MPC Approach for Synchronized Supply Chains of Perishable Goods

Trabalho apresentado em 7th International Conference on Industrial Engineering and Systems Management (IESM’17), 2017,Saarbrucken, GermanyThe movement of perishable goods is growing worldwide. Perishable goods need to be available to the market before the expiration date. With the decrease in inventory levels the components of a supply chain become even more integrated and dependent on coordinated decisions. Information regarding perishable goods must be visible throughout the supply chain for avoiding goods loss. A Model Predictive Control (MPC) heuristic to address operations management at supply chains of perishable goods is proposed in this paper. The approach is capable to follow the remaining time until expiration date which is critical to avoid losses. The supply chain is modeled using a state-space representation and operations management at the supply chain is formulated as an MPC Problem. In order to cope with operational decisions, the problem is solved on a periodic basis. The proposed approach is capable to deal with production decisions, monitor work-in-progress (WIP), and make transport assignments while monitoring the remaining time until the expiration date. Flows over the supply chain can be synchronized and therefore we named this type of supply chain a Synchronized Supply Chain (SSC). The approach is modular and easily scalable for largescale supply chains. Numerical results illustrate these statements.info:eu-repo/semantics/publishedVersio

A Multi-Agent Control Architecture for Supply Chains using a Predictive Pull-Flow Perspective

Com o apoio RAADRI.Supply chains are large-scale distribution networks in which multiple types of commodities are present. In this paper, the operations management in supply chains is posed as a tracking control problem. All inventory levels in the network should be kept as close as possible to the desired values over time. The supply chain state is disturbed due to client demand at the end nodes. A multiagent control architecture to restore all inventory levels over the supply chain is proposed. First the model for the supply chain is broken down into smaller subsystems using a flow decomposition. The operations management for each subsystem will be decided upon by a dedicated control agent. The control agents solve their problems using a pull-flow perspective, starting at the end nodes and then propagating upstream. Adding new components to the supply chain will have as a consequence the inclusion of more control agents. The proposed architecture is easily scalable to large supply chains due to its modular feature. The multi-agent control architecture performance is illustrated using a supply chain composed of four levels (suppliers, consolidation, distribution, end nodes) using different levels of predictions about client demands. With the increase of prediction demand accuracy the proposed control architecture is able to keep the desired inventory level at the end nodes over time, which makes it suitable for use for just in time production strategies

Incidence, clinical characteristics and management of inflammatory bowel disease in Spain: large-scale epidemiological study

(1) Aims: To assess the incidence of inflammatory bowel disease (IBD) in Spain, to describe the main epidemiological and clinical characteristics at diagnosis and the evolution of the disease, and to explore the use of drug treatments. (2) Methods: Prospective, population-based nationwide registry. Adult patients diagnosed with IBD—Crohn’s disease (CD), ulcerative colitis (UC) or IBD unclassified (IBD-U)—during 2017 in Spain were included and were followed-up for 1 year. (3) Results: We identified 3611 incident cases of IBD diagnosed during 2017 in 108 hospitals covering over 22 million inhabitants. The overall incidence (cases/100, 000 person-years) was 16 for IBD, 7.5 for CD, 8 for UC, and 0.5 for IBD-U; 53% of patients were male and median age was 43 years (interquartile range = 31–56 years). During a median 12-month follow-up, 34% of patients were treated with systemic steroids, 25% with immunomodulators, 15% with biologics and 5.6% underwent surgery. The percentage of patients under these treatments was significantly higher in CD than UC and IBD-U. Use of systemic steroids and biologics was significantly higher in hospitals with high resources. In total, 28% of patients were hospitalized (35% CD and 22% UC patients, p < 0.01). (4) Conclusion: The incidence of IBD in Spain is rather high and similar to that reported in Northern Europe. IBD patients require substantial therapeutic resources, which are greater in CD and in hospitals with high resources, and much higher than previously reported. One third of patients are hospitalized in the first year after diagnosis and a relevant proportion undergo surgery. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Trends and outcome of neoadjuvant treatment for rectal cancer: A retrospective analysis and critical assessment of a 10-year prospective national registry on behalf of the Spanish Rectal Cancer Project

Introduction: Preoperative treatment and adequate surgery increase local control in rectal cancer. However, modalities and indications for neoadjuvant treatment may be controversial. Aim of this study was to assess the trends of preoperative treatment and outcomes in patients with rectal cancer included in the Rectal Cancer Registry of the Spanish Associations of Surgeons. Method: This is a STROBE-compliant retrospective analysis of a prospective database. All patients operated on with curative intention included in the Rectal Cancer Registry were included. Analyses were performed to compare the use of neoadjuvant/adjuvant treatment in three timeframes: I)2006–2009; II)2010–2013; III)2014–2017. Survival analyses were run for 3-year survival in timeframes I-II. Results: Out of 14, 391 patients, 8871 (61.6%) received neoadjuvant treatment. Long-course chemo/radiotherapy was the most used approach (79.9%), followed by short-course radiotherapy ± chemotherapy (7.6%). The use of neoadjuvant treatment for cancer of the upper third (15-11 cm) increased over time (31.5%vs 34.5%vs 38.6%, p = 0.0018). The complete regression rate slightly increased over time (15.6% vs 16% vs 18.5%; p = 0.0093); the proportion of patients with involved circumferential resection margins (CRM) went down from 8.2% to 7.3%and 5.5% (p = 0.0004). Neoadjuvant treatment significantly decreased positive CRM in lower third tumors (OR 0.71, 0.59–0.87, Cochrane-Mantel-Haenszel P = 0.0008). Most ypN0 patients also received adjuvant therapy. In MR-defined stage III patients, preoperative treatment was associated with significantly longer local-recurrence-free survival (p < 0.0001), and cancer-specific survival (p < 0.0001). The survival benefit was smaller in upper third cancers. Conclusion: There was an increasing trend and a potential overuse of neoadjuvant treatment in cancer of the upper rectum. Most ypN0 patients received postoperative treatment. Involvement of CRM in lower third tumors was reduced after neoadjuvant treatment. Stage III and MRcN + benefited the most

