Simulation optimization for a flexible jobshop scheduling problem using an estimation of distribution algorithm

Pérez-Rodríguez, Ricardo; Jöns, S.; Hernández-Aguirre, Arturo; Alberto-Ochoa, Carlos

doi:10.1007/s00170-014-5759-x

Cited by 17 publications

(4 citation statements)

References 19 publications

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“…The development of EDAs, which considers high order interactions between variables, does not finish here. Conversely, different combinations of complex probability models have been used for solving combinatorial problems, such as in [32]. The authors compute more than one complex model to address a flexible job shop scheduling problem.…”

Section: Complex Problems Complex Probability Modelsmentioning

confidence: 99%

A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

Pérez-Rodríguez

2021

MCA

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The aim of the quay crane scheduling problem (QCSP) is to identify the best sequence of discharging and loading operations for a set of quay cranes. This problem is solved with a new hybrid estimation of distribution algorithm (EDA). The approach is proposed to tackle the drawbacks of the EDAs, i.e., the lack of diversity of solutions and poor ability of exploitation. The hybridization approach, used in this investigation, uses a distance based ranking model and the moth-flame algorithm. The distance based ranking model is in charge of modelling the solution space distribution, through an exponential function, by measuring the distance between solutions; meanwhile, the heuristic moth-flame determines who would be the offspring, with a spiral function that identifies the new locations for the new solutions. Based on the results, the proposed scheme, called QCEDA, works to enhance the performance of those other EDAs that use complex probability models. The dispersion results of the QCEDA scheme are less than the other algorithms used in the comparison section. This means that the solutions found by the QCEDA are more concentrated around the best value than other algorithms, i.e., the average of the solutions of the QCEDA converges better than other approaches to the best found value. Finally, as a conclusion, the hybrid EDAs have a better performance, or equal in effectiveness, than the so called pure EDAs.

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Section: Complex Problems Complex Probability Modelsmentioning

confidence: 99%

A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

Pérez-Rodríguez

2021

MCA

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“…Based on the concept of properly modeling the main variables that intervene in the performance of the process has been a priority in the solution of real-world scheduling problems [11]. Such variables or characteristics could be inside or outside of the shop floor and these should be incorporated to efficiently solve the scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Simulation Optimization for the Multihoist Scheduling Problem

Pérez-Rodríguez

2021

Applied Computational Intelligence and Soft Computing

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Although the Multihoist Scheduling Problem (MHSP) can be detailed as a job-shop configuration, the MHSP has additional constraints. Such constraints increase the difficulty and complexity of the schedule. Operation conditions in chemical processes are certainly different from other types of processes. Therefore, in order to model the real-world environment on a chemical production process, a simulation model is built and it emulates the feasibility requirements of such a production system. The results of the model, i.e., the makespan and the workload of the most loaded tank, are necessary for providing insights about which schedule on the shop floor should be implemented. A new biobjective optimization method is proposed, and it uses the results mentioned above in order to build new scenarios for the MHSP and to solve the aforementioned conflicting objectives. Various numerical experiments are shown to illustrate the performance of this new experimental technique, i.e., the simulation optimization approach. Based on the results, the proposed scheme tackles the inconvenience of the metaheuristics, i.e., lack of diversity of the solutions and poor ability of exploitation. In addition, the optimization approach is able to identify the best solutions by a distance-based ranking model and the solutions located in the first Pareto-front layer contributes to improve the search process of the aforementioned scheme, against other algorithms used in the comparison.

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“…Multi-Objective optimization algorithms are one of the fast-growing disciplines in the past 30 years. They have evolved from traditional multi-objective optimization methods, like the weighted method, the constraint method, and the objective programming method, to more classic multi-objective optimization methods developed by subsequent scholars on the basis of evolutionary phenomena in nature, such as the Multi-Objective Genetic Algorithm (MOGA) [2,3,4,5], the Vector Evaluated Genetic Algorithm (VEGA) [16,18], the Niched Pareto Genetic Algorithm (NPGA) [6], the Non-dominated Sorting Genetic Algorithm (NSGA) [8,13], the Particle Swarm Optimization Algorithm (PSO) [9,11,20], the Artificial Immune System (AIS) [12], and the NSGA-II [1,7,14]. In recent years, NSGA-II has attracted more attention for its high efficiency in providing multi-objective solutions.…”

Section: Introductionmentioning

confidence: 99%

Improved NSGA-II for the Job-Shop Multi-Objective Scheduling Problem

Xiao-yun¹

2018

IJPE

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Job-shop scheduling is essential to advanced manufacturing and modern management. In light of the difficulty of obtaining the optimal solution using simple genetic algorithms in the process of solving multi-objective job-shop scheduling problems, with maximum customer satisfaction and minimum makespan in mind, we constructed a multi-objective job-shop scheduling model with factory capacity constraints and propose an improved NSGA-II algorithm. This algorithm not only uses an improved elitism strategy to dynamically update the elite solution set, but also enhances the Pareto sorting algorithm to make density computations more accurate, thereby ensuring population diversity. An example is given to verify that this algorithm can effectively enhance global search capabilities, save computing resources, and lead to a better optimal solution. Using this algorithm for job-shop scheduling optimization oriented towards multiobjective decision-making can provide corporate executives with a scientific quantitative basis for management and decision-making, thereby enhancing their companies' competitiveness.

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Simulation optimization for a flexible jobshop scheduling problem using an estimation of distribution algorithm

Cited by 17 publications

References 19 publications

A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

Simulation Optimization for the Multihoist Scheduling Problem

Improved NSGA-II for the Job-Shop Multi-Objective Scheduling Problem

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