Multi-disciplinary design optimization of life cycle eco-efficiency for heavy-duty vehicles using a genetic algorithm

Transport Efficiency of Delivery Trucks: A Study of Coupling Vehicle Design and Transport System Through Functional Modelling and Optimisation

“…Other studies has shown that, in general larger vehicle configurations are more cost efficient per payload unit (see e.g. Ghandriz et al (2021); Wolff et al (2021)). However, here it is also shown that larger vehicle configurations are potentially more transport efficient as well.…”

“…Furthermore, in the short-term run, the environmental performance of vehicles that function within a transport system can be improved by integrative design approaches such as multidisciplinary design optimisation (MDO) approaches Martins and Ning, 2021). In contrast to conventional top-down design methodology that breaks down designs into several independent subproblems with independent functional constraints leading to sub-optimal designs (O'Reilly et al, 2016), by MDO approaches, multiple aspects of a transportation-vehicle problem can be examined simultaneously yielding better system performance much quicker, cheaper, and more robust (Wolff et al, 2021). Conventional design methodologies are more convenient for industry applications, where engineers are grouped in a disciplinary-oriented manner working with complex engineering systems which makes the approach as the only perceived choice.…”

Fuzzy-logic Approach to Estimating the Fleet Efficiency of a Road Transport Company: A Case Study of Agricultural Products’ Deliveries in Kazakhstan

“…The directions of research in recent scientific publications in the field of choosing a vehicle models and fleet structure are the following:  considering the probabilistic components of the transport process [8,10,12,[15][16][17][18][19][20],  considering the complex impact of technical and operational indicators [7,8,12,[21][22][23][24][25][26][27],  optimizing of environmental [9,28,29], economic [7,11,18,22,19,30] and social impacts, as well as a combination of these parameters [13,[31][32][33][34],  determining and optimizing of truck loading schedules [4,8,10,16,35,36],  considering the energy efficiency of freight transportation [21,24,37],  minimizing the size of the vehicles fleet [17,20],  ensuring the tractive effort reserves [32],  considering the trajectory of trucks according to the global positio...…”

“…In addition to the classic vehicles used in road transportation that are analyzed by most of the mentioned research, the following vehicles were considered as the vehicles under study:  mining dump trucks [7,9,45],  trucks for the transportation of heavy loads [37],  garbage trucks [17],  hybrid, battery, and electric trucks [32],  trucks for concrete deliveries [40],  hydrogen vehicles [29],  trucks with a capacity of up to 3.5 tons [25],  and vehicles equipped with a hybrid diesel or high-pressure gas engine [33].…”

“…The above statement is also based on the fact that genetic algorithms and their modifications [1,4,10,11,[15][16][17]29], which are characterized by poor scalability to the complexity of the problem being solved, were often used to solve the optimization problems formulated in the listed above works. As other methods, the following approaches were used: hybrid Pareto optimal approaches [31], binary integer programming under uncertainty [8], fuzzy programming [12], generalized disjunctive programming [35], fuzzy-tuned models [43], and multiperiod multiplicative analysis [37], recursive logic modeling [38], binary probit and logit models [2], discrete continuum econometric models [18], agent-based modeling [32], and discrete event simulation modeling [6,9].…”