discrete event simulation

The supply chain of perishable products has high complexity due to the short lifespan of the products. For this reason, it is necessary to evaluate various issues in the supply chain such as location, inventory and logistics in an integrated manner by using flexible tools. The purpose of this research is to present an optimization approach based on event-factor-based discrete hybrid simulation for the integrated optimization of facility location, inventory control, fleet composition and routing in the supply chain of perishable products.The optimization process is performed using meta-heuristic algorithms. In order to conduct a case study, the supply chain of one of the largest producers of dairy products in the country has been considered, and due to the high demand and production of ice cream, this product has been considered as a perishable product of the supply chain. The results show that the integrated optimization of the research topic leads to a significant improvement in product waste, reducing the time of processing and delivering orders, and reducing the number of fleets.

Objective : Reducing waste and improving productivity are crucial challenges in today’s competitive manufacturing landscape. Lean production tackles these issues by eliminating activities that do not add value, cutting costs, and enhancing quality. However, the success of lean implementation relies on selecting strategies that align with an organization’s operational context. This study evaluates four fundamental lean strategies under various production conditions: Work-in-Progress (WIP) Inventory Reduction, Batch Size Reduction, Setup Time Reduction, and Multi-skilled Workforces. Methods : A hybrid methodology was utilized, integrating discrete-event simulation (DES) with multi-criteria decision-making (MCDM). Six scenarios were modeled, varying production capacity (low, medium, and high) and work shift schedules (one or two shifts). The Best-Worst Method (BWM) was employed to determine the weights of the evaluation criteria: total cost, available inventory, waiting time, and lead time. The VIKOR method was then used to rank the strategies for each scenario. Results : The results indicate that total cost (weight = 0.54) is the most critical evaluation criterion, followed by available inventory (0.27), waiting time (0.11), and lead time (0.08). Both simulation and VIKOR analyses demonstrated a contextual pattern: reducing setup time was more effective than other strategies in low-capacity environments. In contrast, reducing batch size consistently ranked highest in medium and high-capacity environments, regardless of the shift schedule. Conclusion : The findings highlight that lean strategies' effectiveness depends on the context. Reducing setup time is most beneficial for resource-limited systems, while reducing batch size offers greater advantages in high-output environments. The hybrid simulation-MCDM framework created in this study is a structured and objective tool for managers, allowing them to choose lean strategies aligned with their specific operational conditions. This, in turn, enhances supply chain performance and fosters long-term competitiveness.