Combining Monte Carlo simulation with heuristics to solve a rich and real-life multi-depot vehicle routing problem

Alemany, Gabriel; Armas, Jésica de; Juan, Ángel A.; Garcia-Sanchez, Alvaro; Garcia-Meizoso, Roberto; Ortega-Mier, Míguel

doi:10.1109/wsc.2016.7822285

Cited by 2 publications

(1 citation statement)

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“…Characteristics regarding input data, decision management components, vehicles, time constraints, among others, turns a classical VRP into a rich VRP (Lahyani et al, 2015b). For instance, Alemany et al (2016) combine the well-known savings heuristic (Clarke and Wright, 1964) with Monte Carlo simulation to solve a heterogeneous-fleet, multi-depot, multi-compartment, multiproduct, and multi-trip VRP. In general, vehicles can be classified according to their physical characteristics, e.g., they can be homogeneous or heterogeneous, or compartmentalized or not.…”

Section: Introductionmentioning

confidence: 99%

An agile and reactive biased-randomized heuristic for an agri-food rich vehicle routing problem

Tordecilla¹

2021

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Operational problems in agri-food supply chains usually show characteristics that are scarcely addressed by traditional academic approaches. These characteristics make an already NP-hard problem even more challenging; hence, this problem requires the use of tailor-made algorithms in order to solve it efficiently. This work addresses a rich vehicle routing problem in a real-world agri-food supply chain. Different types of animal food products are distributed to raising-pig farms. These products are incompatible, i.e., multicompartment heterogeneous vehicles must be employed to perform the distribution activities. The problem considers constraints regarding visit priorities among farms, and not-allowed access of large vehicles to a subset of farms. Finally, a set of flat tariffs are employed to formulate the cost function. This problem is solved employing a reactive savings-based biased-randomized heuristic, which does not require any time-costly parameter fine-tuning process. Our results show savings in both cost and traveled distance when compared with the real supply chain performance.

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Section: Introductionmentioning

confidence: 99%