Research on Shortest Path BFS Strategy in Multi-AGV Scheduling System

With the increasing maturity of automated guided vehicles(AGV)technology and the widespread application of flexible manufacturing systems,enhancing the efficiency of AGVs in complex environments has become crucial.This paper analyzes the challenges of path planning and scheduling in multi-AGV systems,introduces a map-based path search algorithm,and proposes the BFS algorithm for shortest path planning.Through optimization using the breadth-first search(BFS)algorithm,efficient scheduling of multiple AGVs in complex environments is achieved.In addition,this paper validated the effectiveness of the proposed method in a production workshop experiment.The experimental results show that the BFS algorithm can quickly search for the shortest path,reduce the running time of AGVs,and significantly improve the performance of multi-AGV scheduling systems.

Optimizing Vaccine Access: A Web-Based Scheduling System with Geo-Tagging Integration and Decision Support for Local Health Centers

The system created aims to produce an online vaccination appointment scheduling system with geo-tagging integration and a decision-support mechanism for neighborhood health clinics. With a decision support mechanism that suggests the essential vaccines based on their account details, it is made to meet the unique vaccination needs of each patient. The system includes immunizations that are accessible locally, and patients and midwives can manage their own corresponding information through personal accounts. Viewers of websites can visualize the distribution of vaccines by purok thanks to geotagging. The Agile Scrum Methodology was modified by the researchers for early delivery, change flexibility, and continual system improvement in order to accomplish the study’s main goal. In order to assess the system’s acceptability in terms of functional adequacy, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability, it was designed in accordance with the ISO 25010 Product Software Quality Standards. Following the assessment, the system was given an average total weighted mean score of 4.62, which represents a verbal interpretation of “strongly agree”. This score demonstrates that the evaluators were in agreement that the system met the requirements of ISO 25010 for Product Software Quality Standards.

Rolling horizon scheduling algorithm for dynamic vehicle scheduling system

Dynamic exclusive pickup and delivery problem with time windows (DE-PDPTW), aspecial dynamic vehicle scheduling problem, is proposed. Its mathematical description is given andits static properties are analyzed, and then the problem is simplified asthe asymmetrical travelingsalesman problem with time windows. The rolling horizon scheduling algorithm (RHSA) to solve thisdynamic problem is proposed. By the rolling of time horizon, the RHSA can adapt to the problem'sdynamic change and reduce the computation time by dealing with only part of the customers in eachrolling time horizon. Then, its three factors, the current customer window, the scheduling of thecurrent customer window and the rolling strategy, are analyzed. The test results demonstrate theeffectiveness of the RHSA to solve the dynamic vehicle scheduling problem.

Research on the ITOC Based Scheduling System for Ship Piping Production

Manufacturing of ship piping systems is one of the major production activities in shipbuilding. The schedule of pipe production has an important impact on the master schedule of shipbuilding. In this research, the ITOC concept was introduced to solve the scheduling problems of a piping factory, and an intelligent scheduling system was developed. The system, in which a product model, an operation model, a factory model, and a knowledge database of piping production were integrated, automated the planning process and production scheduling. Details of the above points were discussed. Moreover, an application of the system in a piping factory, which achieved a higher level of performance as measured by tardiness, lead time, and inventory, was demonstrated.

Workshop Scheduling Based on a Rule-restrained Colored Petri Net and the Development of a Scheduling System on the Internet/Intranet

In the paper, the gap between theoretical research and practical applicationsof workshop scheduling is analyzed. According to practical application requirements, thetraditional Petri net is expanded and a Rule-restrained Colored Petri Net (RCPN) is put forward tomodel workshop activities. Then, the architecture of the workshop scheduling system based on RCPN ispresented. Finally, the scheduling system that adopts a 3 -layer B/S/D mode is developed on theInternet/Intranet by using the Web database and Java.

