Modeling and Performance Evaluation of Inventory Systems Using Batch Deterministic and Stochastic Petri Nets

Labadi, Karim; Chen, Haoxun; Amodeo, Lionel

doi:10.1109/tsmcc.2007.905860

Cited by 27 publications

(24 citation statements)

References 23 publications

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“…Among the formalisms used to model the dynamic systems, Petri nets are one of the graphical and formal specification techniques for the description of the operational behavior of the systems. They are widely used in a number of different disciplines including engineering, manufacturing, business, chemistry, mathematics, and even within the judicial system [17]- [18]- [40]- [32]- [42]- [41]. They have been accepted as a powerful formal specification tool for a variety of systems including concurrent, distributed, asynchronous, parallel, deterministic and non-deterministic.…”

Section: A Review Of the Literaturementioning

confidence: 99%

“…The same modelling concept is introduced for modelling inventory control systems and supply chains in [17]- [6]. Precisely, the Petri net model of an inventory control system whose inventory replenishment decision is based on the inventory position of the stock and the reorder and order-up-to-level parameters.…”

Section: Petri Nets With Variable Arc Weightsmentioning

confidence: 99%

“…The model represents an inventory control system with continue review (s, S) policy (here s = 4 and S = 10) [17]. The different operations of the system are modelled by using a set of transitions: generation of replenishment orders (t3); inventory replenishment (t2); and order delivery (t1).…”

Section: Petri Nets With Variable Arc Weightsmentioning

confidence: 99%

“…Our simulation tool has been developed by exploiting the BDSPN simulator recently realized by us [17]- [6] for the simulation of BDSPN models developed for the modelling and performance evaluation of inventory control systems and logistic systems where we also used the "marking dependent weights" concept. The developed simulation tool provides functions for calculating some important perfomance indices of the Petri net model.…”

Section: Performance Evaluation With Simulationmentioning

confidence: 99%

“…After, the rest of this chapter deals with an original Petri net approach dedicated for PBS systems modelling for control purposes [2]- [19]- [20]- [21]. The section 3 provides an introduction for Petri nets with the "marking dependent weights" concept [17] as it is used throughout this chapter to model PBS systems for control purposes. In the sections 4 to 6, a modular framework based on Petri nets with marking dependent weights is developed for modelling and performance evaluation of PBS systems.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Petri Nets Models for Analysis and Control of Public Bicycle-Sharing Systems

Labadi¹,

Benarbia²,

Hamaci³

et al. 2012

Petri Nets - Manufacturing and Computer Science

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Section: A Review Of the Literaturementioning

confidence: 99%

Section: Petri Nets With Variable Arc Weightsmentioning

confidence: 99%

Section: Petri Nets With Variable Arc Weightsmentioning

confidence: 99%

Section: Performance Evaluation With Simulationmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Petri Nets Models for Analysis and Control of Public Bicycle-Sharing Systems

Labadi¹,

Benarbia²,

Hamaci³

et al. 2012

Petri Nets - Manufacturing and Computer Science

Self Cite

View full text Add to dashboard Cite

Meta-heuristic Approaches for Multi-objective Simulation-based Optimization in Supply Chain Inventory Management

Sánchez¹,

Amodeo²,

Prins³

2010

Springer Series in Advanced Manufacturing

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A supply chain (SC) is a complex network of facilities with dissimilar and conflicting objectives. Discrete-event simulation is often used to model and capture the dynamic interactions in SCs and to provide performance indicators. However, a simulator by itself is not an optimizer. Optimization algorithms can be coupled with a simulation module in order to find the most suitable SC policies. Nevertheless, because the simulation of an SC can take a considerable amount of time, the optimization tool must be well chosen. This chapter considers the hybridization of evolutionary algorithms, well known for their multi-objective capabilities, with an SC simulation module in order to determine the inventory policy (order-point or order-level) of a single product SC, taking into account two conflicting objectives: maximizing customer service level and minimizing total inventory cost. Four algorithms (SPEA-II, SPEA-IIb, MOPSO and NSGA-II) are evaluated on five different SC configurations to determine which algorithm gives the best results and makes the best use of the simulator. The results indicate that SPEA-2 favours a rapid convergence and that modifying its crossover or its archive truncation rule (variant SPEA-IIb) may improve the results even further.

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