Modelling public transport accessibility with Monte Carlo stochastic simulations: A case study of Ostrava

Activity-based micro-scale simulation models for transport modelling provide better evaluations of public transport accessibility, enabling researchers to overcome the shortage of reliable real-world data. Current simulation systems face simplifications of personal behaviour, zonal patterns, non-optimisation of public transport trips (choice of the fastest option only), and do not work with real targets and their characteristics. The new TRAMsim system uses a Monte Carlo approach, which evaluates all possible public transport and walking origin-destination (O-D) trips for k-nearest stops within a given time interval, and selects appropriate variants according to the expected scenarios and parameters derived from local surveys. For the city of Ostrava, Czechia, two commuting models were compared based on simulated movements to reach (a) randomly selected large employers and (b) proportionally selected employers using an appropriate distance-decay impedance function derived from various combinations of conditions. The validation of these models confirms the relevance of the proportional gravity-based model. Multidimensional evaluation of the potential accessibility of employers elucidates issues in several localities, including a high number of transfers, high total commuting time, low variety of accessible employers and high pedestrian mode usage. The transport accessibility evaluation based on synthetic trips offers an improved understanding of local situations and helps to assess the impact of planned changes.Web of Science1124art. no. 709

An agent-based dynamic information network for supply chain management

One of the main research issues in supply chain management is to improve the global efficiency of supply chains. However, the improvement efforts often fail because supply chains are complex, are subject to frequent changes, and collaboration and information sharing in the supply chains are often infeasible. This paper presents a practical collaboration framework for supply chain management wherein multi-agent systems form dynamic information networks and coordinate their production and order planning according to synchronized estimation of market demands. In the framework, agents employ an iterative relaxation contract net protocol to find the most desirable suppliers by using data envelopment analysis. Furthermore, the chain of buyers and suppliers, from the end markets to raw material suppliers, form dynamic information networks for synchronized planning. This paper presents an agent-based dynamic information network for supply chain management and discusses the associated pros and cons

Insights on Multi-Agent Systems Applications for Supply Chain Management

In this paper, we review relevant literature on the development of multi-agent systems applications for supply chain management. We give a general picture of the state of the art, showing the main applications developed using this novel methodology for analyzing diverse problems in industry. We also analyze generic frameworks for supply chain modelling, showing their main characteristics. We discuss the main topics addressed with this technique and the degree of development of the contributions.Universidad de Sevilla V PPIT-USPiano della Ricerca Dipartimentale 2016-2018 of DICAR-UniC

Special Session on Industry 4.0

No abstract available

OVAP: A strategy to implement partial information sharing among supply chain retailers

This paper analyses the impact on supply chain performance of adopting different strategies to implement partial information sharing among heterogeneous retailers. Supply chains are modelled using a multi-agent systems approach. We find that the strategy adopted to construct the partial information sharing structure (i.e., the retailers who share information) has a significant impact on supply chain performance. We propose a practical strategy, named Order VAriance Prioritization (OVAP), which gives priority to the retailers with higher order variance. OVAP outperforms the worst (i.e. naive) implementation method by 27.2% and 7.8% with respect to the levels of bullwhip and average inventory.Ministerio de Ciencia e Innovación DPI201680750P