Introduction
Changing market conditions in the chemical industry are driving demand for fast, custom-engineered chemicals, shifting away from mass production. This move requires flexible production planning and chemical industry logistics, often involving manual processes with risks of human-chemical contact. Using tank containers directly connected to production plants can enhance flexibility and safety.
In this paper, the authors propose a framework, shown in the figure below, that combines simulation and optimization. The framework is used to plan tank container use, select storage materials, and evaluate chemical industry logistics impacts, focusing on general cargo container management.
The authors then introduce a real-world use case to evaluate the framework for tactical planning challenges, focusing on the use of tank containers. The case involves a chemical company trying to determine whether tank containers should be utilized, which raw materials to store, and the appropriate storage duration based on production orders.
Simulation model
The writers of this paper examined how mathematically optimized raw material choices for tank containers impact chemical industry logistics using simulations.
The simulation model, developed in AnyLogic 8.8.5, utilizes discrete-event and agent-based simulation modeling to analyze different levels of abstraction. A detailed goal and task specification were established during development, alongside key performance indicators (KPIs) to evaluate different scenarios.
KPIs included metrics like the number of general cargo containers (GCCs), employee-chemical interactions, resource utilization, and logistics process times. Data was collected and categorized into technical, organizational, and system to achieve the defined objectives.
The next step involved developing a conceptual model, with raw material reception and storage defined as the source and finished product outbound flow and GCC cleaning as the sink.
The production process was divided into individual event-driven process chains to simplify logistics implementation in the simulation. The model included various agents, such as production buildings, logistics buildings, containers, and orders.
Automated parameterization was added, allowing scenario adjustments by updating the database. The model was validated using expert feedback from chemical companies.
Results
This study experiment evaluated two scenarios using simulation:
- Where no tank containers are used, and raw materials are manually supplied with GCCs.
- One that incorporates tank containers, optimized for occupancy.
Ten simulation runs were conducted for each scenario, and their mean values were analyzed. The comparison, illustrated below, between the scenarios shows that incorporating tank containers significantly reduced the number of GCCs in the logistics system.
By the end of the observation period, GCC demand dropped by up to 1,500 units—a reduction of nearly 30%. This highlights the efficiency gains of using optimized tank containers alongside GCCs.
The simulation also focused on a single GCC type, which is stored in tank containers. Results of this study experiment showed that storing these materials in tank containers significantly reduces the demand for GCCs of this type. For instance, in the third month, the demand for GCC type B dropped by nearly 75%, highlighting the efficiency of this storage method.
The study demonstrates that the use of tank containers in chemical industry logistics significantly reduces direct interactions between employees and chemicals, including handling, transportation, and bundling processes. Interactions decreased by approximately 25%, from over 90,000 to less than 68,000.
Future research will integrate stationary tanks and develop reusable simulation blocks to simplify adoption by chemical companies. Additionally, an expanded use case will address supply-type impacts on production buildings, including bottlenecks like freight elevators.