Distributed Scheduling Research Articles

Multi-Objective Q-Learning-Based Brain Storm Optimization for Integrated Distributed Flow Shop and Distribution Scheduling Problems

In recent years, integrated production and distribution scheduling (IPDS) has become an important subject in supply chain management. However, IPDS considering distributed manufacturing environments is rarely researched. Moreover, reinforcement learning is seldom combined with metaheuristics to deal with IPDS problems. In this work, an integrated distributed flow shop and distribution scheduling problem is studied, and a mathematical model is provided. Owing to the problem’s NP-hard nature, a multi-objective Q-learning-based brain storm optimization is designed to minimize makespan and total weighted earliness and tardiness. In the presented approach, a double-string representation method is utilized, and a dynamic clustering method is developed in the clustering phase. In the generating phase, a global search strategy, a local search strategy, and a simulated annealing strategy are introduced. A Q-learning process is performed to dynamically choose the generation strategy. It consists of four actions defined as the combinations of these strategies, four states described by convergence and uniformity metrics, a reward function, and an improved ε-greedy method. In the selecting phase, a newly defined selection method is adopted. To assess the effectiveness of the proposed approach, a comparison pool consisting of four prevalent metaheuristics and a CPLEX optimizer is applied to conduct numerical experiments and statistical tests. The results suggest that the designed approach outperforms its competitors in acquiring promising solutions when handling the considered problem.

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.

Deep Reinforcement Learning-Based Rescue Resource Distribution Scheduling of Storm Surge Inundation Emergency Logistics

A high-intensity storm surge hazard exposes residents and facilities to inundation danger. A continuously operating robust inundation emergency logistics system is vital to guarantee lives and support the control of hazardous materials. A massive inundation area, large number of affected facilities, and instantly changing flooding situation bring tremendous challenges to rescue resource allocation. In this article, a rescue resource distribution scheduling of storm surge inundation logistics is proposed to quantitatively formulate the rescue time minimization problem in emergency logistics. The mixed-integer linear programming (MILP) method is proposed for the emergency logistics scheduling model validation and optimality comparison. To enhance the efficiency of creating a good quality allocation strategy when facing large-scale problems, a deep reinforcement learning algorithm—deep deterministic policy gradient (DDPG)—is utilized to search the solutions. Based on a rescue resource scheduling model of storm surge inundation logistics targeting storm surge Mangkhut in September, 2018, a case study of the Futian District, Shenzhen, China, was conducted to verify the correctness and efficiency of the MILP and the DDPG. The optimal schedule solution designed by the MILP had a duration of 3.5138 h, while the solution calculated by DDPG had a duration of 5.065 h for the rescue. The execution time of DDPG was stable and under a second, while the execution time of the MILP was over two hours.

Energy-efficient anchor activation protocol for non-cooperative localization of Industrial Internet of Things (IIoT)

The Industrial Internet of Things (IIoT) necessitates low-latency and ultra-reliable communication to support various applications. Besides, the data generated in IIoT must be geo-tagged to take location-aware timely actions. Therefore, this paper proposes an accurate and energy-efficient localization scheme for localizing the devices in IIoT. We introduce a novel anchor activation protocol for the non-cooperative localization of IIoT devices. The proposed method is sequential, distributed, and error adaptive, that first performs distributed scheduling followed by adaptive selection. This distributed localization scheme is well-suited for IIoT due to the non-cooperative nature of the devices; however, at the same time, it is more challenging because no centralized node can disseminate the anchors’ network information. Therefore, the anchors rely only on the local information from other anchors in their vicinity. The distributed approach can significantly benefit such networks because the centralized node can be a system’s bottleneck. Besides the distributed nature, the decision process of anchor selection for localization considers the transmission energy of the anchors to make it energy efficient. Our results show the effectiveness of the proposed method both in terms of localization accuracy and energy efficiency.

