This paper analyses the Stochastic Team Orienteering Problem (STOP), a stochastic variant of the Team Orienteering Problem (TOP). In the STOP, travel times are represented by random variables. The objective is to determine a set of routes that maximises the expected collected profit. We model the problem as a two-stage stochastic integer program and propose an exact solution method based on the Integer L-shaped method. We also consider a set of non-linear chance constraints to restrict the search to highly reliable routes. In the first-stage, a set of routes is selected, while in the second-stage the recourse evaluates the expected profit of the selected routes. We present a new set of optimality cuts and strengthen the formulation by considering valid inequalities and constraints liftings. Computational results provide a comprehensive analysis of the algorithm using the standard TOP benchmark dataset from the literature.
A Simheuristic method integrating Monte Carlo simulations within an Adaptive Large Neighborhood Search (SimALNS) to address the Stochastic Clustered Team Orienteering Problem (SCTOP), a variant of the recently defined CluTOP where travel times are non-negative random variables.
D. Ferone, P. Festa, Tommaso Pastore· Annals of Operations Researc...· 0 citations
Over the past 25 years, the Traveling Tournament Problem (TTP) has become one of the most extensively studied optimization problems in sports scheduling. At its core, the TTP seeks to minimize the total travel distance incurred by teams that travel directly between opponents'venues during consecutive away games. The pr...
David Van Bulck, Fan Yang, Dries R. Goossens et al.· 0 citations
This work proposes a matheuristic approach that combines heuristic techniques with mathematical optimization to address a bi-objective, multi-source team orienteering problem. During the exploration phase, nodes are assigned to depots using a biased-randomized round-robin heuristic, guided by a ranking criterion based...
Sandra Oltra-Crespo, Lucía Agud-Albesa, N. Garrido et al.· Mathematics· 0 citations
The scalability of a multi-criteria optimization for the Service Team Transport Scheduling (STTS) problem is investigated, minimizing total travel time, maximum vehicle worktime, and total vehicle engagement time to define scale-aware algorithmic boundaries essential for real-time decision support systems.
Jarosław Rudy, G. Radzki· IEEE Access· 0 citations
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