18183230
Simulation of Order Fulfillment in Divergent Assembly Supply Chains
Management of supply chains is a difficult task involving coordination and decision-making across organizational boundaries. Computational modeling using multi-agent simulation is a tool that can provide decision support for supply chain managers. We identify the components of a supply chain model and implement it in the Swarm multi-agent simulation platform. The model is used to study the impact of information sharing on order fulfillment in divergent assembly supply chains (commonly associated with the computer and electronics industries). We find that efficient information sharing enables inventory costs to be reduced while maintaining acceptable order fulfillment cycle times. This is true because information, which provides the basis for enhanced coordination and reduced uncertainty, can substitute for inventory.
9583425
Modeling supply-chain networks by a multi-agent system
The article proposes a multi-agent information system (MAIS) approach to model the order fulfilment process (OFP) in supply chain networks (SCNs). An order fulfilment process starts with receiving orders from customers and ends with delivery of the finished goods. As manufacturing practice is shifting toward the outsourcing paradigm, the OFP is more likely to be executed throughout SCNs. It becomes imperative to integrate the OFP into SCNs to improve the OFP. The proposed multi-agent information system (MAIS) approach is used for modeling the OFP in SCNs, and evaluating OFP performance by applying the proposed strategies. The objective of reengineering the OFP is to achieve agility of the process in terms of efficiency, flexibility, robustness and adaptability. A multi-agent simulation platform, called Swarm, is enhanced and applied for modeling the MAIS, and experiments are conducted to simulate the OFP in SCNs in a multi-agent environment. Based an the Swarm simulation platform, we model the OFP in SCNs, simulate the OFP, and then evaluate the potential OFP improvement strategies to identify useful strategies for improving the OFP. The results shed light on identifying the main effects of various strategies on OFP performance. The insights of utilizing various strategies in different SCNs help redesign the OFP in SCNs.
42225177
Using multi-agent simulation and learning to design new business processes
Business process modeling, analysis, and then redesign are the central task in the efforts of re-engineering business processes. Frequently reviewing and promptly changing business processes to adapt to new business environment is the key to maintain agility under the competitive global market. It is imperative to enhance the adaptability of business processes. This paper proposes a multi-agent information system based on Swarm, a multi-agent simulation platform, to simulate business processes and incorporate reinforcement learning to obtain better process adaptability. The resulting system, called BPSLS, is elaborated and evaluated by the order fulfilment process in supply chain networks.
7842388
Mining time dependency patterns in clinical pathways
Clinical pathways are widely adopted by many large hospitals around the world in order to provide high-quality patient treatment and to reduce the length of hospital stay of each patient. The development of clinical pathways is a lengthy process, and may require the collaboration among physicians, nurses and other staff in a hospital. However, individual differences cause great variance in the execution of clinical pathways. This calls for a more dynamic and adaptive process to improve the performance of clinical pathways. This paper proposes a data mining technique to discover the time-dependency patterns of clinical pathways for curing brain strokes. The purpose of mining time-dependency patterns is to discover patterns of process execution sequences and to identify the dependent relations between activities in a majority of cases. By obtaining the time-dependency patterns, we can predict the paths for new patients when they are admitted to a hospital, and, in turn, the health care procedure will then be more effective and efficient.
8994770
Industrial Training Using Virtual Reality
17668486
A Methodology for Feature Interaction Detection in the AIN 0.1 Framework
We propose an integrated methodology for specifying AIN (advanced intelligent networks) and switch based features and analyzing their interactions in the AIN 0.1 framework. The specification of each individual feature is tied to the AIN call model and requires only a minimum amount of information in terms of control and data for interaction analysis. Once a feature is specified, its specification is then validated for consistency with respect to control and data. Interaction analysis is conducted for a set of features based on the sharing of call variables between the SSP and the SCP. With this approach, one can detect the following interactions involving AIN features: (1) side effects, where a call variable modified by one feature is used by another feature and (2) disabling, where one feature disconnects a call, preventing another feature from execution. We also develop a theory that is based on the computation of sequences of messages exchanged between the SSP and the SCP and their call variable usage. This theory is shown to dramatically reduce the number of cases considered during the analysis. A brief overview of a tool that makes use of this methodology to aid in the task of feature interaction detection is also given.
14454228
Protocol validation for large-scale applications
A threefold strategy incorporating formal modeling, structural decomposition and functional abstraction, and reachability analysis is described, and the role of each part in managing complexity is examined. Formal modeling helps weed out extraneous detail so that later work on decomposition and abstraction is easier. Decomposition and abstraction help divide complexity into manageability parts that the reachability algorithm can solve. Examples of the use of each are given.<<ETX>>
11056764
Integrating multi-agent negotiation to resolve constraints in fulfilling supply chain orders
Abstract As the order fulfillment process (OFP) in supply chains shifts to outsourcing paradigm, the OFP performance relies on the coordination among supply chain partners to reach executable and effective plans. The coordination of OFP among supply chain partners can be viewed as a distributed constraint satisfaction problem (DCSP). This study adds the multi-agent negotiation mechanism to enhance the existing methods to solve the DCSP, and then evaluates the integrated system’s performance through experimentation on the OFP in the context of the metal industry. The experimental results show that the integrated system outperforms the existing distributed constraint satisfaction algorithms in various demand patterns.
18858030
Applications of Cluster Perturbation Theory Using Quantum Monte Carlo Data
We study cluster perturbation theory [Phys. Rev. Lett. 84, 522 (2000)] when auxiliary field quantum Monte Carlo method is used for solving the cluster hamiltonian. As a case study, we calculate the spectral functions of the Hubbard model in one and two dimensions and compare our results for the spectral functions to results obtained using exact diagonalization to solve the cluster hamiltonian. The main advantage of using quantum Monte Carlo results as a starting point is that the initial cluster size can be taken to be considerably larger and hence potentially capture more of the relevant physics. The drawback is that quantum Monte Carlo methods yield results at imaginary times with stochastic errors.