job shop scheduling problem (FJSP) with worker flexibility has gained significant attention in the upcoming Industry 5.0 era because of its computational complexity and its importance in production processes. It is normally assumed that each machine is typically operated by one worker at any time; therefore, shop-floor managers need to decide on the most efficient assignments for machines and workers. However, the processing time is variable and uncertain due to the fluctuating production environment caused by unsteady operating conditions of machines and learning effect of workers. Meanwhile, they also need to balance the worker workload while meeting production efficiency. Thus a dual resource-constrained FJSP with worker’s learning effect and fuzzy processing time (F-DRCFJSP-WL) is investigated to simultaneously minimise makespan, total machine workloads and maximum worker workload. Subsequently, the reinforcement learning enhanced multi-objective memetic algorithm based on decomposition (RL-MOMA/D) is proposed for solving F-DRCFJSP-WL. For RL-MOMA/D, the Q-learning is incorporated into memetic algorithm to perform variable neighbourhood search and further strengthen the exploitation capability for the algorithm. Finally, comprehensive experiments on extensive test instances and a case study of aircraft overhaul shop-floor are conducted to demonstrate effectiveness and superiority of the proposed method.

A hybrid optimization algorithm for energy-aware multi-objective task scheduling in heterogeneous multiprocessor systems

A hybrid optimization algorithm for energy-aware multi-objective task scheduling in heterogeneous multiprocessor systems

Grid-based artificial bee colony algorithm for multi-objective job shop scheduling with manual loading and unloading tasks

This paper investigates a multi-objective job shop scheduling problem with manual loading and unloading tasks (MOJSSPLU) aiming to minimize makespan and total workload simultaneously. Taking human factors into consideration achieves a better balance between production efficiency and resource utilization. To address this problem, we first establish a bi-objective mixed-integer linear programming (MILP) model. Afterward, we combine the artificial bee colony algorithm with the grid technique to develop a grid-based artificial bee colony algorithm (GABC) for solving MOJSSPLU. The algorithm uses a decomposition approach with the earliest-shortest dispatching rule to reduce the complexity of MOJSSPLU. A grid coordinate system is constructed to divide the objective space into cells, facilitating individuals’ location and evaluation in the decision space. Moreover, we focus on improving the cooperation between the employed and onlooker bees and design a mathematical formula based on priority weights to generate onlooker bees to maximize information utilization and improve the exploration and exploitation capabilities of the algorithm. To ensure that the most promising individuals are retained and protected from being eliminated during the evolutionary, we propose an elitist non-dominated solutions reservation strategy for improving the diversity of the population. We further propose a dynamic adjustment of the grid-division method that focuses on improving the exploitation capacity of the algorithm in the objective space to enhance the diversity and convergence of the population. To test the performance of the GABC algorithm, we compare it to the MILP approach and other algorithms using modified benchmark instances. Furthermore, we conduct the Friedman and Wilcoxon nonparametric tests to validate the comparison results among the different algorithms. The computational results demonstrate that the algorithm GABC is efficient.

Multi-objective evolutionary scheduling based on collaborative virtual workflow model and adaptive rules for flexible production process with operation reworking

Dynamic event disturbances during production such as reworking of defective operations more challenge scheduling optimization. Efficient response to operation reworking is crucial for collaborative optimization of production quality and scheduling performance. Given the complex constraints within production processes of jobs involving key operation reworking and uncertainty of dynamic disturbances, a two-level virtual workflow modeling method and a multi-objective evolutionary scheduling algorithm with adaptive rules (MOESA-AR) based on collaborative virtual workflow framework are proposed. Virtual workflow modeling fulfills linear abstraction of tasks to be scheduled and priority constraints. Through decision arrangement, task state tracking and information feedback, collaborative virtual workflow model constitutes framework for dynamic update of task workflows and real-time response to rescheduling triggered by event disturbances. MOESA-AR adopts two-dimensional chromosome evolution and heuristic rule training to adapt to the complexity of scheduling optimization. Adaptive strategy of dispatching rules enhances the dynamic adaptability of decision-making to variable scheduling scenarios, thereby improving the dominance level of multi-objective trade-off optimization under changing demand conditions. Experiments confirm the superiority of MOESA-AR in improving scheduling performance and the effectiveness of collaborative virtual workflow scheduling framework in responding to dynamic disturbances.

