Abstract
In today’s highly competitive environment, the high speed of evolutions has increased the uncertainty of decisions, which creates a high level of uncertainty in the supply chain and impairs its ability to predict future conditions. To plan better and more accurately, reliable planning should be performed. One of the reliable approaches is robust optimization. In this study, a forward oil supply chain is considered to minimize the shipping costs as well as the number of loads under certain and uncertain conditions. To solve the model under certain conditions, two meta-heuristic algorithms, including particle swarm optimization (PSO) and multi objective particle swarm optimization (MOPSO) are used, and in the uncertain condition, the Mulvey approach is implemented . The empirical results illustrate the efficiency of proposed models under both conditions.
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Abdolazimi, O., Abraham, A. (2021). Meta-heuristic Based Multi Objective Supply Chain Model for the Oil Industry in Conditions of Uncertainty. In: Abraham, A., Sasaki, H., Rios, R., Gandhi, N., Singh, U., Ma, K. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2020. Advances in Intelligent Systems and Computing, vol 1372. Springer, Cham. https://doi.org/10.1007/978-3-030-73603-3_13
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DOI: https://doi.org/10.1007/978-3-030-73603-3_13
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