An Enhanced Branch and Price Algorithm for the Time-Dependent Vehicle Routing Problem with Time Windows

In this paper we implement a branch and price (BP) algorithm for a time dependent vehicle routing problem with time windows (TDVRPTW) in which the goal is to minimize the total route duration (DM-TDVRPTW). The travel time between two customers depends on the departure time and, thus, it need not remain fixed along the planning … Read more

New facets for the consecutive ones polytope

A 0/1 matrix has the Consecutive Ones Property if a permutation of its columns exists such that the ones appear consecutively in each row. In many applications, one has to find a matrix having the consecutive ones property and optimizing a linear objective function. For this problem, literature proposes, among other approaches, an Integer Linear … Read more

A Lagrange decomposition based Branch and Bound algorithm for the Optimal Mapping of Cloud Virtual Machines

One of the challenges of cloud computing is to optimally and efficiently assign virtual machines to physical machines. The aim of telecommunication operators is to mini- mize the mapping cost while respecting constraints regarding location, assignment and capacity. In this paper we first propose an exact formulation leading to a 0-1 bilinear constrained problem. Then … Read more

A Dual Approximate Dynamic Programming Approach to Multi-stage Stochastic Unit Commitment

We study the multi-stage stochastic unit commitment problem in which commitment and generation decisions can be made and adjusted in each time period. We formulate this problem as a Markov decision process, which is “weakly-coupled” in the sense that if the demand constraint is relaxed, the problem decomposes into a separate, low-dimensional, Markov decision process … Read more

Data-Driven Chance Constrained Programs over Wasserstein Balls

We provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constraints with right-hand side uncertainty, our reformulation amounts to a mixed-integer conic program. In the special case of a Wasserstein ball with the $1$-norm or the $\infty$-norm, the cone is the nonnegative … Read more

The Value of Randomized Solutions in Mixed-Integer Distributionally Robust Optimization Problems

Randomization refers to the process of taking decisions randomly according to the outcome of an independent randomization device such as a dice or a coin flip. The concept is unconventional, and somehow counterintuitive, in the domain of mathematical programming, where deterministic decisions are usually sought even when the problem parameters are uncertain. However, it has … Read more

Multi-stage Stochastic Programming for Demand Response Optimization

The increase in the energy consumption puts pressure on natural resources and environment and results in a rise in the price of energy. This motivates residents to schedule their energy consumption through demand response mechanism. We propose a multi-stage stochastic programming model to schedule different kinds of electrical appliances under uncertain weather conditions and availability … Read more

The Integrated Last-Mile Transportation Problem

Last-mile transportation (LMT) refers to any service that moves passengers from a hub of mass transportation (MT), such as air, boat, bus, or train, to destinations, such as a home or an office. In this paper, we introduce the problem of scheduling passengers jointly on MT and LMT services, with passengers sharing a car, van, … Read more

Convergence rates for an inertial algorithm of gradient type associated to a smooth nonconvex minimization

We investigate an inertial algorithm of gradient type in connection with the minimization of a nonconvex differentiable function. The algorithm is formulated in the spirit of Nesterov’s accelerated convex gradient method. We show that the generated sequences converge to a critical point of the objective function, if a regularization of the objective function satis es the … Read more

Benders Decomposition for Very Large Scale Partial Set Covering and Maximal Covering Problems

Covering problems constitute an important family of facility location problems. This paper intro- duces a new exact algorithm for two important members of this family: i) the maximal covering location problem, which requires finding a subset of facilities that maximizes the amount of demand covered while respecting a budget constraint on the cost of the … Read more