Abstract
The traditional multi-objective cold chain logistics deployment path planning model does not build the logistics transportation supply system dynamics model, which leads to the problems of long planning time and high overlapping rate. Therefore, this paper designs a multi-objective dynamic programming model of cold chain logistics deployment path based on meme algorithm. This paper deals with the constraint conditions, constructs the penalty function method of cold chain logistics transportation, and realizes the multi-objective cold chain logistics distribution objective sequencing on the basis of constructing the dynamic model of logistics transportation supply system. According to the combination segmentation operator method, the optimal solution of logistics deployment path was obtained, and the multi-objective dynamic programming model of cold chain logistics deployment path was designed to realize the dynamic programming of logistics deployment path. The experimental results showed that this method can effectively reduce the path planning time, reduce the proportion of path overlap, and effectively improve the effect of logistics deployment path planning.
Similar content being viewed by others
References
Chen L, Ma M, Sun L (2019a) Heuristic swarm intelligent optimization algorithm for path planning of agricultural product logistics distribution. J Intell Fuzzy Syst 37(4):1–7
Chen MZ, Qian TH, Zhang SZ, Wang JQ (2019b) Research on multiple robot warehouse scheduling method based on reinforcement learning. Mod Electron Tech 42(14):165–168
Cui YZ, Liu M (2020) Research on port logistics distribution path planning based on artificial fish school algorithm. Ship Sci Technol 42(6):194–196
Ding YJ (2019) Research on location selection of fresh agricultural products logistics park based on multi-objective planning. Value Eng 38(12):83–85
Du LZ, Ke S, Wang Z (2019) Research on multi-load AGV path planning of weaving workshop based on time priority. Math Biosci Eng: MBE 16(4):2277–2292
Duan LM (2018) (2018) Path planning for batch picking of warehousing and logistics robots based on modified a* algorithm. Acad J Manuf Eng 16(2):99–106
Heinemann T, Riedel O, Lechler A (2019) Generating smooth trajectories in local path planning for automated guided vehicles in production. Procedia Manuf 39(1):98–105
Li JT, Lu MM, Li DL, Liu PF (2019) (2019) Research on the logistics path planning of fuzzy time window multi-objective cold chain. J China Agric Univ 24(12):128–135
Liu D, Ji S (2018) Research on efficient online planning of emergency logistics path based on double-layer ant colony optimization algorithm. Int J Comput Appl 12(8):1–7
Liu D, Ji S (2019) Research on efficient online planning of emergency logistics path based on double-layer ant colony optimization algorithm. Int J Comput Appl 41(6):400–406
Novillo SM, Rodriguez A, Garcia GDC (2020) Path planning for mobile robots applied in the distribution of materials in an industrial environment. Adv Intell Syst Comput 1273(1):323–337
Subbiah S, Schoppmeyer C, Valdès JMDL (2013) Optimal management of shuttle robots in a high-rise warehouse using timed automata models. Ifac Proc Vol 46(9):1358–1363
Tao ZW, Zhang ZY, Shi Y, Zhang YW, Shi YQ (2019) Optimization of multi-objective cold chain logistics distribution routes under carbon tax system. J Wuhan Univ Technol 41(1):55–60
Wang R, Qi MZ, Han AQ (2018) Comparative study of emergency logistics path planning. J Donghua Univ 35(03):274–278
Zhao X, Wang Z, Huang CK, Zhao YW (2018) Mobile robot path planning based on an improved a*algorithm. Robot 40(6):903–910
Zhou ZY, Jiang HY (2020) Multi-objective optimization of cold chain logistics network based on bi-level programming model. Logist Technol 39(02):71–76
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, Y. Design of Dynamic Programming Model for Multi-Objective Cold Chain Logistics Deployment Path Based on Meme Algorithm. Iran J Sci Technol Trans Civ Eng 46, 2553–2560 (2022). https://doi.org/10.1007/s40996-021-00639-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40996-021-00639-2