Multileveled Symbiotic Evolutionary Algorithm: Application to FMS Loading Problems

Kim, Jae Yun; Kim, Yeo Keun

doi:10.1007/s10791-005-6621-4

Cited by 12 publications

(3 citation statements)

References 22 publications

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“…They applied priority rules such as the shortest processing time for the genetic search to devise a hybrid genetic algorithm. Kim et al (2003) presented a new evolutionary algorithm to solve FMS loading problems with machine, tool, and process flexibilities. Kim et al (2004) introduced an asymmetric multi-leveled symbiotic evolutionary algorithm, and applied it to the integrated problem of process planning and scheduling in FMS.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithms for job shop scheduling problems with alternative routings

Moon

Lee

Bae

2008

International Journal of Production Research

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The purpose of this research is to solve a general job shop problem with alternative machine routings. We consider four performance measures: mean flow time, makespan, maximum lateness, and total absolute deviation from the due dates. We first develop mixed-integer linear programming (MILP) formulations for the problems. The MILP formulations can be used either to compute optimal solutions for small-sized problems or to test the performance of existing heuristic algorithms. In addition, we have developed a genetic algorithm that can be used to generate relatively good solutions quickly. Further, computational experiments have been performed to compare the solution of the MILP formulations with that of existing algorithms.

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Section: Introductionmentioning

confidence: 99%

Genetic algorithms for job shop scheduling problems with alternative routings

Moon

Lee

Bae

2008

International Journal of Production Research

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“…Workload balancing is a key problem in FMSs; each type of unbalanced measure facilitates a balancing goal. Kumar and Shanker [63] and Kim and Kim [64] compared these goals in terms of their effectiveness. Automated manufacturing systems improve the flexibility and efficiency of automated systems, with load decisions playing a critical role in determining the efficiency of a manufacturing system.…”

Section: Facilitating Decision Making Through Simulationmentioning

confidence: 99%

The Academic Development Trajectories and Applications of Flexible Manufacturing Systems Based on Main Path Analysis Method

Chen

2023

Processes

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Rapid shifts in consumer preferences have prompted enterprises to offer products in small quantities and various options. To meet market demands, enterprises must be able to research the development of modern conceptions of manufacturing systems which has revolved around new practical and scientific results that are able to meet the assumptions of focused flexible manufacturing systems (FMSs) and the challenges of the Industry 4.0 philosophy. These FMSs, which incorporate automated facilities and computer control systems, play a crucial role in boosting the productivity of enterprises. In this study, the development trajectory and applications of FMS research were investigated. Scopus was used to collect and organize voluminous data, and main path analysis was used to identify the most relevant studies on FMS research. The results revealed that early FMS research concentrated on fundamental property analysis. After the flexibility and productivity of these systems were enhanced, the elimination of loading problems was discussed. Generally, FMS research has emphasized factor identification, flexibility evaluation, pre-simulation, and optimization. In this study, cluster analysis was used to identify five subfields: loading problem mitigation through scheduling, decision-making facilitation through simulation, FMS deadlock prevention, FMS flexibility measurement, and FMS composition. This study provides planning directions for industry, and the findings serve as a reference for manufacturing systems. The integrated analysis successfully determined the trajectory of FMS based technological development and applications as well as forecast the direction of future research.

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“…Later, they live together in symbiosis and evolve into a eukaryote. This evolutionary process may be simulated for solving multileveled problems (Kim and Kim, 2005;Kim et al, 2007).…”

Section: The Basic Conceptmentioning

confidence: 99%

A two‐leveled multi‐objective symbiotic evolutionary algorithm for the hub and spoke location problem

Shin

Kim

2009

J of Advced Transportation

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We consider a hub and spoke location problem (HSLP) with multiple scenarios. The HSLP consists of four subproblems: hub location, spoke location, spoke allocation, and customer allocation. Under multiple scenarios, we aim to provide a set of well-distributed solutions, close to the true Pareto optimal solutions, for decision makers. We present a novel multi-objective symbiotic evolutionary algorithm to solve the HSLP under multiple scenarios. The algorithm is modeled as a two-leveled structure, which we call the twoleveled multi-objective symbiotic evolutionary algorithm (TMSEA). In TMSEA, two main processes imitating symbiotic evolution and endosymbiotic evolution are introduced to promote the diversity and convergence of solutions. The evolutionary components suitable for each subproblem are defined. TMSEA is tested on a variety of test-bed problems and compared with existing multi-objective evolutionary algorithms. The experimental results show that TMSEA is promising in solution convergence and diversity.

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Multileveled Symbiotic Evolutionary Algorithm: Application to FMS Loading Problems

Cited by 12 publications

References 22 publications

Genetic algorithms for job shop scheduling problems with alternative routings

Genetic algorithms for job shop scheduling problems with alternative routings

The Academic Development Trajectories and Applications of Flexible Manufacturing Systems Based on Main Path Analysis Method

A two‐leveled multi‐objective symbiotic evolutionary algorithm for the hub and spoke location problem

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