Optimal Resource Scheduling Research Articles

Human-robot collaboration in assembly line balancing problems: Review and research gaps

Researchers have been working on assembly line optimization problems, especially assembly line balancing, for decades. Integration of Industry 4.0 technologies into assembly lines creates new challenges in optimal resource selection, task allocation, line balance, and scheduling decisions, to name a few. Over the last decade, researchers have developed novel frameworks for conventional assembly line optimization problems that comply with the recent industrial revolution and leading technologies. State-of-the-art Balancing problems in assembly lines with collaborative robots are the core of this study as a key decision in Assembly 4.0. The collaboration of humans and robots in a fenceless environment requires a more comprehensive approach than the traditional task-station assignment in a balancing model. Resource selection/allocation and scheduling are the most common decisions that researchers combine with balancing models. Thus, the studies on all these optimization challenges in the last ten years through the Web of Science and Engineering Village are present in the current review. We analyze the articles’ statistics (e.g., year and journals) and contents to find the research trends and gaps for future research direction in collaborative robotic assembly line optimization which incorporates occupational health and safety.

Survey on Resource Management Solutions to Speed up Processing Small Files in Hadoop Cluster

High performance data analytics is a computing paradigm involving optimal placement of data, analytics and other computational resources such that superior performance is achieved with lesser resource consumption. Resource allocation and scheduling are the two major functionalities to be addressed in Hadoop clusters to satisfy the service level agreements of users for High performance data analytics applications. Though many solutions have been proposed for optimal resource allocation and scheduling, those schemes are designed for large Hadoop files. Recently with Internet of Things (IoT) convergence with big data, there is need to process large volumes of small files whose size is lower than block size of Hadoop. This creates huge storage overhead and exhausts Hadoop clusters computational resources. This survey analyzes the existing works on resource allocation and scheduling in Hadoop clusters and their suitability for small files. The aim is to identify the problems in existing resource allocation and scheduling approaches while handling small files. Based on the problems identified, prospective solution architecture is proposed.

Integrated model for optimal scheduling and allocation of water resources considering fairness and efficiency: A case study of the Yellow River Basin

Water is an important natural resource with economic and social attributes. The most important aspects of water resource regulations are fairness and efficiency. Achieving a balance between fairness and efficiency is a challenging but popular topic in related research. In this study, three optimal water-resource allocation models were constructed: efficiency priority (E-P), fairness priority (F-P), and stratified water supply (SWS). The three models were applied to the Yellow River Basin (YRB), and the results show that, compared with the E-P and F-P models, the SWS model is a more equilibrium model with significant advantages. Considering the impact of reservoirs on water allocation, an optimal operation model for cascade reservoirs groups, considering fairness and efficiency, was constructed based on the principle of welfare economics and coupled with the SWS model. Proxy modeling was used in the coupling process to enhance the computational efficiency of the integrated model. Compared with the SWS model, the integrated model can better satisfy the water supply demand outside a river while ensuring the ecological environment and sand flushing demand inside the river; its fairness and efficiency are also significantly improved. In addition, integrated models can effectively address hydrological uncertainty and reduce losses. The model proposed in this study has significant flexibility and application potential and can provide a reference for other governments and water-resource management institutions worldwide.

An optimized resource scheduling algorithm based on GA and ACO algorithm in fog computing

An optimized resource scheduling algorithm based on GA and ACO algorithm in fog computing

Optimal planning of self-healing multi-carriers energy systems considering integration of smart buildings and parking lots energy resources

This paper presents a new framework for optimal planning of electrical, heating, and cooling distributed energy resources and networks considering smart buildings' contribution scenarios in normal and external shock conditions. The main contribution of this paper is that the impacts of smart buildings' commitment scenarios on the planning of electrical, heating, and cooling systems are explored. The proposed iterative four-stage optimization framework is another contribution of this paper, which utilizes a self-healing performance index to assess the level of resiliency of the multi-carrier energy system. In the first stage, the optimal decision variables of planning are determined. Then, in the second stage, the smart buildings and parking lots contribution scenarios are explored. In the third stage, the optimal hourly scheduling of the energy system for the normal condition is performed considering the self-healing performance index. Finally, in the fourth stage, the optimization process determines the optimal scheduling of system resources and the switching status of electrical switches, heating, and cooling pipelines’ control valves. The proposed method was successfully assessed for the 123-bus IEEE test system. The proposed framework reduced the expected values of aggregated system costs and energy not supplied costs by about 49.92% and 93.64%, respectively, concerning the custom planning exercise.