Bad-scenario-set Robust Optimization Framework With Two Objectives for Uncertain Scheduling Systems

This paper proposes a robust optimization framework generally for scheduling systems subject to uncertain input data, which is described by discrete scenarios. The goal of robust optimization is to hedge against the risk of system performance degradation on a set of bad scenarios while maintaining an excellent expected system performance. The robustness is evaluated by a penalty function on the bad-scenario set. The bad-scenario set is identified for current solution by a threshold, which is restricted on a reasonable-value interval. The robust optimization framework is formulated by an optimization problem with two conflicting objectives. One objective is to minimize the reasonable value of threshold, and another is to minimize the measured penalty on the bad-scenario set. An approximate solution framework with two dependent stages is developed to surrogate the biobjective robust optimization problem. The approximation degree of the surrogate framework is analyzed. Finally, the proposed bad-scenario-set robust optimization framework is applied to a scenario job-shop scheduling system. An extensive computational experiment was conducted to demonstrate the effectiveness and the approximation degree of the framework. The computational results testified that the robust optimization framework can provide multiple selections of robust solutions for the decision maker. The robust scheduling framework studied in this paper can provide a unique paradigm for formulating and solving robust discrete optimization problems. © 2014 Chinese Association of Automation.

Modeling of Agile Intelligent Manufacturing-oriented Production Scheduling System

Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises, in which the management and control of the production system have surpassed the scope of individual enterprise and embodied some new features including complexity, dynamicity, distributivity, and compatibility. The agile intelligent manufacturing paradigm calls for a production scheduling system that can support the cooperation among various production sectors, the distribution of various resources to achieve rational organization, scheduling and management of production activities. This paper uses multi-agents technology to build an agile intelligent manufacturing-oriented production scheduling system. Using the hybrid modeling method, the resources and functions of production system are encapsulated, and the agent-based production system model is established. A production scheduling-oriented multi-agents architecture is constructed and a multi-agents reference model is given in this paper.

Artificial intelligence and end user tools to develop a nurse duty roster scheduling system

Objectives A nurse duty roster is usually prepared monthly in a hospital ward.It is common for nurses to make duty shift requests prior to scheduling.A ward manager normally spends more than a working day to manually prepare and subsequently to optimally adjust the schedule upon staff requests and hospital policies.This study aimed to develop an automatic nurse roster scheduling system with the use of open-source operational research tools by taking into account the hospital standards and the constraints from nurses.Methods Artificial intelligence and end user tools operational research tools were used to develop the code for the nurse duty roster scheduling system.To compare with previous research on various heuristics in employee scheduling,the current system was developed on open architecture and adopted with real shift duty requirements in a hospital ward.Results The schedule can be generated within 1 min under both hard and soft constraint optimization.All hard constraints are fulfilled and most nurse soft constraints could be met.Compared with those schedules prepared manually,the computer-generated schedules were more optimally adjusted as real time interaction among nurses and management personnel.The generated schedules were flexible to cope with daily and hourly duty changes by redeploying ward staff in order to maintain safe staffing levels.Conclusions An economical but yet highly efficient and user friendly solution to nurse roster scheduling system has been developed and adopted using open-source operational research methodology.The open-source platform is found to perform satisfactorily in scheduling application.The system can be implemented to different wards in hospitals and be regularly updated with new hospital polices and nurse manpower by hospital information personnel with training in artificial intelligence.

Remote Scheduling System for Drip Irrigation System Using Geographic Information System

The Internet is widely accessible in Tanzania. Most of the technologies used in different organizations have changed to address their functions using web based information systems. In this paper, attempt is made to design software system using geographical information system (GIS) for the spatial and temporal distribution of irrigation supply for large-scale drip irrigation systems in Tanzania. Map based information system has gained popularity after evolution of simple tools to present spatial information using Internet. Due to water scarcity, it is envisioned that by 2050 the world won’t have enough water for communities, industries and agriculture. Web based precision irrigation system refers to deployment of remotely precision irrigation services using the application interface that connects to the Internet. Hence, this study presents the GIS in the context of precision farming to achieve precision irrigation strategy with special reference to precision farming of tea in Tanzania. The GIS-based irrigation scheduling system was designed for the scheduling daily drip irrigation water deliveries and regular monitoring of irrigation delivery performance for maximum yield. The “Scheduling” program computes the right amount of irrigation deliveries based on tea water requirements. The “Monitoring” program gives information on the uniformity of water distribution and the shortfall or excess.

