On the NP-Completeness of the Multi-Period Minimum Spanning Tree Problem

In this note, we consider the Multi-period Minimum Spanning Tree Problem (MMST), a variant of the well known Minimum Spanning Tree Problem (MST), that consists in the fol- lowing. Given a connected and undirected graph G and a finite discrete time horizon, one has to schedule the moment in time edges are added to a … Read more

Bid Markup Decision and Resource Allocation for Cost Estimation in Competitive Bidding

To receive a project contract through competitive bidding, contractors submit a bid price determined by putting a markup on the estimated project cost. Since a bid is inevitably affected by an inaccurate cost estimate, sufficient resources should be allocated to cost estimation. This paper develops a novel optimization model for determining the bid markup and … Read more

A Two-Stage Stochastic Program for Multi-shift, Multi-analyst, Workforce Optimization with Multiple On Call Options

Motivated by a cybersecurity workforce optimization problem, this paper investigates optimizing staffing and shift scheduling decisions given unknown demand and multiple on call staffing options at a 24/7 firm with three shifts per day, three analyst types, and several staffing and scheduling constraints. We model this problem as a two-stage stochastic program and solve it … Read more

Solving Large Aircraft Landing Problems on Multiple Runways by Applying a Constraint Programming Approach

Aircraft Landing Problem is to assign an airport’s runways to the arrival aircraft as well as to schedule the landing time of these aircraft. In this paper, due to the complexity of the problem, which is NP-hard, we develop an iterative-based heuristic by exploiting special characteristics of the problem. Computational results show the developed approach … Read more

New solution methods for the block relocation problem

This technical report presents new solution methods for the block relocation problem (BRP). Although most of the existing work focuses on the restricted BRP, we tackle the unrestricted BRP, which yields more opportunities for optimisation. Our contributions include fast heuristics able to tackle very large instances within seconds, fast metaheuristics that provide very competitive performance … Read more

A Polyhedral Study of the Static Probabilistic Lot-Sizing Problem

We study the polyhedral structure of the static probabilistic lot-sizing (SPLS) problem and propose facets that subsume existing inequalities for this problem. In addition, the proposed inequalities give the convex hull description of a related stochastic lot-sizing problem. We propose a new compact formulation that exploits the simple recourse structure, which can be applied to … Read more

A joint routing and speed optimization problem

Fuel cost contributes to a significant portion of operating cost in cargo transportation. Though classic routing models usually treat fuel cost as input data, fuel consumption heavily depends on the travel speed, which has led to the study of optimizing speeds over a given fixed route. In this paper, we propose a joint routing and … Read more

A decomposition approach for single allocation hub location problems with multiple capacity levels

In this paper we consider an extended version of the classical capacitated single allocation hub location problem in which the size of the hubs must be chosen from a finite and discrete set of allowable capacities. We develop a Lagrangian relaxation approach that exploits the problem structure and decomposes the problem into a set of … Read more

Numerical Solution of Linear-Quadratic Optimal Control Problems for Switching System

In this paper we obtained an approach to the optimal switching control problem with unknown switching points which it is described in reference [1, 2]. In reference [1], the authors studied the Decomposition of Linear-Quadratic Optimal Control Problems for Two-Steps Systems. In [1], the authors assumed the switching point t1 is xed in the interval … Read more

A Distributed Interior-Point KKT Solver for Multistage Stochastic Optimization

Multistage stochastic optimization leads to NLPs over scenario trees that become extremely large when many time stages or fine discretizations of the probability space are required. Interior-point methods are well suited for these problems if the arising huge, structured KKT systems can be solved efficiently, for instance, with a large scenario tree but a moderate … Read more