Data-Driven Stochastic Optimization for Transportation Road Network Design Under Uncertainty

Data-Driven Stochastic Optimization for Transportation Road Network Design Under Uncertainty

Suh-Wen Chiou (National Dong Hwa University, Taiwan)
Copyright: © 2020 |Pages: 48
DOI: 10.4018/978-1-7998-0106-1.ch012


A data-driven stochastic program for bi-level network design with hazardous material (hazmat) transportation is proposed in this chapter. In order to regulate the risk associated with hazmat transportation and minimize total travel cost on interested area under stochasticity, a multi-objective stochastic optimization model is presented to determine generalized travel cost for hazmat carriers. Since the bi-level program is generally non-convex, a data-driven bundle method is presented to stabilize solutions of the proposed model and reduce relative gaps between iterations. Numerical comparisons are made with existing risk-averse models. The results indicate that the proposed data-driven stochastic model becomes more resilient than others in minimizing total travel cost and mitigating risk exposure. Moreover, the trade-offs among maximum risk exposure, generalized travel costs, and maximum equitable risk spreading over links are empirically investigated in this chapter.
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For most urban road networks, transportation of hazardous material (hazmat) is of primary concern to decision makers due to serious safety, human health, and environmental risks associated with the release of hazmat. Because of the danger associated with the accidental release of hazmat, the people living and working around the roads heavily used for hazmat thus incur most of the risk during transportation. The reliability of a transportation road network with signal settings for hazmat traffic thus heavily depends on its vulnerability to a dangerous mix of probabilistic threats such as lane closure and road capacity loss. A multi-objective program has long been regarded as one of the most popular approaches to tackle a hazmat network design problem where the interest of stakeholders is in conflict. For example, Zhao and Verter (2015) presented a bi-objective model for the location and routing problem to simultaneously minimize the total environment risk and the total cost. A weighted goal programming was employed to solve the location-routing problem with a case study in the Chongqing of southwest China. Zhang and Huang (2013) considered a multi-objective program for greenhouse gas emissions control from municipal solid waste management facilities. Two conflicting objectives are integrally considered, including minimization of total system cost and minimization of total greenhouse gas emissions from waste management facilities. The multi-objective program often assumes that the multiple objectives which are established by the same decision makers and located at the same level. As the objectives have to be optimized simultaneously, a tradeoff needs to be determined for compromising the multiple objectives. On the other hand, the bi-level decision-making follows a leader and follower relationship and attempts to sequentially optimize the objectives according to the levels of decision makers. Since multi-objective programs can hardly solve the problems with multiple decision makers at different levels, a bi-level decision making program has recently been noticed. He et al. (2011) gave a mixed-integer bi-level programming model for municipal solid waste management and greenhouse gases control with decision-makers at different levels. Empirical comparisons were made among various bi-level single objective decision-making models and multi-objective models. More recently, Gang et al. (2015) proposed a multi-objective bi-level location planning problem for stone industrial parks with a hierarchical structure under a random environment. The proposed model captured cost uncertainties and multiple decision makers with conflicting interests and solved by adaptive chaotic particle swarm optimization.

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