Multi-objective secure aware workflow scheduling algorithm in cloud computing based on hybrid optimization algorithm

Cloud computing provides the on-demand service of the user with the use of distributed physical machines, in which security has become a challenging factor while performing various tasks. Several methods were developed for the cloud computing workflow scheduling based on optimal resource allocation; still, the security consideration and efficient allocation of the workflow are challenging. Hence, this research introduces a hybrid optimization algorithm based on multi-objective workflow scheduling in the cloud computing environment. The Regressive Whale Water Tasmanian Devil Optimization (RWWTDO) is proposed for the optimal workflow scheduling based on the multi-objective fitness function with nine various factors, like Predicted energy, Quality of service (QoS), Resource utilization, Actual task running time, Bandwidth utilization, Memory capacity, Make span equivalent of the total cost, Task priority, and Trust. Besides, secure data transmission is employed using the triple data encryption standard (3DES) to acquire enhanced security for workflow scheduling. The method’s performance is evaluated using the resource utilization, predicted energy, task scheduling cost, and task scheduling time and acquired the values of 1.00000, 0.16587, 0.00041, and 0.00314, respectively.

Scheduling Scientific Workflow in Multi-Cloud: A Multi-Objective Minimum Weight Optimization Decision-Making Approach

One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study.

Research on Sparrow Search Optimization Algorithm for multi-objective task scheduling in cloud computing environment

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling.

Knowledge-driven adaptive evolutionary multi-objective scheduling algorithm for cloud workflows

Workflow scheduling in cloud platforms is a highly challenging issue because it faces multiple conflicting optimization objectives and large-scale decision variables. Most of the existing multi-objective workflow scheduling algorithms regard the focused problems as black boxes, and optimize large-scale decision variables as a whole. This leads to inefficiency in searching solution spaces that grow exponentially with the increase of decision variables. To compensate the above deficiency, this paper proposes a knowledge-driven adaptive evolutionary multi-objective scheduling algorithm, KAMSA for short, to optimize makespan and cost of workflow execution in cloud platforms. Specifically, we excavate the knowledge that adjustment of a task’s execution only affects its successor tasks to divide large-scale decision variables into a series of groups, so as to give play to the strengths of divide-and-conquer technology to improve the evolutionary search efficiency. Moreover, we develop an adaptive resource allocation scheme to reward more evolution opportunities for groups with high contributions to further improve the evolutionary search efficiency. We compare the proposed KAMSA with five state-of-the-art competitors in the context of 20 real-world workflows and the Amazon elastic compute cloud (EC2). The comparison results verify the KAMSA’s advantages by prevailing over the five competitors on 18 out of the 20 test cases with respect to the metric hypervolume.

Multiobjective Prioritized Workflow Scheduling in Cloud Computing Using Cuckoo Search Algorithm

Effective workflow scheduling in cloud computing is still a challenging problem as incoming workflows to cloud console having variable task processing capacities and dependencies as they will arise from various heterogeneous resources. Ineffective scheduling of workflows to virtual resources in cloud environment leads to violations in service level agreements and high energy consumption, which impacts the quality of service of cloud provider. Many existing authors developed workflow scheduling algorithms addressing operational costs and makespan, but still, there is a provision to improve the scheduling process in cloud paradigm as it is an nondeterministic polynomial-hard problem. Therefore, in this research, a task-prioritized multiobjective workflow scheduling algorithm was developed by using cuckoo search algorithm to precisely map incoming workflows onto corresponding virtual resources. Extensive simulations were carried out on workflowsim using randomly generated workflows from simulator. For evaluating the efficacy of our proposed approach, we compared our proposed scheduling algorithm with existing approaches, i.e., Max–Min, first come first serve, minimum completion time, Min–Min, resource allocation security with efficient task scheduling in cloud computing-hybrid machine learning, and Round Robin. Our proposed approach is outperformed by minimizing energy consumption by 15% and reducing service level agreement violations by 22%.

A knowledge-based NSGA-II algorithm for multi-objective hot rolling production scheduling under flexible time-of-use electricity pricing

With the increasing demand and shortage of energy, the iron and steel industry has focused on achieving low-carbon and smart manufacturing. This study investigates an energy-oriented scheduling problem arising from hot rolling production under flexible time-of-use (TOU) electricity tariffs, where the electric power comes from both the enterprise-owned power plant and the state-owned power grid. First, we model the studied scheduling problem as a multi-objective prize-collecting vehicle routing problem with special constraints. This model selects slabs from the order pool and sequences them to form rolling units, aiming to simultaneously minimize the transition costs in specifications, penalty costs, and electricity costs. Next, we develop a knowledge-based NSGA-II algorithm (KB-NSGA-II) by introducing a self-adaptive recombination procedure and a shortest path search (SPS)-based local intensification procedure. Furthermore, the comparative experimental results indicate that the KB-NSGA-II outperforms other competing algorithms in terms of the following indicators: (1) the hypervolume indicator is improved by approximately 57%; (2) the Spacing indicator is decreased by approximately 40%; and (3) the success ratio indicator of KB-NSGA-II is at least 60% higher than that of other competing algorithms. Finally, we apply a multi-criteria decision-making method named VIKOR to a case study under flexible TOU pricing strategies. These comparison results and the case study confirm that the KB-NSGA-II algorithm obtained better computational performance than the other competing algorithms and provided more flexible solutions under TOU pricing.

