A dual population multi-operator genetic algorithm for flight deck operations scheduling problem

It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations.In this paper,the precedence constraints and resource constraints in flight deck operations are analyzed,then the model of the multi-aircraft integrated scheduling problem with transfer times(MAISPTT)is established.A dual population multi-operator genetic algorithm(DPMOGA)is proposed for solving the problem.In the algorithm,the dual population structure and random-key encoding modified by starting/ending time of operations are adopted,and multiple genetic operators are self-adaptively used to obtain better encodings.In order to conduct the mapping from encodings to feasible schedules,serial and parallel scheduling generation scheme-based decoding operators,each of which adopts different justified mechanisms in two separated populations,are introduced.The superiority of the DPMOGA is verified by simulation experiments.

A novel two-stage Lagrangian decomposition approach for refinery production scheduling with operational transitions in mode switching

To address large scale industrial processes,a novel Lagrangian scheme is proposed to decompose a refinery scheduling problem with operational transitions in mode switching into a production subproblem and a blending and delivery subproblem.To accelerate the convergence of Lagrange multipliers,some auxiliary constraints are added in the blending and delivery subproblem.A speed-up scheme is presented to increase the efficiency for solving the production subproblem.An initialization scheme of Lagrange multipliers and a heuristic algorithm to find feasible solutions are designed.Computational results on three cases with different lengths of time horizons and different numbers of orders show that the proposed Lagrangian scheme is effective and efficient.

A continuous-time formulation for refinery production scheduling problems involving operational transitions in mode switching

In the refinery scheduling, operational transitions in mode switching are of great significance to formulate dynamic nature of production and obtain efficient schedules. The discrete-time formulation meets two main challenges in modeling: discrete approximation of time and large size of mixed-integer linear problem(MILP).In this article, a continuous-time refinery scheduling model, which involves transitions of mode switching, is presented due to these challenges. To reduce the difficulty in solving large scale MILPs resulting from the sequencing constraints, the global event-based formulation is chosen. Both transition constraints and production transitions are introduced and the numbers of key variables and constraints in both of the discrete-time and continuous-time formulations are analyzed and compared. Three cases with different lengths of time horizons and different numbers of orders are studied to show the efficiency of the proposed model.

An Improved Multi-Objective Hybrid Genetic-Simulated Annealing Algorithm for AGV Scheduling under Composite Operation Mode

This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.

Novel operating theatre scheduling method based on estimation of distribution algorithm

In order to improve the efficiency of operating rooms,reduce the costs for hospitals and improve the level of service qualities, a scheduling method was developed based on an estimation of distribution algorithm（ EDA）. First, a scheduling problem domain is described. Based on assignment constraints and resource capacity constraints, the mathematical programming models are set up with an objective function to minimize the system makespan. On the basis of the descriptions mentioned above, a solution policy of generating feasible scheduling solutions is established. Combined with the specific constraints of operating theatres, the EDA-based algorithm is put forward to solve scheduling problems. Finally, simulation experiments are designed to evaluate the scheduling method. The orthogonal table is chosen to determine the parameters in the proposed method. Then the genetic algorithm and the particle swarm optimization algorithm are chosen for comparison with the EDA-based algorithm, and the results indicate that the proposed method can decrease the makespan of the surgical system regardless of the size of operations. Moreover, the computation time of the EDA-based algorithm is only approximately 5 s when solving the large scale problems, which means that the proposed algorithm is suitable for carrying out an on-line scheduling optimization of the patients.

INTEGRATED OPERATOR GENETIC ALGORITHM FOR SOLVING MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCHEDULING

In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.

Market Operation of Energy Storage System in Smart Grid:A Review

As a flexible resource,energy storage plays an increasingly significant role in stabilizing and supporting the power system,while providing auxiliary services.Still,the current high demand for energy storage contrasts with the fuzzy lack of market-oriented mechanisms for energy storage,the principle of market-oriented operation has not been embodied,and there is no unified and systematic analytical framework for the business model.However,the dispatch management model of energy storage in actual power system operation is not clear.Still,the specific scheduling process and energy storage strategy on the source-load-network side could be more specific,and there needs to be a greater understanding of the collaborative scheduling process of the multilevel scheduling center.On this basis,this paper reviews the energy storage operation model and market-based incentive mechanism,For different functional types and installation locations of energy storage within the power system,the operational models and existing policies for energy storage participation in the market that are adapted to multiple operating states are summarized.From the point of view of the actual scheduling and operation management of energy storage in China,an energy storage regulation and operation management model based on“national,provincial,and local”multilevel coordination is proposed,as well as key technologies in the interactive scenarios of source-load,network and storage.

