Reformulation and decomposition of integer programs

In this survey we examine ways to reformulate integer and mixed integer programs. Typically, but not exclusively, one reformulates so as to obtain stronger linear programming relaxations, and hence better bounds for use in a branch-and-bound based algorithm. First we cover in detail reformulations based on decomposition, such as Lagrangean relaxation, Dantzig-Wolfe column generation and the resulting branch-and-price algorithms. This is followed by an examination of Benders’ type algorithms based on projection. Finally we discuss in detail extended formulations involving additional variables that are based on problem structure. These can often be used to provide strengthened a priori formulations. Reformulations obtained by adding cutting planes in the original variables are not treated here.Integer program, Lagrangean relaxation, column generation, branch-and-price, extended formulation, Benders' algorithm

The two-echelon distribution network design problem under uncertainty: modeling and solution approach

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A Column Generation based Tactical Planning Method for Inventory Routing

International audienceInventory routing problems combine the optimization of product deliveries (or pickups) with inventory control at customer sites. Our application concerns the planning of single product pickups over time; each site accumulates stock at a deterministic rate; the stock is emptied on each visit. At the tactical planning stage considered here, our objective is to minimize a surrogate measure of routing cost while achieving some form of regional clustering by partitioning the sites between the vehicles. The fleet size is given but can potentially be reduced. Planning consists in assigning customers to vehicles in each time period, but the routing, i.e., the actual sequence in which vehicles visit customers, is considered as an ''operational'' decision. The planning is due to be repeated over the time horizon with constrained periodicity. We develop a truncated branch-and-price-and-cut algorithm combined with rounding and local search heuristics that yields both primal solutions and dual bounds. On a large scale test problem coming from industry, we obtain a solution within 6.25% deviation from the optimal. A rough comparison between an operational routing resulting from our tactical solution and the industrial practice shows a 10% decrease in number of vehicles as well as in travel distance. The key to the success of the approach is the use of a state-space relaxation technique in formulating the master program to avoid the symmetry in time

BaPCod - a generic branch-and-price code

This document presents a user guide for BaPCod version 0.63, a C++ library implementing a generic branch-cut-and-price solver. We give guidelines for installing BaPCod, using its modelling language, BaPCod parameterization, retrieving BaPCod statistics, and understanding BaP-Cod output. We also present the VRPSolver extension of BaPCod which allows one to model and efficiently solve a large number of vehicle routing and related problems

Reformulation and Decomposition Approaches for Traffic Routing in Optical Networks

International audienceWe consider a multi-layer network design model arising from a real-life telecommunication application where traffic routingdecisions imply the installation of expensive nodal equipment. Customer requests come in the form of bandwidthreservations for a given origin destination pair. Bandwidth demands are expressed as multiples of nominal granularities. Each request must be single-path routed. Grooming several requests on the same wavelength and multiplexing wavelengths in the same optical stream allow a more efficient use of network capacity. However, each addition or withdrawal of a request from a wavelength requires optical to electrical conversion and the use of cross-connect equipment with expensive ports of high densities. The objective is to minimize the number of required ports of the cross-connect equipment. We deal with backbone optical networks, therefore with networks with a moderate number of nodes (14 to 20) but thousands of requests. Further difficulties arise from the symmetries in wavelength assignment and traffic loading. Traditional multi-commodity network flowapproaches are not suited for this problem. Instead, four alternative models relying on Dantzig-Wolfe and/or Benders' decomposition areintroduced and compared. The formulations are strengthened using symmetry breaking restrictions, variable domain reduction, zero-onediscretization of integer variables, and cutting planes. The resulting dual bounds are compared to the values of primal solutions obtained through hierarchical optimization and rounding procedures. For realistic size instances, our best approaches provide solutions with optimality gap of approximately 5% on average in around two hours of computing time

Reformulation and Decomposition Approaches for Traffic Routing in Optical Networks

