Luigi Moccia

Models and Tabu Search Heuristics for the Berth-Allocation Problem

In the berth-allocation problem (BAP) the aim is to optimally schedule and assign ships to berthing areas along a quay. The objective is the minimization of the total (weighted) service time for all ships, defined as the time elapsed between the arrival in the harbor and the completion of handling. Two versions of the BAP are considered: the discrete case and the continuous case. The discrete case works with a finite set of berthing points. In the continuous case ships can berth anywhere along the quay. Two formulations and a tabu search heuristic are presented for the discrete case. Only small instances can be solved optimally. For these sizes the heuristic always yields an optimal solution. For larger sizes it is always better than a truncated branch-and-bound applied to an exact formulation. A heuristic is also developed for the continuous case. Computational comparisons are performed with the first heuristic and with a simple constructive procedure.

The service allocation problem at the Gioia Tauro Maritime Terminal

The Service Allocation Problem (SAP) is a tactical problem arising in the yard management of a container transshipment terminal. The objective is the minimization of the container rehandling operations inside the yard. This study of the SAP was undertaken for the Gioia Tauro port which is located in Italy and is the main hub terminal for container traffic in the Mediterranean Sea. The SAP can be formulated as a Generalized Quadratic Assignment Problem (GQAP) with side constraints. Two mixed integer linear programming formulations are presented. The first one exploits characteristics of the yard layout at Gioia Tauro where the berth and the corresponding yard positions extend along a line. The second formulation is an adaptation of a linearization for the GQAP. In both cases only small instances can be solved optimally. An evolutionary heuristic was therefore developed. For small size instances the heuristic always yields optimal solutions. For larger sizes it is always better than a truncated branch-and-bound algorithm applied to the exact formulations.

A Memetic Heuristic for the Generalized Quadratic Assignment Problem

In the generalized quadratic assignment problem (GQAP) we are given n weighted facilities, m capacitated sites, a traffic intensity matrix between facilities, a distance matrix between sites, unit traffic costs, and assignment costs of facilities to sites. The aim is to determine an assignment of facilities to sites so that the sum of assignment and traffic costs is minimized and the total weight of all facilities assigned to the same site does not exceed the site capacity. The GQAP is a generalization of the quadratic assignment problem (QAP) in which n = m and exactly one facility must be assigned to each site. The problem has applications in container yard management and in the assignment of equipment to manufacturing sites. This article describes a memetic heuristic for the GQAP, as well as an integer linear programming formulation that can be solved by CPLEX for small instances. For larger instances, feasible solutions can be obtained by a truncated branch-and-bound procedure. Computational experiments show that on small instances the proposed heuristic always yields an optimal solution; on larger instances it always outperforms the truncated branch-and-bound algorithm.

An Incremental Tabu Search Heuristic for the Generalized Vehicle Routing Problem with Time Windows

This paper describes an incremental neighbourhood tabu search heuristic for the generalized vehicle routing problem with time windows. The purpose of this work is to offer a general tool that can be successfully applied to a wide variety of specific problems. The algorithm builds upon a previously developed tabu search heuristic by replacing its neighbourhood structure. The new neighbourhood is exponential in size, but the proposed evaluation procedure has polynomial complexity. Computational results are presented and demonstrate the effectiveness of the approach.

A column generation heuristic for a dynamic generalized assignment problem

This paper studies the dynamic generalized assignment problem (DGAP) which extends the well-known generalized assignment problem by considering a discretized time horizon and by associating a starting time and a finishing time with each task. Additional constraints related to warehouse and yard management applications are also considered. Three linear integer programming formulations of the problem are introduced. The strongest one models the problem as an origin-destination integer multi-commodity flow problem with side constraints. This model can be solved quickly for instances of small to moderate size. However, because of its computer memory requirements, it becomes impractical for larger instances. Hence, a column generation algorithm is used to compute lower bounds by solving the linear program (LP) relaxation of the problem. This column generation algorithm is also embedded in a heuristic aimed at finding feasible integer solutions. Computational experiments on large-scale instances show the effectiveness of the proposed approach.

Multi-objective rapid transit network design with modal competition

We present a mixed integer linear program for the rapid transit network design problem with static modal competition. Previous discrete formulations cannot handle modal competition for realistic size instances because of the complexity of modeling alternatives for each flow in the network. We overcome this difficulty by exploiting a pre-assigned topological configuration. We discuss relevant goals of rapid transit planning, and we propose a multi-objective model conducive to a post-optimization analysis for effectiveness, efficiency, and equity concerns. A case study carried out for a metro proposal in Concepcion, Chile, shows the suitability of the proposed method consisting of the mixed integer linear program coupled with the post-optimization analysis. HighlightsWe model and solve a strategic metro design problem with modal competition.A discrete mathematical program allows a fine modeling of the problems attributes.A pre-assigned topological configuration allows resolution by integer programming.A multi-objective framework addresses efficiency, effectiveness, and equity concerns.A case study on the city of Concepcion illustrates the suitability of the methodology.

A Mixed Integer Linear Program for the Rapid Transit Network Design Problem with Static Modal Competition (Short Paper)

We present a mixed integer linear program for the rapid transit network design problem with static modal competition. Previous discrete formulations cannot handle modal competition for realistic size instances because of the complexity of modeling alternatives for each flow in the network. We overcome this difficulty by exploiting a pre-assigned topological configuration. Results of a case study will be presented at the conference.