Biased-Randomized Discrete-Event Heuristics for Dynamic Optimization with Time Dependencies and Synchronization

Abstract

Many real-life combinatorial optimization problems are subject to a high degree of dynamism, while, simultaneously, a certain level of synchronization among agents and events is required. Thus, for instance, in ride-sharing operations, the arrival of vehicles at pick-up points needs to be synchronized with the times at which users reach these locations so that waiting times do not represent an issue. Likewise, in warehouse logistics, the availability of automated guided vehicles at an entry point needs to be synchronized with the arrival of new items to be stored. In many cases, as operational decisions are made, a series of interdependent events are scheduled for the future, thus making the synchronization task one that traditional optimization methods cannot handle easily. On the contrary, discrete-event simulation allows for processing a complex list of scheduled events in a natural way, although the optimization component is missing here. This paper discusses a hybrid approach in which a heuristic is driven by a list of discrete events and then extended into a biased-randomized algorithm. As the paper discusses in detail, the proposed hybrid approach allows us to efficiently tackle optimization problems with complex synchronization issues.

Stirling Numbers of Uniform Trees and Related Computational Experiments

“…Many interactions between combinatorial analysis and modern machine and statistical learning techniques have focused on the field of combinatorial optimization [21][22][23]. These analyses have both applied learning techniques to generating heuristics or approximate solutions to difficult combinatorial problems [24][25][26][27], as well as motivating interesting new areas of combinatorial research [28]. Another recent area of interest is Graph Neural Networks [29,30] which use graph structures to better represent features in modern datasets.…”

Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization

“…Exact approaches, as well as classic heuristics and meta-heuristics, are not usually designed for such dynamic scenarios. On the other hand, discrete-event heuristics (DEH) combine a fast heuristic algorithm with a discrete-event simulation [53]. As represented in Figure 5, while the heuristic is responsible for making decisions, the simulation updates the state of the system event by event in order to evaluate how the decisions made by the heuristic affect the overall system with all of its parallelisms, dynamism, and synchronization.…”