Operations Research Letters最新文献

We employ scoring functions, used in statistics for eliciting risk functionals, as cost functions in the Monge-Kantorovich (MK) optimal transport problem. This gives rise to a rich variety of novel asymmetric MK divergences, subsuming Bregman-Wasserstein divergences. We show that for distributions on the real line, the comonotonic coupling is optimal for the majority of the new divergences. We conclude with two applications to robust optimisation.

Graphical designs are a framework for sampling and numerical integration of functions on graphs. In this note, we introduce a method to address the trade-off between graphical design sparsity and accuracy. We show how to obtain sparse graphical designs via linear programming and design objective functions that aim to maximize their accuracy. We showcase our approach using yellow taxicab data from New York City.

The total matching polytope generalizes the stable set polytope and the matching polytope. In this paper, we first propose new facet-defining inequalities for the total matching polytope. We then give an exponential-sized, non-redundant description in the original space and a compact description in an extended space of the total matching polytope of complete bipartite graphs.

This paper explores the stability of coalition partitions in dynamic games, specifically introducing a concept of dynamically stable Nash coalition partition for dynamic games. We focus on a Nash equilibrium where coalitions act as unified players and discuss dynamic stability, where players have no incentive to switch coalitions. A case study on “fish wars” illustrates conditions under which dynamic stability occurs, utilizing a time-consistent imputation distribution procedure to allocate payoffs along optimal trajectories.

The inverse-square law states that a source's effect is inversely proportional to the distance from that source squared. In continuous location problems, objective functions often obey the inverse-square law. This work shows that for any region in D dimensions, the maximum inverse-square effect is on the region's boundary if $D<4$, the minimum is on the boundary if $D>4$, and both the maximum and minimum are on the boundary if $D=4$.

Machine learning has shown promises in tackling routing problems yet falls short of state-of-the-art solutions achieved by stand-alone operations research algorithms. This paper introduces “Learning to Guide Local Search” (L2GLS), a novel approach that leverages Local Search operators' strengths and reinforcement learning to adjust search efforts adaptively. The results of comparing L2GLS with the existing cutting-edge approaches indicate that L2GLS attains new levels of state-of-the-art performance, particularly excelling in handling large instances that continue to challenge existing algorithms.

This paper proposes pandemic mitigation vaccination policies for Newfoundland and Labrador (NL) based on two compact mixed integer programming (MIP) models of the distance-based critical node detection problem (DCNDP). Our main focus is on two variants of the DCNDP that seek to minimize the number of connections with lengths of at most one (1-DCNDP) and two (2-DCNDP). A polyhedral study for the 1-DCNDP is conducted, and new aggregated inequalities are provided for the 2-DCNDP. The computational experiments show that the 2-DCNDP with aggregated inequalities outperforms the one with disaggregated inequalities for graphs with a density of at least 0.5%. We also study the strategic vaccine allocation problem as a real-world application of the DCNDP and conduct a set of computational experiments on a simulated contact network of NL. Our computational results demonstrate that the DCNDP-based strategies can have a better performance in comparison with the real-world strategies implemented during COVID-19.

Our paper studies a fair stochastic facility location problem with congestion under the max-min principle. We analyze the price of fairness in a two-location problem and develop a tractable optimization framework for the general multi-location setting. Evaluating the fair solution against the utilitarian solution on Buffalo's demographic data reveals that implementing a fair solution can substantially improve fairness measures (up to 98%) with relatively limited impact on the overall service quality.

In the online hypergraph matching problem, hyperedges of size k over a common ground set arrive online in adversarial order. The goal is to obtain a maximum matching (disjoint set of hyperedges). A naïve greedy algorithm for this problem achieves a competitive ratio of $\frac{1}{k}$. We show that no (randomized) online algorithm has competitive ratio better than $\frac{2+o\left(1\right)}{k}$. If edges are allowed to be assigned fractionally, we give a deterministic online algorithm with competitive ratio $\frac{1-o\left(1\right)}{\ln \left(k\right)}$ and show that no online algorithm can have competitive ratio strictly better than $\frac{1+o\left(1\right)}{\ln \left(k\right)}$. Lastly, we give a $\frac{1-o\left(1\right)}{\ln \left(k\right)}$ competitive algorithm for the fractional edge-weighted version of the problem under a free disposal assumption.

Uncertainty reduction has recently been introduced in the robust optimization literature as a relevant special case of decision-dependent uncertainty. Herein, we identify two relevant situations in which the problem is polynomially solvable. We further provide insights into possible MILP reformulations and the strength of their continuous relaxations.