Genetic Algorithm to Evolve Ensembles of Rules for On-Line Scheduling on Single Machine with Variable Capacity

Abstract

On-line scheduling is often required in real life situations. This is the case of the one machine scheduling with variable capacity and tardiness minimization problem, denoted (1, Cap(t)|| Ti). This problem arose from a charging station where the charging periods for large fleets of electric vehicles (EV) must be scheduled under limited power and other technological constraints. The control system of the charging station requires solving many instances of this problem on-line. The characteristics of these instances being strongly dependent on the load and restrictions of the charging station at a given time. In this paper, the goal is to evolve small ensembles of priority rules such that for any instance of the problem at least one of the rules in the ensemble has high chance to produce a good solution. To do that, we propose a Genetic Algorithm (GA) that evolves ensembles of rules from a large set of rules previously calculated by a Genetic Program (GP). We conducted an experimental study showing that the GA is able to evolve ensembles or rules covering instances with different characteristics and so they outperform ensembles of both classic priority rules and the best rules obtained by GP.

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

“…On-line scheduling is another domain. This problem addressed in [92] arose from a charging station where the charging periods for large fleets of electric vehicles (EV) must be scheduled under limited power and other technological constraints. The control system of the charging station requires solving many instances of this problem on-line.…”

“…The goal was to evolve small ensembles of priority rules such that for any instance of the problem at least one of the rules in the ensemble has high chance to produce a good solution. To do that, Genetic Algorithm (GA) that evolves ensembles of rules from a large set of rules previously calculated by a Genetic Program (GP) were used in [92].…”

Learning ensembles of priority rules for online scheduling by hybrid evolutionary algorithms

“…In this section we formalize the Optimal Ensemble of Priority Rules Problem, denoted by OEPRP, and analyze its complexity. Besides, we propose a number of solving methods: an iterated greedy algorithm (IGA) inspired in a similar algorithm for the Maximum Coverage Problem (MCP), a genetic algorithm (GA) that is an extension of that proposed in [31], and a new local search algorithm (LSA), which can be combined with both IGA and GA to improve their solutions.…”

“…and through fine-tuning of their parameters to avoid undesired algorithmic behaviors [41]. In [31], a GA was proposed to solve the OEPRP, whose main components were chosen as follows.…”

“…Earlier work [31] proposed a genetic algorithm for solving the OEPRP. In this paper we extend this work and propose a number of solving methods, namely an iterated greedy algorithm adapted from a classical approach for MCP, a local search algorithm specifically designed for the OEPRP and hybrid algorithms that combine local search with the greedy algorithm and with the genetic algorithm, resulting in a memetic algorithm.…”