Planning Scheduling Research Articles

Studies on Effective Usage of SAP ERP Tool to Control Materials Planned Delivery Time in Oil and Gas Industries

The global competitions are making rapid changes in production. Procedure integration and seamless flow of information and data play a vital role in productivity. The planning order improbability, manufacturing lead time and lack of systematic approach are the main concerns in production industries. Enterprise Resource Planning (ERP) and Lean Manufacturing (LM) are the two distinct processes to deal with the above mentioned concerns for competitive manufacturing. ERP focuses on data and schedule accuracy and ensures repeatable and predictable processes. LM concentrates on identifying and eradicating or minimizing all kinds of manufacturing wastes and focuses on reducing process inconsistency. The paper narrates about some holistic approaches that can be practiced with SAP ERP tool in order to achieve the efficient materials planning, master production schedule and capacity planning.

Scheduling Algorithm for Two-Workshop Production with the Time-Selective Strategy and Backtracking Strategy

To solve the two-workshop integrated scheduling problem with the same device resources, existing algorithms pay attention to the horizontal parallel processing of the process tree and ignore the tightness between vertical serial processes. A scheduling algorithm for two-workshop production with the time-selective strategy and Backtracking Strategy is proposed. The scheduling order of each process in the process tree needs to be determined, which will be completed by the process sequence sequencing strategy. The scheduling plan also needs to be determined, which will be completed using the time-selective scheduling strategy for the two workshops. At the same time, the “reference time” is set for the current scheduling process. To find a better scheduling scheme, the “scheduling reference time” is recorded as T. If the time of the current scheduling process scheme is greater than T, the backtracking adjustment strategy will be used to track the process scheduling scheme. Finally, experiments show that the algorithm not only ensures the parallel processing of parallel processes but also effectively improves the tightness of serial processes and optimizes the results of integrated scheduling.

A space variable-scale scheduling method for digital vehicle-to-grid platform under distributed electric energy storage

With the widespread application of digital technology in the intelligent transportation field, the vehicle-to-grid (V2G) pattern, which utilizes electric vehicles (EVs) as the distributed storage resources of electric energy, has won increasingly more attention from authorities and decision-makers to promote the sustainable development of regenerative clean energy. This paper aims to study the interaction and integration problem between EVs and grid for the digital V2G platform, so as to support load aggregators making scheduling decisions. According to the variable-scale data analysis theory, a space–time scale space model is established to describe the demand response behavioral feature of EV users, and the lightning-Scale Transformation method (LST) is also proposed. Compared to the original unidirectional scale transformation modes, the LST is capable of adjusting the space and time observation scale collaboratively. A vehicle-to-grid scheduling method based on the lightning-Scale Transformation (V2G-LST) is put forward, in order to make scheduling plans by considering EV users’ behavioral feature and charger resource constraints. We collect the real dataset from 863 Beijing charging stations using the APIs of Amap, as well as the investigation data of the State Grid Corporation of China to verify the efficacy of our proposed method. Comparative experimental results verify that the V2G-LST could obtain the lower dispatch cost and higher EV charger usage rate than the maximum dispatchable quantity first method EVC-MDQ under both charging and discharging scenarios.

Efficient sensor placement and online scheduling of bin collection

Several spatially distributed tasks can be completed more efficiently by using a combination of better sensing, prediction, and planning. When an agent needs to regularly move to serve different locations, it is necessary to find a compromise solution between the travel cost and the required quality of service. This problem is present in several contexts, namely waste collection, maintenance, and surveillance. This problem is very complex since it includes sensor placement and routing problems that, due to the uncertainty and combinatorial decision making, cannot be solved optimally in an efficient way. In this work, we develop a new efficient algorithm for sensor placement. It takes into account scheduling and route planning to serve distributed locations, considering not only the value of the information provided by each sensor but also the impact on travel efficiency and quality of service. We present examples of sensor placement and route planning in a waste collection environment.

