Accurate Scheduling Research Articles

KSP-SCOS: new Karp-Steele patching based streamlined customer order scheduling approach

Customer order scheduling (COS) encapsulates one of the most intricate problems in operational optimization, primarily due to the multifaceted interplay of constraints such as temporal dependencies, resource limitations, and cost minimization. This paper introduces the Karp-Steele Patching for Streamlined COS (KSP-SCOS), a cutting-edge framework designed to address these complexities with unparalleled precision and efficiency. By incorporating the dynamic problem decomposition capabilities of Karp-Steele Patching, KSP-SCOS enhances the scheduling landscape through systematic exploration of feasible solutions while eliminating suboptimal paths through strategic pruning. The proposed approach was subjected to rigorous testing across diverse datasets representing a broad spectrum of real-world operational scenarios. These scenarios range from minimally constrained environments to those characterized by extensive resource contention and tight scheduling windows. Experimental findings reveal that KSP-SCOS consistently delivers near-optimal solutions, demonstrating robust scalability and significant reductions in computational overhead, regardless of the problem complexity. This research makes several noteworthy contributions to COS optimization : (1) The integration of the Karp-Steele Patching method within COS, ensuring a systematic yet adaptable solution process. (2) An empirical analysis across datasets of varying complexity, illustrating the algorithm’s effectiveness in balancing computational efficiency and scheduling quality. (3) An exploration of cost-time trade-offs, shedding light on the practical implications of for resource allocation in dynamic operational settings. The adaptability of KSP-SCOS establishes it as a groundbreaking for industries demanding dynamic, accurate, and resource-efficient scheduling strategies. Its capability to seamlessly adjust to evolving operational constraints underscores its critical relevance in scenarios requiring immediate and responsive decision-making. This study marks a pivotal advancement in resolving the pressing challenges of modern COS, paving the way for elevated operational efficacy and adaptability in increasingly complex environments.

A multi-objective optimization algorithm-based capacity scheduling method for photovoltaic power hybrid energy storage systems

In this study, the combination of crossover algorithm and particle swarm optimization—crossover algorithm-particle swarm optimization (CS-PSO) algorithm—to optimize photovoltaic hybrid energy storage scheduling, improving global search and convergence speed, is discussed. The new method reduces energy storage costs and energy losses, ensures supply–demand balance and interaction power constraints, and maintains population diversity through cross-search. The CS-PSO algorithm introduces battery state of charge optimization for energy storage scheduling, improving global search and convergence speed, and obtaining accurate multi-objective scheduling results. The experiment shows that the optimal configuration for photovoltaic energy storage is 10 045 batteries + 687 244 supercapacitors, with a cost of 3.452 × 105 yuan and an energy loss of less than 5%. CS-PSO has similar costs but lower losses and faster convergence compared to traditional methods.

Evaluation of Different Commercial Sensors for the Development of Their Automatic Irrigation System.

Reliable soil moisture information is essential for accurate irrigation scheduling. A wide range of soil moisture sensors are currently available on the market, but their performance needs to be evaluated as most sensors are calibrated under limited laboratory conditions. The aim of this study was to evaluate the performance of six commercially available moisture sensors (HydraProbe, Teros 10, Teros 11, EnviroPro, CS616 and Drill & Drop) and three tensiometers (Irrometer RSU-C-34, Teros 32 and Teros 21) and to establish calibration equations for a typical sandy soil of the Doñana National Park (Huelva, Spain). The calibration process for soil moisture sensors indicated differences between factory and corrected equations. All tested sensors improved with adjustments made to the factory calibration, except for the HydraProbe sensor which had a more accurate factory equation for a sandy soil. Among the various sensors tested, the Teros 10, Teros 11, and HydraProbe were found to be the easiest to install, typically positioned with an auger to prevent preferential pathways and ensure adequate sensor-soil contact. For tensiometers, the Teros 32 sensor requires specialized labor for its correct installation, as it must be positioned at a specific angle and maintained with distilled water. All tensiometers need a stabilization period after installation.

