activity
20092026
most citedMean-Based Error Measures for Intermittent Demand Forecasting

64 citations · 217 across the 24 of their papers we have counts for

collaborators
Showing math.OCShow all

6 papers · 1 filter

math.OC2025

Mixed-Integer Linear Programming Approximations for the Stochastic Knapsack

Roberto Rossi, Steven D. Prestwich, S. Armagan Tarim

We develop mathematical programming approximations to tackle the stochastic knapsack problem. In this problem, the decision maker considers items for which either weights or values…

math.OC2022★ 3 cited

Stochastic Dynamic Programming Heuristic for the (R, s, S) Policy Parameters Computation

Andrea Visentin, Steven Prestwich, Roberto Rossi +1

The (R, s, S) is a stochastic inventory control policy widely used by practitioners. In an inventory system managed according to this policy, the inventory is reviewed at instant R…

math.OC2020★ 21 cited

Computing Optimal (R, s, S) Policy Parameters by a Hybrid of Branch-and-Bound and Stochastic Dynamic Programming

Andrea Visentin, Steven Prestwich, Roberto Rossi +1

A well-know control policy in stochastic inventory control is the (R, s, S) policy, in which inventory is raised to an order-up-to-level S at a review instant R whenever it falls b…

math.OC2020

A computational study for the inventory routing problem

Yasemin Malli, Marco Laumanns, Roberto Rossi +2

In this work we compare several new computational approaches to an inventory routing problem, in which a single product is shipped from a warehouse to retailers via an uncapacitate…

math.OC2013★ 34 cited

Piecewise linear approximations of the standard normal first order loss function

Roberto Rossi, S. Armagan Tarim, Steven Prestwich +1

The first order loss function and its complementary function are extensively used in practical settings. When the random variable of interest is normally distributed, the first ord…

math.OC2011★ 10 cited

Confidence-based Reasoning in Stochastic Constraint Programming

Roberto Rossi, Brahim Hnich, S. Armagan Tarim +1

In this work we introduce a novel approach, based on sampling, for finding assignments that are likely to be solutions to stochastic constraint satisfaction problems and constraint…