Efficient local search algorithms for job-shop scheduling problems

Pongchairerks, Pisut

doi:10.1504/ijmor.2016.077995

Cited by 5 publications

(9 citation statements)

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“…A backward schedule can simply be constructed by reversing the directions of the precedence constraints of all operations, and then allocating jobs to machines in the same way as constructing a forward schedule; after that, the schedule must be turned back to front in order to make it satisfy the original precedence constraints. The uses of the backward scheduling direction are found in published articles, e.g., [7,[28][29][30][31].…”

Section: Preliminariesmentioning

confidence: 99%

“…Because can be adjusted to be any real number between 0 and 1, this parameter thus controls the algorithm's solution space between nondelay schedules and active schedules. Some variants of the hybrid scheduler of [16], which are modified for the purpose of simplification, can be found in published articles such as [15,31,33,34].…”

Section: Preliminariesmentioning

confidence: 99%

“…On the other hand, some other metaheuristic algorithms such as [34,35] adjust the values during their evolutionary processes; thus, their methods of changing the values belong to the classification of selfadaptive parameter control based on the definition given by [10]. In addition, some metaheuristic algorithms adjust the values based on the mathematical functions of another parameter (or other parameters); for example, the local search algorithms of [31] adjust the value based on the iteration's index. The concept of a two-level metaheuristic algorithm in which the upper-level algorithm controls the input parameters including for the lower-level algorithm has been found in [15].…”

Section: Preliminariesmentioning

confidence: 99%

“…LOLA, which is the lower-level algorithm in the proposed two-level algorithm, acts as a local search algorithm. It is similar to the other local search algorithms in its basic framework [36][37][38][39]; in addition, it generates neighbor solutions based on common swap and insert operators [31,40,41]. However, LOLA has three specific features different from general local search algorithms as follows:…”

Section: The Proposed Lower-level Algorithmmentioning

confidence: 99%

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A Two‐Level Metaheuristic Algorithm for the Job‐Shop Scheduling Problem

Pongchairerks

2019

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This paper proposes a novel two-level metaheuristic algorithm, consisting of an upper-level algorithm and a lower-level algorithm, for the job-shop scheduling problem (JSP). The upper-level algorithm is a novel population-based algorithm developed to be a parameter controller for the lower-level algorithm, while the lower-level algorithm is a local search algorithm searching for an optimal schedule in the solution space of parameterized-active schedules. The lower-level algorithm’s parameters controlled by the upper-level algorithm consist of the maximum allowed length of idle time, the scheduling direction, the perturbation method to generate an initial solution, and the neighborhood structure. The proposed two-level metaheuristic algorithm, as the combination of the upper-level algorithm and the lower-level algorithm, thus can adapt itself for every single JSP instance.

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Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: The Proposed Lower-level Algorithmmentioning

confidence: 99%

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A Two‐Level Metaheuristic Algorithm for the Job‐Shop Scheduling Problem

Pongchairerks

2019

Complexity

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“…General of job shop scheduling problems has been studied for more than 50 years (Faccio et al, 2015). Job-shop scheduling problem (JSP), help to allocate the given jobs to the machines over time (Pongchairerks, 2016). Recently job shop scheduling focus of a significant amount of research approaches are formulated and designed to address the static job shop scheduling problem (Kundakc and Kulak, 2016).…”

Section: Introductionmentioning

confidence: 99%

Robust and stable flexible job shop scheduling with random machine breakdowns: multi-objectives genetic algorithm approach

Sajadi

Alizadeh

Zandieh

et al. 2019

IJMOR

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In this paper, robust and stable scheduling for a flexible job-shop problem with random machine breakdowns has been discussed. A two-stage genetic algorithm is used to generate the predictive schedule. The first stage optimises the primary objective, which minimises the makespan, where all data is considered to be deterministic with no expected disruptions. The second stage optimises two objectives, makespan and stability, function in the presence of random machine breakdowns. For the second stage two different versions of multi-objective genetic algorithm, non-dominated sorting genetic algorithm II and non-dominated ranking genetic algorithm, is used. A simulator is proposed to simulate random machine breakdowns. An experimental study and analysis of variance is conducted to study the results of each multi-objective algorithm and breakdown simulator. The results of their comparison indicate that, non-dominated ranking genetic algorithm (NRGA) performs better and also shows a significant difference between various repair times in the proposed breakdown simulator.

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