Mixed Thermal and Renewable Energy Generation Optimization in Non-Interconnected Regions via Boolean Mapping

Global efforts aiming to shift towards renewable energy and smart grid configurations require accurate unit commitment schedules to guarantee power balance and ensure feasible operation under different complex constraints. Intelligent systems utilizing hybrid and high-level techniques have arisen as promising solutions to provide optimum exploration–exploitation trade-offs at the expense of computational complexity. To ameliorate this requirement, which is extremely expensive in non-interconnected renewable systems, radically different approaches based on enhanced priority schemes and Boolean encoding/decoding have to take place. This compilation encompasses various mappings that convert multi-valued clausal forms into Boolean expressions with equivalent satisfiability. Avoiding any need to introduce prior parameter settings, the solution utilizes state-of-the-art advancements in the field of artificial intelligence models, namely Boolean mapping. It allows for the efficient identification of the optimal configuration of a non-convex system with binary and discontinuous dynamics in the fewest possible trials, providing impressive performance. In this way, Boolean mapping becomes capable of providing global optimum solutions to unit commitment utilizing fully tractable procedures without deteriorating the computational time. The results, considering a non-interconnected power system, show that the proposed model based on artificial intelligence presents advantageous performance in terms of generating cost and complexity. This is particularly important in isolated networks, where even a-not-so great deviation between production and consumption may reflect as a major disturbance in terms of frequency and voltage.

Accurate Optimal Scheduling In Cloud Computing Based On Hybrid Bacterial Evolutionary And Bees Mating Optimization Algorithm

Accurate Optimal Scheduling In Cloud Computing Based On Hybrid Bacterial Evolutionary And Bees Mating Optimization Algorithm

Structural evaluation and analysis of existing house renovation into shophouse based on Building Information Modeling

Indonesia's population and economic growth must be supported by adequate facilities and infrastructure, including residences and business premises. Renovating residential houses into shophouses offers an effective way to optimize land use and reduce construction costs. A thorough evaluation of existing structural elements is critical to determining which components can be retained, alongside accurate cost and scheduling assessments to ensure smooth renovation. This research utilizes Building Information Modeling (BIM) to integrate structural, architectural, and MEP work, as well as to streamline scheduling and cost estimation. ETABS is employed for structural design, while Autodesk Revit and Navisworks are used for cost and schedule planning. The assessment follows ASCE 41-17 guidelines to evaluate existing structures, and SNI 1726:2019 and SNI 2847:2019 standards for retrofitting and designing new structures. The evaluation reveals deficiencies in global strength and component ductility, necessitating structural retrofits using concrete jacketing. This study not only addresses the renovation of a residential house into a shophouse but also contributes to broader research in construction. The estimated cost for the structural renovation is Rp. 159,692,607.93, and the duration is projected at 58 days, with 11 days for demolition and 47 days for upper structure construction.

Virtual Simulation-Based Optimization for Assembly Flow Shop Scheduling Using Migratory Bird Algorithm.

As industrial informatization progresses, virtual simulation technologies are increasingly demonstrating their potential in industrial applications. These systems utilize various sensors to capture real-time factory data, which are then transmitted to servers via communication interfaces to construct corresponding digital models. This integration facilitates tasks such as monitoring and prediction, enabling more accurate and convenient production scheduling and forecasting. This is particularly significant for flexible or mixed-flow production modes. Bionic optimization algorithms have demonstrated strong performance in factory scheduling and operations. Centered around these algorithms, researchers have explored various strategies to enhance efficiency and optimize processes within manufacturing environments.This study introduces an efficient migratory bird optimization algorithm designed to address production scheduling challenges in an assembly shop with mold quantity constraints. The research aims to minimize the maximum completion time in a batch flow mixed assembly flow shop scheduling problem, incorporating variable batch partitioning strategies. A tailored virtual simulation framework supports this objective. The algorithm employs a two-stage encoding mechanism for batch partitioning and sequencing, adapted to the unique constraints of each production stage. To enhance the search performance of the neighborhood structure, the study identifies and analyzes optimization strategies for batch partitioning and sequencing, and incorporates an adaptive neighborhood structure adjustment strategy. A competition mechanism is also designed to enhance the algorithm's optimization efficiency. Simulation experiments of varying scales demonstrate the effectiveness of the variable batch partitioning strategy, showing a 5-6% improvement over equal batch strategies. Results across different scales and parameters confirm the robustness of the algorithm.

