Vehicle and personnel routing optimization in the service sector: application to water distribution and treatment

We present an overview of the author’s Ph.D. thesis, supervised by P. Dejax and N. Bostel, which was defended in February 2006 at école des Mines de Nantes, France. The thesis is written in French, and is available at . It was conducted in the context of a research contract with a water distribution company. In a first section, we define multiperiod routing problems for service technicians. In a second section, we present some heuristics and a memetic algorithm used to solve these problems. The third section introduces optimal and near-optimal approaches based on column generation. Finally, we present some applications to the real-life case. The methods presented in Sects. 2, 3 and 4 were tested over several sets of problems, based on real-life statistics provided by the company.      

Performances improvement of the column generation algorithm: application to vehicle routing problems

We survey the main results obtained by the author in his PhD dissertation supervised by Anass Nagih and Lucas Létocart. It was defended in December 2008 at The Computer Science lab of the Paris-Nord University (L.I.P.N.), Villetaneuse, France. The thesis is written in French and is available from . Column generation algorithms are instrumental in many areas of applied optimization, where linear programs with an enormous number of variables need to be solved. Although successfully used in many applications, this method suffers from well-known “instability” issues that somewhat limit its efficiency. This work focuses on accelerating strategies in a column generation scheme; on the first iterations using diversification, on the last iterations using reoptimization and on the overall GC scheme using an improving technique to solve the pricing problems (dynamic programming with blocs). The effectiveness of these approaches is validated on the Vehicle Routing Problem with Time Windows.     

Enhanced mixed integer programming techniques and routing problems

This is a summary of the author’s PhD thesis supervised by Andrea Lodi and Paolo Toth and defended on 16 April 2009 at the Università di Bologna. The thesis is written in English and is available from the author upon request. This work is focused on Mixed Integer Programming (MIP). In particular, the first part of the thesis deals with general purpose cutting planes, which are probably the key ingredient behind the success of the current generation of MIP solvers. The second part is instead focused on the heuristic and exact exploitation of integer programming techniques for hard combinatorial optimization problems in the context of routing applications.     

Model-hierarchical column generation and heuristic for the routing and wavelength assignment problem

The routing and wavelength assignment (RWA) problem typically occurs in wavelength division multiplexing optical networks. Given a number of available wavelengths, we consider here the problem of maximising the number of accepted connections with respect to the clash and continuity constraints. We first propose a new strategy which combines two existing models. This leads to an improved column generation scheme. We also present two heuristics to compute feasible solutions: a hybrid heuristic and the integer solution at the root node of the column generation. Our approaches are compared with the best existing results on a set of classic RWA instances.     

Internal transport in automated semiconductor manufacturing systems

This paper summarizes the main results presented by the author in his PhD thesis (Montoya-Torres 2005), supervised by Stéphane Dauzèere-Pérés, Jean-Pierre Campagne and Hélène Marian, and defended on 29 November 2005 at the école des Mines de Saint-étienne and Université Jean-Monnet. The thesis is written in French and is available upon request from the author. This work deals with a real-life transportation problem in the semiconductor industry. It proposes a new approach by integrating tactical and operational decisions for the control of the automated transport system. At the tactical level, the problem is modeled using integer linear programming models inspired from Location Theory. At the operational level, the solution obtained from the tactical optimization is coupled with a discrete-event computer simulation program and some policies for transportation operations are implemented and compared.     

Optimization of modular machining lines

The paper summarizes the main results of the author’s Ph.D. thesis presented in December 2006 at the école des Mines de Saint étienne. The work was supervised by Alexandre Dolgui and Xavier Delorme. The thesis is written in French and is available upon request from the author. Its purpose is to provide decision support in the design and reconfiguration of modular machining lines with multi-spindle units. This design problem is defined as the selection of spindle units to perform a set of operations needed to produce the parts and subsequently their assignment to workstations. The corresponding optimization problem is solved using different models based on integer and constraint programming.      

