On the heterogeneous vehicle routing problem under demand uncertainty

In this paper we study the heterogeneous vehicle routing problem under demand uncertainty, on which there has been little research to our knowledge. The focus of the paper is to provide a strong formulation that also easily allows tractable robust and chance-constrained counterparts. To this end, we propose a basic Miller-Tucker-Zemlin (MTZ) formulation with the main advantage that uncertainty is restricted to the right-hand side of the constraints. This leads to compact and tractable counterparts of demand uncertainty. On the other hand, since the MTZ formulation is well known to provide a rather weak linear programming relaxation, we propose to strengthen the initial formulation with valid inequalities and lifting techniques and, furthermore, to dynamically add cutting planes that successively reduce the polyhedral region using a branch-and-cut algorithm. We complete our study with extensive computational analysis with different performance measures on different classes of instances taken from the literature. In addition, using simulation, we conduct a scenario-based risk level analysis for both cases where either unmet demand is allowed or not

A compact variant of the QCR method for quadratically constrained quadratic 0-1 programs

Quadratic Convex Reformulation (QCR) is a technique that was originally proposed for quadratic 0-1 programs, and then extended to various other problems. It is used to convert non-convex instances into convex ones, in such a way that the bound obtained by solving the continuous relaxation of the reformulated instance is as strong as possible. In this paper, we focus on the case of quadratically constrained quadratic 0-1 programs. The variant of QCR previously proposed for this case involves the addition of a quadratic number of auxiliary continuous variables. We show that, in fact, at most one additional variable is needed. Some computational results are also presented

Optimization Methods: an Applications-Oriented Primer

Effectively sharing resources requires solving complex decision problems. This requires constructing a mathematical model of the underlying system, and then applying appropriate mathematical methods to find an optimal solution of the model, which is ultimately translated into actual decisions. The development of mathematical tools for solving optimization problems dates back to Newton and Leibniz, but it has tremendously accelerated since the advent of digital computers. Today, optimization is an inter-disciplinary subject, lying at the interface between management science, computer science, mathematics and engineering. This chapter offers an introduction to the main theoretical and software tools that are nowadays available to practitioners to solve the kind of optimization problems that are more likely to be encountered in the context of this book. Using, as a case study, a simplified version of the bike sharing problem, we guide the reader through the discussion of modelling and algorithmic issues, concentrating on methods for solving optimization problems to proven optimality

Risk factors associated with the occurrence of autoimmune diseases in adult coeliac patients

Objectives. Autoimmune diseases (AD) may be associated with coeliac disease (CD), but specific risk factors have been poorly investigated. The aim of this study was to assess the spectrum of AD and its specific risk factors associated in a series of adult coeliac patients. Materials and Methods. We performed a single-center case-control study including adult newly diagnosed CD patients. To evaluate the risk factors of the association between AD and CD, 341 coeliac patients included were categorized on the basis of AD presence: 91 cases with at least one AD and 250 controls without AD were compared for clinical, serological, and histological features. Eighty-seven cases were age-gender-matched with 87 controls. Results. Among 341 CD patients, 26.6% of CD patients had at least one AD. Endocrine and dermatological diseases were the most prevalent AD encountered: autoimmune thyroiditis was present in 48.4% of cases, psoriasis in 17.6%, and type I diabetes and dermatitis herpetiformis in 11%, respectively. At logistic regression, factors associated with AD were a positive 1st-degree family history of AD (OR 3.7, 95% CI 1.93–7), a body mass index ≥ 25 kg/m2 at CD diagnosis (OR 2.95%, CI 1.1–3.8), and long standing presentation signs/symptoms before CD diagnosis (>10 years) (OR 2.1, 95% CI 1.1–3.7). Analysis on age-gender-matched patients confirmed these results. Conclusions. CD patients with family history of AD, overweight at CD diagnosis, and a delay of CD diagnosis had an increased risk of having another AD. The benefit of CD screening in these specific subsets of patients with AD awaits further investigation

A binarisation heuristic for non-convex quadratic programming with box constraints

Non-convex quadratic programming with box constraints is a fundamental problem in the global optimization literature, being one of the simplest NP-hard nonlinear programs. We present a new heuristic for this problem, which enables one to obtain solutions of excellent quality in reasonable computing times. The heuristic consists of four phases: binarisation, convexification, branch-and-Bound, and local optimisation. Some very encouraging computational results are given

A Binarisation Heuristic for Non-Convex Quadratic Programming with Box Constraints

Non-convex quadratic programming with box constraints is a fundamental problem in the global optimization literature, being one of the simplest NP-hard nonlinear programs. We present a new heuristic for this problem, which enables one to obtain solutions of excellent quality in reasonable computing times. The heuristic consists of four phases: binarisation, convexification, branch-and-bound, and local optimisation. Some very encouraging computational results are given

On Linearising Mixed-Integer Quadratic Programs via Bit Representation

It is well known that, under certain conditions, one can use bit representation to transform both integer quadratic programs and mixed-integer bilinear programs into mixed-integer linear programs (MILPs), and thereby render them easier to solve using standard software packages. We show how to convert a more general family of mixed-integer quadratic programs to MILPs, and present several families of strong valid linear inequalities that can be used to strengthen the continuous relaxations of the resulting MILPs

Optimal joint path computation and rate allocation for real-time traffic

Computing network paths under worst-case delay constraints has been the subject of abundant literature in the past two decades. Assuming Weighted Fair Queueing scheduling at the nodes, this translates to computing paths and reserving rates at each link. The problem is NP-hard in general, even for a single path; hence polynomial-time heuristics have been proposed in the past, that either assume equal rates at each node, or compute the path heuristically and then allocate the rates optimally on the given path. In this paper we show that the above heuristics, albeit finding optimal solutions quite often, can lead to failing of paths at very low loads, and that this could be avoided by solving the problem, i.e., path computation and rate allocation, jointly at optimality. This is possible by modeling the problem as a mixed-integer second-order cone program and solving it optimally in split-second times for relatively large networks on commodity hardware; this approach can also be easily turned into a heuristic one, trading a negligible increase in blocking probability for one order of magnitude of computation time. Extensive simulations show that these methods are feasible in today's ISPs networks and they significantly outperform the existing schemes in terms of blocking probability

Delay-constrained Routing Problems: Accurate Scheduling Models and Admission Control

As shown in [1], the problem of routing a flow subject to a worst-case end-to-end delay constraint in a packed-based network can be formulated as a Mixed-Integer Second-Order Cone Program, and solved with general-purpose tools in real time on realistic instances. However, that result only holds for one particular class of packet schedulers, Strictly Rate-Proportional ones, and implicitly considering each link to be fully loaded, so that the reserved rate of a flow coincides with its guaranteed rate. These assumptions make latency expressions simpler, and enforce perfect isolation between flows, i.e., admitting a new flow cannot increase the delay of existing ones. Other commonplace schedulers both yield more complex latency formulæ and do not enforce flow isolation. Furthermore, the delay actually depends on the guaranteed rate of the flow, which can be significantly larger than the reserved rate if the network is unloaded. In this paper we extend the result to other classes of schedulers and to a more accurate representation of the latency, showing that, even when admission control needs to be factored in, the problem is still efficiently solvable for realistic instances, provided that the right modeling choices are made. Keywords: Routing problems, maximum delay constraints, scheduling algorithms, admission control, Second-Order Cone Programs, Perspective Reformulatio