Integrated optimization of stochastic resource scheduling and machine maintenance in prefabricated component production processes

Effective resource scheduling and machine maintenance are two important interactive factors to improve prefabricated component productions. However, previous studies focused on machine maintenance without simultaneously considering stochastic resource scheduling, or studied job scheduling but neglected the resource constraint and machine availability. This paper explores the interaction between resource scheduling and machine maintenance to optimize the maintenance decisions and human resources in the prefabricated component production process. To solve this problem, a new integrated optimization model with aim of minimizing the uncertain project duration under the cost constraint is formulated. A differential evolution algorithm is designed by incorporating the simulation method. A real-life example of prefabricated walls is used to demonstrate the significance of jointly optimizing stochastic resource scheduling and machine maintenance, and analyzes the effectiveness of related parameters on the production system. Related results provide a new perspective to simultaneously consider multiple factors in the production process for future research.

A surrogate-assisted heuristic approach for the joint optimization of resource allocation and scheduling of an aircraft final assembly line

In an aircraft final assembly line, there exists flexibility in resource allocation in the sense that processing time of a task can be reduced by allocating more workers to the task. As task processing times change, assembly task scheduling needs to be adjusted accordingly, which in turn has an impact on resource allocation. This paper studies the joint optimization problem of resource allocation and task scheduling. Both resource requirement and starting time of each task need to be determined such that the total resource investment cost is minimized. We propose a surrogate-assisted heuristic approach to solve the problem by decomposing it into two phases. In the first phase, a multi-start iterative search algorithm is developed to perform a dedicated exploration on resource allocation. A surrogate model trained on numerical data is applied to evaluate the resource allocation strategy. In the second phase, a hybrid genetic algorithm embedded with a novel local search procedure is designed for task scheduling. Meanwhile, a fine search for resource allocation can also be achieved. Computational experiments demonstrate the superiority of our proposed approach and that the surrogate plays a significant role in terms of improving solution quality. A realistic case study provides valuable managerial insights for real production.

A New Gaining-Sharing Knowledge Based Algorithm with Parallel Opposition-Based Learning for Internet of Vehicles

Heuristic optimization algorithms have been proved to be powerful in solving nonlinear and complex optimization problems; therefore, many effective optimization algorithms have been applied to solve optimization problems in real-world scenarios. This paper presents a modification of the recently proposed Gaining–Sharing Knowledge (GSK)-based algorithm and applies it to optimize resource scheduling in the Internet of Vehicles (IoV). The GSK algorithm simulates different phases of human life in gaining and sharing knowledge, which is mainly divided into the senior phase and the junior phase. The individual is initially in the junior phase in all dimensions and gradually moves into the senior phase as the individual interacts with the surrounding environment. The main idea used to improve the GSK algorithm is to divide the initial population into different groups, each searching independently and communicating according to two main strategies. Opposite-based learning is introduced to correct the direction of convergence and improve the speed of convergence. This paper proposes an improved algorithm, named parallel opposition-based Gaining–Sharing Knowledge-based algorithm (POGSK). The improved algorithm is tested with the original algorithm and several classical algorithms under the CEC2017 test suite. The results show that the improved algorithm significantly improves the performance of the original algorithm. When POGSK was applied to optimize resource scheduling in IoV, the results also showed that POGSK is more competitive.

Application of Improved Artificial Immune System Algorithm Based on Applied Mathematics for Optimization of Manpower Allocation in Construction Engineering

The outbreak of the COVID-19 pandemic has led construction companies to prioritize the intelligent and optimal scheduling of human resources in construction projects to reduce costs. This study addresses the problem of heterogeneity in human resource scheduling in construction projects, presents a mathematical model with generic human resources as an example, proposes an improved artificial immune system (NAIS) algorithm to solve the problem, and verifies its effectiveness. Experimental results show that the NAIS algorithm achieves the optimal duration of 9 days in just 2 s using the Matrix Laboratory (MATLAB), which is significantly faster than mathematical optimization technique software (CPLEX), thus confirming the feasibility of the NAIS algorithm. Additionally, the average PD values for the NAIS algorithm, calculated for different worker counts, skills, and the number of tasks, were lower compared to the comparison algorithm. Overall, the NAIS algorithm effectively addresses the heterogeneous problem of human resource scheduling in construction projects with multiple modes, thereby optimizing construction engineering labor allocation.

Joint Optimization of Platoon Control and Resource Scheduling in Cooperative Vehicle-Infrastructure System

Vehicle platooning technology is essential in achieving group consensus, on-road safety, and fuel-saving. Meanwhile, Vehicle-to-Infrastructure (V2I) communication significantly facilitates the development of connected vehicles. However, the coupled effects of the longitudinal vehicle's mobility, platoon control and V2I communication may result in a low reliable communication network between the platoon vehicle and the roadside unit, there is a tradeoff between the platoon control and communication reliability. In this paper, we investigate a bi-objective joint optimization problem where the first objective is to maximize the success probability of data transmission (communication reliability) and the second objective function is to minimize the traffic oscillation flow. The vehicle's mobility state of the platoon vehicle affects the channel capacity and transmission performance. In this context, we deeply explore the relationship between control signals and resource scheduling and theoretically deduce a closed-form expression of the optimal communication reliability objective. Through this closed expression, we transform the bi-objective model into a single objective MPC model by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -constraint method. We design an efficient algorithm for solving the joint optimization model and prove the convergence. To verify the effectiveness of the proposed method, we finally evaluate the spacing error, speed error, and resource scheduling of platooning vehicles through simulation experiments in two experimental scenarios. The results show that the proposed control-communication co-design can improve the platoon control performance while satisfying the high reliability of V2I communications.