Energy management strategy for accurate thermal scheduling of the combined hydrogen, heating, and power system based on PV power supply

To solve the negative impact of renewable energy volatility on the power system, a combined hydrogen, heating and power system integrating alkaline water electrolysis, metal hydride hydrogen storage, and proton exchange membrane fuel cells (PEMFCs) is proposed. However, water electrolysis, hydrogen adsorption and PEMFC all generate heat, and how to use this heat is the key to improving the system efficiency. Therefore, four energy scheduling schemes are proposed in this study, aiming at making full use of waste heat to improve its energy utilization efficiency. The results show that the system heat is almost entirely utilized under the proposed four strategies. Meanwhile, the peak-valley strategy is more suitable for on-grid operation, and heat priority, peak clipping and load reduction strategy are more suitable for off-grid operation. Under the heat priority strategy, residual electricity and heat are the lowest. Under the peak clipping strategy, the current density of the PEMFC fluctuates the least and is maintained at 0.59 A cm−2 during the heat-led period. Under the load reduction strategy, during the heat-led period (5:00 p.m. ∼ 6:00 a.m.), the system electric power is lower than the power load for the shortest time, accounting for 23.93% of this operating duration.

Smart Medi Box

This paper addresses the prevalent issue of medication mismanagement among older individuals in families, a problem observed not only in our personal context but also widespread across various demographics. Many elderly family members struggle with the complexity of multiple medications, leading to missed doses and inadvertent intake of incorrect medicines. Recognizing the urgency of this concern, we embarked on a mission to devise a solution, resulting in the conceptualization and development of a Smart Medi Box. This innovative device aims to streamline medication management by incorporating smart technology to facilitate accurate dosage scheduling and timely reminders. The impetus for this initiative stems from the staggering statistic that 16 crore (160 million) Indians rely on daily medications. Through the Smart Medi Box, we aspire to enhance medication adherence, mitigate risks associated with incorrect usage, and ultimately improve the overall well-being of individuals managing chronic illnesses. Key Words: Medication management, Smart Medi Box, adherence, medication scheduling, elderly care, health technology, chronic illnesses, reminder system, healthcare innovation, Indian population.

Short-term stochastic multi-objective optimization scheduling of wind-solar-hydro hybrid system considering source-load uncertainties

Uncertainties in energy outputs (source side) and load side are simultaneously present and cannot be ignored in the actual operation of multi-energy systems. Adopting a reasonable uncertainty modeling method and understanding the impact of source-load uncertainties on optimization scheduling are key to formulating accurate and effective multi-energy scheduling plans. To address the uncertainties on both the source side and the load side in wind-solar-hydro hybrid systems, this paper proposes a multi-objective optimization scheduling model based on stochastic programming theory. The model aims to maximize the net profit of the system's power generation and minimize the fluctuation of the remaining load. It employs Vine-Copula coupled with Monte Carlo simulation and the deep learning method TimeGAN to generate joint wind and solar power output and load scenario sets. The generated source-load uncertainty scenarios are then reduced to representative scenarios using the K-Means clustering method, which are used as inputs for the scheduling model. The proposed model is applied to a wind-solar-hydro energy base in China, and the results show that: 1) The Vine-Copula-based source-side scenario generation method can quantitatively consider the correlations among meteorological factors. The relative errors of the generated scenarios' statistics compared to the original data are all less than 5%, and the relative errors of the correlation coefficients are less than 10%. 2) The TimeGAN-based load-side scenario generation method avoids the presupposition of the load probability distribution. Compared to the original data, the generated scenarios have R2 and Pearson correlation coefficients of 0.77 and 0.87, respectively. Additionally, TimeGAN shows significant advantages over traditional random sampling methods in simulating extreme scenarios. 3) Both source-side and load-side uncertainties significantly impact the optimization scheduling results of multi-energy systems, leading to increased fluctuation of the remaining load and decreased net profit. 4) The combined source-load uncertainties have a synergistic negative impact on the multi-objective optimization scheduling results. 5) The Pareto front of the optimization results is a concave function with low marginal benefits. Decision-makers should adopt a compromise solution as a guide for the operation of multi-energy systems.

