Simulation/optimization using "real-world" applications

Hsu

Journal of the Chinese Institute of Industrial Engineers

2006

Spare part stock management attempts to ensure that the failed equipment items can be replaced immediately to maintain a sufficient productivity level. In maintenance, the inventory policy determination for spare parts is an important issue. The age-based preventive replacement policy may seek the least total cost for spare part replacement. Considering the criticality of equipment where it is installed, demand for a certain spare part can be categorized into critical and non-critical. The stock level for critical demand can be set to reserve certain spare parts in stock for critical equipment users. This stock policy is named the inventory rationing policy. The stock policy has a significant impact on production system performance, particularly when a machine breakdown causes a large amount of production lost. This study optimizes the joint age-based preventive replacement and inventory rationing policy in a multi-echelon spare part logistics network. In this study, there are equipment users, spare part distribution center and spare part suppliers in the multi-echelon system. Inventory policy optimization for the spare part logistics network is extremely complicated. This paper adopts the scatter search based simulation-optimization method to obtain the optimal configurations with respect to maximizing the total profit of the spare parts logistics network. An implementation example of a spare part logistics system is used to demonstrate the advantages of employing the joint policy.

Section: Subset Generation Methodmentioning

confidence: 99%

Section: The Simulation-optimization Approachmentioning

confidence: 99%

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Optimizing Joint Maintenance and Stock Provisioning Policy for a Multi-Echelon Spare Part Logistics Network

Hsu

Journal of the Chinese Institute of Industrial Engineers

2006

Information Systems Research

“…April et al (2001) and Glover et al (1999) provide a useful primer on the merits of combining simulation and optimization in managing the complexity and uncertainty posed by many real-world problems. Our simulation-based technique builds on the results from Section 3 but additionally incorporates the notion that the expected surplus from a server ( ji U ) is a function of the query set.…”

Section: A Simulation Based Technique Under Convex Cognitive Costmentioning

confidence: 99%

Usercentric Operational Decision Making in Distributed Information Retrieval

Hosanagar

2011

Information specialists in enterprises regularly use distributed information retrieval (DIR) systems that query a large number of information retrieval (IR) systems, merge the retrieved results, and display them to users. There can be considerable heterogeneity in the quality of results returned by different IR servers. Further, because different servers handle collections of different sizes and have different processing and bandwidth capacities, there can be considerable heterogeneity in their response times. The broker in the DIR system has to decide which servers to query, how long to wait for responses, and which retrieved results to display based on the benefits and costs imposed on users. The benefit of querying more servers and waiting longer is the ability to retrieve more documents. The costs may be in the form of access fees charged by IR servers or user's cost associated with waiting for the servers to respond. We formulate the broker's decision problem as a stochastic mixed-integer program and present analytical solutions for the problem. Using data gathered from FedStats-a system that queries IR engines of several U.S. federal agencies-we demonstrate that the technique can significantly increase the utility from DIR systems. Finally, simulations suggest that the technique can be applied to solve the broker's decision problem under more complex decision environments. Electronic copy available at: http://ssrn.com/abstract=926928 User-Centric Operational Decision-Making in Distributed Information RetrievalKartik Hosanagar + AbstractInformation specialists in enterprises regularly use Distributed Information Retrieval (DIR) systems that query a large number of Information Retrieval (IR) systems, merge the retrieved results and display them to users. There can be considerable heterogeneity in the quality of results returned by different IR servers. Further, since different servers handle collections of different sizes, have different processing and bandwidth capacities, there can be considerable heterogeneity in their response times. The broker in the distributed IR system has to decide which servers to query, how long to wait for responses and which retrieved results to display based on the benefits and costs imposed on users. The benefit of querying more servers and waiting longer is the ability to retrieve more documents. The costs may be in the form of access fees charged by IR servers or user's cost associated with waiting for the servers to respond. We formulate the broker's decision problem as a stochastic mixed integer program and present analytical results for the optimal query set and wait time. Using data gathered from Fedstats -a system that queries IR engines of several US federal agencies -we demonstrate that the technique can significantly increase the utility from DIR systems. Finally, we present a simulation-based optimization technique to solve the broker's decision problem under more complex decision environments. The technique is computationally efficient and can be used to ge...

Journal of the Operational Research Society

“…Also based on metaheuristics, the OptQuest software 50 has been adopted as the optimisation engine for a number of packages including Arena, Enterprise Dynamics and Simul8. Although these optimisers are readily available, anecdotal evidence and case studies do not suggest that their use is widespread, with simulation users seeming to rely on more manual methods of experimentation.…”

Section: Simulation Optimisationmentioning

confidence: 99%

Discrete-event simulation: from the pioneers to the present, what next?

Robinson

2005

176

Discrete-event simulation is one of the most popular modelling techniques. It has developed significantly since the inception of computer simulation in the 1950s, most of this in line with developments in computing. The progress of simulation from its early days is charted with a particular focus on recent history. Specific developments in the past 15 years include visual interactive modelling, simulation optimisation, virtual reality, integration with other software, simulation in the service sector, distributed simulation and the use of the world wide web.The future is then speculated upon. Potential changes in model development, model use, the domain of application for simulation and integration with other simulation approaches are all discussed. The desirability of continuing to follow developments in computing, without significant developments in the wider methodology of simulation, is questioned.