Roads Development Optimization for All-Season Service Accessibility Improvement in Rural Nepal using a Novel Cost-Time Model and Evolutionary Algorithm

Abstract

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Optimisation of rural roads planning based on multi-modal travel: a multi-service accessibility study in Nepal’s remote Karnali Province

“…17, the trade-offs between improving accessibility to health facilities during different seasons is highly linear and reveal that gains in accessibility in one season scale favourably to gains in another season. While similar patterns were also observed comparing solutions for financial institutions and municipal headquarters in the same manner, these were not as striking as for health facilities (such observations for financial institutions and municipal headquarters are visually presented and discussed in more detail by Heyns et al (2020), albeit for a different heuristic solution set). A possible cause for health facilities' gains being so significantly linear between seasons may be the wider spatial distribution and higher absolute number of health facilities (refer to Fig.…”

“…3 An illustration of the notion of Pareto optimality and the translation of candidate roads plans to objective function space. Numerous candidate solutions are evaluated with respect to the objective functions, and those that are determined to be Pareto optimal (non-dominated) are sought for decision making purposes (Heyns et al 2020) investigating additional solutions determined between these points by ILP and heuristics approaches (Heyns et al 2021). An illustration of such corner points is provided in Fig.…”

“…4) may be traced out. The weighted-sum approach remains a popular choice to solve multi-objective optimisation problems from various recent applications, such as building design (Machairas et al 2014), carpool matching (Xia et al 2019), and land-use 4 An example of a three-dimensional Pareto front determined with respect to three maximisation objectives (Heyns et al 2020). The corner points are determined with consideration given to a single objective only, while other solutions along the front may be determined using a weighted-sum objective function allocation (Yao et al 2018), and has also been used for rural roads planning research (Shrestha et al 2014;Shrestha 2018).…”

“…to normalise the values). This allows the objective weights in the weighted-sum approach to more effectively direct the search towards desired solution regions (Heyns et al 2020) objectives 1 and 2 is desired-e.g. a solution that reflects equal importance between these corner points and does not consider objective 3-weights of w 1 = 0.5 , w 2 = 0.5 and w 3 = 0 would be assigned.…”

“…Mutation promotes the diversity of sequences in the chromosomes-and the population in general-by stochastically introducing unexplored sequences into the chromosomes (Deb et al 2002). The mutation process followed here is a multi-objective modification to the classic Teitz-Bart Algorithm (TBA) (Teitz and Bart 1968)-a more detailed discussion on the TBA is provided by Heyns et al (2020). When utilised within the NSGA-II, this modification returned impressive results for large geospatial optimisation problems when compared to the NSGA-II using traditional mutation approaches (Heyns 2016), and has already been used for solving a real-world optimisation problem related to anti-poaching (Heyns 2021).…”