57,222 research outputs found
A framework for smart production-logistics systems based on CPS and industrial IoT
Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems
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A modular hybrid simulation framework for complex manufacturing system design
For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABMâDES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABMâDES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated
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
Modelling an End to End Supply Chain system Using Simulation
Within the current uncertain environment industries are predominantly faced with various challenges
resulting in greater need for skilled management and adequate technique as well as tools to manage
Supply Chains (SC) efficiently. Derived from this observation is the need to develop a generic/reusable
modelling framework that would allow firms to analyse their operational performance over time (Mackulak
and Lawrence 1998, Beamon and Chen 2001, Petrovic 2001, Lau et al. 2008, Khilwani et al. 2011, Cigollini et
al. 2014). However for this to be effectively managed the simulation modelling efforts should be directed
towards identifying the scope of the SC and the key processes performed between players.
Purpose: The research attempts to analyse trends in the field of supply chain modelling using simulation
and provide directions for future research by reviewing existing Operations Research/Operations
Management (OR/OM) literature. Structural and operational complexities as well as different business
processes within various industries are often limiting factors during modelling efforts. Successively, this
calls for the end to end (E2E) SC modelling framework where the generic processes, related policies and
techniques could be captured and supported by the powerful capabilities of simulation.
Research Approach: Following Mitroffâs (1974) scientific inquiry model and Sargent (2011) this research will
adopt simulation methodology and focus on systematic literature review in order to establish generic OR
processes and differentiate them from those which are specific to certain industries. The aim of the
research is provide a clear and informed overview of the existing literature in the area of supply chain
simulation. Therefore through a profound examination of the selected studies a conceptual model will be
design based on the selection of the most commonly used SC Processes and simulation techniques used
within those processes. The description of individual elements that make up SC processes (Hermann and
Pundoor 2006) will be defined using building blocks, which are also known as Process Categories.
Findings and Originality: This paper presents an E2E SC simulation conceptual model realised through
means of systematic literature review. Practitioners have adopted the term E2E SC while this is not
extensively featured within academic literature. The existing SC studies lack generality in regards to
capturing the entire SC within one methodological framework, which this study aims to address.
Research Impact: A systematic review of the supply chain and simulation literature takes an integrated and
holistic assessment of an E2E SC, from market-demand scenarios through order management and planning
processes, and on to manufacturing and physical distribution. Thus by providing significant advances in
understanding of the theory, methods used and applicability of supply chain simulation, this paper will
further develop a body of knowledge within this subject area.
Practical Impact: The paper will empower practitionersâ knowledge and understanding of the supply chain
processes characteristics that can be modelled using simulation. Moreover it will facilitate a selection of
specific data required for the simulation in accordance to the individual needs of the industry
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Decision support for build-to-order supply chain management through multiobjective optimization
This paper aims to identify the gaps in decision-making support based on
multiobjective optimization for build-to-order supply chain management (BTOSCM).
To this end, it reviews the literature available on modelling build-to-order
supply chains (BTO-SC) with the focus on adopting multiobjective optimization
(MOO) techniques as a decision support tool. The literature has been classified based
on the nature of the decisions in different part of the supply chain, and the key
decision areas across a typical BTO-SC are discussed in detail. Available software
packages suitable for supporting decision making in BTO supply chains are also
identified and their related solutions are outlined. The gap between the modelling and
optimization techniques developed in the literature and the decision support needed in
practice are highlighted and future research directions to better exploit the decision
support capabilities of MOO are proposed
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