Reflective simulation for on-line workload planning and control

Proceedings of the 2009 …, 2009

Journal of Manufacturing Systems, 2007

This paper illustrates how simulation-based shop-floor planning and control can be extended to enterprise-level activities (top floor). First, the general planning and control concept are discussed, followed by an overview of simulation-based shop-floor planning and control. Analogies between the shop floor and top floor are discussed in terms of the components required to construct simulation-based planning and control systems. Analogies are developed for resource models, coordination models, physical entities, and simulation models. Differences between the shop floor and top floor are also discussed in order to identify new challenges faced for top-floor planning and control. A major difference between the top floor and the shop floor is the way a simulation model is constructed for use in planning, depending on whether time synchronization among member simulations becomes an issue or not. Another difference is in the distributed communication/computing platform. This work uses a distributed computing platform using Web services technology to integrate heterogeneous simulations and systems in a distributed top-floor control environment. The research results reveal that simulation-based planning and control is extensible to the top-floor environment’s evolving new research challenges.

This presentation covers different phases of the manufacturing system life cycle. Starting from conceptual system design to planning of operations. Material handling and logistic are the key factors in modern networking manufacturing. The author proposes use of discrete event simulation as a system design and operation-planning tool. Traditionally simulation tools have been used in the system planning and design; today the simulation models are used in all the different phases of manufacturing system life cycle. This paper presents two case studies. First case shows a modular semiautomatic assembly system planning using simulation. Second case presents a simulation tool developed for operations planning, management of production capacity and decision helping for planning of operations.

International Journal of Computer Integrated Manufacturing, 2017

In the last decades, many researchers have studied open shop scheduling (OSS) problem by considering deterministic parameters using mathematical modelling, heuristics and meta-heuristics. However, it is important to study the problem as close as possible to real world conditions which consists of uncertainty and stochastic parameters. In this study, dispatching rules, as accepted tools for real-time scheduling, are applied for optimising the OSS problem. Since none of conventional dispatching rules performs well for all performance measures, a simulation-based real-time scheduling composite dispatching rule is developed. For this purpose, a multi response optimisation approach based on computer simulation for scheduling a non-preemptive open shop with stochastic ready times is presented in order to minimise the mean waiting time of jobs. The presented approach composed of design of experiments, discrete event simulation, multi-layer perceptron artificial neural network, radial basis function and data envelopment analysis to determine the most efficient dispatching rule for each machine.

Winter Simulation …, 2009

Simulation tools allow its users to computationally model real-life systems in order to determine their best future outcome. One real-life system that can benefit from simulation is that of the retail industry. This paper describes how simulation can be an effective tool for this type of industry, especially for process improvement projects. In addition, a small case study is presented to demonstrate the use of simulation for a large retailer which needs to improve its unloading and receiving processes. Among the future ideas for research, this paper shows that less obvious methods for process improvement, such as tracking customer loyalty, can be analyzed using simulation to determine which route a retailer should take in order to please its customers. Other topics on this subject are suggested at the conclusion of this paper.

Logistics networks are complex systems since they are made up by many elements interconnected by a great variety of non linear relationships. This property makes it impossible to study such systems with traditional analytical approaches. Therefore, computer simulation is a valuable tool to virtually replicate the structure of a logistics network, the relationships among its components, and the rules governing its functioning. The output of this process, the simulation model, can be run in order to reproduce the evolution of the real system over a specific time interval, and analyze its patterns of behavior. This work focuses on simulation approaches for managing the performance of logistics networks in manufacturing supply chains. With the aim of providing a reference framework for choosing the best tool, five approaches are reviewed and compared: Neural Networks, Petri Nets, Agent Based Modeling, Discrete Event Simulation, and System Dynamics. The results show that the complexity o...

Zeszyty Naukowe Politechniki Częstochowskiej Zarządzanie, 2019

The article presents the concept of modern simulation systems applicable in the analysis of the logistics chain configuration. The main goal of the article is to present advanced possibilities of using various methods of simulation of a logistic system, or to determine the appropriateness of introducing changes in the configuration of a logistics chain or the assessment of already prepared solutions for given logistics processes. The article contains a description of information technologies that can be used in simulation, including specialized simulation programs, spreadsheets, languages and simulation interfaces etc. The article presents the basic issues related to the creation of a logistics project simulation, as well as the possibilities of the FlexSim 3D Simulation software, which easily allows to map complex logistic processes. The simulation, including the data generated during its implementation on individual logistic processes, allows the selection of the most advantageous and optimization of the solutions used so far in a given process. The article presents an example of simulation of logistics issues in the production process and examples are presented to explain the cognitive appeal of this method based on the use of computer technology and FlexSim 3D Simulation software.