Polynomial approximation: a structural and operational study

This is a summary of the most important results of the author’s PhD thesis. This thesis, supervised by Vangelis Th. Paschos, was defended in October 2005 at the Université Paris Dauphine. It is written in French and is available on-line. The thesis is focused on combinatorial optimization problems, studied from the standpoint of polynomial approximation theory. We were interested both in structural concerns (mainly completeness in approximation classes and logical expressivity) and operational ones (with the study of satisfiability, coloring and covering problems). http://www.lamsade.dauphine.fr/~escoffier/fichiers/TheseEscoffier.pdf     

Metaheuristics for multiobjective optimisation

This is a summary of the author’s PhD thesis supervised by Laetitia Jourdan and El-Ghazali Talbi and defended on 8 December 2009 at the Université Lille 1. The thesis is written in French and is available from . This work deals with the design, implementation and experimental analysis of metaheuristics for solving multiobjective optimisation problems, with a particular interest on hard and large combinatorial problems from the field of logistics. After focusing on a unified view of multiobjective metaheuristics, we propose new cooperative, adaptive and parallel approaches. The performance of these methods are experimented on a scheduling and a routing problem involving two or three objective functions. We finally discuss how to adapt such metaheuristics during the search process in order to handle uncertainty that may occur from many different sources.     

Exact and approximate resolution of integral multiflow and multicut problems: algorithms and complexity

This is a summary of the author’s PhD thesis supervised by Marie- Christine Costa and Frédéric Roupin and defended on 20 November 2006 at the Conservatoire National des Arts et Métiers in Paris (France). The thesis is written in French and is available upon request from the author. This work deals with two well-known optimization problems from graph theory: the maximum integral multiflow and the minimum multicut problems. The main contributions of this thesis concern the polynomial-time solvability and the approximation of these two problems (and of some of their variants) in classical classes of graphs: bounded tree-width graphs, planar graphs and grids, digraphs, etc.      

Coordination of production planning and distribution

This is a summary of the main results presented in the author’s Ph.D thesis, supervised by C. Prins and defended at the Université de Technologie de Troyes in October 2006. The thesis, written in French, is available from the author upon request. It deals with the integrated optimization of production planning and distribution in supply chains. A single product case and a multiproduct case are investigated. Integer linear programming models are proposed and different approaches based on heuristics, metaheuristics and cooperative methods are developed. Significant savings are obtained, compared to classical decoupled methods. These results confirm both the interest of integrating production and distribution decisions and of using metaheuristics for the largest instances.      

Column generation based heuristic for tactical planning in multi-period vehicle routing

The periodic vehicle routing problem (PVRP) consists in establishing a planning of visits to clients over a given time horizon so as to satisfy some service level while optimizing the routes used in each time period. The tactical planning model considered here restricts its attention to scheduling visits and assigning them to vehicles while leaving sequencing decisions for an underlying operational model. The objective is twofold: to optimize regional compactness of the routes in a desire to specialize routes to restricted geographical area and to balance the workload evenly between vehicles. Approximate solutions are constructed using a truncated column generation procedure followed by a rounding heuristic. This mathematical programming based procedure can deal with problems with 50–80 customers over five working days which is the range of size of most PVRP instances treated in the literature with meta-heuristics. The paper highlights the importance of alternative optimization criteria not accounted for in standard operational models and provides insights on the implementation of a column generation based rounding heuristic.     

Arc routing problems with time-dependent service costs

This paper studies an arc routing problem with capacity constraints and time-dependent service costs. This problem is motivated by winter gritting applications where the “timing” of each intervention is crucial. The exact problem-solving approach reported here first transforms the arc routing problem into an equivalent node routing problem. Then, a column generation scheme is used to solve the latter. The master problem is a classical set covering problem, while the subproblems are time-dependent shortest path problems with resource constraints. These subproblems are solved using an extension of a previously developed algorithm. Computational results are reported on problems derived from a set of classical instances of the vehicle routing problem with time windows.     

