## 2 Literature Review
[38] have framed the problem in the humanitarian logistics field, providing an agile optimization solving methodology-which combines parallel computing with biased-randomized heuristics-which was proposed for solving it in real-time. Competitive results were found in milliseconds, enabling the agile optimization technique able to outperform other heuristics from the literature. On the other hand, [PERSON] et al. [37] formulated the OM-VRP as a mixed-integer program (MIP) and proposed a two-phase local search with a discrete-event heuristic for solving the problem. In contrast to [PERSON] et al. [1]'s model, their formulation avoids the need to directly assign retailers to vehicles, since the pick-up and delivery constraints are modeled through routing variables. The first phase of the approach employs a discrete-event constructive heuristic, while the second phase aims to refine the most promising solutions obtained in stage one, by applying a sequence of local search neighborhoods. The authors have found new best-known solutions for the vast majority of problem instances, and improved lower bounds for a set of small instances were obtained. Constructive heuristics are simple deterministic procedures that follow a logical sequence of decisions and which always generate the same solution when starting from the same point. Biased-randomized algorithms incorporate non-symmetric random sampling in order to diversify the behavior of a base constructive heuristic [39]. At each stage of the base constructive heuristic, a list of candidate decisions is considered. The list is sorted in decreasing order of the benefit concerning an objective function, and a candidate is then randomly selected. In this random selection process, the higher-ranked candidates receive a higher probability of being selected. Biased-randomized algorithms have been employed for solving different COPs in transportation [40; 41; 42], scheduling [43; 44], and facility location problems [39; 45]. Simheuristic algorithms combine the use of approximate methods, such as heuristics or metaheuristics, with simulation techniques, in order to cope with stochastic combinatorial optimization problems. They have proven to be an efficient approach to solve stochastic problems [46]. The extension of traditional metaheuristics into simheuristics is gaining popularity, and they have already been applied to solve a wide set of stochastic optimization problems, in different fields such as permutation flow-shop [47], facility location problems [48], inventory routing problems [49; 50; 48], telecommunication networks [51] and finances [52]. Other examples of simheuristics include [PERSON] et al. [53], [PERSON] et al. [54], and [PERSON] et al. [55]. [PERSON] et al. [53] employs a simheuristic approach for evolving agent behavior in the exploration of novel combat tactics. They use a genetic algorithm to find the states, during a flight maneuver, at which an aircraft should transit into the next phase of the maneuver. [PERSON] et al. [54] tackles a stochastic assembly line problem using a simheuristic approach. Demands for different product types are stochastic and occur in real-time. The optimization task lies in assigning tasks to stations in such a way that task precedence constraints are respected and the expected throughput is maximized. [PERSON] et al. [55] develops a simheuristic based decision support tool for an iron ore crusher circuit. They propose and validate a simulation model of throughput efficiency depending on the amount of equipment active in each phase of the crusher circuit. The simheuristic integrates the simulation model with an iterated local search (ILS) metaheuristic. Their solutions improve throughput and reduce energy consumption compared to the all active equipment solution. Despite the fact that travel times are stochastic in most real-life transportation activities, in the past only deterministic versions of the OM-VRP have been considered.