Pierre Tolla

A path relinking algorithm for the generalized assignment problem

The Generalized Assignment Problem (GAP) consists in finding a maximum-profit assignment of tasks to agents with capacity constraints. In this paper, a path relinking heuristic is proposed for the GAP. The main feature of our path relinking is that both feasible and infeasible solutions are inserted in the reference set of elite solutions, trade-off between feasibility and infeasibility being ruled through a penalty coefficient for infeasibility. Since exploration of the solution space is very sensitive to this penalty coefficient, the coefficient is dynamically updated at each round of combinations so that a balance is kept between feasible and infeasible solutions in the reference set. Numerical experiments reported on classical testbed instances of the OR-library show that the algorithm compares favorably to several other methods in the literature. In particular, more than 95% of the instances in the test-file were solved to optimality with short computation time.

Exact solution method to solve large scale integer quadratic multidimensional knapsack problems

Abstract In this paper we develop a branch-and-bound algorithm for solving a particular integer quadratic multi-knapsack problem. The problem we study is defined as the maximization of a concave separable quadratic objective function over a convex set of linear constraints and bounded integer variables. Our exact solution method is based on the computation of an upper bound and also includes pre-procedure techniques in order to reduce the problem size before starting the branch-and-bound process. We lead a numerical comparison between our method and three other existing algorithms. The approach we propose outperforms other procedures for large-scaled instances (up to 2000 variables and constraints).

An interactive multiobjective nonlinear programming procedure

Abstract This paper develops a method for interactive MultiObjective NonLinear Programming procedure (MONLP). It provides a detailed description of an efficient algorithm, and reports on promising computational results. It also discusses several alternative strategies for implementing GRG code (Generalized Reduced Gradient), which is known as one of the ‘best’ methods for solving NonLinear optimization Problems (Abadie, 1978). The method relies on three steps: 1) generation of a subset of feasible efficient solutions; 2) interactive definition by Decision Maker (DM) of his preference structure according to desired outcome; 3) determination of a compromise solution using nonlinear optimization; a global analysis based on “reference point search procedure” is performed (in the criteria space). Following this methodology, it is possible for the DM to find his final solution. A microcomputer version (for medium problems) of the method is available.

A hybrid search combining interior point methods and metaheuristics for 0–1 programming

Our search deals with methods hybridizing interior point processes and metaheuristics for solving 0-1 linear programs. This paper shows how metaheuristics can take advantage of a sequence of interior points generated by an interior point method. After introducing our work field, we present our hybrid search which generates a diversified population. Next, we explain the whole method combining the solutions encountered in the previous phase through a path relinking template. Computational experiments are reported on 0-1 multiconstraint knapsack problems.