A three-phase heuristic for last-mile delivery with spatial-temporal consolidation and delivery options

The parcel demand in last-mile delivery increases sharply with the growth of e-commerce, which has been accelerated by the COVID-19 pandemic. The current delivery scheme follows a first-in-first-out (FIFO) rule, i.e., parcels arriving at different periods for the same customer are likely to become separate deliveries, which incurs a high transportation cost. To address this, an innovative spatial-temporal based consolidation (STC) strategy is proposed to consolidate parcels based on their delivery due dates and spatial distributions to reduce redundant delivery trips and lower the total cost. In addition to reducing the operational cost, improving customer satisfaction by offering diversified delivery preferences is key for enterprises to maintain competitiveness. This paper investigates a new variant of last-mile delivery that integrates parcel consolidation and flexible delivery options, including serving through home delivery and parcel lockers. A bi-objective model is formulated to balance the total cost and the service satisfaction. Then a three-phase heuristic is developed to solve the model. In the three-phase heuristic, the first phase determines the parcel distribution schedule, delivery option and node visiting schedule, while the second one optimizes vehicle routing by solving a series of traveling salesman problems. The third phase improves the solution by applying a general improvement routing procedure. The performance of the three-phase heuristic is demonstrated through extensive computational experiments based on a realistic parcel distribution network in the city of Melbourne, Australia. Experimental results reveal that the STC strategy has significant advantages over the FIFO scheme. Moreover, the advantage increases with the unit transportation cost, vehicle capacity and consumer distribution dispersion.

Integrated production scheduling and vehicle routing problem with energy efficient strategies: Mathematical formulation and metaheuristic algorithms

This paper addresses integrated production and distribution scheduling problem in which orders/jobs are undergone a single operation on any one of the identical machines in parallel and upon the completion of the production they are distributed to destined customers by limited number of vehicles. Customers located in dispersed regions place their orders with predetermined demand size and time windows. On production side, machines operate under discrete speed modes, low of which requires less energy cost or vice versa. On distribution side, energy consumed by a vehicle varies depending on the size of load on it. Therefore, objective is minimizing the sum of the weighted cost emanating from early and tardy deliveries plus production and distribution costs. Operational decisions for (i) production are to determine the allocation of jobs to the machines and sequence of jobs on any machine as well as speed mode of each machine for a particular job. As for (ii) distribution: We need to decide vehicle assignment to specific subset of consolidated jobs and the sequence of customer visitation for each vehicle. We develop a formulation for the problem at hand involving parallel machine scheduling and vehicle routing to obtain solutions to optimality. Not surprisingly, however, CPLEX only provides optimum solution for all instances with customer number up to and 6, for which reason we present two metaheuristics, namely Memetic Algorithm (MA) and Iterated Local Search (ILS) for practical sized instances. Computational results indicate that ILS yields better solutions in shorter times as compared to its counterpart.

A multi-objective discrete differential evolution algorithm for energy-efficient distributed blocking flow shop scheduling problem

ABSTRACT The energy problem in green manufacturing has attracted enormous attention from researchers and practitioners in the manufacturing domain with the global energy crisis and the aggravation of environmental pollution. The distributed blocking flow shop scheduling problem (DBFSP) has considerable application scenarios in connection with its widespread application in the industry under the background of intelligent manufacturing. A multi-objective discrete differential evolution (MODE) algorithm is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with the objectives of the makespan and total energy consumption (TEC) in this paper. The cooperative initialisation strategy is proposed to generate the initial population of the EEDBFSP. The mutation, crossover, and selection operators are redesigned to enable the MODE algorithm as applied to discrete space. A local search strategy based on the knowledge of five operators is introduced to enhance the exploitation capability of the MODE algorithm in the EEDBFSP. The non-critical path energy-efficient strategy is proposed to reduce energy consumption according to the specific constraints in the EEDBFSP. The effectiveness of each strategy in the MODE algorithm is verified and compared with the state-of-the-art algorithms. The numerical results demonstrate that the MODE algorithm is the efficient optimiser for solving the EEDBFSP.

Two-stage distributionally robust integrated scheduling of oxygen distribution and steelmaking-continuous casting in steel enterprises

In the steel industry, the imbalance between fluctuating oxygen demand and stable supply generally results in excessive oxygen emissions and power waste. Independent optimal scheduling of oxygen distribution (OD) or steelmaking-continuous casting (SCC) has limited ability to handle this issue. To address this challenge, we propose an integrated scheduling model that allows for flexible interaction between OD and SCC. We first develop an SCC scheduling model that applies to machine sharing of duplex and conventional smelting, and derive an OD scheduling model. A critical demand interface is further designed to connect the scheduling models of SCC and OD, resulting in a mixed integer linear programming-based integrated scheduling model. Existing research overlooks the probability information of demand uncertainty posed by unpredictable interferences, which limits the ability to handle uncertain demands. To address this limitation, we embed demand probability distribution via a data-driven ambiguity set and upgrade the deterministic integrated scheduling model via two-stage distributionally robust optimization. Our model was tested with real data and resulted in a 15% reduction in total costs compared to independent scheduling of OD and SCC. Additionally, it effectively reduced uncertainty risk to 8.3% with low conservativeness, demonstrating superior performance compared to recent methods.

