Multiple-path selection for new highway alignments using discrete algorithms

Abstract

This paper addresses the problem of finding multiple near-optimal, spatially-dissimilar paths that can be considered as alternatives in the decision making process, for finding optimal corridors in which to construct a new road. We further consider combinations of techniques for reducing the costs associated with the computation and increasing the accuracy of the cost formulation. Numerical results for five algorithms to solve the dissimilar multipath problem show that a "bidirectional approach" yields the fastest running times and the most robust algorithm. Further modifications of the algorithms to reduce the running time were tested and it is shown that running time can be reduced by an average of 56% without compromising the quality of the results.

A 3D Model for Optimizing Infrastructure Costs in Road Design

“…In this situation, taking turns and slope changes, the problem has been solved by the two‐stage method described in Section . To look for solutions in different corridors, at Stage 1 we have considered seven ad hoc alignments to start NOMAD (a more general method to generate dissimilar alignments can be seen in Pushak et al., ) and a population of 20 individuals for executing GA and PSwarm (the seven used in the NOMAD multistart and other 13 random layouts). In this numerical experiment, the combination of any of these three algorithms for solving , with the SQP method for solving , leads to the same layout.…”

“…(), give a classification of the papers on this topic, based on the optimization approach used. Nowadays, the most common are genetic algorithms (GAs) (Kang et al., ; Davey et al., ; Li et al., ), derivative‐free optimization algorithms (Mondal et al., ; Hirpa et al., ), shortest path (Pushak et al., ), and distance transform methods (Li et al., ). In any case, the selection of a suitable numerical method must be done according to the characteristics of the stated problem.…”

A three‐dimensional distance transform for optimizing constrained mountain railway alignments

“…The usual goal of alignment optimization is to find an alignment with the lowest comprehensive cost between two given endpoints. In the literature, representative methods for optimizing alignments include particle swarm optimization (Shafahi & Bagherian, ; Babapour, Naghdi, Ghajar, & Mortazavi, ; Pu et al., ), two‐stage method that combines global optimization methods with a gradient type algorithm (Vázquez‐Méndez, Casal, Santamarina, & Castro, ), derivative‐free algorithms (Mondal, Lucet, & Hare, ), discrete algorithms (Hirpa, Hare, Lucet, Pushak, & Tesfamariam, ; Pushak, Hare, & Lucet, ), dynamic programming (Hogan, ; Li, Pu, Zhao, & Liu, ), mixed integer programming (Easa & Mehmood, ), linear programming (Revelle, Whitlatch, & Wright, ; Chapra & Canale, ), network optimization (Trietsch, , ), heuristic neighborhood search with mixed integer programming (Cheng & Lee, ; Lee, Tsou, & Liu, ), calculus of variations (Howard, Bramnick, & Shaw, ), numerical search (Robinson, ), enumeration (Easa, ), average‐end‐area method for improving earthwork calculation accuracy from 2D to 3D (Cheng & Jiang, ), genetic algorithms (Maji & Jha, , ), and distance transforms (DTs) (de Smith, ; Li et al., ; Li et al, ; Pu et al., ).…”

Methodology for optimizing constrained 3-dimensional railway alignments in mountainous terrain

“…Network optimization Turner and Miles (1971), OECD (1973), Athanassoulis and Calogero (1973), Parker (1977), and Trietsch (1987a,b) Dynamic programming Hogan (1973), Nicholson et al (1976), Puy Huarte (1973), Murchland (1973), Goh et al (1988), Fwa (1989), Li et al(2013) Mixed integer programming Easa and Mehmood (2008) Neighborhood search heuristic with mixed integer programming Cheng and Lee (2006), Lee et al (2009) Distance transform Mandow and Perez-de-la-Cruz (2004) and De Smith (2006) Discrete algorithms Mondal et al (2015), Hirpa et al (2016), Pushak et al (2016) Among direct methods, genetic algorithms (GAs) have been the most popularly adopted methods for optimizing railway or highway alignments in the past decade (Shafahi and Bagherian, 2013). They have been developed by a University of Maryland research group.…”

“…They developed a novel bi-objective optimization framework for three-dimensional road alignments, in which earthwork cost and utility costs are cast (Hirpa et al, 2016). Their comparative numerical results from five algorithms used to solve a dissimilar multipath problem show that a bidirectional approach yields the fastest running times and the most robust algorithm (Pushak et al, 2016). The NOMAD (Nonlinear Optimization with Mesh Adaptive Direct Search) and the HOPSPACK (Hybrid Optimization Parallel Search Package) were used by researchers from the same team for optimizing horizontal alignment in a specified corridor (Mondal et al, 2015).…”