A Novel Modified Discrete Differential Evolution Algorithm to Solve the Operations Sequencing Problem in CAPP Systems

Oscar Alberto Alvarez-Flores, Raúl Rivera-Blas (), Luis Armando Flores-Herrera, Emmanuel Zenén Rivera-Blas, Miguel Angel Funes-Lora and Paola Andrea Niño-Suárez
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Oscar Alberto Alvarez-Flores: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica Azcapotzalco, CDMX 02250, Santa Catarina, Mexico
Raúl Rivera-Blas: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica Azcapotzalco, CDMX 02250, Santa Catarina, Mexico
Luis Armando Flores-Herrera: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica Azcapotzalco, CDMX 02250, Santa Catarina, Mexico
Emmanuel Zenén Rivera-Blas: Instituto Tecnológico Superior de Alvarado, Veracruz, Departamento de Ingeniería en Sistemas Computacionales, La Trocha, Alvarado 95270, Veracruz, Mexico
Miguel Angel Funes-Lora: Department of Mechanical Engineering, The University of Michigan, Ann Arbor, MI 48109, USA
Paola Andrea Niño-Suárez: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica Azcapotzalco, CDMX 02250, Santa Catarina, Mexico

Mathematics, 2024, vol. 12, issue 12, 1-20

Abstract: Operation Sequencing (OS) is one of the most critical tasks in a CAPP system. This process could be modelled as a combinatorial problem where finding a suitable solution within a reasonable time interval is difficult. This work implements a novel Discrete Differential Evolution Algorithm (DDEA) to solve the OS problem, focusing on parts of up to 76 machining operations; the relationships among operations are represented as a directed graph; the contributions of the DDEA are as follows: (1) operates with a discrete representation in the space of feasible solutions; (2) employs mutation and crossover operators to update solutions and to reduce machining and setup costs, (3) possess a local search strategy to achieve better solutions, and (4) integrates a statistical method based on quantiles to measure the quality and likelihood for an achieving a solution. To demonstrate the efficiency and robustness of the DDEA, five prismatic parts with different numbers of machining operations as benchmarks to address the OS problem were selected. The results generated the same OS for parts with a few machining operations (up to 23 machining operations). Conversely, for parts with more machining operations, the DDEA needs more runs to achieve the best solution.

Keywords: Discrete Differential Evolution; operation sequencing; statistical method based on quantiles; combinatorial optimisation; feasible operation sequences (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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