Abstract:For several decades, simulation has been used as a descriptive tool by the operations research community in the modeling and analysis of a wide variety of complex real systems. With recent developments in simulation optimization and advances in computing technology, it now becomes feasible to use simulation as a prescriptive tool in decision support systems. In this paper, we present a comprehensive survey on techniques for simulation optimization with emphasis given on recent developments. We classify the exi… Show more
“…For a general review of supply chain simulation study, please refer to Terzi and Cavalieri (2004). For a general review of simulation optimisation, please refer to Tekin and Sabuncuoglu (2004).…”
“…For a general review of supply chain simulation study, please refer to Terzi and Cavalieri (2004). For a general review of simulation optimisation, please refer to Tekin and Sabuncuoglu (2004).…”
“…Besides the traditional analytical approaches, another type of approaches for analysing complex distribution networks is simulation, which is increasingly utilized with the rapid development of information technology [9,12]. Apart from the traditional view that simulation serves only as "a descriptive tool in the modelling and analysis of a wide variety of complex real systems", recent advancement in optimization approach and computing technology make it "a prescriptive tool in support decision making" [13]. In Barton's work, the simulation-based optimization is defined as "repeated analysis of the simulation model with different values of design parameters, in an attempt to identify best simulated system performance" [14].…”
Section: State-of-the-art Approaches For Optimization Of Inventory Pomentioning
In today's dynamic market numerous dynamic influencing factors have seriously aggravates the difficulties of inventory planning of the complex distribution networks. This paper proposes a simulation-based hybrid approach for the optimization of the inventory policies in complex distribution networks. The initial multi-echelon inventory policies are handed over to a simulation model, which is capable of modelling complexity and uncertainties of the distribution network and simulating them under respective scenarios. Through comprehensively analysing the KPIs (logistic service level and logistic costs) of this set of multi-echelon inventory policies, their levels of robustness can be clearly ascertained. Based on the simulation results, a metaheuristic-based optimizer regenerates improved (more robust) multi-echelon inventory policies, which are once again comprehensively and precisely evaluated through simulation. This closed feedback loop forms a simulation optimization process that enables the autonomous evolution of multi-echelon inventory policies of complex distribution networks. Since the simulation results can truly reflect the performance of certain inventory policies in real market environment, the new Simulation-Based Hybrid Approach is quite useful for decision making process.
Simulation Optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation-discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise-various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in simulation optimization as compared to mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.
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