Integrated Scheduling of Automated Yard Cranes and Automated Guided Vehicles with Limited Buffer Capacity of Dual-Trolley Quay Cranes in Automated Container Terminals

Background: The key performance index for the container terminals is the vessel berthing time which is highly affected by the scheduling of the different handling equipment. Proper integrated scheduling of the handling equipment is crucial, especially in automated container terminals, where all the handling equipment is automated and must be coordinated to avoid interference. One of the most challenging problems both scholars and terminal operators face is introducing a proper scheduling plan for different equipment, considering the buffer capacity of dual-trolley quay cranes (QCs) and the limited storage locations of import containers. Methods: A mathematical model is proposed to integrate the scheduling of automated yard cranes and automated guided vehicles (AGVs), considering the limited buffer capacity beneath dual-trolley QCs and the storage allocation of import containers. Results: different instances were solved to evaluate the proposed model’s performance and investigate the impact of using dual-trolley QCs instead of single-trolley QCs, and the impact of using different buffer capacities. Conclusions: The results show that the model provides detailed scheduling and assigning plans for the YCs and AGVs besides allocating import containers. Additionally, the dual-trolley QCs can significantly decrease the completion time and increase AGVs’ utilization compared to the single-trolley QCs.

Research Status and Development Trend of Unmanned Driving Technology in Coal Mine Transportation

Unmanned driving technology has always been one focus of mine smart transportation, which is a crucial component of smart mines. However, the descriptions of both the intelligent transportation system and its industrial application are not comprehensive. In order to have an all-encompassing and comprehensive understanding of both the intelligent coal mine transportation system and its industrial application with the intelligent system, this paper summarizes and analyzes the current research status of unmanned driving technology in mines and the industrial application of current mining transportation vehicles. It begins by outlining the current research state of unmanned driving technology in mines and then assesses the advancement of unmanned driving technology. The unmanned transportation system in mines is also introduced, together with its components for perception, location, path planning, vehicle control, and multi-vehicle scheduling. In addition, each component is described in combination with the current artificial intelligent unmanned technology individually. Then, some typical categories of intelligent industrial vehicles are introduced for learning about the conditions of their actual application. There are almost four hundred coal mines that have researched unmanned driving technology, and some companies have applied the unmanned technology to realize transportation with an efficiency enhancement of 70~80%. Finally, currently existing challenges and future research are analyzed and proposed. This review may provide more comprehensive knowledge of the intelligent coal mine, accelerating the development of intelligent technology and helping to build a new management and control model.

A Distributed Coordination Method for Coupled Traffic-Power Network Equilibrium Incorporating Behavioral Theory

The increasing penetration of electric vehicles (EVs) gradually evolves the urban transportation network (UTN) and power distribution network (PDN) from being independent to being coupled. To analyze the impact of drivers' behavior on the coupled network, and reveal the relationship between the two networks, a coupled traffic-distribution (CTD) model is proposed, in which the total traveling cost of UTN and the power service cost of PDN is minimized. In the CTD model, A stochastic user equilibrium traffic assignment problem (SUE-TAP) with elastic traffic demand and discrete path selection of drivers is formulated to capture the traffic flow distribution comprised of EVs and gasoline vehicles (GVs). An alternative current optimal power flow (ACOPF) model of PDN is utilized to provide the optimal charging price and scheduling plan. Besides, the SUE-TAP with nonlinear functions is difficult to solve, a novel piecewise linear approximation method is presented to deal with the nonlinear items of the SUE-TAP caused by drivers' traveling cost and traveling tendency incorporating perception. Incorporating the behavioral theory, a distributed coordination method derived from optimal condition decomposition is proposed for the coupled traffic-power network equilibrium. The proposed distributed scheme is examined using a coupled network, which consists of a 20-road UTN and a 33-bus PDN. Numerical results demonstrate the effectiveness and practicability of the proposed model and the distributed coordination method.

Employee scheduling and maintenance planning for safety systems at the remotely located oil and gas industrial facilities

Abstract The safety of operations is vital in any process in the oil and gas sector, especially given that increasingly more hydrocarbon reserves are discovered in non-conventional remote and Arctic locations. Safety systems are designed as a part of a complex IT system for process control. The design of these systems is conducted in the form of an engineering project. This research presents a decision-making framework to facilitate formulating clear and comprehensive recommendations for the requirements specification developed for the safety systems. The contribution of this research to the strategic planning area of IT solutions for hazardous industrial facilities is integrating the problems of designing a safety system, planning its maintenance, and scheduling the employees to conduct the required maintenance. With this joint decision-making, it is possible to explore trade-offs between investments into the systems’ complexity and workforce-related expenditures throughout the solution’s lifecycle. The reliability modelling is conducted with the help of Markov analysis. The multi-objective decision-making framework is employed to deduce straightforward requirements to the safety system design, maintenance strategy, and workforce organisation. This research is relevant to managing the petroleum sector engineering projects with regard to the design of technological solutions.