Long Short-Term Renewable Energy Sources Prediction for Grid-Management Systems Based on Stacking Ensemble Model

The transition towards sustainable energy systems necessitates effective management of renewable energy sources alongside conventional grid infrastructure. This paper presents a comprehensive approach to optimizing grid management by integrating Photovoltaic (PV), wind, and grid energies to minimize costs and enhance sustainability. A key focus lies in developing an accurate scheduling algorithm utilizing Mixed Integer Programming (MIP), enabling dynamic allocation of energy resources to meet demand while minimizing reliance on cost-intensive grid energy. An ensemble learning technique, specifically a stacking algorithm, is employed to construct a robust forecasting pipeline for PV and wind energy generation. The forecasting model achieves remarkable accuracy with a Root Mean Squared Error (RMSE) of less than 0.1 for short-term (15 min and one day ahead) and long-term (one week and one month ahead) predictions. By combining optimization and forecasting methodologies, this research contributes to advancing grid management systems capable of harnessing renewable energy sources efficiently, thus facilitating cost savings and fostering sustainability in the energy sector.

Sync Alert Notice Board

The SyncAlert Notice Board project represents a significant advancement in communication technology, leveraging a sophisticated integration of hardware components to create a versatile communication hub. At its core, the project features a 45x6 LED matrix, enabling vivid and customizable display capabilities for messages and notifications. This matrix is complemented by an RTC (Real-Time Clock) module, ensuring accurate timekeeping and scheduling functionalities essential for managing events and deadlines effectively. In addition to visual communication, the project incorporates an MP3 module, adding an auditory dimension to message delivery and enhancing user engagement. The integration of a Bluetooth HC05 module further enhances the project's utility by enabling seamless wireless transmission of data, allowing users to update the notice board remotely and in real time. This combination of features transforms the SyncAlert Notice Board into a dynamic and interactive communication tool suitable for a wide range of environments, from educational institutions to corporate offices. By addressing the specific needs of users for efficient information dissemination and scheduling management, the SyncAlert Notice Board project offers a comprehensive solution that not only streamlines communication but also enhances organizational productivity and engagement. Its versatility, coupled with user-friendly functionality, makes it an invaluable asset for any setting where clear and timely communication is paramount

A multi-timescale optimization method for integrated energy systems with carbon capture and accounting