2007 Winter Simulation Conference, 2007

Simulation has long been a significant and powerful force for the improvement of manufacturing operations. More recently, it has been used to increase the efficiency, efficacy, and economy of service operations. In this case study, we describe the valuable contributions simulation made to the improvement of operations at numerous business locations of a company renting vehicles (without drivers). Specifically, discrete-event process simulation analyses played a pivotal role in the construction and implementation of the "Demand-Driven Workforce Scheduler" (DdWS) now used at the client company.

Organizacija, 2012

We discuss the concept of simulation and its application in the resolution of problems in complex industrial systems. Most problems of serious scale, be it an inventory problem, a production and distribution problem, a management of resources or process improvement, all real world problems require a mix of generic, data algorithmic and ad-hoc solutions making the best of available information. We describe two projects in which analytical solutions were applied or contemplated. the first case study uses linear programming in the optimal allocation of advertising resources by a major internet service provider. the second study, in a series of projects, analyses options for the expansion of the production and distribution network of mining products, as part of a sensitive strategic business review. using the examples, we make the case for the need of simulation in complex industrial problems where analytical solutions may be attempted but where the size and complexity of the problem forces a Monte carlo approach.

1997

The Law of Industrial Dynamics ensures that if a production control system can amplify then it will surely find a way of doing so despite the best efforts of production schedulers to take corrective action. In fact practical studies show that such human intervention frequently aggravates the situation with both stock levels and order rates fluctuating alarmingly. The solution is to design an effective system via simulation.

2021

The article gives a simulation model of a warehouse and logistics department of a medium-sized company. Based on the pre-specified workload, the following characteristics of the department are established, such as the load of the warehouses, the size of the queues for import / export of goods and the waiting time in them, the volume of the warehouse and the number of service personnel (warehouse workers). The model can be easily changed for various similar situations.

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)

Supply chain logistics planning is a complex process in both military and civilian operations. Poor planning may lead to system instability that might seriously influence the ability of the supply chain to satisfy its customers or might affect a combat mission. Therefore, correct decisions need to be made to optimize the performance of the system. It is important that the right information is transferred to the concerned unit that needs to receive the right information. Our model features a decision support system that aids human in making decisions and studies the role of a decision support system in enhancing the performance of the supply chain logistics system. The model is object oriented in nature, which helps in rapid prototyping of the different components of the system.

2002

Techniques based on discrete-event simulation have been widely used for network analysis and policy optimization in the domain of supply chain management. Previous researchers have developed and implemented architectures for simulation-based control for shop floor. A more detailed and high-fidelity simulation model is used for control purposes as opposed to that used for analytical purposes alone. This paper discusses the issues related to implementing a simulation based control architecture for actively controlling supply chain interactions.

Collaborative Engineering, 2008

In this chapter, we introduce the basic concept of the simulation-based optimization and illustrate its usefulness and applicability for generating the manpower planning of airline's cargo service call center. Because of the continuous increase in oil prices, and combined with many other factors, the airline industry is currently facing new challenges to keep its customers satisfied. One of the most important drivers of the customer satisfaction is the customer service. The excellent customer service can give an airline company the edge over its competitors. Airline companies need to insure the appropriate level of staffing at their service call centers in order to maintain a high level of customer satisfaction with the appropriate level of the overall cost. With the high level of uncertainty in the customer demand and a number of complicated factors in the problem, it becomes necessary to apply the simulation-based optimization technique to help managers generate the efficient staffing policy for the airline's cargo service call center. In this work, the technique called reinforcement learning and Markov decision process are used to build and solve the mathematical model to determine the appropriate staffing policy at the airline's cargo service call center on the monthly basis. Simulation and optimization models are incorporated together so as to solve the overall problem. The results of the case study are thoroughly analyzed, discussed, and compared with the current staffing policies. All results illustrate the impressive performance of the recommended staffing policies over the current staffing policies.

Decision Support Systems Advances in, 2010