ISPAD Clinical Practice Consensus Guidelines 2022: Ramadan and other religious fasting by young people with diabetes.

ISPAD Clinical Practice Consensus Guidelines 2022: Ramadan and other religious fasting by young people with diabetes.

Data-Driven Flexible Vehicle Scheduling and Route Optimization

The flexible transit service reflects a trend of demand on the flexibility and convenience in urban public transport systems, within which the vehicle scheduling and passenger insertion are two challenging issues. Especially, finding the optimal solution for a flexible transit system can be viewed as an extension of the traveling salesman problem which is NP-complete. Yet most of the existing research mainly focuses on one aspect, i.e. route planning, stop selection or vehicle scheduling, where a combined integration and optimization of the whole system is largely neglected. In this paper, we propose a data-driven flexible transit system that integrates the origin-destination insertion algorithm and the milp-based (mixed-integer linear programming) scheduling scheme. Specifically, stops are mined from the historical datasets and some stops act as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$backbone$ </tex-math></inline-formula> stops that should be visited by the vehicles; and a heuristic backbone-based origin-destination insertion algorithm is proposed to schedule the routing path of vehicles, where the time loss caused by the optimal insertion positions is calculated for the vehicles to decide whether to accept the requests or not when constructing a path for the flexible routes. Moreover, a vehicle scheduling model based on milp is proposed to minimise the gap between the passenger flow and available seats. The proposed flexible transit systems are simulated in real-world taxi datasets, and experimental results show that the proposed flexible transit system can effectively increase the delivery ratio and decrease the passengers’ waiting time compared with existing methods.

Integrated maintenance, inventory and quality engineering decisions for multi-product systems

Abstract It is essential for manufacturers to consider the interrelation among quality, inventory, and maintenance decisions to detect imperfect quality products, keep the production system in good operating condition, and manage quality and inventory costs. Hence, this paper aims to develop an integrated model of inventory planning, quality engineering, and maintenance scheduling in which the expected total cost per time unit is minimised by determining the sample size, sampling interval, control limit coefficient, along with production cycle time. In this regard, an imperfect multi-product manufacturing system is considered, in which the inventory shortage in satisfying the demand for each product type and the idle time during the production cycle are not allowed. It is assumed that the process starts in an in-control condition where most produced units are conforming. However, due to the occurrence of an assignable cause (AC), the process mean moves to an out-of-control condition in which a significant fraction of non-conforming units is produced. The efficiency of the proposed mathematical model is evaluated by a numerical example, and then the sensitivity of the proposed model to important inputs is analysed. Finally, a comparative study based on the Taguchi design approach is given to confirm the capability of the proposed model to achieve remarkable cost savings.

Integrated Scheduling of the Production and Maintenance of Parallel Machine Job-shop Considering Stochastic Machine Breakdowns

The integrated scheduling of production and maintenance can make equipment maintenance in line with the production pace, so as to effectively prevent anormal interruptions of the production process due to equipment failure, and ensure the smooth implementation of the production scheduling plan. Aiming at the parallel machine job-shop environment, and considering stochastic machine failures and different degradation speeds of parallel machines, this paper introduced the minimal maintenance and preventive maintenance strategies to establish an integrated scheduling model for production and maintenance, designed a genetic algorithm based on process coding and binary hybrid coding to solve the model, and verified the correctness of the proposed model and the effectiveness of the algorithm through an instance. This study provided an effective decision-making method for parallel machine job-shop scheduling problems.

