Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-26T17:28:39.507Z Has data issue: false hasContentIssue false
  • English
  • Français

Fleet optimization considering overcapacity and load sharing restrictions using genetic algorithms and ant colony optimization

Published online by Cambridge University Press:  13 January 2020

Fredy Kristjanpoller ,
Kevin Michell ,
Werner Kristjanpoller Open the ORCID record for Werner Kristjanpoller [Opens in a new window]  and
Adolfo Crespo
Fredy Kristjanpoller
Affiliation:
Department of Industrial Engineering, Universidad Técnica Federico Santa María, Av. España 1680, Valparaiso, Chile
Kevin Michell
Affiliation:
Department of Industrial Engineering, Universidad Técnica Federico Santa María, Av. España 1680, Valparaiso, Chile
Werner Kristjanpoller*
Affiliation:
Department of Industrial Engineering, Universidad Técnica Federico Santa María, Av. España 1680, Valparaiso, Chile
Adolfo Crespo
Affiliation:
Department of Industrial Management, School of Engineering, University of Seville, Camino de los Descubrimientos s/n. 41092, Seville, Spain
*
Author for correspondence: Werner Kristjanpoller, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a fleet model explained through a complex configuration of load sharing that considers overcapacity and is based on a life cycle cost (LCC) approach for cost-related decision-making. By analyzing the variables needed to optimize the fleet size, which must be evaluated in combination with the event space method (ESM), the solution to this problem would normally require high computing performance and long computing times. Considering this, the combined use of an integer genetic algorithm (GA) and the ant colony optimization (ACO) method was proposed in order to determine the optimal solution. In order to analyze and highlight the added value of this proposal, several empirical simulations were performed. The results showed the potential strengths of the proposal related to its flexibility and capacity in solving large problems with a near optimal solution for large fleet size and potential real-world applications. Even larger problems can be solved this way than by using the complete enumeration approach and a non-family fleet approach. Thus, this allows for a more real solution to fleet design that also considers overcapacity, availability, and an LCC approach. The simulations showed that the model can be solved in much less time compared with the base model and allows for the resolution of a fleet of at least 64 trucks using GA and 130 using ACO, respectively. Thus, the proposed framework can solve real-world problems, such as the fleet design of mining companies, by offering a more realistic approach.

