Optimization Models of Production Planning Problems

Missbauer, Hubert; Uzsoy, Reha

doi:10.1007/978-1-4419-6485-4_16

Cited by 53 publications

(31 citation statements)

References 106 publications

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“…The significance of automatically generating simulation and optimization models for the entire supplydemand network for the production of semiconductor is well documented (Missbauer andUzsoy 2011, Fordyce, et al 2011). Automatic model generation is invaluable for large, complex supply-demand networks where many hundreds to thousands of products are manufactured in dynamical settings and stages under varying demand forecasts.…”

Section: Related Workmentioning

confidence: 97%

Developing composed simulation and optimization models using actual supply-demand network datasets

Gholami

Sarjoughian

Godding

et al. 2014

Proceedings of the Winter Simulation Conference 2014

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Large, fine-grain data collected from an actual semiconductor supply-demand system can help automated generation of its integrated simulation and optimization models. We describe how instances of Parallel DEVS and Linear Programming (LP) models can be semi-automatically generated from industry-scale relational databases. Despite requiring the atomic simulation models and the objective functions/constraints in the LP model to be available, it is advantageous to generate system-wide supply-demand models from actual data. Since the network changes over time, it is important for the data contained in the LP model to be automatically updated at execution intervals. Furthermore, as changes occur in the models, the interactions in the Knowledge Interchange Broker (KIB) model, which composes simulation and optimization models, are adjusted at run-time. INTRODUCTIONSimulation methods are used for modeling and simulation of supply-demand networks for predicting operations schedules and costs. For example, optimizing large-scale semiconductor supply-chain systems is very complex and the number of configurations of their processes and parameters are many. Therefore, there has been interest in using optimization modules for optimizing the operation of simulation. In this paper, we consider an actual semiconductor manufacturing supply-demand system (aka supply-chain system). Efficiency in the management of such systems can reduce costs by many millions of dollars each year (Wu, Erkoc and Karabuk 2005, Kempf 2004). Semiconductor supply-demand networks are a collection of suppliers, inventories, processes, transportations, and customers (Kempf 2006). Raw material is processed in sequential and parallel stages to produce many different kinds of products to customers. This enterprise can be divided to manufacturing processes and decision planning parts. The key variables of interest for discrete processes and logistics include stochastic processing times in building products in multiple stages and in different geographies. Another important variable is inventory holdings across manufacturing plants and logistics stages. Both the scale and complexity in manufacturing requires complex decision making, often supported with linear programming where reduced cost and just-in-time (raw material, semi-finished goods, packaging, etc.) delivery across the supply-demand is highly sought after. To simulate the operation of such dynamic enterprises, we can use discrete event modeling specifications such as Discrete Event System Specification (DEVS) and optimization modeling such as Linear Programming (LP) methods. Two important factors in creating realistic models of such enterprise systems are scale and model integration. Manufacturing has many tens of processes and inventories with alternative source to target routes and shipping modes. Similarly, logistics has many inventories and hubs for transportation and delivery to customers in different geographies. Simplifying generation of these simulation models is attractive. Considering the optim...

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Section: Related Workmentioning

confidence: 97%

Developing composed simulation and optimization models using actual supply-demand network datasets

Gholami

Sarjoughian

Godding

et al. 2014

Proceedings of the Winter Simulation Conference 2014

View full text Add to dashboard Cite

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“…Missbauer (2002) proposes a CF model which distinguishes between potential bottlenecks and non-bottlenecks. Reviews of production planning models with workload-dependent lead times can be found in Pahl et al (2005) and Missbauer and Uzsoy (2010).…”

Section: Approaches Assuming Deterministic Demandmentioning

confidence: 99%

“…Orcun et al (2006) give an example of how a simple CF may be estimated for such a system. An extensive discussion of the concepts related to clearing functions, their estimation and their use in production planning models is given by Missbauer and Uzsoy (2010).…”

Section: Effect Of Capacity Loading On Wip and Lead Timesmentioning

confidence: 99%

Production planning with load-dependent lead times and safety stocks for a single product

Ravindran

Kempf

Uzsoy

2011

IJPS

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Queuing models have demonstrated the nonlinear relationship between resource utilisation, which is determined by production planning decisions, and lead times. Both production planning decisions and safety stock levels are driven by lead times, and are thus tightly interrelated. However, production planning and safety stock calculations have generally been carried out separately. We propose a production planning model for a single-stage single-product system that integrates release planning and safety stock considerations. Concave clearing functions are used to capture the nonlinear relationship between resource utilisation and lead times, while chance constraints represent the need to plan inventories to achieve desired service levels. Extensive computational experiments indicate that the proposed model provides an excellent tradeoff between customer service and inventory costs.

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“…This paper is concerned with production planning in manufacturing, which can be loosely defined as the problem of finding a release schedule of jobs into the facility over time so that the actual outputs over time satisfy, as closely as possible, the predetermined requirements (Missbauer and Uzsoy 2010). Clearly, production planning is an optimization problem with the decision variables being the quantity of jobs released into the system for processing over a planning horizon, and the objective is usually to minimize the total cost (or sometimes profit) associated with a release plan.…”

Section: Introductionmentioning

confidence: 99%

Production planning for semiconductor manufacturing via simulation optimization

Liu¹,

Li²,

Yang³

et al. 2011

Proceedings of the 2011 Winter Simulation Conference (WSC)

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This paper is concerned with production planning in manufacturing, which can be loosely defined as the problem of finding a release plan for jobs that minimizes the total cost (or maximizes the total profit). Production planning is a challenging optimization problem due to the variability in manufacturing systems and uncertainty in future demand, both of which have not been adequately addressed by existing production planning models. To address both these issues, this paper formulates the production planning problem as a simulation-based multi-objective optimization problem, and adapts a genetic algorithm to search for a set of release plans that are near-Pareto optimal. The solutions from the simulation optimization approach can serve as a useful benchmark for existing and new production planning methods.

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Optimization Models of Production Planning Problems

Cited by 53 publications

References 106 publications

Developing composed simulation and optimization models using actual supply-demand network datasets

Developing composed simulation and optimization models using actual supply-demand network datasets

Production planning with load-dependent lead times and safety stocks for a single product

Production planning for semiconductor manufacturing via simulation optimization

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