A Construction Simulation Model for Production Planning at the Pentagon Renovation Project

2002 ◽  
Author(s):  
John I. Messner ◽  
Theodore Lynch
Keyword(s):  
Simulation Model ◽  
Production Planning ◽  
Construction Simulation

Experiences in Modeling, Simulation, and Optimization of Chemical Industry Supply Chains

Manufacturing ◽  
2002 ◽  
Author(s):  
Charles R. Standridge ◽  
David R. Heltne
Keyword(s):  
Supply Chain ◽  
Simulation Model ◽  
Production Planning ◽  
Simulation Optimization ◽  
Process Industry ◽  
Plant Production ◽  
Optimization Models ◽  
Individual Plant ◽  
Simulation And Optimization ◽  
Combined Simulation

We have developed and applied simulation as well as combined simulation – optimization models to represent process industry plant logistics and supply chain operations. The simulation model represents plant production, inventory, and shipping operations as well as inter-plant shipments. When a combined simulation-optimization approach is used, the simulation periodically invokes a classical production planning optimization model to set production and shipping levels. These levels are retrieved by and used in the simulation model. Process industry supply chain operations include stochastic elements such as customer demands whose expected values may vary in time as well as transportation lead times. The complexity of individual plant operations and logistics must be considered. Simulation provides the methods needed to integrate these elements in a single model. Periodically during a simulation run, production planning decisions that require optimization models may be made. Simulation experimental results are used to determine service levels to end customers as well as to set rail fleet sizes, inventory capacities, and capital equipment requirements for logistics as well as to assess alternative shipping schedules.

The Methodology of Virtual Foundry Developing

2013 ◽  
Vol 58 (3) ◽  
pp. 867-870 ◽  
Author(s):  
M. Brzeziński ◽  
A. Stawowy ◽  
R. Wrona
Keyword(s):  
Simulation Model ◽  
Production Planning ◽  
Production Systems ◽  
State Of The Art ◽  
The State ◽  
Systemic Approach ◽  
Integrative Approach ◽  
Efficient Production ◽  
Economic Aspects

Abstract Systemic approach to design of factories requires that engineering, organisational and economic aspects should be considered concurrently. That prompts the need to develop a solution, based on the state-of-the-art IT technologies, to enable us to solve the problems associated with foundry production planning. The paper outlines a methodology of creating the simulation model of a virtual foundry, as a tool for foundry design. An integrative approach is suggested for development of a complete foundry model, enabling the design of more efficient production systems. The underlying principles of such models are discussed, the basic stages involved in the methodology are outlined and the range of its applicability is defined.

Using systems dynamics to evaluate the tradeoff among supply chain aggregate production planning policies

2014 ◽  
Vol 34 (8) ◽  
pp. 1055-1079 ◽  
Author(s):  
Juan D. Mendoza ◽  
Josefa Mula ◽  
Francisco Campuzano-Bolarin
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Simulation Model ◽  
Production Planning ◽  
Systems Dynamics ◽  
Aggregate Production Planning ◽  
Aggregate Production ◽  
Content Type ◽  
Action Strategies ◽  
Demand Variability

Purpose – The purpose of this paper is to explore different aggregate production planning (APP) strategies (inventory levelling, validation of the workforce and flexible production alternatives: overtime and/or outsourcing) by using a system dynamics model in a two-level, multi-product, multi-period manpower intensive supply chain (SC). Therefore, the appropriateness of using systems dynamics as a research method, by focusing on managerial applications, to analyse APP policies is proven. From the combination of systems dynamics and APP, recommendations and action strategies are considered for each scenario to understand how the system performs and to improve decision making on APP in the SC context. Design/methodology/approach – The research design analyses a typical factory setting with representative parameter settings for five different conventional APP policies – inventory levelling, workforce variation, overtime, outsourcing and a combination of overtime and outsourcing – through deterministic systems dynamics-based simulation. In order to validate the simulation model, the results from published APP models were replicated. Then, optimisation is conducted for this deterministic setting to determine the performance of all these typical policies with optimal parameter settings. Next, a Monte Carlo stochastic simulation is used to assess the robustness of such performances in a variety of demand settings. Different aggregate plans are tested and the effect that events like demand variability and production times have on the SC performance results is analysed. Findings – The results support the assertion that the greater the demand variability, the higher the flexibility costs (overtime, outsourcing, inventory levelling, and contracts and firings). As greater inter-month oscillations appear, which must be covered with additional alternatives, the optimum number of employees must be determined by analysing the interchanges and marginal costs between capacity oversizing costs (wages, idle time, storage) and the costs to undersize it (penalties for lowering safety stocks, delayed demand, greater use of overtime and outsourcing). Accordingly, controlling the times to avoid increased costs and penalties incurred by delayed demand becomes an essential important task, but one that also depends on the characteristics of this variability. Practical implications – This paper has developed a modelling approach for APP in a manpower intensive SC by applying system dynamics. It includes a simulation model, the analysis of several scenarios, the impact on performance caused by variability events in the parameters, and some recommendations and action strategies to be subsequently applied. The modelling methodology proposed can be employed to design-specific models for each SC. Originality/value – This paper proposes an APP system dynamics approach in a two-level, multi-product, multi-period manpower intensive SC for the first time. This model bridges the gap in the literature relating to simulation, specifically system dynamics and its application for APP. The paper also provides a qualitative description of the various pros and cons of each analysed policy and how they can be combined.

Logistic Simulator for Steel-Making Process

2010 ◽  
Vol 154-155 ◽  
pp. 712-715
Author(s):  
La Mei He ◽  
Yan Hu
Keyword(s):  
Simulation Model ◽  
System Design ◽  
Production Planning ◽  
Process Parameters ◽  
Production System ◽  
Steel Plant ◽  
Decision Makers ◽  
Production Parameters ◽  
Steel Making ◽  
Design And Manufacture

Setting up an adaptable and flexible simulation model for steel-making process is very important to improve production system design and manufacture automation. To assist the decision-makers in steel-making plants, the Logistical simulator for steel-making has been developed using em-Plant software. The Simulator can be used to rapidly model any steel plant, including the movement of the operation equipments and the changes of production parameters. With the help of Simulator, the influences of lay-out changes, process parameters, and changes in planning could be revealed vividly, and the realistic production planning could be created. Results include production Gantt charts, display of cranes track, utilization figures and production statistics. Simulation cases show that the simulator is valid for simulating different steel-making processes, and production states on different conditions can be investigated by using this model.

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