As the goal of “double carbon”, integrated energy systems aiming at the development of low-carbon economy are developing rapidly, and carbon capture and other emission reduction technologies are gradually gaining more extensive development space. For controlling carbon emissions and enhance the consumption of renewable energy. This work proposes to introduce carbon capture technology in the framework of integrated energy system and optimize the energy dispatching of integrated energy system in multiple time scales, and design a multi-time scale optimization model of integrated energy system with carbon capture. Based on the basic architecture of a low-carbon integrated energy system, this study analyzes the power characteristics of each unit of the integrated energy system, which consists of thermal power units, gas turbines, electric boilers, batteries, gas storage, heat storage, etc. By studying the energy conversion and storage processes of each unit, a power model of each unit of the integrated energy system is established. On this basis, the relationship between carbon emissions and unit output of thermal power units and gas turbines was studied, and a carbon emission model for the energy supply unit in the comprehensive energy system was established. At the same time, in order to solve the problem of carbon emission reduction under the day ahead scheduling plan of the integrated energy system, considering the emission reduction goals and system operation security factors, the study analyzed the economic model and carbon emission model of the integrated energy system, established the day ahead low-carbon scheduling model of the integrated energy system, and reasonably planned the output of each unit that can achieve the carbon emission reduction goals on the premise of meeting the balance of supply and demand. The innovation of the research method of this paper is that this paper establishes a multi time scale rolling optimization model under the emission reduction goal of the integrated energy system. Based on the day ahead scheduling scheme obtained in the day ahead low-carbon scheduling phase, the day ahead plan is first revised through 4 h rolling scheduling in the day; Then, with the goal of minimizing the adjustment amount, fine tune the unit output within 15 minutes to provide a daily output plan for subsequent low-carbon emission reduction targets. The outcomes indicate that in the practical application, the carbon emission of the optimized model in the peak hour 11:00 to 12:00 phase is 118 tons, which is 7 tons less than the 125 tons of the traditional model. In summary, it demonstrates that the studied multi-timescale optimization model of integrated energy system with carbon capture has good application. We have studied and analyzed the low-carbon implementation mechanism of coordinated cooperation in multiple time scales, and constructed a multi time scale rolling optimization model, laying a theoretical foundation for subsequent low-carbon scheduling research. This enables the system to formulate more accurate and reasonable scheduling plans, while improving the low-carbon performance and economic benefits of the system, providing reference for the low-carbon development of the power system.

A multi-function integrated PVDF transducer for fault detection and speed measurement of cylindrical roller bearings

Monitoring the condition of bearings in rotating machinery is crucial to ensure reliability, safety, and efficiency. Recently, integrated sensors have become more popular than traditional accelerometers in bearing condition monitoring because of their compact design, cost-effectiveness, and improved efficiency in fault detection through a short transmission path. This study presents a new approach to bearing condition monitoring by developing a bearing with an integrated Polyvinylidene Fluoride transducer. This polymer-based transducer, known as PVDF, has a piezoelectric effect which generates electric charge in response to mechanical excitation. The PVDF transducer was selected for its high sensitivity, flexibility, and thin structure. The developed bearing with the integrated PVDF transducer can detect faults under constant and variable speeds, and the damage size can be estimated with an accuracy of 10%. It outperformed a commercial accelerometer in noisy environments with artificial impacts from an air motor, making it a highly reliable option for bearing condition monitoring in noisy industrial settings. The PVDF transducer can also be used as a low-cost speed sensor with an accuracy of 5.2%, offering a versatile solution for machine monitoring. By simultaneously recording rotational speed and vibration, the developed integrated transducer provides a comprehensive view of machine health and performance as a multi-function transducer, enabling more accurate and efficient maintenance scheduling.

A Refined Wind Power Forecasting Method with High Temporal Resolution Based on Light Convolutional Neural Network Architecture

With a large proportion of wind farms connected to the power grid, it brings more pressure on the stable operation of power systems in shorter time scales. Efficient and accurate scheduling, operation control and decision making require high time resolution power forecasting algorithms with higher accuracy and real-time performance. In this paper, we innovatively propose a high temporal resolution wind power forecasting method based on a light convolutional architecture—DC_LCNN. The method starts from the source data and novelly designs the dual-channel data input mode to provide different combinations of feature data for the model, thus improving the upper limit of the learning ability of the whole model. The dual-channel convolutional neural network (CNN) structure extracts different spatial and temporal constraints of the input features. The light global maximum pooling method replaces the flat operation combined with the fully connected (FC) forecasting method in the traditional CNN, extracts the most significant features of the global method, and directly performs data downscaling at the same time, which significantly improves the forecasting accuracy and efficiency of the model. In this paper, the experiments are carried out on the 1 s resolution data of the actual wind field, and the single-step forecasting task with 1 s ahead of time and the multi-step forecasting task with 1~10 s ahead of time are executed, respectively. Comparing the experimental results with the classical deep learning models in the current field, the proposed model shows absolute accuracy advantages on both forecasting tasks. This also shows that the light architecture design based on simple deep learning models is also a good solution in performing high time resolution wind power forecasting tasks.

