# Integrating Simulation-Based Optimization for Lean Logistics: A Case Study
## 2 Literature Review
### Techniques for Optimization in Logistics  
Traditional framework to perform the logistics process, it is to maintain FG high inventories to ensure customer satisfaction and avoid lost sales costs. However, maintaining high inventories hides many inefficiencies in the activities for this process, which are directly reflected in the logistics cost. By this reason, leading companies apply best practices, such as direct store deliveries, which dramatically reduce inventory levels and cycle times in product distribution [20]. [PERSON] and [PERSON] [21] analyze the impact of using different supply chain planning models and propose the approach to use is depending on the conditions. A structured framework for comparison and evaluation of supply chain planning models under different conditions had been provided, including external transportation costs. Future research must include improvements in shipment rules may allow Lean Production to improve its transportation performance. In this simulation study, a simple general shipment rule of minimum truck saturation had been applied. They recommend further studies analyzing more complex rules that can increase efficiency of transportation, as milk-run or compound deliveries. Likewise, a simulation study on a multi-product supply chain could lead to more insights on the potential of lean approach adoption along the supply chain. Regarding to environmental impact [PERSON] et al. [4] tested the hypotheses that since just-in-time inventory management significantly increases the frequency of transport it will also increase greenhouse gas emissions in a supply chain; this by using a simulation model of a manufacturing-retailer supply chain. They used a discrete event simulation model of a two-echelon supply-chain composed of distribution and retailing operations to test the hypotheses. The model included fixed infrastructural elements in the warehousing and retailing facilities, while integrating mobile infrastructural elements in the form of transportation of goods from the warehouse to the retailingfacility and the corresponding backhauling operations. In a parallel way, [PERSON] et al. [22] proposed a timed colored Petri net simulation-based self-adaptive collaboration method for Internet of Things-enabled production-logistics systems. They combined the schedule of token sequences in the timed colored Petri net with real-time status of key production and logistics equipment. The key equipment is made'smart' to actively publish or request logistics tasks. A simulation experiment was conducted to validate the performance and applicability of the proposed method and computational experiments demonstrate that the proposed method outperforms the event-driven method in terms of reductions of waiting time, makespan and electricity consumption. This proposed method could be applicable to other manufacturing systems to implement production-logistics collaboration. In this way, [PERSON] et al. [23] conducted a sustainability analysis of concrete innovative and already tested retail logistics solutions addressing the research question \"what are the effects of retail logistics solutions on total costs and sustainability performance?\" For the analysis, they developed and applied an indicator-based framework based on the key sustainability components (economy, environment, society) and enriched by the addition of the transport component. External costs analysis showed that higher degree of internalization is achieved in the line-haul transport. As a conclusion, they affirmed the impact of innovative and already tested solutions relies on a variety of factors: organizational, urban context, type of goods transported, engagement of stakeholders and so forth. So that, innovation is crucial for urban retail logistics impacting on transport service, society, economy and environment. [PERSON] et al. [24] proposed the scheduling problem in the context of a three-stage supply chain comprising a supplier, a manufacturer and a customer.