A collaborative iterated greedy algorithm with reinforcement learning for energy-aware distributed blocking flow-shop scheduling

Energy-aware scheduling has attracted increasing attention mainly due to economic benefits as well as reducing the carbon footprint at companies. In this paper, an energy-aware scheduling problem in a distributed blocking flow-shop with sequence-dependent setup times is investigated to minimize both makespan and total energy consumption. A mixed-integer linear programming model is constructed and a cooperative iterated greedy algorithm based on Q-learning (CIG) is proposed. In the CIG, a top-level Q-learning is focused on enhancing the utilization ratio of machines to minimize makespan by finding a scheduling policy from four sequence-related operations. A bottom-level Q-learning is centered on improving energy efficiency to reduce total energy consumption by learning the optimal speed governing policy from four speed-related operations. According to the structure characteristics of solutions, several properties are explored to design an energy-saving strategy and acceleration strategy. The experimental results and statistical analysis prove that the CIG is superior to the state-of-the-art competitors with improvement percentages of 20.16 % over 2880 instances from the well-known benchmark set in the literature.

Energy saving management technology for electrical automation and power distribution network dispatching

With the rapid development of the economy, the demand for electric power is increasing, and the operation quality of the power system directly affects the quality of people’s production and life. The electric energy provided by the electric power system is the foundation of social operation. Through continuous optimization of the functions of the electric power system, the efficiency of social operation can be improved, and economic benefits can be continuously created, thereby promoting social progress and people’s quality of life. In the power system, the responsibility of the power distribution network (PDN) is to transmit electricity to all parts of the country, and its transmission efficiency would directly affect the operational efficiency of the power system. PDN scheduling plays an important role in improving power supply reliability, optimizing resource allocation, reducing energy waste, and reducing environmental pollution. It is of great significance for promoting social and economic development and environmental protection. However, in the PDN scheduling, due to the inflexibility of the power system scheduling, it leads to the loss and waste of electric energy. Therefore, it is necessary to upgrade the operation of the PDN automatically and use automation technology to improve the operational efficiency and energy utilization rate of the power system. This article optimized the energy-saving management of PDN dispatching through electrical automation technology. The algorithm proposed in this paper was a distribution scheduling algorithm based on electrical automation technology. Through this algorithm, real-time monitoring, analysis, and scheduling of PDNs can be achieved, thereby improving the efficiency and reliability of distribution systems and reducing energy consumption. The experimental results showed that before using the distribution scheduling algorithm based on electrical automation technology, the high loss distribution to transformation ratios of power distribution stations in the first to fourth quarters were 21.93%, 22.95%, 23.61%, and 22.47%, respectively. After using the distribution scheduling algorithm, the high loss distribution to transformation ratios for the four quarters were 15.75%, 13.81%, 14.77%, and 13.12%, respectively. This showed that the algorithm can reduce the high loss distribution to transformation ratio of power distribution stations and reduce their distribution losses, which saved electric energy. The research results of this article indicated that electrical automation technology can play an excellent role in the field of PDN scheduling, which optimized the energy-saving management technology of PDN scheduling, indicating an advanced development direction for intelligent management of PDN scheduling.

An improved iterative greedy athm for energy-efficient distributed assembly no-wait flow-shop scheduling problem

An improved iterative greedy athm for energy-efficient distributed assembly no-wait flow-shop scheduling problem

Supply Chain Scheduling Method for the Coordination of Agile Production and Port Delivery Operation

The cost-reducing potential of intelligent supply chains (ISCs) has been recognized by companies and researchers. This paper investigates a two-echelon steel supply chain scheduling problem that considers the parallel-batching processing and deterioration effect in the production stage and sufficient vehicles in the port delivery stage. To solve this problem, we first analyze several sufficient and necessary conditions of the optimal scheme. We then propose a heuristic algorithm based on a dynamic programming algorithm to obtain the optimal solution for a special case where the assignment of all ingots to the soaking pits is known. Based on the results of this special case, we develop a modified biased random-key genetic algorithm (BRKGA), which incorporates genetic operations based on the flower pollination algorithm (FPA) to obtain joint production and distribution schedules for the general problem. Finally, we conduct a series of computational experiments, the results of which indicate that BRKGA-FPA has certain advantages in solving quality and convergence speed issues.

