Enrique Salomone

Transforming collaborative business process models into web services choreography specifications

Languages for web services choreography are becoming more and more important for B2B integration. However, the development of web services-based systems is complex and time-consuming. Enterprises have to agree on collaborative business processes and then derive their respective web services choreographies in order to implement B2B collaboration. To support it, this paper presents a MDA approach for collaborative processes. We describe the components and techniques of this approach. We show how collaborative process models defined with the UP-ColBPIP language can be used as the main development artifact in order to derive choreography specifications based on WS-CDL. The transformations to be carried out are also discussed. The main advantage of this MDA approach is that it guarantees that the generated web services choreographies fulfill the collaborative processes agreed between the partners in a business level.

Agent-based monitoring service for management of disruptive events in supply chains

HighlightsSubsystem able to manage the distributed nature of disruptive events.Agent technology made it possible to address the complexity of monitoring services.Tailored monitoring processes are dynamically generated at execution time.Model-driven development approach used to generate tailored monitoring processes.Subsystem able to decide on the most appropriate tool to perform the monitoring task. Schedules of supply chains are generated with buffers to absorb the effect of disruptive events that could occur during their execution. Schedules can be systematically repaired through specific modifications within buffers by using appropriate decision models that consider the distributed nature of a supply chain. To this aim, information of disruptive events at occurrence or in advance allows decision models to make better decisions. To detect and predict disruptive events along a schedule execution, a service-oriented monitoring subsystem that uses a reference model for defining monitoring models was proposed. This subsystem offers services for collecting execution data of a schedule and environment data, and assessing them to detect/anticipate disruptive events. Because of the distributed nature and the complexity of these services functionalities, this paper presents an agent-based approach for their implementation. This technology allows dealing with supply chain monitoring by structuring monitoring subsystem functionalities as a set of autonomous entities. These entities are able to perform tailored plans created at execution time to concurrently monitor different schedules. A case study is described to try out the implemented prototype system.

A model driven development approach based on a reference model for predicting disruptive events in a supply process

Due to the impossibility of predicting with certainty the occurrence of disruptive events, buffers defined to obtain a robust schedule could not absorb all the changes. Then, local modifications of the schedule are usually performed to avoid a new planning task. For this task, obtaining disruptive event information in advance can help to make better decisions. As a result, ability to predict disruptive events that affect the execution of the supply process an order represents is required. With the objective of satisfying this requirement, this work proposes a model driven development approach based on a reference model to automate the generation of the monitoring model of a supply process able to anticipate the occurrence of a disruptive event by monitoring variables that can explain it. The approach proposes both a reference model to represent the monitoring model independently of the implementation platform, and a specific model to represent the monitoring model with the particular language of the implementation platform. An engine based on transformation rules allows automating the generation of a platform dependent monitoring model from an instance of a platform independent metamodel. The monitoring component of a SCEM system has been developed, which implements the transformation engine as a Bayesian Network model, and uses an appropriate tool to execute it. For an empirical validation of the model three case studies are presented.

An autonomous multi-agent approach to supply chain event management

Event management is a problem in the supply chain context that requires a solution with the goal of mitigate the event effect during the execution plan. We present an autonomous agent-based approach to support a system for this problem. Our proposal introduces two novel aspects: we conceive the system as a collaborative inter-organizational information system and we aim to provide autonomous mechanisms for the system to perform proactive control actions. We develop an example illustrating the principal concept and how this decomposition and collaborative negotiations allow finding a solution to an exception.

Service Oriented Approach for Autonomous Exception Management in Supply Chains

Risk and uncertainty are inherent to Supply Chains; at the execution level unexpected events can disrupt the normal flow of supply processes creating a gap between planned operations and what is actually executed. These disruptions increment rescheduling frequency, generating reconfiguration costs and system’s nervousness. This work proposes a web service based Business Process to support Autonomous Exception Management in Supply chains.

Using conceptual models for the synthesis and design of batch distillations

We present a methodology to estimate the maximum recovery of each component in an azeotropic multicomponent mixture processed at a batch distillation column. Based in the most recent advances in the field of separation feasibility we have adapted an algorithm to predict all the distillation regions for the multicomponent mixture with their corresponding natural and distillation boundaries. The results of the maximum separation problem togheter with those corresponding to conceptual dynamic runs are used to illustrate several important issues related to the synthesis and design of highly non-ideal azeotropic mixtures.

Framework for modelling and simulating the supply process monitoring to detect and predict disruptive events

Abstract Disruptive events that take place during supply process execution produce negative effects that propagate throughout a supply chain. Event management systems for supply chains have emerged to provide functionality for monitoring schedules, managing disruption, and repairing schedules affected by a disruptive event. A Web service that provides a schedule monitoring functionality for supply chain event management was developed. This paper provides a framework to allow enterprises that hire this service to develop simulation models of monitoring processes and evaluate their ability to detect and anticipate disruptive events. The framework, based on discrete event simulation, is implemented in a library that can be used for developing and testing monitoring processes by means of a friendly interface. A marine freight transport process was used as a case study to show how a supply process and its environment can be modelled and simulated by using the library. Simulation results show the ability of this approach to anticipate disruptive events and identify critical stages of a supply process in order to prevent disruptive events.

Compound Web Service for Supply Processes Monitoring to Anticipate Disruptive Event

The execution of supply process orders in a supply chain is conditioned by different types of disruptive events that must be detected and solved in real time. In this work we present a compound web service that performs the monitoring and notification functions of a supply chain event management system. This web service is designed based on a reference model that we have proposed to improve the event management activity through a deeper analysis of the occurrence and causality of events, leading to anticipate an exception during the execution of a supply process order. The web service composition is defined based on business processes. The ability to proactively detect, analyze and notify disruptive events is given through of a Bayesian network with decision nodes.

Model Based on Bayesian Networks for Monitoring Events in a Supply Chain

The execution of supply process orders in a supply chain is conditioned by different types of disruptive events that must be detected and solved in real time. This requires the ability to proactively monitor, analyze and notify disruptive events. In this work we present a model that captures this functionality and was used as the foundation to design a software agent. A reactive-deliberative hybrid architecture provides the ability to proactively detect, analyze and notify disruptive events that take place in a supply chain. For the deliberative performance of the agent, a cause-effect relation model based on a Bayesian network with decision nodes is proposed.