A Light Weight Optimal Resource Scheduling Algorithm for Energy Efficient and Real Time Cloud Services

Abstract: Cloud computing has become an essential technology for providing various services to customers. Energy consumption and response time are critical factors in cloud computing systems. Therefore, scheduling algorithms play a vital role in optimizing resource usage and improving energy efficiency. In this paper, we propose a lightweight optimal scheduling algorithm for energy-efficient and real-time cloud services. Our algorithm considers the trade-off between energy consumption and response time and provides a solution that optimizes both factors. We evaluate our algorithm on a cloud computing testbed using the Cloud Sim simulation toolkit and compare it with load balancing round robin, ant colony, genetic algorithm, and analytical algorithm. The results show that our proposed algorithm outperforms these algorithms in terms of energy efficiency and response time.

Optimal power system flexibility-based scheduling in iron and steel production: A case of steelmaking–refining–continuous casting process

Steelmaking–refining–continuous casting (SRCC), as a key electricity-intensive and flexible process in the integrated iron and steel production route, has great potential in providing demand-side flexibility. Optimal scheduling of flexible resources is important for ensuring maximal flexibility. However, it is not considered in existing SRCC scheduling studies, leading to a low ability to provide operational flexibility at iron and steel production sites and an inability to support the sites to participate in flexibility transactions and reduce energy costs. To fill in the research gaps, a Basic resource–task network (RTN) model and a Flex RTN model for SRCC scheduling considering refining process flexibility are established. With minimizing the power cost of the process as the objective function, the Flex RTN model takes various load modes of refining ladle furnaces into account and provides an approach for the scheduling of flexible resources in the iron and steel sites. The energy cost of iron and steel sites under the Flex RTN model considering flexible resources is compared with that of the Basic RTN model without considering flexible resources. The results of a 2-min time step case study show that 12,000 yuan can be saved by implementing the flexibility-based scheduling, proving the technical and economic superiority of the proposed method.

A global optimal resource scheduling algorithm in muti-layer optical networks

Abstract This paper analyzed the characteristic of business flow and divided the business flow into two categories according to the flow distribution: elephant flow and mice flow, combined with the characteristics of the elephant flow’s high delay tolerance and bandwidth demand flexibility, focused on the multi-layer traffic coordination problem in the optical transport network, and the lack of fairness guarantee and multi-path transmission mechanism in the competition of bandwidth resources between elephant flows, realized the coordination of service traffic between the IP layer and the optical layer, proposed a periodic resource scheduling mechanism and a cross-layer global routing and bandwidth scheduling algorithm based on fairness and multi-path transmission under the unified control “IP over OTN” transport network architecture based on the SDN architecture and the OpenFlow protocol.

Design and Application of Vague Set Theory and Adaptive Grid Particle Swarm Optimization Algorithm in Resource Scheduling Optimization.

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.

Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN

To satisfy the continuously high energy consumption and high computational capacity requirements for IoT applications, such as video monitoring, we integrate solar harvesting and multi-access edge computing (MEC) technologies to develop a solar-powered MEC system. Considering the stochastic nature of solar arrivals and channel conditions, we formulate a stochastic optimization problem to maximize network energy efficiency under the constraints of energy queue stability, task queue stability, peak transmission power, and maximum CPU frequency of each sensor. To solve the long-term stochastic optimization problem, we propose a Lyapunov-based online joint computational offloading and resource scheduling optimization algorithm, transforming the long-term stochastic problem into a series of deterministic subproblems in each time slot. Simulation results show that the proposed algorithm can find the optimal solution to tradeoff long-term energy efficiency and queueing backlog without requiring a priori knowledge of the channel state and energy arrival, which is a more realistic solution for practical solar-powered MEC systems.

Research on Railway Emergency Resources Scheduling Model under Multiple Uncertainties

This paper discusses the optimization of emergency resource scheduling for major railway emergencies under multiple uncertainties while considering the uncertainties in demand, reserve, and transportation costs of resources. We introduce a novel approach that integrates stochastic mathematical programming, interval parameter programming, and fuzzy mathematical programming to study uncertain parameter interactions and coupling. A two-stage interval fuzzy credibility-constrained model is established and solved using an interval interactive algorithm. Finally, through a case study on China Railway Nanchang Group Co., Ltd., the novelty and effectiveness of the proposed method for optimizing emergency resource scheduling strategies under multiple uncertainties are demonstrated.

