Juan José Ramos

Combining probabilistic algorithms, Constraint Programming and Lagrangian Relaxation to solve the Vehicle Routing Problem

This paper presents an original hybrid approach to solve the Capacitated Vehicle Routing Problem (CVRP). The approach combines a Probabilistic Algorithm with Constraint Programming (CP) and Lagrangian Relaxation (LR). After introducing the CVRP and reviewing the existing literature on the topic, the paper proposes an approach based on a probabilistic Variable Neighbourhood Search (VNS) algorithm. Given a CVRP instance, this algorithm uses a randomized version of the classical Clarke and Wright Savings constructive heuristic to generate a starting solution. This starting solution is then improved through a local search process which combines: (a) LR to optimise each individual route, and (b) CP to quickly verify the feasibility of new proposed solutions. The efficiency of our approach is analysed after testing some well-known CVRP benchmarks. Benefits of our hybrid approach over already existing approaches are also discussed. In particular, the potential flexibility of our methodology is highlighted.

A simheuristic algorithm for solving the arc-routing problem with stochastic demands

This paper proposes a simheuristic algorithm for solving the Arc-Routing Problem with Stochastic Demands. Our approach combines Monte Carlo Simulation (MCS) with the RandSHARP metaheuristic, which was originally designed for solving the Capacitated Arc-Routing Problem with deterministic demands (CARP). The RandSHARP metaheuristic is a biased-randomized version of a savings-based heuristic for the CARP, which allows it to obtain competitive results for this problem in low computational times. The RandSHARP is then combined with MCS to cope with the stochastic variant of the problem in a natural and efficient way. Our work is based on the use of a safety stock during the route-design stage. This safety stock can then be used during the delivery stage to satisfy unexpected demands. A reliability index is also defined to evaluate the robustness of each solution with respect to possible route failures caused by random demands. Some numerical experiments contribute to validate our approach and to illustrate its potential benefits.

Causal simulation models for facing third millennium air transport sustainability

Aeronautics and air transport is a vital sector of our society and economy. Air transport logistics is one of the key players to support efficient globalization; however, sustainable mobility is at stake, due to facts such as the interdependencies with the financial system, climate change and an increasing scarcity of resources. This paper highlights the consequences of a lack of a proper understanding of air-side and land-side, leading to an unsustainable air transport system. A system approach for knowledge sharing between air traffic controllers, handling operators, airlines and airport managers is justified by means of causal models to design a mitigation mechanism to tackle perturbations instead of increasing the latent capacity. The simulation benefits to design indicators of sustainability are also mentioned.

Airport Logistics Operations

In recent years, air traffic has increased dramatically while airport capacity has remained stagnant. This has resulted in congestion problems which degrade the performance of the air traffic control system and cause excessive costs. Despite recent technological advances in the airport airside area, some procedures and operational rules in the landside area are years behind airside capability. In this chapter, a discrete-event system view of airport operations is introduced. The main aspects of delay propagation due to a lack of coordination policies will be illustrated using an Arena© simulation model.

A bi-objective approach for scheduling ground-handling vehicles in airports

In the present paper, we propose a new approach for scheduling ground-handling vehicles, tackling the problem with a global perspective. Preparing an aircraft for its next flight requires a set of interrelated services involving different types of vehicles. Planning decisions concerning each resource affect the scheduling of the other activities and the performance of the other resources. Considering the different operations and vehicles instead of scheduling each resource in isolation allows integrating decisions and contributing to the optimization of the overall ground-handling process. This goal is defined through two objectives: (i) minimizing the waiting time before an operation starts and the total reduction of corresponding time windows and (ii) minimizing the total completion time of the turnarounds. We combine different technologies and techniques to solve the problem efficiently. A new method to address this bi-objective optimization problem is also proposed. The approach has been tested using real data from two Spanish airports, thereby obtaining different solutions that represent a trade-off between both objectives. Experimental results permit inferring interesting criteria on how to optimize each resource, considering the effect on other operations. This outcome leads to more robust global solutions and to savings in resources utilization. Novel approach to cope with the ground-handling scheduling as a whole.Planning integration of the resources modeled as a bi-objective optimization problem.Solution robustness and completion time considered simultaneously.Have been evaluated on a real problem from two of the busiest airports in Spain.

Solving Vehicle Routing Problems Using Constraint Programming and Lagrangean Relaxation in a Metaheuristics Framework

This paper presents a methodology based on the Variable Neighbourhood Search metaheuristic, applied to the Capacitated Vehicle Routing Problem. The presented approach uses Constraint Programming and Lagrangean Relaxation methods in order to improve algorithm’s efficiency. The complete problem is decomposed into two separated subproblems, to which the mentioned techniques are applied to obtain a complete solution. With this decomposition, the methodology provides a quick initial feasible solution which is rapidly improved by metaheuristics’ iterative process. Constraint Programming and Lagrangean Relaxation are also embedded within this structure to ensure constraints satisfaction and to reduce the calculation burden. By means of the proposed methodology, promising results have been obtained. Remarkable results presented in this paper include a new best-known solution for a rarely solved 200-customers test instance, as well as a better alternative solution for another benchmark problem.

PML: An Object-Oriented Language for Modelling Automation

To cope with the growing demands for simulation models of ever increasing complex industrial systems, the research community effort has been mainly focused in creating different software tools which simplify the modeling process. This work describes how the Object-Oriented Modeling language PML (Physical Modeling Language) automates the modeling process by using physical knowledge in order to set the mathematical model of the system. PML introduces new modular structures to represent the physical knowledge required to model a system, making a clear separation between the physical behaviour representation (declarative knowledge) and the computational aspects of model simulation (procedural knowledge).Copyright

Needs of object-oriented languages for physics knowledge representation in the simulation field

To cope with the growing demands for simulation models of ever increasing complex industrial systems, the research community effort has been mainly focused on creating different software tools which simplify the modelling task. There is a recognised necessity of modelling tools supporting libraries of non-causal models which could be coupled in the same way as physical units are assembled in a system. This work presents an object-oriented modelling language, PML, designed to support a modelling methodology where system models are described by linking system component models analogously as the system components are linked. This modelling language introduces a new modularization of physical knowledge, making a clear separation between the physical behaviour representation (declarative knowledge) and the computational aspects of model simulation (procedural knowledge).