Energy management method of integrated energy system based on collaborative optimization of distributed flexible resources

Under the background of energy internet and low-carbon power, integrated energy system (IES) has become an important carrier of energy conservation and emission reduction. The IES utilizes innovative energy management mode to coordinate various energy sources such as natural gas, electric energy and heat energy. It is composed of energy production, conversion, storage and consumption subsystems, which emphasizes breaking the isolation of energy subsystems through reasonable scheduling, realizing energy cascade utilization and improving energy utilization efficiency. In this paper, an energy management model with two-stage scheduling before day and in real time is proposed aiming at the collaborative optimization of generator-load-storage of IES. Firstly, the first stage is the day-ahead economic dispatch, which aims to realize the power distribution of units in the system. The day-ahead economic dispatching model takes the maximization of economic benefits, the maximization of exergy efficiency and the minimization of carbon emission cost as the optimization objectives, so as to make the day-ahead global optimal dispatching decision. Secondly, the second stage is real-time optimal scheduling, which aims at real-time power adjustment of the previous scheduling plan. The real-time optimal scheduling model takes the minimum interactive power deviation punishment cost, wind abandonment punishment cost and user satisfaction loss cost as optimization objectives, so as to balance the energy supply and load demand deviation between planned output and actual output. Thirdly, according to the characteristics of the model, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the first-stage day-ahead economic scheduling, and YALMIP toolbox is used to solve the real-time optimal scheduling model. Finally, based on the established model, a typical IES is selected for case simulation, which verifies that the proposed method can effectively improve the economy of system operation.

Memetic Algorithm for Dynamic Joint Flexible Job Shop Scheduling with Machines and Transportation Robots

In this study, the dynamic joint scheduling problem for processing machines and transportation robots in a flexible job shop is investigated. The study aims to minimize the order completion time (makespan) of a job shop manufacturing system. Considering breakdowns, order insertion and battery charging maintenance of robots, an event-driven global rescheduling strategy is adopted. A novel memetic algorithm combining genetic algorithm and variable neighborhood search is designed to handle dynamic events and obtain a new scheduling plan. Finally, numerical experiments are conducted to test the effect of the improved operators. For successive multiple rescheduling, the effectiveness of the proposed algorithm is verified by comparing it with three other algorithms under dynamic events, and through statistical analysis, the results verify the effectiveness of the proposed algorithm.

Improved event-driven simulation method for fuel transport in a mesh-like pipeline network

One of the tasks of an oil company is to fulfil site demands. Since not all sites have production, it is essential to transport products between sites. The order of pumping operations is determined by the scheduling plan. The scheduling plan includes the type and quantity of products to be delivered, the start of deliveries, and routes. The type and quantity of products to be delivered, the start of deliveries and routes are included in the scheduling plan. A scheduling plan is feasible if it does not contain inconsistencies. Checking a one-month schedule is a complex task in real multi-source mesh-like pipeline networks, thus it is advisable to use a discrete event simulation system. This work presents improvements on our previous simulation model. In the improved simulation model, it is possible to reroute, split, and reverse the flow direction of the products in the pipe, amongst other things.

Implementation of video laryngoscopes and the development in airway management strategy and prevalence of difficult tracheal intubation: A national cohort study.

We aimed to determine the development in the use of video laryngoscopy over a 9-year period, and its possible impact on airway planning and management. We retrieved 822,259 records of tracheal intubations recorded from 2008 to 2016 in the Danish Anaesthesia Database. The circumstances regarding pre-operative airway assessment, the scheduled airway management plan and the actual airway management concerning video laryngoscopy were reported for each year of observation. Further, the association between year of observation and various airway management related outcomes was evaluated by multivariate logistic regression. There was a significant increase in airway management with 'advanced technique successfully used within two attempts' from 2.7% in 2008 to 15.5% in 2016 (p <.0001). This predominantly reflects use of video laryngoscopy. The prevalence of tracheal intubations 'scheduled for video laryngoscopy' increased from 3.5% in 2008 to 10.6% in 2016 (p <.0001). We found a significant increase in the prevalence of anticipated difficulties with intubations by direct laryngoscopy from 1.8% in 2008 to 5.2% in 2016 (p <.0001). The prevalence of failed tracheal intubations decreased from 0.14% in 2008 to 0.05% in 2016 (p <.0001). From 2008 to 2016, a period of massive implementation of video laryngoscopes, a significant change in airway management behaviour was recorded. Increasingly, video laryngoscopy is becoming a first-choice device for both acute and routine airway management. Most importantly, the data showed a noticeable reduction in failed intubation over the time of observation.

