A Modified Genetic Algorithm for Distributed Hybrid Flowshop Scheduling Problem

Sun, Xueyan; Shen, Weiming; Sun, Bomin

doi:10.1109/cscwd49262.2021.9437786

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The above search process continues until the termination criterion is met (Hansen et al, 2010). Researchers have employed this method to successfully address numerous combinatorial optimization problems (Peng et al, 2019(Peng et al, , 2023Sun et al, 2023). In this section, a specialized local search is designed based on VNS to enhance the local search capabilities of the GA. Its primary characteristics are the following two aspects:…”

Section: Local Search Based On Vnsmentioning

confidence: 99%

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Model and algorithm for pharmaceutical distribution routing problem considering customer priority and carbon emissions

Li,

Peng,

Deng

et al. 2024

DCE

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Pharmaceutical distribution routing problem is a key problem for pharmaceutical enterprises, since efficient schedules can enhance resource utilization and reduce operating costs. Meanwhile, it is a complicated combinatorial optimization problem. Existing research mainly focused on delivery route lengths or distribution costs minimization, while seldom considered customer priority and carbon emissions simultaneously. However, considering the customer priority and carbon emissions simultaneously will not only help to enhance customer satisfaction, but also help to reduce the carbon emissions. In this article, we consider the customer priority and carbon emission minimization simultaneously in the pharmaceutical distribution routing problem, the corresponding problem is named pharmaceutical distribution routing problem considering customer priority and carbon emissions. A corresponding mathematical model is formulated, the objectives of which are minimizing fixed cost, refrigeration cost, fuel consumption cost, carbon emission cost, and penalty cost for violating time windows. Moreover, a hybrid genetic algorithm (HGA) is proposed to solve the problem. The framework of the proposed HGA is genetic algorithm (GA), where an effective local search based on variable neighborhood search (VNS) is specially designed and incorporated to improve the intensification abilities. In the proposed HGA, crossover with adaptive probability and mutation with adaptive probability are utilized to enhance the algorithm performance. Finally, the proposed HGA is compared with four optimization algorithms, and experimental results have demonstrated the effectiveness of the HGA.

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Section: Local Search Based On Vnsmentioning

confidence: 99%

“…In order to further enhance intensification abilities of GA, some researchers have integrated local search into the GA, resulting in hybrid genetic algorithm (HGA). The HGA has been utilized to solve various NP-hard combinatorial optimization problems (Sun et al, 2023;Mahmoudinazlou and Kwon, 2024).…”

Section: Introductionmentioning

confidence: 99%