Antoni Guasch

Simplifying the encoding of a simulation conceptual model representation reduces the number of errors that will be detected in the verification phase. In this paper, we present a mapping between Petri nets, a well-known formalism, and FlexSim, a well-known simulation tool. The proposal is illustrated through an example of how a model specified in a Petri net can be encoded easily, reducing the time needed to understand and verify the model. In the proposed methodology, the mapping must be defined at the initial stage of the encoding, starting from (in this case) a Petri net conceptual model, and ending at the encoding tool (FlexSim in this case). The main advantages of the proposed methodology are discussed.

In this paper we present a discrete event simulation model of a cruise terminal for decision support and strategic planning. The main objective is to provide an analysis tool for systems with high capacity constraints, heterogeneous subsystems, time-dependent demand and stochastic passenger transfer and travel times. Cruise terminals are connected by bus to a transport interchange or intermobility center where passengers are transferred to taxis. All terminals are affected by capacity constraints such as the number of bus platforms for passenger pick-up and drop-off and the limited queuing capacity for taxis and passengers. Passenger arrivals are time-dependent and cruise-type-dependent. The purpose of this model is to support the strategic decision-making process through what-if-scenarios. Decisions involve determining the required number of bus platforms, taxi buffer sizes and passenger distribution policies, among other factors. The results identified and proposed the solution of bottlenecks and enabled a sensitivity analysis to be performed.

Firefighting resource management is crucial to contain and extinguish wildfires. Resource optimization in wildfire containment can help to reduce the dangers and risks to both human (firemen and area inhabitants) and natural environment. The use of simulation to predict wildfire evolution combined with optimization techniques can lead to an optimal resource deployment and management to minimize natural and human risks. This article proposes a simulation and optimization architecture; a well-defined data format to represent firefighting resources and an experimental platform to simulate wildfire spread, wildfire containment, resource dispatching and management and resource optimization. The simulation and optimization environment will be tested in the Catalonia region (Spain) in collaboration with Catalan Firefight Department.

In this paper we present a state space exploration method implemented as part of a Decision Support Tool designed to improve the performance of logistics and production systems. An efficient depth-first search strategy, together with a reduction method known as the ‘Old Node Method’, is introduced to improve the descendant path choice by means of an evaluation function that allows the selection of the most promising node to be expanded at any level of the path. The sub-set of the state space representing the most promising scenarios holds a set of feasible and quasi-optimal solutions.

The Newspaper Production/Distribution Problem (NDP) involves a perishable good. Newspaper publishers cannot print the news sections of newspapers in advance, as they must be up to date. Additionally, delivery must be completed before morning commuting starts in metropolitan areas. Therefore, delivery delays at the printing plant are not acceptable, as they cause significant disturbances to the distribution chain. In this paper, we analyse the impact of using a second transelevator on the performance of a printing plant's automatic warehouse with the aim to avoid delivery delays to the printing press.

In this paper we present a methodological approach designed to automate the decision-making in logistic systems, with deterministic time, by solving optimization problems. The Colored Petri Net (CPN) formalism has been used as a base to develop a methodology that integrates the features of operational research, artificial intelligence and simulation fields. At the same time, it combines the modeling of discrete event systems with simulation, analysis and system optimization, transforming a conceptual model into a simulation model, and a decision problem into a search problem. The use of the CPN formalism has allowed the integration of all of these different research fields into a unique decision support tool.

Simulation models have proved to be useful for examining the performance of different system configurations and/or alternative operating procedures for complex logistic or manufacturing systems. However, when applying simulation techniques to increase the performance of those systems, several limitations arise due to their inability to evaluate more than a fraction of the immense range of options available. Simulation-optimization is one of the most popular approaches to improve the use of simulation models as a tool to obtain the best (optimal or quasi-optimal) decision variable values that minimize a certain objective function. However, despite the success of several simulation-optimization packages, many technical barriers still remain. The authors describe a new approach to integrate evaluation (simulation) methods with search methods (optimization) based on not only simulation results but also information from the simulation model.