Production planning of a multi-site manufacturing system by hybrid modelling: A case study from the automotive industry

Gnoni, Maria Grazia; Iavagnilio, Raffaello; Mossa, Giorgio; Mummolo, Giovanni; Leva, Antonio Di

doi:10.1016/s0925-5273(03)00113-0

Cited by 139 publications

(46 citation statements)

References 23 publications

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“…The goal of these decisions is to handle incoming customer orders in the best possible way, given the already set supply chain design and planning policies. At the operational level the main problems pursued are lot sizes, replenishment orders, and service levels [51].…”

Section: Distinguishing Between Strategic and Operational/tactical Lsmentioning

confidence: 99%

The application of discrete event simulation and system dynamics in the logistics and supply chain context

Tako

Robinson

2012

Decision Support Systems

314

136

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Citation: TAKO, A.A. and ROBINSON, S., 2012. The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems, 52 (4), pp. 802 -815.Additional Information:• This article was published in the journal, Decision Support Sys- AbstractDiscrete event simulation (DES) and system dynamics (SD) are two modelling approaches widely used as decision support tools in logistics and supply chain management (LSCM). A widely held belief exists that SD is mostly used to model problems at a strategic level, whereas DES is used at an operational/tactical level. This paper explores the application of DES and SD as decision support systems (DSS) for LSCM by looking at the nature and level of issues modelled. Peer reviewed journal papers that use these modelling approaches to study supply chains, published between 1996 and 2006 are reviewed. A total of 127 journal articles are analysed to identify the frequency with which the two simulation approaches are used as modelling tools for DSS in LSCM. Our findings suggest that DES has been used more frequently to model supply chains, with the exception of the bullwhip effect, which is mostly modelled using SD. Based on the most commonly used modelling approach, issues in LSCM are categorised into four groups: the DES domain, the SD domain, the common domain and the less common domain. The study furthermore suggests that in terms of the level of decision making involved, strategic or operational/tactical, there is no difference in the use of either DES or SD. The results of this study inform the existing literature about the use of DES and SD as DSS tools in LSCM.

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Section: Distinguishing Between Strategic and Operational/tactical Lsmentioning

confidence: 99%

The application of discrete event simulation and system dynamics in the logistics and supply chain context

Tako

Robinson

2012

Decision Support Systems

314

136

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“…Each plant outsources some components to suppliers for producing a product and selects the best one to collaborate with. The distributed production planning in this paper was aimed at the lowest total cost by scheduling the operations but not allocating the optimal production quantities Gnoni et al (2003) have dealt with lot sizing and scheduling problem (LSSP) of a multi-site manufacturing system with capacity constraints and uncertain multi-product and multi-period demand. They used a hybrid model combining a mixed-integer linear programming model with a simulation one to solve LSSP.…”

Section: Distributed Production Planningmentioning

confidence: 99%

Distributed production planning based on ATC and MOILP considering different coordination patterns

Chen

et al. 2014

J Intell Manuf

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This paper is aimed at employing analytical target cascading (ATC) to solve the distributed production planning problem. In the ATC hierarchy, by setting a parent element to the core enterprise and a child element to each manufacturer, the core enterprise can negotiate the plans with the manufactures. Individual manufactures are able to make production plans autonomously, and finally achieve the global objective under the coordination of the core enterprise. ATC offers an independent decision making ability to each member, a decentralize coordination strategy to the core enterprise and a parallel computing method to the planning model. In order to obtain the concrete production quantities and balance the costs and the service levels, a multi-objective integer linear programming has also been introduced in the model, where different costs have different weights. By setting different weights to different optimization objectives, the core enterprise can obtain the lowest overall cost or the best service quantity. Meanwhile, from a strategic point of view, two coordination patterns, competitive and cooperative, through modifying the iterative mode of ATC, are addressed here. The competitive pattern make the core enterprise achieve a lower cost, while the cooperative pattern make the core enterprise maintain a better relationship with the manufactures. A series of comparative analyses are conducted to identify the strengths of the ATC method and demonstrate theeffectiveness of the proposed coordination patterns. In

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“…Finally, it should be noted that the general structure of the proposed approach naturally would allow the consideration of other types of problems, especially such, where the physical problem provides a basis for decomposition. (Gnoni et al, 2003) present a case study from the automotive industry dealing with the lot sizing and scheduling decisions in a multi-site manufacturing system with uncertain multi-product and multi-period demand. They use a hybrid approach which combines mixedinteger linear programming model and simulation to test local and global production strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

Supply Chain Optimization: Centralized vs Decentralized Planning and Scheduling

Saharidis¹

2011

Supply Chain Management

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Production planning of a multi-site manufacturing system by hybrid modelling: A case study from the automotive industry

Cited by 139 publications

References 23 publications

The application of discrete event simulation and system dynamics in the logistics and supply chain context

The application of discrete event simulation and system dynamics in the logistics and supply chain context

Distributed production planning based on ATC and MOILP considering different coordination patterns

Supply Chain Optimization: Centralized vs Decentralized Planning and Scheduling

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