Operator scheduling strategy for LBS-based intelligent transportation system

With the development of location technologies, advanced LBS-based ITS increasingly requires the capability of database technologies to manage the continuously arrived vehicles’ location, traffic jam and other interrelated information of large amounts of traffic in the following years. And some burst arrival stream data will challenge the real-time performance and the allocation of limited resource. However, choosing a desirable database operator scheduling strategy can significantly improve the performance of the system. The path capability strategy was chosen and improved as ITS’ operator scheduling strategy to meet the real-time response and the minimal memory requirement of the system.

Filtered-beam-search-based approach for operating theatre scheduling

To improve the efficiency of operating rooms, reduce the hospital＇ s costs and improve the level of service qualities, a scheduling method is presented based on a filtered-beam-search-based algo- rithm. Firstly, a scheduling problem domain is described. Mathematical programming models are al- so set up with an objective function of minimizing related costs of the system. On the basis of the de= scriptions mentioned above, a solving policy of generating feasible scheduling solutions is estab- lished. Combining with the speeific constraints of operation theatres, a filtered-beam-search-based algorithm is put forward to solve scheduling problems. Finally, simulation experiments are designed. The performance of the proposed algorithm is evaluated and compared with that of other approaches through simulations. Results indicate that the proposed algorithm can reduce costs, and are of prac- ticality and effectiveness.

IOPS:computational graph optimization based on inter-operators parallel scheduling

To improve the inference efficiency of convolutional neural networks(CNN),the existing neural networks mainly adopt heuristic and dynamic programming algorithms to realize parallel scheduling among operators.Heuristic scheduling algorithms can generate local optima easily,while the dynamic programming algorithm has a long convergence time for complex structural models.This paper mainly studies the parallel scheduling between operators and proposes an inter-operator parallelism schedule(IOPS)scheduling algorithm that guarantees the minimum similar execution delay.Firstly,a graph partitioning algorithm based on the largest block is designed to split the neural network model into multiple subgraphs.Then,the operators that meet the conditions is replaced according to the defined operator replacement rules.Finally,the optimal scheduling method based on backtracking is used to schedule the computational graph.Network models such as Inception-v3,ResNet-50,and RandWire are selected for testing.The experimental results show that the algorithm designed in this paper can achieve a 1.6×speedup compared with the existing sequential execution methods.

A Decision Method of Cogeneration Operation Schedule by Combining Use of SOFCs and PEFCs

In this paper, the authors propose a cogeneration system by combining two kinds of FCs （fuel cells） for a collective housing. The good points which each FC has are applied to the cogeneration operation schedule. In this study, some rooms interchange electric power and heat with each other for high efficiency and reduction of energy loss. The authors determine an operation schedule of FCs by multi-evaluation from viewpoints of energy cost and CO2 emissions.

关于Scheduling中文译名的注记

Scheduling是运筹学和组合最优化的一个分支。国际上,Scheduling发展已经有50年。1960年,越民义编写国内第1本排序理论讲义至今也有半个世纪。Scheduling的理论和应用涉及到数学、管理、自动化、系统工程和工业工程等许多学科。回顾和比较国内外使用排序、调度等Scheduling中文译名的情况,介绍新近提出的排序与调度译法,建议在泛指Scheduling时,采用该译名。

The prediction formula and a risk-based sensor scheduling method in target detection with guiding information

Sensor scheduling is used to improve the sensing performance in the estimation of targets’states.However,few papers are on the sensor scheduling for target detection with guiding information.This letter can remedy this deficiency.A risk-based target detection method with guiding information is provided firstly,based on which,the sensor scheduling approach is aiming at reducing the risk and uncertainty in target detection,namely risk-based sensor scheduling method.What should be stressed is that the Prediction Formula in sensor scheduling is proposed.Lastly,some examples are conducted to stress the effectiveness of this proposed method.

Operation optimization of the steel manufacturing process: A brief review

Against the realistic background of excess production capacity, product structure imbalance, and high material and energy consumption in steel enterprises, the implementation of operation optimization for the steel manufacturing process is essential to reduce the production cost, increase the production or energy efficiency, and improve production management. In this study, the operation optimization problem of the steel manufacturing process, which needed to go through a complex production organization from customers' orders to workshop production, was analyzed. The existing research on the operation optimization techniques, including process simulation, production planning, production scheduling, interface scheduling, and scheduling of auxiliary equipment, was reviewed. The literature review reveals that, although considerable research has been conducted to optimize the operation of steel production, these techniques are usually independent and unsystematic.Therefore, the future work related to operation optimization of the steel manufacturing process based on the integration of multi technologies and the intersection of multi disciplines were summarized.

Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections

The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–continuous casting sections(SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to conduct the evaluation, due to the lack of full-scale consideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multiprocess collaborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation(process matching) degrees of SCCSs were obtained as 0.638(0.610) and 1.000(0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster coordinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied,the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process collaborative operation level was improved with fewer adjustments and interruptions in casting.

Flow shop rescheduling problem under rush orders

In the environment of customization, disturbances such as rush orders and material shortages often occur in the manufacturing system, so rescheduling is necessary for the manufacturing system. The rescheduling methodology should be able to dispose of the disturbance efficiently so as to keep production going smoothly. This aims researching flow shop rescheduling problem (FSRP) necessitated by rush orders. Disjunctive graph is employed to demonstrate the FSRP. For a flow shop processing n jobs, after the original schedule has been made, and z out of n jobs have been processed in the flow shop, x rush orders come, so the original n jobs together with x rush orders should be rescheduled immediately so that the rush orders would be processed in the shortest time and the original jobs could be processed subject to some optimized criteria. The weighted mean flow time of both original jobs and rush orders is used as objective function. The weight for rush orders is much bigger than that of the original jobs, so the rush orders should be processed early in the new schedule. The ant colony optimization (ACO) algorithm used to solve the rescheduling problem has a weakness in that the search may fall into a local optimum. Mutation operation is employed to enhance the ACO performance. Numerical experiments demonstrated that the proposed algorithm has high computation repeatability and efficiency.

Multi-ACO Application in Routing and Scheduling Optimization of Maintenance Fleet (RSOMF) Based on Conditions for Offshore Wind Farms

Reducing the operation and maintenance (O & M) cost is one of the potential actions that could reduce the cost of energy produced by offshore wind farms. This article attempts to reduce O & M cost by improving the utilization of the maintenance resources, specifically the efficient scheduling and routing of the maintenance fleet. Scheduling and routing of maintenance fleet is a non-linear optimization problem with high complexity and a number of constraints. A heuristic algorithm, Ant Colony Optimization (ACO), was modified as Multi-ACO to be used to find the optimal scheduling and routing of maintenance fleet. The numerical studies showed that the proposed methodology was effective and robust enough to find the optimal solution even if the number of offshore wind turbine increases. The suggested approaches are helpful to avoid a time-consuming process of manually planning the scheduling and routing with a presumably suboptimal outcome.

Scheduling Aircraft Landings at LSS Airport

This paper presents a short contribution in air transportation, specifically in scheduling aircraft (plane) landings at Léopol Sédar Senghor (LSS) airport of Dakar. The safety of air navigation of LSS is managed by ASECNA: Agency for Air Navigation Safety in Africa and Madagascar. Scheduling aircraft landing is the problem of deciding a landing time on an appropriate runway for each aircraft in a given set of aircraft such that each aircraft lands within a predetermined time window. The separation criteria between the landing of an aircraft, and the landing of all successive aircraft, are respected. Our objective is to minimize the cost of deviation from the target times. We present a mixed-integer 0 - 1 formulation for the single runway case. Numerical experiments and comparisons based on real datasets of LSS airport are presented.

Varying Response Ratio Priority: A Preemptive CPU Scheduling Algorithm (VRRP)

In present era, one of the most important resources of computer machine is CPU. With the increasing number of application, there exist a large number of processes in the computer system at the same time. Many processes in system simultaneously raise a challenging circumstance of managing the CPU in such a manner that the CPU utilization and processes execution gets optimal performance. The world is still waiting for most efficient algorithm which remains a challenging issue. In this manuscript, we have proposed a new algorithm Progressively Varying Response Ratio Priority a preemptive CPU scheduling algorithm based on the Priority Algorithm and Shortest Remaining Time First. In this scheduling algorithm, the priority is been calculated and the processes with high priority get CPU first or next. For new process, the priority of it becomes equal to inverse of burst time and for the old processes the priority calculation takes place as a ratio of waiting time and remaining burst time. The objective is to get all the processes executed with minimum average waiting time and no starvation. Experiment and comparison show that the VRRP outperforms other CPU scheduling algorithms. It gives better evaluation results in the form of scheduling criteria. We have used the deterministic model to compare the different algorithms.

Uncertain optimal model and solving method to platform scheduling problem in battlefield

The platform scheduling problem in battlefield is one of the important problems in military operational research.It needs to minimize mission completing time and meanwhile maximize the mission completing accuracy with a limited number of platforms.Though the traditional certain models obtain some good results,uncertain model is still needed to be introduced since the battlefield environment is complex and unstable.An uncertain model is prposed for the platform scheduling problem.Related parameters in this model are set to be fuzzy or stochastic.Due to the inherent disadvantage of the solving methods for traditional models,a new method is proposed to solve the uncertain model.Finally,the practicability and availability of the proposed method are demonstrated with a case of joint campaign.