A branch-and-price approach to the feeder network design problem

Abstract

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Transit Route Network Design for Low-Mobility Individuals Using a Hybrid Metaheuristic Approach

“…As shown in Figure 3, the tra c network of Wenling has 259 stops (corresponding to the node number) and 406 links (the length can be obtained based on the scale). In our previous survey [1], the total survey results (12,013) consisted of 4,319 low-mobility respondents and 7,694 general respondents. Here, we used two parts of trip O-D (Origin-Destination) from survey results to formulate our travel demands: (1) transit trip O-D of respondents; (2) trip O-D of low-mobility respondents who made mid-and long-distance travels (at least 20 minutes) by riding a bicycle, electric bicycle or motorcycle.…”

“…Exiting studies related to transit route network design (TRND) have been presented by many researchers in di erent public transportation networks. For example, transit network accessibility [7,8], multi-user class transit network design [9,10], feeder network design [11,12]. However, most of exiting studies related to public transportation for LMIs mainly focus on public transit accessibility (i.e., walking to the bus stop) [4,5,13,14], rather than on the TPND.…”

Research on feeder network design: a case study of feeder service for the port of Kotka

“…In an effort to make the network design problem more practical, Shintani et al [7] examined the network design problem with empty container repositioning. Santini, Plum, and Ropke [8] investigated a feeder network design by considering the operational characteristics. The container transport demand is uncertain during network design and cargo assignment periods [9].…”

“…First, a single decision-making objective does not reflect the multi-objective behaviors of liner shipping in practice. The current research was based largely on the maximization of revenue, profit, or minimization of cost (e.g.,Hwa-Joong et al [6] and Santini et al [8]). However, in practice, the decision objective was made in reference to a host of factors including: cost, sailing cycle, equipment balance, allocation utilization, service route competitiveness, and stability.…”

VRPy: A Python package for solving a range of vehicle routing problems with a column generation approach

“…For more advanced users, there are different pricing strategies (approaches for solving subproblems), namely sparsification strategies (Dell'Amico et al, 2006;Santini et al, 2018), as well as pre-pricing heuristics available that can lead to faster solutions. The heuristics implemented include a greedy randomized heuristic (for the CVRP and the CVRP with resource constraints) (Santini et al, 2018). Also, a diving heuristic (Sadykov et al, 2019) can be called to explore part of the branch-and-price tree, instead of solving the restricted master problem as a MIP.…”