Damp trend Grey Model forecasting method for airline industry

This paper presents a modification of the Grey Model (GM) to forecast routes passenger dema nd growth in the air transportation industry. Forecast methods like Holt-Winters, autoreg ressive models, exponential smoothing, neural network, fuzzy logic, GM model calculate very high airlines routes pax growth. For this reason, a modification has been done to the GM model to damp trend calculations as time grows. The simulation results show that the modified GM model reduces the model exponential estimations grow. It allows the GM model to forecast reasonable routes passenger demand for long lead-tim es forecasts. It makes this model an option to calculate airlines routes pax flow when few data points are availabl e. The United States domestic air transport market data are used to compare the performance of the GM model wit h the proposed model.info:eu-repo/semantics/publishedVersio

Hierarchical Model Predictive Control for Multi-Commodity Transportation Networks

Transportation networks are large scale complex systems spatially distributed whose objective is to deliver commodities at the agreed time and at the agreed location. These networks appear in different domain fields, such as communication, water distribution, traffic, logistics and transportation. A transportation network has at the macroscopic level storage capability (located in the nodes) and transport delay (along each connection) as main features. Operations management at transportation networks can be seen as a flow assignment problem. The problem dimension to solve grows exponentially with the number of existing commodities, nodes and connections. In this work we present a Hierarchical Model Predictive Control (H-MPC) architecture to determine flow assignments in transportation networks, while minimizing exogenous inputs effects. This approach has the capacity to keep track of commodity types while solving the flow assignment problem. A flow decomposition of the main system into subsystems is proposed to diminish the problem dimension to solve in each time step. Each subsystem is managed by a control agent. Control agents solve their problems in a hierarchical way, using a so-called push-pull flow perspective. Further problem dimension reduction is achieved using contracted projection sets. The framework proposed can be easily scaled to network topologies in which hundreds of commodities and connections are present.info:eu-repo/semantics/publishedVersio

A Multi-Agent MPC Scheme for Vertically Integrated Manufacturing Supply Chains

Trabalho apresentado em 6th International Conference on Management and Control of Production Logistics, 2013, Fortaleza, Brasilinfo:eu-repo/semantics/publishedVersio

A Constrained MPC Heuristic to Achieve a Desired Transport Modal Split at Intermodal Hubs

Trabalho apresentado em 16th IEEE Conference of Intelligent Transportation Systems (ITSC'13), 2013,Haia, HolandaIntermodal hubs are a component of freight transportation networks that have as main goal to deliver cargo at the agreed time and at the agreed location. Currently, authorities are forcing transport operators to act in more sustainable ways. For intermodal hubs this is translated into making a preferable choice for sustainable transport modalities. In some cases, this is no longer a choice and is imposed on the intermodal hub in terms of a desired transport modal split. In this paper, a heuristic based on Model Predictive Control (MPC) to achieve a desired transport modal split at intermodal hubs is proposed. A terminal state constraint is used for the quantity of cargo assigned per modality over the prediction horizon to guide the cargo assignment. Feasibility of the optimization problem and cargo delivery at the agreed time are assured by relaxing the terminal state constraint. The proposed heuristic can anticipate the transport of cargo due to the inclusion of predictions, leading to a push of cargo towards the final destination. As cargo is moving in anticipation to the due time the transport is more robust to unforseen events, such as jams and weather conditions. The proposed heuristic is a step towards sustainable and synchromodal transportation networks. Simulation experiments illustrate the validity of these statements.info:eu-repo/semantics/publishedVersio

Setting Cooperative Relations Among Terminals at Seaports Using a Multi-Agent System

Trabalho apresentado em 16th IEEE Conference of Intelligent Transportation Systems (ITSC'13), 2013, Haia, HolandaSeaports are gateways between the over sea and the hinterland commerce, where different cargo types are handled at dedicated terminals. Currently, seaports are facing traffic congestion leading to a decrease in its performance. Prior to increase the existing infrastructures in terms of transport capacity between the seaport and the hinterland it is important to improve cooperation among terminals. A multi-agent system to guarantee cooperation among terminals within a seaport is proposed in this paper. A control agent is assigned to each terminal and is responsible for the cargo assignment to the transport capacity at its disposal such that cargo arrives on time at the agreed location. Control agents solve in parallel an optimization problem formulated in accordance to the Model Predictive Control (MPC) strategy. Cooperation among control agents is established using a coordinator agent that updates the transport capacity assigned to each control agent based on the marginal costs provided by all control agents. The proposed framework does not require the exchange of private information and assumes an altruist behavior for all control agents. The proposed approach can perform similarly to a central approach. The framework performance is illustrated with simulation studies considering a seaport composed of 3 container terminalsinfo:eu-repo/semantics/publishedVersio