A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility

Pérez-Rodríguez, Ricardo; Hernández-Aguirre, Arturo

doi:10.1007/s10489-018-1160-z

Cited by 17 publications

(8 citation statements)

References 44 publications

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“…[42], detailing the school bus routing problem with bus stop selection; Ref. [43], depicting a flexible job shop scheduling problem with process plan flexibility; and [44], illustrating the vehicle routing problem with time windows. For the aforementioned papers, an interaction estimation between variables is not required.…”

Section: Distance Based Ranking 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: Distance Based Ranking Modelsmentioning

confidence: 99%

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

Pérez-Rodríguez

2021

MCA

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“…MHEDA contains differences and similarities with respect to a recent published algorithm, i.e., the MEDA (Mallows Estimation of Distribution Algorithm). e MEDA algorithm mentioned above can be consulted in Pérez-Rodríguez and Hernández-Aguirre [51]. e details of the MHEDA and also the differences and similarities between the algorithms, MHEDA and MEDA, are detailed below.…”

Section: Mheda For the Mhspmentioning

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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“…Over the years, several researchers have applied GAs to solve such problems. For instance, authors in [44] dealt applied the GA to address the issue of scheduling class timetables. The differential feature in this work is the use of crossover operators only between rows and, later, columns.…”

Section: A Genetic Algorithmsmentioning

confidence: 99%

A Review of Optimization Algorithms for University Timetable Scheduling

Alghamdi

Alsubait

Alhakami

et al. 2020

Eng. Technol. Appl. Sci. Res.

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The university course timetabling problem looks for the best schedule, to satisfy given criteria as a set of given resources, which may contain lecturers, groups of students, classrooms, or laboratories. Developing a timetable is a fundamental requirement for the healthy functioning of all educational and administrative parts of an academic institution. However, factors such as the availability of hours, the number of subjects, and the allocation of teachers make the timetable problem very complex. This study intends to review several optimization algorithms that could be applied as possible solutions for the university student course timetable problem. The reviewed algorithms take into account the demands of institutional constraints for course timetable management.

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A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility

Cited by 17 publications

References 44 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

A Review of Optimization Algorithms for University Timetable Scheduling

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