Keywords

Ant colony optimizationavailability analysisfleet designlife cycle costovercapacity
Type
Research Article
Information
AI EDAM , Volume 34 , Issue 1: Thematic Collection on “Cognitive and Learning processes for transition to Design for Sustainability (DfS)” , February 2020 , pp. 104 - 113
Copyright
Copyright © Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Amado, L (2013) Reservoir exploration and appraisal. Gulf Professional Publishing. Chapter 9 Capex and Opex Expenditures, pp. 39–42.CrossRefGoogle Scholar
Chaowasakoo, P, Seppälä, H, Koivo, H and Zhou, Q (2017) Improving fleet management in mines: the benefit of heterogeneous match factor. European Journal of Operational Research 261, 1052–1065.CrossRefGoogle Scholar
Coelho, VN, Souza, MJ, Coelho, IM, Guimarães, FG, Lust, T and Cruz, RC (2012) Multi-objective approaches for the open-pit mining operational planning problem. Electronic Notes in Discrete Mathematics 39, 233–240.CrossRefGoogle Scholar
Coelho, VN, Grasas, A, Ramalhinho, H, Coelho, IM, Souza, MJ and Cruz, RC (2016) An ILS-based algorithm to solve a large-scale real heterogeneous fleet VRP with multi-trips and docking constraints. European Journal of Operational Research 250, 367–376.CrossRefGoogle Scholar
Czerny, AI, van den Berg, VA and Verhoef, ET (2016) Carrier collaboration with endogenous fleets and load factors when networks are complementary. Transportation Research Part B: Methodological 94, 285–297.CrossRefGoogle Scholar
Ding, L, Wang, H, Jiang, J and Xu, A (2017) SIL verification for SRS with diverse redundancy based on system degradation using reliability block diagram. Reliability Engineering & System Safety 165, 170–187.CrossRefGoogle Scholar
Distefano, S and Puliafito, A (2009) Reliability and availability analysis of dependent–dynamic systems with DRBDs. Reliability Engineering & System Safety 94, 1381–1393.CrossRefGoogle Scholar
Dorigo, M and Gambardella, LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1, 53–66.CrossRefGoogle Scholar
Durairaj, SK, Ong, SK, Nee, AY and Tan, RB (2002) Evaluation of life cycle cost analysis methodologies. Corporate Environmental Strategy 9, 30–39.CrossRefGoogle Scholar
Gavalas, D, Konstantopoulos, C and Pantziou, G (2016) Design and management of vehicle-sharing systems: a survey of algorithmic approaches. In Smart Cities and Homes. Morgan Kaufmann, pp. 261–289.CrossRefGoogle Scholar
Hoff, A, Andersson, H, Christiansen, M, Hasle, G and Løkketangen, A (2010) Industrial aspects and literature survey: fleet composition and routing. Computers & Operations Research 37, 2041–2061.CrossRefGoogle Scholar
Holland, JH and Reitman, JS (1977) Cognitive systems based on adaptive algorithms. ACM SIGART Bulletin 63, 49.CrossRefGoogle Scholar
Hristakeva, M and Shrestha, D (2004) Solving the 0-1 knapsack problem with genetic algorithms. Midwest Instruction and Computing Symposium, April 16–17, 2004, University of Minnesota, Morris.Google Scholar
Johannknecht, F, Gatzen, MM and Lachmayer, R (2016 a) Life cycle cost model for considering fleet utilization in early conceptual design phases. Procedia CIRP 48, 68–72.CrossRefGoogle Scholar
Johannknecht, F, Gatzen, MM, Hahn, D and Lachmayer, R (2016 b) Holistic life cycle costing approach for different development phases of drilling tools. International Petroleum Technology Conference, 14–16 November 2016, Bangkok, Thailand.CrossRefGoogle Scholar
King, B, Goycoolea, M and Newman, A (2017) Optimizing the open pit-to-underground mining transition. European Journal of Operational Research 257, 297–309.CrossRefGoogle Scholar
Kjærsgaard, J (2010) Quest for appropriate overcapacity in the fisheries industry. Socio-Economic Planning Sciences 44, 141–150.CrossRefGoogle Scholar
Klosterhalfen, ST, Kallrath, J and Fischer, G (2014) Rail car fleet design: optimization of structure and size. International Journal of Production Economics 157, 112–119.CrossRefGoogle Scholar
Kumar, SN and Panneerselvam, R (2012) A survey on the vehicle routing problem and its variants. Intelligent Information Management 4, 66.CrossRefGoogle Scholar
Li, X and Epureanu, BI (2018) An agent-based approach for optimizing modular vehicle fleet operation. arXiv preprint arXiv:1811.04112.Google Scholar
López-Campos, M, Viveros-Gunckel, P, Crespo-Márquez, A, Kristjanpoller-Rodríguez, F and Stegmaier-Bravo, R (2014) Metodología para auditar la asignación de recursos a las actividades críticas de mantenimiento. DYNA-Ingeniería e Industria 89(1), 89–97.CrossRefGoogle Scholar
Oliveira, BB, Carravilla, MA and Oliveira, JF (2017) Fleet and revenue management in car rental companies: a literature review and an integrated conceptual framework. Omega 71, 11–26.CrossRefGoogle Scholar
Parra, C, Crespo, A, Kristjanpoller, F and Viveros, P (2012) Stochastic model of reliability for use in the evaluation of the economic impact of a failure using life cycle cost analysis. Case studies on the rail freight and oil industries. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 226, 392–405.Google Scholar
Sha, M and Srinivasan, R (2016) Fleet sizing in chemical supply chains using agent-based simulation. Computers & Chemical Engineering 84, 180–198.CrossRefGoogle Scholar
Vestergaard, N, Squires, D and Kirkley, J (2003) Measuring capacity and capacity utilization in fisheries: the case of the Danish Gill-net fleet. Fisheries Research 60, 357–368.CrossRefGoogle Scholar
Woodward, DG (1997) Life cycle costing – theory, information acquisition and application. International Journal of Project Management 15, 335–344.CrossRefGoogle Scholar
Xiong, W, Wang, L and Yan, C (2006) Binary ant colony evolutionary algorithm. International Journal of Information Technology 12, 10–20.Google Scholar