International audienceWe consider a multi-layer network design model arising from a real-life telecommunication application where traffic routingdecisions imply the installation of expensive nodal equipment. Customer requests come in the form of bandwidthreservations for a given origin destination pair. Bandwidth demands are expressed as multiples of nominal granularities. Each request must be single-path routed. Grooming several requests on the same wavelength and multiplexing wavelengths in the same optical stream allow a more efficient use of network capacity. However, each addition or withdrawal of a request from a wavelength requires optical to electrical conversion and the use of cross-connect equipment with expensive ports of high densities. The objective is to minimize the number of required ports of the cross-connect equipment. We deal with backbone optical networks, therefore with networks with a moderate number of nodes (14 to 20) but thousands of requests. Further difficulties arise from the symmetries in wavelength assignment and traffic loading. Traditional multi-commodity network flowapproaches are not suited for this problem. Instead, four alternative models relying on Dantzig-Wolfe and/or Benders' decomposition areintroduced and compared. The formulations are strengthened using symmetry breaking restrictions, variable domain reduction, zero-onediscretization of integer variables, and cutting planes. The resulting dual bounds are compared to the values of primal solutions obtained through hierarchical optimization and rounding procedures. For realistic size instances, our best approaches provide solutions with optimality gap of approximately 5% on average in around two hours of computing time

The two-echelon distribution network design problem under uncertainty: modeling and solution approach

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Solving the robust CVRP under demand uncertainty

International audienceIn this paper, we propose a Branch-Cut-and-price algorithm for the robust counterpart of the classical Capacitated Vehicle Routing Problem (CVRP). The deterministic version of this problem consists of finding a set of vehicle routes to serve a given set of customers with associated demands such that the sum of demands served by each vehicle does not exceed its capacity, and each customer is served exactly once. The total travel cost, given by the sum of distances traversed by all vehicles must be minimized. Here, only customer demands are assumed to be uncertain. We consider two types of uncertainty sets for the vector of customer demands: the classical budget polytope introduced by Bertsimas and Sim (2003), and a partitioned budget polytope, proposed by Gounaris et al (2013) for the CVRP with uncertain demands. The method proposed in this paper uses a set partitioning formulation to solve the problem, where each binary variable determines whether a given route is included or not in the solution. It considers only the routes that satisfy the capacity constraints for all possible demand vectors allowed by the uncertainty polytope. The linear relaxation for this formulation is solved by column generation, where the pricing subproblem is decomposed into a small number of deterministic subproblems with modified demand vectors. This reformulation allows the use of state-of-the-art techniques such as ng-routes, rank-1 cuts, specialized labeling algorithms, fixing by reduced costs and route enumeration. As a result, we were able to solve all 47 open instances proposed by Gounaris et al (2013), the largest one having 150 customers

The Two-Echelon Stochastic Multi-period Capacitated Location-Routing Problem

International audienceGiven the emergence of two-echelon distribution systems in several practical contexts, this paper tackles, at the strategic level, a distribution network design problem under uncertainty. This problem is defined as the two-echelon stochastic multi-period capacitated location-routing problem (2E-SM-CLRP). It considers a network partitioned into two capacitated distribution echelons: each echelon involves a specific location-assignment-transportation schema that must cope with the future demand. It aims to decide the number and location of warehousing/storage platforms (WPs) and distribution/fulfillment platforms (DPs), and on the capacity allocated from first echelon to second echelon platforms. In the second echelon, the goal is to construct vehicle routes that visit ship-to locations (SLs) from operating distribution platforms under a stochastic and time-varying demand and varying costs. This problem is modeled as a two-stage stochastic program with integer recourse, where the first-stage includes location and capacity decisions to be fixed at each period over the planning horizon, while routing decisions of the second echelon are determined in the recourse problem. We propose a logic-based Benders decomposition approach to solve this model. In the proposed approach, the location and capacity decisions are taken by solving the Benders master problem. After these first-stage decisions are fixed, the resulting sub-problem is a capacitated vehicle-routing problem with capacitated multiple depots (CVRP-CMD) that is solved by a branch-cut-and-price algorithm. Computational experiments show that instances of realistic size can be solved optimally within a reasonable time and provide relevant managerial insights on the impact of the stochastic and multi-period settings on the 2E-CLRP