A data-driven ensemble algorithm of black widow optimizer and simulated annealing algorithms for multi-objective buffer allocation in production lines

The multi-objective buffer allocation problem of production lines is a non-deterministic-polynomial-hard problem. Many metaheuristic algorithms have been proposed to solve this problem. However, further investigation of new algorithms is still required because metaheuristic algorithms highly depend on the problem types. Furthermore, the balance between the solution quality and computational efficiency requires further improvement. Therefore, a data-driven algorithm consisting of the black widow optimizer and simulated annealing algorithm is proposed to maximize throughput and minimize energy consumption in production lines. Numerical examples demonstrate that the proposed algorithm achieves better solution quality than other state-of-the-art algorithms without losing computational efficiency. This study contributes to multi-objective optimization of resource scheduling in production lines.

Forecasting of water consumption by integrating spatial and temporal characteristics of short-term water use in cities

Urban water consumption prediction is an important basis for urban water resource management and optimal scheduling of water supply. Most existing prediction methods disregard the spatial–temporal characteristics of water consumption data and utilize incomplete data mining, resulting in prediction models with low accuracy. To accurately predict short-term water demand, a data-cleaning algorithm based on statistical distributions and velocity constraints is proposed in this study. The long- and short-term time-series network (LSTNet), automated spatial–temporal graph prediction (AutoSTG), and attention-based spatial–temporal graphical convolutional neural network (ASTGCN) deep learning models were investigated for automatically capturing the spatial–temporal dynamic features. Additionally, the short-term predictions for 1, 3, 6, 12, and 24 h forecasting periods were investigated based on the water consumption data of water plants in Tianjin. The results revealed that the proposed data-cleaning algorithm can effectively manage single-point anomalies and continuous outliers, thus improving prediction accuracy. The LSTNet, AutoSTG, and ASTGCN deep learning models, particularly the ASTGCN model based on the spatial–temporal attention mechanism, significantly improved the prediction accuracy and adaptive spatial–temporal feature extraction. The final prediction model that combines data-cleaning algorithms with the ASTGCN deep learning method achieved root mean square error, mean absolute error, accuracy, and relative squared error values of 566.4 m3, 377.7 m3, 0.964, and 0.112, respectively. The proposed forecasting framework can improve short-term water use forecasting at multiple sites in the city and provide support for fine-grained water allocation.

Day-Ahead Optimal Scheduling of Resources in a Distribution System in Joint Energy, Regulation and Ramp Markets

The increasing trend of integrating Renewable Energy Sources (RESs) into power systems could cause operational challenges in the system from flexibility and reliability perspectives. Respectively, utilities would be relied on local flexible resources to address the flexibility requirements in power systems. In this respect, local resources in distribution systems could participate in ancillary markets along with the wholesale energy markets to address the flexibility capacity shortage in the system. Consequently, this paper aims to provide a framework to optimize the scheduling of resources in distribution networks while participating in energy, regulation, and ramp markets. Correspondingly, this approach would maximize the profits of the local resources in distribution systems whereas providing Flexible Ramp Product (FRP) and regulation reserve for the system operators. Respectively, an optimized bidding strategy is developed to maximize the profits of the local resources, while modeling the operational constraints of the distribution grids and power losses to improve the accuracy of the proposed scheme. Furthermore, the chance constrained concept is taken into account in the proposed scheme to model the uncertainty of RESs. Finally, the model is applied on the IEEE-37-bus-test-system and the sensitivity analysis is employed to investigate its effectiveness in the management of the system.

A Federated Learning Multi-Task Scheduling Mechanism Based on Trusted Computing Sandbox.

At present, some studies have combined federated learning with blockchain, so that participants can conduct federated learning tasks under decentralized conditions, sharing and aggregating model parameters. However, these schemes do not take into account the trusted supervision of federated learning and the case of malicious node attacks. This paper introduces the concept of a trusted computing sandbox to solve this problem. A federated learning multi-task scheduling mechanism based on a trusted computing sandbox is designed and a decentralized trusted computing sandbox composed of computing resources provided by each participant is constructed as a state channel. The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the data privacy of the participant node and the reliability of the calculation during the training process. In addition, considering the resource heterogeneity of participant nodes, the deep reinforcement learning method was used in this paper to solve the resource scheduling optimization problem in the process of constructing the state channel. The proposed algorithm aims to minimize the completion time of the system and improve the efficiency of the system while meeting the requirements of tasks on service quality as much as possible. Experimental results show that the proposed algorithm has better performance than the traditional heuristic algorithm and meta-heuristic algorithm.