Genetic Algorithm for Scheduling Charging Times of Electric Vehicles Subject to Time Dependent Power Availability

Mencía, Carlos; Sierra, María R.; Mencía, Raúl; Varela, Ramiro

doi:10.1007/978-3-319-59740-9_16

Cited by 5 publications

(11 citation statements)

References 8 publications

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“…Algorithm 1 shows the pseudocode of the schedule builder proposed in [31,32] for the (1, Cap(t)|| ∑ T i ) problem. It maintains a set US containing the jobs to be scheduled, which is initialized to the set of all jobs J .…”

Section: Schedule Buildermentioning

confidence: 99%

“…Algorithm 2 depicts the main structure of the genetic algorithm proposed in [31,32] for solving the (1, Cap(t)|| ∑ T i ) problem. The GA has four parameters: crossover and mutation probabilities (P c and P m ), number of generations (#Gen) and population size (PopSize).…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…In its formal definition, a set of jobs has to be scheduled on a single machine whose capacity varies over time, with the aim of minimizing the total tardiness objective function. This problem is denoted (1, Cap(t)|| ∑ T i ) in the conventional (α|β|γ) notation [30] and it is NP-hard [31,32].…”

Section: Introductionmentioning

confidence: 99%

“…In [29], it was solved by means of the Apparent Tardiness Cost (ATC) priority rule [33], which is of common use in scheduling problems with tardiness objectives. Later, a genetic algorithm [31] was shown to compute much better schedules than classical priority rules, including ATC, at the expense of longer running times. More recently, this genetic algorithm was combined with an efficient local search procedure, resulting in a memetic algorithm [32].…”

Section: Introductionmentioning

confidence: 99%

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One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

Mencía

2021

Mathematics

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This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles and it is NP-hard. Recent work proposed an efficient memetic algorithm for solving the problem, combining a genetic algorithm and a local search method. The local search procedure is based on swapping consecutive jobs on a C-path, defined as a sequence of consecutive jobs in a schedule. Building on it, this paper develops new memetic algorithms that stem from new local search procedures also proposed in this paper. The local search methods integrate several mechanisms to make them more effective, including a new condition for swapping pairs of jobs, a hill climbing approach, a procedure that operates on several C-paths and a method that interchanges jobs between different C-paths. As a result, the new local search methods enable the memetic algorithms to reach higher-quality solutions. Experimental results show significant improvements over existing approaches.

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Section: Schedule Buildermentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

Mencía

2021

Mathematics

Self Cite

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“…In [12], the (1, Cap(t)|| P T i ) problem is solved by means of the Apparent Tardiness Cost (ATC) priority rule, commonly used in the context of scheduling with tardiness objectives. The problem was then solved in [21] by means of a genetic algorithm and later in [20] by a memetic algorithm producing better solutions. However, none of these approaches is suitable for on-line scheduling as it is required to solve the EVCSP.…”

Section: Introductionmentioning

confidence: 99%

Evolving priority rules for on-line scheduling of jobs on a single machine with variable capacity over time

Gil-Gala

Mencía

Sierra

et al. 2019

Applied Soft Computing

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On-line scheduling is often required in a number of real-life settings. This is the case of distributing charging times for a large fleet of electric vehicles arriving stochastically to a charging station working under power constraints. In this paper, we consider a scheduling problem derived from a situation of this type: one machine scheduling with variable capacity and tardiness minimization, denoted (1, Cap(t)|| P T i). The goal is to develop new priority rules to improve the results from some classical ones as Earliest Due Date (EDD) or Apparent Tardiness Cost (ATC). To this end, we developed a Genetic Programming (GP) approach. The e ciency of this algorithm relies on some smart representation of the expression trees. Besides, we restrict the search space to that of dimensionally compliant expressions, which allows GP to reach single and clear solutions. We conducted an experimental study showing that GP is able to evolve new rules that outperform ATC and EDD using the same problem attributes and operations.

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New mathematical model for machine scheduling with variable capacity over time

Esmaelian,

Momeni,

Mohammadi

2024

International Journal of Management Science and Engineering Man

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Genetic Algorithm for Scheduling Charging Times of Electric Vehicles Subject to Time Dependent Power Availability

Cited by 5 publications

References 8 publications

One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

Evolving priority rules for on-line scheduling of jobs on a single machine with variable capacity over time

New mathematical model for machine scheduling with variable capacity over time

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