## 4 Simulation Models and Sustainability: Characterization
### Agent-Based Simulation
The reasons for this are that there is greater propagation, availability of software, and education. The reasons cited for this have been a positive feedback [8]. The use of ABS in case of sustainability has been proposed in place of tools such as lifecycle assessment (LCA), Strategic environment assessment (SEA), Environmental impact assessment (EIA), and multi-criteria analysis (MCA), among others [108]. ABM is increasingly being used to investigate the formation of complex nonlinear phenomena in connected human and natural systems [109]. With the ABM, various agents, including organizations, agencies, and actors can be modeled, while also simulating the environment containing them. The ABM, unlike other methods, can simulate heterogeneity which finds greater use in contexts where individual decision-making with diverse objectives is taken into account in addition to the changing social, economical, and environmental behaviors [110]. Modeling sustainability with ABS has shown to be a promising strategy. It is claimed that detecting macro-behaviors aids in the comprehension of complicated physical and ecological systems [111]. The primary idea of an ABS model is to describe interactions between social-economic and ecological processes. Along the general environmental theme, the ABS has found a great deal of application. The ABM provides for flexibility in modeling such that it may be as sophisticated as the computing power allows for. Such that when it comes to sustainability, the ABM can also be integrated with other frameworks including the LCA. The degree of integration is assessed by the expected outcome. Research also shows that the use of ABM enhances studies where limited computational power is available [112]. Those models which were formerly developed using econometric methods and nonlinear programming techniques were enhanced using the ABM [113]. Furthermore, ABM also allows for incorporating human behaviors. The case of modeling human behavior in the case of sustainable logistic management may be cited [114]. Sustainability has been addressed using ABM in research along one of the four categories. The first category includes studies modeling traditional issues of pollution control [115]. The second category deals with resource modeling such as energy, oil, land, etc. [116]. The third category involves studies that deal with diffusion of sustainable behavior which also include market and policy-based models [117; 118]. The fourth category includes modeling global environmental issues. Examples of which include [119; 120]. This study has included research covering the first and second categories. SCs can be conceived as consisting of supply (procurement logistics), production (manufacturing systems), distribution (delivery), and reverse logistics (planning and implementation) [121]. In case of manufacturing SCs, ABS has been put to use across all the four areas, instances of which include waste paper [122], manufacturing resource efficiency [123], carbon-based freight consolidation [124], and SC planning [125]. With regards to magnification, ABS finds use at all three levels of operation. A study encompassing the strategic level seeks to model sustainable industrial policy using by coupling macro-economic dimensions with environmental parameters while optimizing the resource efficiency [123]. The application of ABS at the tactical level is exemplified in a study where industrial collaboration is modeled wherein a space of cooperation between industrial partners is explored in an economically win-win manner [126]. At the operational level, the ABS in optimizing the real-time energy consumption of resources in job-shop manufacturing processes. The data used for simulation are collected from industrial robots [127].