Boris Detienne

A mixed integer linear programming approach to minimize the number of late jobs with and without machine availability constraints

This study investigates scheduling problems that occur when the weighted number of late jobs that are subject to deterministic machine availability constraints have to be minimized. These problems can be modeled as a more general job selection problem. Cases with resumable, non-resumable, and semi-resumable jobs as well as cases without availability constraints are investigated. The proposed efficient mixed integer linear programming approach includes possible improvements to the model, notably specialized lifted knapsack cover cuts. The method proves to be competitive compared with existing dedicated methods: numerical experiments on randomly generated instances show that all 350-job instances of the test bed are closed for the well-known problem

The two-machine flowshop total completion time problem: Branch-and-bound algorithms based on network-flow formulation

1|r_i|\sum w_iU_i

Two phase solution for an intelligent moving target search problem based on a 0–1 linear model

. For all investigated problem types, 98.4% of

A Column Generation Based Heuristic for the Dial-A-Ride Problem

500

Optimizing power generation in the presence of micro-grids

-job instances can be solved to optimality within one hour.

Robust coverage optimization approach in Wireless Sensor Networks

We consider the flowshop problem on two machines with sequence-independent setup times to minimize total completion time. Large scale network flow formulations of the problem are suggested together with strong Lagrangian bounds based on these formulations. To cope with their size, filtering procedures are developed. To solve the problem to optimality, we embed the Lagrangian bounds into two branch-and-bound algorithms. The best algorithm is able to solve all 100-job instances of our testbed with setup times and all 140-job instances without setup times, thus significantly outperforming the best algorithms in the literature.

Scheduling nuclear outage with cut and price (Snowcap)

This paper presents a generic discrete model for the moving, intelligent target problem. Our objective is to maximise the probability of detection of the moving target with respect to target and searcher’s constraints. The solution method proposed here is composed on two stages. The first one aims at providing a large-scale strategy by solving an Integer Linear Program approach. As a direct solution of this problem is not practically possible, we use a decomposition of the problem into a searcher’s strategy on one side, and the target’s strategy on the other side. A good strategy for the searcher is determined using a sliding window procedure. Concerning the target, our approach consists in simulating some of the target’s possible strategies and considering each of these possibilities as an independent and deterministic entity. The second stage is dedicated to adjusting the large-scale strategy provided by stage 1. Finally, numerical results are presented so as to assess the impact of our approach.