Trunk dielectric permittivity correlates with irrigation based on soil water content in fruit trees

Time-domain reflectometry (TDR) is an electromagnetic technique that measures the dielectric permittivity (K) which is a surrogate property influenced by water content. Advances in nanoelectronics have enabled the development of a TDR probe (TDR-305 N) to monitor changes in K, bulk electrical conductivity (ECbulk) and temperature (T) in a porous medium, such as a tree trunk. The main objective of this study was to assess the effectiveness of the TDR-305 N sensors for real-time monitoring changes in water content in the trunk of nectarine trees. Throughout the summer of 2022, irrigation was automatically managed with threshold values of soil water content (θv-soil) measured with capacitance probes. Different management allowed depletion (MAD) values were set to trigger irrigation: 50 % in July (moderate water deficit), 100 % in August (severe water deficit), and recovery to well-irrigated conditions in September. Discrete measurements of midday stem water potential (Ψs,md) and leaf gas exchange were made frequently. The results showed a progressive reduction of the measured physiological parameters, as well as of K and ECbulk and θv-soil decreased. Notably, Ψs,md reached a critically low value of -2.03 MPa, coinciding with pronounced and severe stomatal closure. Both K and Ψs,md, were able to explain the variations of θv-soil by more than 75 %. Daily, a positive relationship of K and ECbulk was observed, although ECbulk exhibited a stronger dependence on Ttrunk compared to K. Furthermore, K did not return to its initial values prior to the onset of water stress, possibly influenced by xylem cavitation and a reduction in leaf area during its senescence stage. The findings suggest that trunk permittivity measurements obtained using TDR-305 N sensors could be a reliable indicator for monitoring tree water status. However, further research is needed to determine the threshold values of trunk water content under non-limiting soil water conditions for accurate irrigation scheduling.

Optimal Operation Scheduling of Multi-energy Complementary Systems Including Offshore Wind Power

Background: With the rapid development of offshore wind power in China, offshore wind power is accounting for an increasing proportion of the whole installed power generation in China, which put forward higher requirements for operation scheduling of multi-energy complementary systems including offshore wind power. However, existing studies have paid little attention to the complementary output scheduling of multi-energy systems with a high proportion of offshore wind power, this phenomenon is not conducive to the realization of reasonable operation scheduling. Objective: To realize the reasonable operation scheduling of multi-energy systems with a high proportion of offshore wind power. Methods: Firstly, the output characteristics of offshore wind power were analyzed and summarized, and modeling was conducted from the perspectives of time distribution, spatial correlation, volatility and randomness, which would serve as the theoretical basis for subsequent researches. Secondly, the complementary rule among offshore wind power and the multi-energy system was analyzed, and the complementary characteristic index was put forward. Then the safety operation was taken as the constraint, and the two-stage operation model of the integrated energy system was established, which considers the auxiliary service costs. In the first stage, the initial generation scheduling was developed according to the day-ahead forecast values of the offshore wind power; in the second stage, the more accurate intra-day operation scheduling was further achieved by considering the intra-day forecast values of offshore wind power and the day-ahead scheduling results. Costs of rotating reserve capacity could be reduced by correcting the deviation of generating capacity, then the revenue-maximizing scheduling strategies were obtained and the output of the pumped-storage power plants was optimized. Results: The effectiveness of the model proposed in this paper was verified based on the simulation examples. Compared with the traditional scheduling strategy, the scheduling strategy considering auxiliary service cost proposed in this paper can promote the consumption of offshore wind power, reduce the wind curtailment proportion and improve the electricity sales revenues in the end. Conclusion: The research results in this paper provide a reference for the reasonable consumption of multi-energy complementary systems with a high proportion of offshore wind power and improving electricity sales revenues