Research on Fine Scheduling and Assembly Planning of Modular Integrated Building: A Case Study of the Baguang International Hotel Project

There exist various challenges in constructing a large in-city project, such as narrow construction sites, limited surrounding roads, heavy construction periods and tasks, various types of vehicles, and affected cargo transport. Considering construction needs, transportation characteristics, and site conditions, this paper puts forward the overall planning for modular integrated construction (MiC) transportation and on-site assembly. Meanwhile, the traffic organization and transportation scheduling method are designed for smart construction sites and different engineering materials are coordinated in the space-time dimension during the overall period from construction delivery. Meanwhile, an integer programming model is developed to solve the truck scheduling matching problem between the supply side and the construction side. The weighted loss time of the truck is set as the optimization objective function, and time, space, and material type are the constraints. For this model, this paper proposes an operations scheduling solution method by combining operations research and actual field construction scheduling experience. The traditional empirical scheduling method and the proposed operations research scheduling model are compared through a case study of actual engineering scheduling data. The experimental results show that the operations research scheduling model is better than the traditional empirical scheduling method at different traffic levels. In addition, the implementation of the scheme is guaranteed through measures such as pre-data analysis, management framework, and information technology equipment. The planning and scheduling cover the whole process of MiC module transportation and on-site assembly, which have practical guiding significance for the project and ensure the timely success and acceptance of the project.

Manpower Planning Scheduling in hospital operating rooms

The International Journal of Integrated Care (IJIC) is an online, open-access, peer-reviewed scientific journal that publishes original articles in the field of integrated care on a continuous basis.IJIC has an Impact Factor of 2.913 (2021 JCR, received in June 2022)The IJIC 20th Anniversary Issue was published in 2021.

Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal

The uninterrupted operation of the quay crane (QC) ensures that the large container ship can depart port within lay-time, which effectively reduces the handling cost for the container terminal and ship owners. The QC waiting caused by automated guided vehicles (AGVs) delay in the uncertain environment can be alleviated by dynamic scheduling optimization. A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems, in which the scheduling scheme determines the starting and ending nodes of paths, and the choice of paths between nodes affects the scheduling of subse-quentAGVs. This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime. A dynamic optimization algorithm, including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm, is designed to solve the optimal AGV scheduling and path schemes. A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.

CMSE 2022 Preface

The proceedings provide a selection of thirty-eight peer-reviewed papers presented at the 11th Global Conference on Materials Science and Engineering (CMSE 2022), held as a virtual conference on September 16-19, 2022.By the tradition annually followed in the full decade of its history, CMSE 2022 was dedicated to various aspects of Materials Science and Engineering. Given the aggravated Covid situation, the conference format was fully converted to the online mode to ensure the safety of participants, abandoning the previously scheduled on-site preparation plans in Shenzhen, China.CMSE 2022 was conducted via Microsoft Teams. The full three days of the conference comprised namely keynote speeches, oral presentations, and poster presentations, each part followed by a Q&A discussion. Firstly, the conference audience containing over a hundred participants from 23 countries enjoyed the keynote speeches of three distinguished professors: Prof. Qixin Guo from Saga University, Japan, Prof. Yarub Al-Douri from the American University of Iraq, Iraq, and Prof. Rodrigo Martins from Universidade Nova de Lisboa, Portugal, who shared their deep insights on rare earth doped semiconductors, quinternary alloys’ multi-application, and green and low-cost printed electronics, respectively. Next, over eighty oral and poster presentations were delivered successfully with informative results from related materials science and engineering fields. More detail can be found in the online version of the conference program. The online format of the conference somewhat limited the informal discussion opportunities for its participants. However, despite Covid-induced restrictions, this conference fulfilled its ultimate goal of bringing together experts from universities, academic institutions, industrial companies, and so on, updating the state-of-the-art developments and challenges.The selected thirty-eight contributions cover the latest advances in innovative materials, materials technology and materials properties, microstructure, testing & characterization, processing, etc. They focus on theoretical and analytical methods, numerical simulations, and experimental techniques with regard to industrial applications. All papers passed a rigorous reviewing process by the International Committee experts, whose reviews and comments were crucial for enhancing the scientific merit and quality of the proceedings.List of Committee Members is available in this Pdf.