Modelling and simulating multi-echelon food systems

Vorst, J.G.A.J. van der; Beulens, A.J.M.; Beek, P. van

doi:10.1016/s0377-2217(99)00238-6

Cited by 178 publications

(68 citation statements)

References 15 publications

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“…It is a highly flexible tool that can be used effectively for analyzing complex systems, like supply chains, and enables us to model such systems in details (Van Der Vorst et al, 2000;Jansen et al 2001). One of the advantages of simulation is the ability to study hypothetical models as close to the real situations as possible (Sirias and Mehra, 2005).…”

Section: Simulation Approachesmentioning

confidence: 99%

“…When more complex settings are involved, like supply chain dynamics with demand and supply uncertainties, this approach may not be satisfactory in providing good results (Riddalls et al, 2000;Van Der Vorst et al, 2000). Therefore, the strength of traditional mathematical modelling techniques, which is to obtain robust optimal solution, may not be easily achieved in solving supply chain problems.…”

Section: Analytical Approaches 31 Introductionmentioning

confidence: 99%

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A review of coordination studies in the context of supply chain dynamics

Chan

2009

International Journal of Production Research

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Section: Simulation Approachesmentioning

confidence: 99%

Section: Analytical Approaches 31 Introductionmentioning

confidence: 99%

A review of coordination studies in the context of supply chain dynamics

Chan

2009

International Journal of Production Research

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“…The specific characteristics of agricultural products that differ from other products are seasonality, perishability, safety, and traceability throughout an end-to-end supply network (Taylor & Fearne, 2006;Van der Vorst, Beulens, & Van Beek, 2000). Furthermore, intermediary trading organizations in the food industry have been facing the challenges of coordinating retail promotions with lead time requirements and a generally low degree of supply chain flexibility and limited supply requirements (Adebanjo, 2009).…”

Section: Agricultural Product Characteristicsmentioning

confidence: 99%

“…"… (i) fresh products are not standard and are subject to quality deterioration, (ii) there is a lack of clear product descriptions and coding standardization, (iii) information requirements differ with each customer, making standardization complex, and (iv) farmers utilize a relatively low degree of automation" (Van der Vorst et al, 2000).…”

Section: Agricultural Product Characteristicsmentioning

confidence: 99%

A simulation of contract farming using agent based modeling

Handayati¹,

Simatupang²,

Perdana³

et al. 2016

JOSCM

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This study aims to simulate the effects of contract farming and farmer commitment to contract farming on supply chain performance by using agent based modeling as a methodology. Supply chain performance is represented by profits and service levels. The simulation results indicate that farmers should pay attention to customer requirements and plan their agricultural activities in order to fulfill these requirements. Contract farming helps farmers deal with demand and price uncertainties. We also find that farmer commitment is crucial to fulfilling contract requirements. This study contributes to this field from a conceptual as well as a practical point of view. From the conceptual point of view, our simulation results show that different levels of farmer commitment have an impact on farmer performance when implementing contract farming. From a practical point of view, the uncertainty faced by farmers and the market can be managed by implementing cultivation and harvesting scheduling, information sharing, and collective learning as ways of committing to contract farming.

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“…The simulation models are exible tools that can be used to analyze complex problems and enable us to study such systems in detail (van der Vorst et al 2000, Jansen et al 2001). Value of parameters in a simulation model can be manipulated easily in a way that analysis of dierent combinations of parameters are possible; therefore, with getting insight about the system behavior, the cost to make a decision could be reduced, and response to such modications could be obtained very fast (Manzini et al 2005).…”

Section: System Characteristicsmentioning

confidence: 99%

An agent-based model to support negotiation-based order acceptance in a multi-plant enterprise

Mobini

Behdani

Lukszo

et al. 2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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SummaryOrder acceptance (OA) has a large inuence on the performance of an enterprise. On the one hand, always accepting an order when capacity is available may restrict the system to accept more protable orders in the future. On the other hand, rejecting too many orders leads to low capacity utilization and also has repercussions for future customer relations. In addition, accepting too many orders leads to an over-loaded production environment, where lead times increase and orders are delivered late. Most of these aspects of order acceptance decision-making can be handled by having proper negotiations with customers on order terms. However, this is a complex problem primarily because there are dierent actors who have various (conicting) interests, needs and objectives. Accordingly, to provide insight for the decision makers in the rms in order to develop proper strategies for order acceptance, developing appropriate models seems necessary.The objective in this research is to improve the order acceptance performance of a make-to-order multi-plant enterprise (MPE) by incorporating buyer-seller negotiations in its OA procedure with the main research question of How can the buyer-seller negotiation process contribute to the improvement of order acceptance performance in the multi-plant enterprise?For this purpose, we have developed an agent-based model to support order acceptance decisions in a multi-plant enterprise particularly using dierent negotiation settings.The research starts with literature review on order acceptance in general and negotiation in particular. A gap is identied in the OA literature: the major perspectives on the OA procedure rest on the underlying assumption that order terms are already determined which is not always tenable. In reality, order terms are usually specied by negotiation between the buyer and the seller and they are not predetermined. A negotiation-based order acceptance approach is consequently suggested in order to consider the whole activities in a negotiation process with three phases. The proposed process involves the initiation conditions for negotiation (pre-negotiation phase), the main interaction (negotiation phase) and the consequences of negotiation on the future decisions of the involved actors (post-negotiation phase). This negotiation process is a part of more general negotiation framework which is then extended into a negotiation ontology. The ontology introduces the main concepts and their relations in a negotiation problem. Next, the conceptualization of the order negotiation process in OA and the negotiation ontology are used in developing an agent-based model for buyer-seller negotiations in a case of multi-plant enterprise. The general assumptions and settings for model development are taken from a model have been presented by Behdani et al. (2010) for a lube oil multi-plant case study motivated by a real supply chain.In this research, the previous model is extended by incorporating negotiation between the ii enterprise and its customers on order terms. Final...

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Modelling and simulating multi-echelon food systems

Cited by 178 publications

References 15 publications

A review of coordination studies in the context of supply chain dynamics

A review of coordination studies in the context of supply chain dynamics

A simulation of contract farming using agent based modeling

An agent-based model to support negotiation-based order acceptance in a multi-plant enterprise

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