Predictive modelling of total operating room time for Laparoscopic Cholecystectomy using pre-operatively known indicators to guide accurate surgical scheduling in a critical access hospital

The financial margin of rural and critical access hospitals highly depends on their surgical volume. An efficient operating room is necessary to maximise profit and minimise financial loss. OR utilisation is a crucial OR efficiency metric requiring accurate case duration estimates. The patient's age, ASA, BMI, Mallampati score, previous surgery, the planned surgery, the surgeon, the assistant's level of experience and the severity of the patient's disease are also associated with operative duration. Although complex machine-learning models are accurate in operative prediction, they are not always available in resource-limited hospitals. Laparoscopic cholecystectomy (LC) is one of the most common surgical procedures performed and is one of the few procedures performed at critical access and rural hospitals. The accurate estimation of the operative duration of LC is essential for efficient OR utilisation. We hypothesise that a multivariate linear regression prediction model can be constructed from a set of pre-operatively known, easily collected variables to maximise OR utilisation and improve operative scheduling accuracy for LC. We further hypothesise that this model can be implemented in resource-limited environments, such as critical access hospitals.

An optimization model for detailed scheduling of heterogeneous fleet of log trucks considering synchronization

ABSTRACT Log transportation accounts for a significant portion of the total delivered cost of logs due to the considerable number of truckloads between origins and destinations. Hence, an efficient transportation plan at the operational level can generate cost savings for forest companies. In this paper, a mixed integer linear programming model is developed for daily routing and scheduling of heterogeneous trucks considering synchronization constraints. Continuous time representation is used for modeling the problem to generate accurate schedules and to synchronize the trucks and loaders at cut blocks and sort yards. Compatibility requirements, overtime, and decisions related to the trucking contractors are incorporated in the model. The outputs of the model include the number of trucks utilized by each contractor, the arrival times and waiting times of trucks at each location, the detailed schedule of loaders, and the amount of overtime assigned to the drivers. To validate the model, it is applied to test problems from a case of a Canadian forest company, where transportation activities are contracted out. Results show it is more economical to pay overtime than dispatching additional trucks to carry logs. Additionally, variable costs and maximum driving time have the most impact on the total transportation cost.

Research on Production Scheduling of Industrial Big Data for Internet of Things Based on Dynamic Planning Algorithm

Abstract In this paper, the optimized Dual Heuristic Dynamic Programming (DHP) algorithm is studied in depth, compared and analyzed with the other three algorithms through experimental simulation, and the DHP algorithm is applied to different industrial accurate scheduling tests to explore the loads of the industrial equipment during scheduling operation. The simulation results show that the dual-heuristic dynamic programming DHP algorithm has a significant decrease in both the mean waiting time (MWT) and the mean response time (MRT), where the highest peak value of the mean waiting time is only 0.06901 μs, and the highest peak value of the mean response time is 0.24493 μs. In industrial accurate production scheduling, the improved DHP algorithm performs the best for textile industry’’s task scheduling with the best performance. Specifically, the task scheduling completion time for the textile industry is 4522 seconds when the number of automated guided vehicles (AGVs) is the minimum value of 3. In comparison, the completion time is 8541 seconds when the number of AGVs is the maximum value of 15. The textile industry shows the fastest task completion time compared to the other industry types for different settings of the number of AGVs. In addition, the analysis of the operation of the equipment engines shows that although there are high and low torques during operation, the Torque of most of the engines stays within the range of 29 Nm to 30.7 Nm, which is generally stable and has little impact on the performance of the equipment. The improved dynamic programming algorithm proposed in this paper has effective application potential in industrial extensive data production scheduling, which can improve the efficiency and responsiveness of production scheduling. It has important practical significance for industrial production management.