An improved genetic algorithm with an overlapping strategy for solving a combination of order batching and flexible job shop scheduling problem

Liu, Zhifeng; Zha, Jiming; Yan, Jun; Zhang, Yueze; Zhao, Tianzuo; Cheng, Qiang; Cheng, Chenyang

doi:10.1016/j.engappai.2023.107321

Cited by 6 publications

(1 citation statement)

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“…GA is a population-based metaheuristic that has been successfully applied to solve a variety of combinatorial optimization problems, such as VRP and job shop scheduling problems (Chien and Lan, 2021; Wang et al, 2023; Liu et al, 2024). In order to further enhance intensification abilities of GA, some researchers have integrated local search into the GA, resulting in hybrid genetic algorithm (HGA).…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%