Distributed surgical scheduling across collaborating hospitals considering stochastic duration and emergency demand

In this paper, we address the need for distributed surgical scheduling across multiple hospitals and multiple days. We develop an effective ex-ante distributed schedule to determine elective service operation and elective patient assignment plan in a hospital alliance with the goal of minimizing the total expected cost considering possible realizations of treatment durations and emergency demands. The problem is formulated into a two-stage stochastic integer programming where uncertainty is characterized by a scenario set. Cancellations of scheduled elective patients and inter-hospital transfers of materialized emergency patients are explicitly modeled as second-stage decisions. To solve the problem, we propose an integer decomposition algorithm that incorporates a novel logic-based optimality cut along with a validity proof. Additionally, we introduce several enhancements to improve the algorithm. Through numerical experiments, we demonstrate that our proposed algorithm outperforms both the commercial solver and the existing integer decomposition algorithm for the proposed problem. It provides effective solutions that closely approach the best upper bounds achieved by the solver in less time, or even surpass the solver’s best upper bounds. Furthermore, we provide operational insights into other commonly-used scheduling approaches through numerical comparisons, including the strengths and limitations of the most common way of scheduling elective patients and two strategies for handling emergencies within a hospital alliance.

A performance-impact based multi-task distributed scheduling algorithm with task removal inference and deadlock avoidance

A performance-impact based multi-task distributed scheduling algorithm with task removal inference and deadlock avoidance

A dynamic material distribution scheduling of automotive assembly line considering material-handling errors

PurposeIn a kitting supply system, the occurrence of material-handling errors is unavoidable and will cause serious production losses to an assembly line. To minimize production losses, this paper aims to present a dynamic scheduling problem of automotive assembly line considering material-handling mistakes by integrating abnormal disturbance into the material distribution problem of mixed-model assembly lines (MMALs).Design/methodology/approachA multi-phase dynamic scheduling (MPDS) algorithm is proposed based on the characteristics and properties of the dynamic scheduling problem. In the first phase, the static material distribution scheduling problem is decomposed into three optimization sub-problems, and the dynamic programming algorithm is used to jointly optimize the sub-problems to obtain the optimal initial scheduling plan. In the second phase, a two-stage rescheduling algorithm incorporating removing rules and adding rules was designed according to the status update mechanism of material demand and multi-load AGVs.FindingsThrough comparative experiments with the periodic distribution strategy (PD) and the direct insertion method (DI), the superiority of the proposed dynamic scheduling strategy and algorithm is verified.Originality/valueTo the best of the authors’ knowledge, this study is the first to consider the impact of material-handling errors on the material distribution scheduling problem when using a kitting strategy. By designing an MPDS algorithm, this paper aims to maximize the absorption of the disturbance caused by material-handling errors and reduce the production losses of the assembly line as well as the total cost of the material transportation.

SOLAR ENERGY FORECASTING WITH DEEP LEARNING TECHNIQUE

The increasing reliance on renewable energy sources, such as solar power, necessitates accurate forecasting to ensure efficient grid integration and stability. This study explores the application of deep learning techniques, particularly deep neural networks (DNNs), in predicting solar irradiance and power output. By leveraging advanced algorithms and large datasets, this research aims to enhance the precision of solar energy forecasts, thereby optimizing grid management and resource allocation. Deep learning techniques, characterized by their ability to model complex nonlinear relationships, offer significant advantages over traditional statistical methods in forecasting solar energy. This study focuses on the development and evaluation of deep neural networks for predicting solar irradiance, which directly influences the power output of photovoltaic (PV) systems. The models are trained on extensive historical weather data, satellite imagery, and real-time solar output measurements to capture temporal and spatial variations in solar energy. Key components of the research include data preprocessing to handle missing values and noise, feature extraction to identify relevant patterns, and model training using various architectures of DNNs such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The study also employs ensemble learning techniques to combine predictions from multiple models, further enhancing forecast accuracy. Results demonstrate that deep neural networks significantly outperform traditional forecasting methods in terms of accuracy and reliability. The improved forecasts enable better scheduling of energy generation and distribution, reducing reliance on fossil fuels and minimizing grid imbalances. Additionally, accurate solar energy predictions support the efficient integration of solar power into the grid, enhancing overall system stability and reducing operational costs. The study also addresses challenges in implementing deep learning models, such as computational requirements and the need for large, high-quality datasets. Solutions include leveraging cloud computing resources and developing standardized data collection protocols to facilitate broader application and scalability of the models. Application of deep learning techniques in solar energy forecasting represents a promising advancement for the renewable energy sector. By improving the accuracy of solar irradiance and power output predictions, deep neural networks contribute to more reliable grid integration and efficient energy management. This research advocates for the continued exploration and adoption of advanced machine learning methods to support the transition to a sustainable energy future. Keywords: Deep Learning Techniques; Solar Energy; Forecasting; Grid Integration; Power Output.

