Bi-objective optimization for road vertical alignment design. (July 2022)

• Record Type: Journal Article
• Authors: Akhmet, Ayazhan; Hare, Warren; Lucet, Yves
• Is Part Of: Computers & operations research. Volume 143(2022)
• Publication Date: 2022-07
• Subjects: Vertical alignment -- Road design optimization -- Multi-objective optimization -- Fuel consumption; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2022.105764
• Languages: English
• ISSNs: 0305-0548

Abstract

Abstract: This manuscript defines a bi-objective optimization model to finds road profiles that optimize the road construction cost and the vehicle operating costs, specifically the fuel consumption. The research implements and validates the formula for the fuel consumption cost. It further presents and examines a variety of well-known methods: three classical scalarization techniques (the ɛ -constraint method, weighted sum method, and weighted metric methods) and two evolutionary methods (NSGA-II and FP-NSGA-II). Moreover, to accelerate the performance of the chosen scalarization approaches, a warm start strategy is proposed. Numerical experiments are performed on 30 road samples for Caterpillar 793D off-highway trucks to determine the most robust approach for the proposed problem. The results are analyzed using the commonly-used performance indicators: hypervolume (to assess the convergence of solutions), spacing (to assess the diversity of solutions), and CPU time (to assess the speed). The research finds that the warm start strategy improves the performance of all the scalarization techniques and concludes that the most promising method for the proposed problem is the ɛ -constraint method with a warm start. Highlights: Defines a bi-objective optim. model for road design with construction and fuel costs. Validates a formula for the fuel consumption with resulting R 2 = 0 . 93 . Presents a warmstart strategy and shows that it improves performance by 5% to 15%. Analyzes 10 methods on 30 road samples using hypervolume, spacing, and CPU time. Finds that ɛ -constraint with warmstart is the most promising and robust method.