An efficient multi-objective scheduling algorithm based on spider monkey and ant colony optimization in cloud computing

Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users’ jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed SMO_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits.

A dynamic multipopulation genetic algorithm for multiobjective workflow scheduling based on the longest common sequence

A dynamic multipopulation genetic algorithm for multiobjective workflow scheduling based on the longest common sequence

A Proposed Scheduling Algorithm for IoT Applications in a Merged Environment of Edge, Fog, and Cloud

With the rapid increase of Internet of Things (IoT) devices and applications, the ordinary cloud computing paradigm soon becomes outdated. Fog computing paradigm extends services provided by a cloud to the edge of network in order to satisfy requirements of IoT applications such as low latency, locality awareness, low network traffic, mobility support, and so forth. Task scheduling in a Cloud-Fog environment plays a great role to assure diverse computational demands are met. However, the quest for an optimal solution for task scheduling in the such environment is exceedingly hard due to diversity of IoT applications, heterogeneity of computational resources, and multiple criteria. This study approaches the task scheduling problem with aims at improving service quality and load balancing in a merged computing system of Edge-Fog-Cloud. We propose a Multi-Objective Scheduling Algorithm (MOSA) that takes into account the job characteristics and utilization of different computational resources. The proposed solution is evaluated in comparison to other existing policies named LB, WRR, and MPSO. Numerical results show that the proposed algorithm improves the average response time while maintaining load balancing in comparison to three existing policies. Obtained results with the use of real workloads validate the outcomes.

Multi-objective reinforcement learning framework for dynamic flexible job shop scheduling problem with uncertain events

The economic benefits for manufacturing companies will be influenced by how it handles potential dynamic events and performs multi-objective real-time scheduling for existing dynamic events. Based on these, we propose a new dynamic multi-objective flexible job shop scheduling problem (DMFJSP) to simulate realistic production environment. Six dynamic events are involved in the problem including job insertion, job cancellation, job operation modification, machine addition, machine tool replacement and machine breakdown. As well as three objectives of longest job processing time (makespan), average machine utilization and average job processing delay rate with a set of constraints are also raised in the study. Then this research designs a novel dynamic multi-objective scheduling algorithm based on deep reinforcement learning. The algorithm uses two deep Q-learning networks and a real-time processing framework to process each dynamic event and generate complete scheduling scheme. In addition, an improved local search algorithm is adopted to further optimize the scheduling results and the idea of combination is used to make the scheduling rules more comprehensive. Experiments on 27 instances show the superiority and stability of our approach compared to each proposed combined rule, well-known scheduling rules and standard deep Q-learning based algorithms. Compared to the current optimal deep Q-learning method, the maximum performance improvement for our three objectives are approximately 57%, 164% and 28%.

An adaptive multiobjective evolutionary algorithm for dynamic multiobjective flexible scheduling problem

There are various uncertain disturbances in the actual manufacturing environment, which makes dynamic multiobjective flexible scheduling problem of flexible job shop (MDFJSP) become the research focus in the field of optimal scheduling. In this paper, MDFJSP in the environment of temporary order insertion uncertainty is studied, and a multiobjective dynamic scheduling scheme based on rescheduling index and adaptive nondominated sorting genetic algorithm (NSGA-II) is proposed. First, based on the actual manufacturing environment, the mathematical model of the traditional flexible job shop scheduling problem is improved, and the multiobjective dynamic rescheduling model of flexible work center is established. Then, the existing rescheduling mechanisms are summarized, and a rescheduling hybrid driving mechanism based on the rescheduling index is proposed to enable it to reschedule and drive according to the actual situation. Finally, the shortcomings of the traditional multiobjective scheduling algorithm NSGA-II are analyzed, the adaptive cross mutation strategy and the simplified harmonic normalized distance measure method are proposed to improve it, and an adaptive multiobjective dynamic scheduling algorithm NSGA-II (MDSA-NSGA-II) is formed. To analyze the performance of this algorithm, the performance of this algorithm is compared with five classical flexible job shop multiobjective scheduling algorithms in international general examples, and the effectiveness is verified by real aircraft production examples. The experimental results fully show that MDSA-NSGA-II has good performance in solving MDFJSP.