A Scheduling System Based on Rules of the Machine Tools in FMS

In this paper, a model of the scheduling of machine tools in the flexiblemanufacturing line is presented by intensive analysis and research of the mathematical method oftraditional scheduling. The various factors correlative with machine tools in the flexiblemanufacturing line are fully considered in this system. Aiming at this model, an intelligentdecision system based on rules and simulation technology integration is constructed by using the OOP( Object-Orented Programming) method, and the simulation experiment analysis is carried out. It isshown from the results that the model is better in practice.

Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.

A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

Hot dry rock(HDR)is rich in reserve,widely distributed,green,low-carbon,and has broad development potential and prospects.In this paper,a distributionally robust optimization(DRO)scheduling model for a regionally integrated energy system(RIES)considering HDR co-generation is proposed.First,the HDR-enhanced geothermal system(HDR-EGS)is introduced into the RIES.HDR-EGS realizes the thermoelectric decoupling of combined heat and power(CHP)through coordinated operation with the regional power grid and the regional heat grid,which enhances the system wind power(WP)feed-in space.Secondly,peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing.Finally,the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS.By simulating a real small-scale RIES,the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.

Optimized scheduling of integrated energy systems for low carbon economy considering carbon transaction costs

With the introduction of the“dual carbon”goal and the continuous promotion of low-carbon development,the integrated energy system(IES)has gradually become an effective way to save energy and reduce emissions.This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs.With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads,an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established.The role of flexible loads in improving the economy of an energy system was investigated using examples,and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios.The results showed that the total cost of the system in different scenarios was reduced by 18.04%,9.1%,3.35%,and 7.03%,respectively,whereas the total carbon emissions of the system were reduced by 65.28%,20.63%,3.85%,and 18.03%,respectively,when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously.Flexible electrical and thermal loads did not have the same impact on the system performance.In the analyzed case,the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account.Photovoltaics have an excess of carbon trading credits and can profit from selling them,whereas other devices have an excess of carbon trading and need to buy carbon credits.

A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Two-Stage Optimal Scheduling of Community Integrated Energy System

From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.

Layered power scheduling optimization of PV hydrogen production system considering performance attenuation of PEMEL

To analyze the additional cost caused by the performance attenuation of a proton exchange membrane electrolyzer(PEMEL)under the fluctuating input of renewable energy,this study proposes an optimization method for power scheduling in hydrogen production systems under the scenario of photovoltaic(PV)electrolysis of water.First,voltage and performance attenuation models of the PEMEL are proposed,and the degradation cost of the electrolyzer under a fluctuating input is considered.Then,the calculation of the investment and operating costs of the hydrogen production system for a typical day is based on the life cycle cost.Finally,a layered power scheduling optimization method is proposed to reasonably distribute the power of the electrolyzer and energy storage system in a hydrogen production system.In the up-layer optimization,the PV power absorbed by the hydrogen production system was optimized using MALTAB+Gurobi.In low-layer optimization,the power allocation between the PEMEL and battery energy storage system(BESS)is optimized using a non-dominated sorting genetic algorithm(NSGA-Ⅱ)combined with the firefly algorithm(FA).A better optimization result,characterized by lower degradation and total costs,was obtained using the method proposed in this study.The improved algorithm can search for a better population and obtain optimization results in fewer iterations.As a calculation example,data from a PV power station in northwest China were used for optimization,and the effectiveness and rationality of the proposed optimization method were verified.

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

A review of artificial intelligence applications in high-speed railway systems

In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.