11. Hybrid simulation for complex manufacturing value-chain environments
Simulation is very popular for modelling complex systems. Recent demands from global business optimization, integration of human decision making, and increased complexity of modern systems, push researchers for combining different simulation methods and getting deeper understanding into complex interactions between processes of very different nature, calling for hybrid simulation approaches. These approaches combine at least two of three simulation methods – System Dynamics, Discrete Event Simulation, and Agent Based Simulation.
Even though there is a growing interest in hybrid simulation, many questions remain unsolved, as the lack of a unified use of terms and definitions in the literature, which introduces ambiguity. Literature in hybrid simulation is very sparse, hampering the work of researchers interested in the topic. Many challenges concern authors when using more than one simulation method, as establishing information sharing between the models, converting time units for proper information exchange, the skills required for building the models, among others.
This work aims at providing insight on the use of hybrid simulation in the context of business, supply chains (SCs), manufacturing, and logistics; and the most important advantages and challenges of using hybrid simulation. We try to answer two research questions:
RQ1: How has hybrid simulation been used in business, SCs, manufacturing, and logistics?
RQ2: Which are the challenges and benefits of hybrid simulation compared to standalone simulation?
This paper reviews literature related to hybrid simulation, focusing on different combinations of methods and the advantages and challenges of using hybrid simulations.
For the structured literature review, a set of keywords considering the dispersion of terms in the literature was selected, and three databases (Scopus, Science Direct, and Emerald Insight) were chosen. The papers were filtered based on abstract and full-text reading. Forward and backward search were used for increasing the range of papers analysed. More than 50 papers were fully analysed, across more than 20 years of publications.
Despite all the papers fully analysed targeted hybrid simulations, the modes of operation and relationships established between the models differ. Therefore, it is relevant understanding the different approaches to hybrid simulations. We present a classification scheme for the analysed papers, based on the classification scheme proposed by Swinerd and McNaught (2012), which included interfaced, sequential and integrated classifications, and adding the enrichment taxonomy as in Morgan, Howick and Belton (2016).
Even though hybrid simulation approaches are more frequent, combining two methods is only justified when the developed models are of equal importance to the overall goal of the simulation. Combining models using different methods requires effort and precision to establish information sharing. Common problems which arise include the different time units used in the models. Hybrid models require knowledge about different simulation methods; high skills and flexibility. In spite of the high demands of hybrid simulation, many advantages can be achieved. One of the benefits of hybrid simulation is flexibility. It is possible to simulate different levels of aggregation, avoiding problems of model consistency. Among others, hybridism allows using complementary methods, coupling methods, and exploration of multilateral problems.