Towards a supply chain simulation reference model for the semiconductor industry

Ehm,; Wenke,; Monch,; Ponsignon, Thomas; Forstner,

doi:10.1109/wsc.2011.6147925

Cited by 18 publications

(8 citation statements)

References 15 publications

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“…Rothlauf (2011) classifies metaheuristics and demonstrates how such heuristics can be designed using problem-and instancespecific knowledge. Ehm et al (2011) consider the simulation of supply chains in semiconductor manufacturing. Reduced simulation techniques are applied for a small-size supply chain.…”

Section: Examples Of Initial Resultsmentioning

confidence: 99%

Model-Based Decision Support in Manufacturing and Service Networks

et al. 2014

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Section: Examples Of Initial Resultsmentioning

confidence: 99%

Model-Based Decision Support in Manufacturing and Service Networks

et al. 2014

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“…Rastogi et al [11] further extend the work and propose a model to minimize expected total cost in the SC network when the demand is uncertain. Ehm et al [12] describe steps to build a supply chain simulation reference model for the semiconductor industry and identify the main building blocks of such models. They present an approach to obtain a supply chain network as a set of reduced simulation models.…”

Section: Literature Reviewmentioning

confidence: 99%

Global Optimization of a Semiconductor IC Supply Chain Network

Kalir¹,

Grosbard²

2017

JSSM

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In this paper we develop a framework for the semiconductor manufacturing process, including front-end (fab) operations and backend (assembly/test) operations. We then propose a Quadratic Programming (QP) formulation for the profit maximizing objective with flexible demand and price ranges (per product) and fab capacities. We demonstrate the model by applying it to a case study that is based on a real-world (distorted) dataset and show how the solution varies between a local optimization of a single fab (for minimum wafer cost) and a globally optimal solution for maximum profit of a network of fabs.

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“…The complex production system is motivated by a real-world semiconductor backend facility (Ehm et al, 2011). It consists of M = 23 machine groups.…”

Section: Production Environmentsmentioning

confidence: 99%

“…The required capacities and the number of parallel machines for each machine group are given in the appendix in Tables 11, 12, and 13. We use the same machines and processing times as described in Ehm et al (2011). For simplicity, however, we exclude some additional characteristics of the production system, namely splitting orders, batching, sequence-dependent setup times and random machine down times.…”

Section: Production Environmentsmentioning

confidence: 99%

Revenue Management in a Multi-Stage Make-to-Order Production System

et al. 2015

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In the present paper, we consider the demand management decisions of a manufacturer facing stochastic demand. While in the previous literature, either the order release decisions are fixed upon arrival or a single-stage production environment is assumed, we make online order acceptance and order release decisions in a multi-stage production system. After describing the problem formally as a stochastic dynamic program, we develop a bid-pricebased revenue management approach in which the order acceptance and order release decisions are made based on previously computed bid prices and feasibility according to a linear program describing the multi-stage production system. A numerical study shows the good performance of the approach compared to an ex-post optimal solution in various supply and demand settings as well as the benefits relative to existing models, which work under simplifying assumptions.

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Towards a supply chain simulation reference model for the semiconductor industry

Cited by 18 publications

References 15 publications

Model-Based Decision Support in Manufacturing and Service Networks

Model-Based Decision Support in Manufacturing and Service Networks

Global Optimization of a Semiconductor IC Supply Chain Network

Revenue Management in a Multi-Stage Make-to-Order Production System

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