H. de Swaan Arons

Knowledge-based modeling of discrete-event simulation systems

Modeling a simulation system requires a great deal of customization. At first sight no system seems to resemble exactly another system and every time a new model has to be designed the modeler has to start from scratch. The present simulation languages provide the modeler with powerful tools that greatly facilitate building models (modules for arrivals or servers, etc.). Yet, also with these tools the modeler constantly has the feeling that he is reinventing the wheel again and again. Maybe the model he is about to design already exists (maybe the modeler has designed it himself some time ago) or maybe a model already exists that sufficiently resembles the model to be designed. In this article an approach is discussed that deploys knowledge-based systems to help selecting a model from a database of existing models. Also, if the model is not present in the database, would it be possible to select a model that in some sense is close to the model that the modeler had in mind?.

Coordination in a supply chain for bulk chemicals

A chemical plant in the Netherlands uses large annual supplies of a bulk chemical. A number of suppliers deliver their parcels from overseas by short sea vessel to a transshipment point where they are stored using a tank farm. Transportation from the transshipment point to the plant takes place by barge. Coordination of the schedules of vessels and barge provides the opportunity for board to board loading. Board to board loading provides clear benefits for the plants operator, as it requires less handling and intermediate storage at the transshipment point. We demonstrate this by experiments conducted with a simulation model. The results are confirmed by analytical means.

Computer assistance for model definition

Modeling requires considerable knowledge of the various stages of the simulation process. The modeler needs to know a great deal of the system to be modeled (domain specific knowledge), the ins and outs of the modeling process itself (the degree of detail of the model) and how to implement the model in a simulation language. Each of these stages would benefit from some kind of knowledgeable support. In this article a decision-making process is described that supports the modeler to build a model step by step. As a vehicle the Arena simulation environment has been used. The support is based on information provided by the modeler and is essentially data-driven. It suggests which modules could be used best, which parameters need to be determined and helps to formulate route information. This research aims for an implementation of this support using a knowledge-based system.Modeling requires considerable knowledge of the various stages of the simulation process. The modeler needs to know a great deal of the system to be modeled (domain specific knowledge), the ins and outs of the modeling process itself (the degree of detail of the model) and how to implement the model in a simulation language. Each of these stages would benefit from some kind of knowledgeable support. In this article a decision-making process is described that supports the modeler to build a model step by step. As a vehicle the Arena simulation environment has been used. The support is based on information provided by the modeler and is essentially data-driven. It suggests which modules could be used best, which parameters need to be determined and helps to formulate route information. This research aims for an implementation of this support using a knowledge-based system.

Ranking a list of discrete-event models

It has been demonstrated that Arena simulation models can be parameterized and stored in a relational database for later use. In this way an Arena model can be uniquely described by assigning the right values to the corresponding parameters. These parameters contain detailed information concerning type and number of modules, entities and routes which entities follow through the system. A modeler could query such a database hoping that it would contain the Arena (sub)model that one has in mind. Unfortunately, an exact match will seldom occur but the database might contain one or more similar models. In earlier work a numerical algorithm was proposed resulting in a ranked list of simulation models in the database. According to this the lower a model is ranked the more it resembles the specified model. This model depends on a number of weight factors. In this paper the ranking algorithm is evaluated by carrying out a number of experiments.