Multi-Objective Optimisation in Manufacturing Supply Chain Systems Design: A Comprehensive Survey and New Directions

Integrating system dynamics and multi-objective optimisation for manufacturing supply chain analysis

“…A comprehensive literature survey was carried out by Aslam et al (2011), which showed that most of the research conducted on MOO for supply chain management is based on mathematical approaches, e.g., linear programming, mixed integer programming, mixed integer linear programming, and so on. In comparison to the large number of publications on applying simulation approaches to SCM problems, it seems that exploring the use of SBO, especially in the context of MOO, is far from adequate.…”

“…As Angerhofer and Angelides (2000) point out, the use of SD modelling within the supply chain domain has just recently re-emerged after a slack period. Despite the increase in research within the domain of utilising SD for SCM issues, Aslam et al (2011) have found that in the literature there are only a few articles related to the integration of simulation-based optimisation (SBO) with SD models. In particular, they conclude that the integration of multi-objective optimisation (MOO) and SD models can provide a novel method that can be used for not only for the optimisation, but also the analysis in SCM studies.…”

Strategy evaluation using system dynamics and multi-objective optimization for an internal supply chain

“…They discussed the use of multi-criteria utility functions. It is only recently that the research community has initiated experimentation with multi-objective optimization in combination with system dynamics models, such as Duggan (2008), Aslam et al (2011) and Dudas et al (2011). A comprehensive literature survey, presented in Aslam et al (2011) where the authors have investigated 42 journal papers which concern multi-objective optimization (MOO) for supply chain management (SCM) problems, shows that the majority of papers, or more exactly 53%, have used a mathematical approach e.g., linear programming, mixed integer programming, mixed integer linear programming, etc.…”

“…It is only recently that the research community has initiated experimentation with multi-objective optimization in combination with system dynamics models, such as Duggan (2008), Aslam et al (2011) and Dudas et al (2011). A comprehensive literature survey, presented in Aslam et al (2011) where the authors have investigated 42 journal papers which concern multi-objective optimization (MOO) for supply chain management (SCM) problems, shows that the majority of papers, or more exactly 53%, have used a mathematical approach e.g., linear programming, mixed integer programming, mixed integer linear programming, etc. By further investigating the authors showed that the most popular mathematical approach used to model supply chains was mixed integer non-linear programming, which accounts for 33% of the papers which is then followed by mixed integer linear programming, as the second most implemented mathematical approach at 21%, while the rest of the methods are fairly equally distributed.…”

Aggregated line modeling for simulation and optimization of manufacturing systems

“…The application of these methods has mostly been conducted on production line level and has generated very successful results. Some applications on higher level systems, such as complete factories and supply chains has been done using mathematical models and methods (Aslam et al 2011). But since most of these mathematical models are deterministic, like system dynamics models, using them for modeling stochastic systems can be misleading (Madan et al 2005).…”