Genetic fuzzy schedules for charging electric vehicles

García-Álvarez, Jorge; González-Rodríguez, Inés; Vela, Camino R.; González, Miguel A.; Afsar, Sezin

doi:10.1016/j.cie.2018.05.019

Cited by 14 publications

(4 citation statements)

References 36 publications

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“…(c) , and t=0 are set (d) is set where randomly represents the continuous uniform distribution and defines as a large random number and lastly (e) is finally set. Triangular fuzzy numbers are usually implemented to denote the job shop scheduling problems with uncertain durations and furthermore random numbers are considered in generating initial solutions for the experiments [20] - [37]. However, this procedure may produce illegal solutions.…”

Section: Methodsmentioning

confidence: 99%

Population Initialisation Methods for Fuzzy Job-Shop Scheduling Problems: Issues and Future Trends

Shaheed¹,

Shukor²,

Abdullah³

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Scheduling job shops in the real-world manufacturing environment is a multifarious task that involved various and multiple components, solutions and approach. Fuzzy Job-Shop Scheduling Problems (Fuzzy JSSPs) are most commonly addressed by the population-based Meta-heuristic algorithms. These algorithms usually derive and develop near-optimum results within credible computational times, almost by two main steps; the initialisation and then improvement step. Numerous theoretical studies pointed out that a Meta-heuristic performance is mainly affected by the performance of its initialisation method. The main motivation of this work is to perceive the existing pattern and concerns on population initialisation issues for Fuzzy JSSPs current work by scrutinizing the published articles. Furthermore, this paper focusing on providing comprehension insight and future direction on these methods. Therefore, this paper determined to review and classify the existing literature on Fuzzy JSSPs and analyse the performance of the initialisation methods used to identify their possible limitations. In consequence, previous works outlined three potential methods for initial solutions generation, which are Random-based, priority rules-based, and heuristic methods. However, the current analysis showed that Heuristic-based initialisation approach remains lacking in the Fuzzy JSSPs domain in spite of its successful performance in the crisp JSSP domain, especially, its capability to generate high-quality initial population that consists of optimal or near optimal solutions. Furthermore, this paper identifies probable gaps and reveals several performance limitations in the existing methods, which demands for an urgent solution to develop alternatives. Promising suggestions for future studies are also provided that may lead to new Heuristic Initialisation methods that can be proposed to solve current weaknesses.

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Section: Methodsmentioning

confidence: 99%

Population Initialisation Methods for Fuzzy Job-Shop Scheduling Problems: Issues and Future Trends

Shaheed¹,

Shukor²,

Abdullah³

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Formula (13) shows that the closer the charging trajectory of the EV is to the upper boundary, the smaller the compensation cost is, where s l,max and c l,max are respectively the charging benefit and cost corresponding to the upper boundary of l (broken line ab 1 c); s l is the charging benefit corresponding to the broken line abc; ϕ and E l are respectively the discharging compensation coefficient and the total discharging energy.…”

Section: Construction Of Robust Optimization Modelmentioning

confidence: 99%

“…In reference [12], a virtual battery model was established from the perspective of producers and consumers to describe the boundary constraints of the charging and discharging process. In reference [13], only the physical constraints of EV power batteries were considered and the uncertainty of charging time was simulated by fuzzy numbers. In reference [14], data mining was used to analyze EV plugged-in characteristics based on a historical data set of EVs, and then the EV load model was calculated by a fuzzy model.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility

Wang

Zhang

et al. 2022

Energies

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Aggregated electric vehicles (EVs) integrated to the grid and intermittent wind and solar energy increased the complexity of the economic dispatch of the power grid. Aggregated EVs have a great potential to reduce system operating costs because of their dual attributes of load and energy storage. In this paper, plugged-in EV is refined into three categories: rated power charging, adjustable charging, and flexible charging–discharging, and then control models are established separately; the concept of temporal flexibility for EV clusters is proposed for the adjustable charging and flexible charging–discharging of EV sets; then, the schedule boundary of EV clusters is determined under the flexibility constraints. The interval is used to describe the intermittent nature of renewable energy, and the minimum operating cost of the system is taken as the goal to construct a distributed energy robust optimization model. By decoupling the model, a two-stage efficient solution is achieved. An example analysis verifies the effectiveness and superiority of the proposed strategy. The proposed strategy can minimize the total cost while meeting the demand difference of EV users.

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“…Depending on the amount of consecutive previous job deadlines missed, novel scheduling technique is provided which reduces systems operations cost without giving up on stability. Scheduling of electric vehicles charging in real-world station used for charging is being formulated with sets of physical conditions to decrease the total tardiness with regard to preferred departure date [4]. To solve the problem, genetic algorithm design is used.…”

Section: Introductionmentioning

confidence: 99%

Algorithm fuzzy scheduling (AFS) for realtime jobs on multiprocessor systems

Holagundi¹,

Ashwathsetty²,

Basthikodi

2022

IJEECS

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The computing in Real-time is rapidly focusing much developments in technologies so that the real-time jobs are to be scheduled and executed on computing systems in particular time frame. The scheduling and load balancing techniques in distributed systems face numerous challenges because of lack of centralized strategy to dispatch the jobs in multiprocessors systems. In this work, we propose an Algorithm Fuzzy Scheduling (AFS) for real-time jobs that includes of Arrival time, Deadline and Computation time as the scheduling parameters of input. The approach AFS is analyzed and compared with Existing Fuzzy Algorithm (EFA) model for evaluation of performances from the outcome of the simulation. The jobs are scheduled on multiprocessor at higher system load by making use of fuzzy mechanisms in the algorithms. The experimental results prove that the proposed AFS achieves a better performance comparatively to EFA at various system load factors with respect to mean turnaroundtime, mean response time and count of missed deadlines. This is the initial phase of the algorithm, that will be enhanced to consider a greater number of parameters to be associated with jobs for better decision making and to investigate the scope for algorithm level parallelism.

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Genetic fuzzy schedules for charging electric vehicles

Cited by 14 publications

References 36 publications

Population Initialisation Methods for Fuzzy Job-Shop Scheduling Problems: Issues and Future Trends

Population Initialisation Methods for Fuzzy Job-Shop Scheduling Problems: Issues and Future Trends

Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility

Algorithm fuzzy scheduling (AFS) for realtime jobs on multiprocessor systems

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