Low-Carbon and Energy-Saving Path Optimization Scheduling of Material Distribution in Machining Shop Based on Business Compass Model

In order to reduce carbon emission and energy consumption in the process of raw material distribution, the workshop material distribution management model was established based on the business compass model; it can help guide enterprises to manage workshop production. Based on the raw material distribution equipment, a path calculation model considering the carbon emission and energy consumption in the process of raw material distribution was established. The dung beetle optimizer was selected for the optimization calculation. The dung beetle optimizer has the characteristics of fast convergence and high solution accuracy. The material distribution of an engine assembly workshop was taken as an example; the results showed that the optimized scheduling model could effectively optimize the material distribution route and reduce energy consumption and carbon emission in the distribution process on the basis of meeting the distribution demand.

Optimal operation of distribution networks and multiple community energy prosumers based on mixed game theory

Community energy systems are currently transitioning from conventional consumers to prosumers. Under this context, the coordinated management of multiple community energy prosumers (CEPs) is explored this study in accordance with the same distribution network. The aim of this study is to facilitate the coordination of unit operations, demand response, and peer-to-peer (P2P) energy trading among CEP coalitions based on the distribution network operator (DSO) by setting electricity prices. Thus, a mixed game-based optimal operation model of DSO and CEP alliances is developed, integrating Stackelberg and cooperative games. The upper layer aims at maximizing DSO revenue, whereas the lower layer minimizes the operation costs of CEP cooperative alliances while allocating benefits in accordance with the Nash-Harsanyi bargaining theory. The model is solved with a two-stage distributed algorithm that integrates the bisection method and the alternating direction method of multipliers (ADMM). Furthermore, cloud energy storage (CES) is introduced to optimize the economics of CEP. The effectiveness of the proposed method is verified through a case study, demonstrating the collaborative optimal scheduling of multiple CEPs and reasonable benefit distribution. Accordingly, this study is conducive to expediting the effective and economical management of CEPs under distribution networks.

Review of Just-in-Time Material Distribution Scheduling for Mixed Flow Assembly Lines Considering Transfer Vehicle Handling Energy Consumption

In recent years, the automotive industry, which uses component assembly methods for production, has grown rapidly as the demand for new energy vehicles has increased. This trend has also led to an increasing number of customers seeking individualized options, which places greater demands on traditional OEMs' vehicle production and assembly processes. In order to meet these challenges, OEMs need to improve their platform production lines by replacing traditional single-product lines with mixed-flow assembly lines. With the development of industrial standards and technology, automated production lines and unmanned vehicles are also being used in assembly line production. Decisions around the scheduling of material distribution on the side of assembly lines with material supermarkets need to be addressed in terms of the start of handling time of the AGV (Automated Guided Vehicle) trolley and the number of loaded bins. This paper presents a review of the research on issues such as distribution patterns under mixed flow assembly lines, combined with the new material supermarket distribution model, and considering the energy consumption of transfer vehicles for handling.

Green city logistics path planning and design based on genetic algorithm.

Effective logistics distribution paths are crucial in enhancing the fundamental competitiveness of an enterprise. This research introduces the genetic algorithm for logistics routing to address pertinent research issues, such as suboptimal scheduling of time-sensitive orders and reverse distribution of goods. It proposes an enhanced scheme integrating the Metropolis criterion. To address the limited local search ability of the genetic algorithm, this study combines the simulated annealing algorithm's powerful local optimization capability with the genetic algorithm, thereby developing a genetic algorithm with the Metropolis criterion. The proposed method preserves the optimal chromosome in each generation population and accepts inferior chromosomes with a certain probability, thereby enhancing the likelihood of finding an optimal local solution and achieving global optimization. A comparative study is conducted with the Ant Colony Optimization, Artificial Bee Colony, and Particle Swarm Optimization algorithms, and empirical findings demonstrate that the proposed genetic algorithm effectively achieves excellent results over these algorithms.