Quadratic convex reformulations for quadratic 0–1 programming

This is a summary of the author’s PhD thesis supervised by A. Billionnet and S. Elloumi and defended on November 2006 at the CNAM, Paris (Conservatoire National des Arts et Métiers). The thesis is written in French and is available from http://www.cedric.cnam.fr/PUBLIS/RC1115. This work deals with exact solution methods based on reformulations for quadratic 0–1 programs under linear constraints. These problems are generally not convex; more precisely, the associated continuous relaxation is not a convex problem. We developed approaches with the aim of making the initial problem convex and of obtaining a good lower bound by continuous relaxation. The main contribution is a general method (called QCR) that we implemented and applied to classical combinatorial optimization problems.      

Multiple criteria decision aiding: a dialectical perspective

This is a summary of the Ouerdane’s PhD thesis supervised by Alexis Tsoukiàs and Nicolas Maudet and defended on 01 December 2009 at the Université Paris-Dauphine, Paris. The thesis is written in English and is available from the author upon request. This work has the aim to investigate the different ways to use argumentation theory in a decision context. More precisely within multi-criteria evaluation models. The principal aim is to meet the needs in terms of explanations and revision during a decision process.     

Metaheuristic algorithms for combinatorial optimization problems

We present the main results in the author’s Ph.D. thesis (Iori 2004), defended at the University of Bologna in April 2004 and supervised by S. Martello. The thesis is written in English and is available from the author upon request. It proposes exact and metaheuristic algorithms for solving some relevant combinatorial optimization problems, with particular emphasis on scheduling, two-dimensional cutting and packing and capacitated vehicle routing. The performance of each algorithm is tested through extensive computational experiments and comparison with other approaches in the literature.Received: 21 September 2004, AMS classification: 90-08, 90C27, 90C59     

Solving VRPTWs with Constraint Programming Based Column Generation

Constraint programming based column generation is a hybrid optimization framework recently proposed (Junker et al., 1999) that uses constraint programming to solve column generation subproblems. In the past, this framework has been used to solve scheduling problems where the associated graph is naturally acyclic and has done so very efficiently. This paper attempts to solve problems whose graph is cyclic by nature, such as routing problems, by solving the elementary shortest path problem with constraint programming. We also introduce new redundant constraints which can be useful in the general framework. The experimental results are comparable to those of the similar method in the literature (Desrochers, Desrosiers, and Solomon, 1992) but the proposed method yields a much more flexible approach.     

On the design of networks with unicyclic connected components

This is a summary of my PhD thesis supervised by Walid Ben-Ameur and defended on September 29 2009, at Télécom SudParis. The thesis is written in French language and is available from the author upon request at makhlouf.hadji@it-sudparis.eu.     

The stochastic bid generation problem in combinatorial transportation auctions

In this paper, we deal with the generation of bundles of loads to be submitted by carriers participating in combinatorial auctions in the context of long-haul full truckload transportation services. We develop a probabilistic optimization model that integrates the bid generation and pricing problems together with the routing of the carrier’s fleet. We propose two heuristic procedures that enable us to solve models with up to 400 auctioned loads.     

Symmetry helps: Bounded bi-directional dynamic programming for the elementary shortest path problem with resource constraints

When vehicle routing problems with additional constraints, such as capacity or time windows, are solved via column generation and branch-and-price, it is common that the pricing subproblem requires the computation of a minimum cost constrained path on a graph with costs on the arcs and prizes on the vertices. A common solution technique for this problem is dynamic programming. In this paper we illustrate how the basic dynamic programming algorithm can be improved by bounded bi-directional search and we experimentally evaluate the effectiveness of the enhancement proposed. We consider as benchmark problems the elementary shortest path problems arising as pricing subproblems in branch-and-price algorithms for the capacitated vehicle routing problem, the vehicle routing problem with distribution and collection and the capacitated vehicle routing problem with time windows.