A Combination of Evolutionary Algorithm, Mathematical Programming, and a New Local Search Procedure for the Just-In-Time Job-Shop Scheduling Problem

Santos, Andre Gustavo dos; Araujo, Rodolfo P.; Arroyo, José Elias Claudio

doi:10.1007/978-3-642-13800-3_2

Cited by 12 publications

(8 citation statements)

References 10 publications

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“…The first one was setup experimentally. For the last we are using the same proposed by the authors of this local search [14]. Note that GTS improves the results for most instances, even when comparing simultaneously to both GR and ILS methods.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Metaheuristic for parallel machines scheduling with resource-assignable sequence dependent setup times

Bicalho

Santos

Arroyo

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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In this paper, we describe and show the results of a combination of two metaheuristics to solve an unrelated parallel machines scheduling problem in which the setup times depend not only on the machine and job sequence, but also on the amount of resource assigned. This problem has been proposed recently on the literature and since then a couple of metaheuristics have been used to address it. The one proposed here, called GTS, consists of two phases: initially, some solutions are generated by the GRASP metaheuristic; subsequently, the Tabu Search (TS) is applied in the best solution found by GRASP. The numerical experiments show that the GTS heuristic was able to improve the results in 70% (251 out of 360) of the larger instances available in the literature.

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Section: Resultsmentioning

confidence: 99%

“…The movement considered in this LS is the permutation of two jobs processed subsequently. The Local Search algorithm used was proposed in [14] for another job scheduling problem. The idea is to work with not only the best neighbor solution, but with a list containing the size_list best solutions from the neighborhood.…”

Section: Local Search Phasementioning

confidence: 99%

Metaheuristic for parallel machines scheduling with resource-assignable sequence dependent setup times

Bicalho

Santos

Arroyo

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

Self Cite

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show abstract

“…More precisely, in each improving step the 2-0pt algorithm selects two edges Both local searches use the approach proposed by [20], where there is an ordered list to keep N of the potential solutions. In this approach the list begins with the initial solution, unchecked.…”

Section: Lsc T Is Important In This Context Because the Energymentioning

confidence: 99%

“…This is repeated while there is solution unchecked in the list. Thus, not only the best neighbor is considered, but also some other that are still present in the list which may be expanded in future iterations, avoiding some local minima [20],…”

Section: Lsc T Is Important In This Context Because the Energymentioning

confidence: 99%

Lifetime maximization of hop-and-delay constrained wireless sensor networks with mobile agent

Romão

Santos

Mateus

2013

2013 IEEE Congress on Evolutionary Computation

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Wireless Sensor Networks (WSNs) have emerged as an attractive and challenging research field. One of the main challenges in such networks lies in the constrained energy resources available to sensor nodes. Since the sensors are usually deployed in hostile environments and in large quantities, it is difficult or impossible to replace or recharge their batteries. A possible solution to save energy is to allow a mobile agent to move through the WSN to collect the data, but this approach increases the delay delivery of messages. In this work we use a communication forest, where the roots (cluster heads) of the trees are the sensors visited by the mobile agent; the other sensors send their information to the cluster heads using one or more hops. The problem is to define the cluster heads, the communication forest and the mobile agent path in order to minimize the total energy consumption within a limited delay. We introduce a Genetic Algorithm (GA) that defines the set of cluster heads using a specialized heuristic to evaluate the solutions. Results are presented for WSN with up to 80 nodes using different limits for the mobile agent path length, in order to control the delivery delay of messages. The optimality of the solutions for some instances with 20 and 30 nodes were confirmed by solving a Mixed-Integer Linear Programming formulation.

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“…Monette et al [24] propose a constraint programming approach, which includes a novel filtering algorithm and dedicated heuristics, applied to the same problem set studied by Baptiste et al [6] and outperforms their model. Using the same problem set, Santos et al [28] propose a combination of evolutionary algorithm and mathematical programming with an efficient local search procedure. Their approach yields improved results than Baptiste et al [5].…”

Section: Previous Workmentioning

confidence: 99%

Job shop scheduling based on earliness and tardiness penalties with due dates and deadlines: an enhanced genetic algorithm

Yang

Sun

Saygin

et al. 2011

Int J Adv Manuf Technol

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This paper studies a job shop scheduling problem with due dates and deadlines in the presence of tardiness and earliness penalties. Due dates are desired completion dates of jobs given by the customer, while deadlines are determined by the manufacturer based on customer due dates. Due dates can be violated at the cost of tardiness, whereas deadlines must be met and cannot be violated. The aforementioned scheduling problem, which is NP-hard, can be formulated with the objective function of minimizing the sum of weighted earliness and weighted tardiness of jobs subject to due dates and deadlines. In order to solve this problem, an enhanced genetic algorithm (EGA) is introduced in this paper. EGA utilizes an operation-based scheme to represent schedules as chromosomes. After the initial population of chromosomes is randomly generated, each chromosome is processed through a three-stage decoder, which first reduces tardiness based on due dates, second ensures deadlines are not violated, and finally reduces earliness based on due dates. After the population size is reached, EGA continues with selection, crossover, and mutation. The proposed algorithm is tested on 180 job shop scheduling problems of varying sizes and its performance is discussed.

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A Combination of Evolutionary Algorithm, Mathematical Programming, and a New Local Search Procedure for the Just-In-Time Job-Shop Scheduling Problem

Cited by 12 publications

References 10 publications

Metaheuristic for parallel machines scheduling with resource-assignable sequence dependent setup times

Metaheuristic for parallel machines scheduling with resource-assignable sequence dependent setup times

Lifetime maximization of hop-and-delay constrained wireless sensor networks with mobile agent

Job shop scheduling based on earliness and tardiness penalties with due dates and deadlines: an enhanced genetic algorithm

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