Abstract
AbstractThis paper presents a stochastic bi-objective model for a single-allocation hub covering problem (HCP) with the variable capacity and uncertainty parameters. Locating hubs can influence the performance of hub and spoke networks, as a strategic decision. The presented model optimizes two objectives minimizing the total transportation cost and the maximum transportation time from an origin to a destination simultaneously. Then, due to the NP-hardness of the multi-objective chance-constrained HCP, the presented model is solved by a well-known meta-heuristic algorithm, namely multi-objective invasive weed optimization. Additionally, the associated results are compared with a well-known multi-objective evolutionary algorithm, namely non-dominated sorting genetic algorithm. Furthermore, the computational results of the foregoing algorithms are reported in terms four well-known metrics, namely quality, spacing, diversification, and mean ideal distance. Finally, the conclusion is reported.
Highlights
A hub location problem is one of the significant classes of facility location problems that after more than two decades from appearance many papers of various journals are titled to hub networks design and applications
Campbell (1994) introduced three types of hub covering problem (HCP) by defining three covering criteria depending on the part of flow route that is focused by the decision-maker for optimizing
The authors propose a new formulation for the problem and solve the multi-objective stochastic HCP using a novel artificial intelligent technique called as discrete invasive weed optimization (IWO) that is more efficient in comparison to the older famous
Summary
A hub location problem is one of the significant classes of facility location problems that after more than two decades from appearance many papers of various journals are titled to hub networks design and applications. Mohammadi, Jolai, and Tavakkoli-Moghaddam (2013) presented a new stochastic multi-mode transportation model for a hub covering location problem under uncertainty. They minimized two objectives, the total current investment costs and the maximum transportation time between each origin–destination pair in the network simultaneously. They proposed a MOICA that compared two wellknown multi-objective evolutionary algorithms. Mohammadi, Torabi, and Tavakkoli-Moghaddam (2014) considered a new hub location problem (HLP) considering environmental aspects for air and noise pollution of vehicles They proposed a mixed possibilistic–stochastic programming method along with two meta-heuristics, namely simulated annealing and imperialist competitive algorithm. The authors propose a new formulation for the problem and solve the multi-objective stochastic HCP using a novel artificial intelligent technique called as discrete invasive weed optimization (IWO) that is more efficient in comparison to the older famous
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