Buffer Allocation via Bottleneck-Based Variable Neighborhood Search

Abstract

This study addresses the challenging problem of efficient buffer allocation in production lines. Suitable locations for buffer allocation are determined to satisfy the desired throughput, while a suitable balance between solution quality and computation time is achieved. A throughput calculation approach that yields the state probability of production lines is adopted to evaluate the effectiveness of candidate buffer allocation solutions. To generate candidate buffer allocation solutions, an active probability index based on state probability is proposed to rapidly detect suitable locations of buffer allocations. A variable neighborhood search algorithm is used to maintain acceptable solution quality; an additional neighborhood structure is used in the case where no satisfactory solution is generated in the initial neighborhood structure. Extensive numerical experiments demonstrate the efficacy of the proposed approach. The proposed approach can facilitate agile design of production lines in industry by rapidly estimating production line topologies.

Optimization of the OEE indicator through meta-models’ simulation in the buffer allocation problem

“…The most common optimization criteria consider maximizing the average throughput rate (Gao, 2022;Gao, Higashi, Kobayashi, Taneda, Rubrico & Ota, 2020;Gao & Liu, 2023;Kassoul, Cheikhrouhou & Zufferey, 2023;Köse, Demir, Tunal & Eliiyi, 2015;Kose & Kilincci, 2015;Koyuncuoğlu & Demir, 2021;Lin & Chiu, 2018;Nahas, Nourelfath & Gendreau, 2014;Narasimhamu, Reddy & Rao, 2014;Patchong & Kerbache, 2017;Wang, Song, Shin & Moon, 2014), minimizing the total buffer size (Li, 2013;Weiss & Stolletz, 2015), minimizing the total cost of allocation (Magnanini, Terkaj & Tolio, 2022;Nahas, 2017;Nahas & Nourelfath, 2018;Ouzineb, Mhada, Pellerin & El Hallaoui, 2018;Tiacci, 2022), among others (Alfieri, Matta & Pastore, 2020;Hernández-Vázquez, Hernández-González, Jiménez-García, Hernández-Ripalda & Hernández-Vázquez, 2019;Hernández-Vázquez, Hernández-González, Hernández-Vázquez, Figueroa-Fernández & Cancino de la Fuente, 2022b;Koyuncuoğlu & Demir, 2023;Shaaban & Romero-Silva, 2021;Shao, Moroni, Li, & Xu, 2022;Xi, Smith, Chen, Mao, Zhang & Yu, 2021;Zhou, Liu, Yu & Tao, 2018).…”

A data-driven ensemble algorithm of black widow optimizer and simulated annealing algorithms for multi-objective buffer allocation in production lines

“…11,12 Search algorithms can obtain the near-optimal solution quickly by using guidance information to update the candidate solutions. 13,14 However, the effectiveness of the guidance information is difficult to determine, which directly affects solution quality. Metaheuristics are widely used to solve resource scheduling problems, such as MBAP.…”