Konferenzbeiträge

Due to numerous uncertainties such as bad weather conditions, frequent changes in the schedules of vessels, breakdowns of equipment, port managers are aiming at providing adaptive and flexible strategic planning of their facilities, especially intermodal terminals (dry ports).

This research shows that the combination of the agent-based modeling with other simulation approaches simplifies the process of designing simulation models and increases their visibility. The developed set of models allows the researchers to compute the balanced values of the parameters. Consequently, it helps achieve effective operation of a seaport – intermodal terminal system. The provided case study on one of the busiest ports in China proves the adequacy and validity of the developed simulation models.

According to a World Health Organization (WHO) report in 2018, 1.35 million people die each year due to road accidents globally. In a country like India, it is becoming increasingly difficult to provide post-accident services on time with an increase in congestion. In this paper, the researchers propose a system which decreases the post-accident response time of Emergency Medical Services (EMS) in India by adding another layer of the patient transport vehicle. Based on a transportation system analysis, the paper discusses a new algorithm and a system design with a transportation optimization simulation model.

The goal of this study is to contribute to commercialization of the second-generation cellulosic biofuels (SGCBs) by reducing its operational cost. A hybrid simulation-based optimization approach is devised to design a cost-effective SGCB supply chain model. The approach includes feedstock yield estimation and location-allocation of feedstock storages between farms and refineries. An agent-based simulation (ABS) implemented in AnyLogic is utilized to estimate operational cost of a SGCB supply chain. The simulation-based optimization with adaptive replication (AR) is devised to find an appropriate SGCB network design in terms of operational cost. The approach is applied to a SGCB transportation network design problem in Southern Great Plains of U.S.

In this paper, we simulate allocation policies for a two-stage inventory system that receives perfect advance demand information (ADI) from customers belonging to different demand classes using AnyLogic as supply chain management software. Demands for each customer class are generated by independent Poisson processes while the processing times are deterministic. All customers in the same class have the same demand lead time (the difference between the due date and the requested date) and back-ordering costs.

Each stage in the inventory system follows order-base-stock-policies where the replenishment order is issued upon arrival of a customer order. The researchers employ discrete event simulation to obtain output parameters such as inventory costs, fill rates, waiting time, and order allocation times. A numerical analysis is conducted to identify a reasonable policy to use in this type of system.

The ever-increasing demand for fresh and healthy products initiated an urgency for transportation system analysis and effective planning for Agri-Fresh Produce Supply Chains (AFPSC). However, AFPSC faces many challenges, including product vulnerability to market disruption and limited shelf-life. In case of a no-deal Brexit (i.e., the UK leaving the EU without an agreement), trade between Ireland and the UK will most probably be subjected to customs control. In effect, transportation delays and products deterioration rates will increase.

Based on interviews with an Irish AFPSC forwarder, a simulation model was developed to investigate different systems’ dynamics and operating rules under different delay patterns on the (yet non-existent) inner-Irish border.

Besides transportation costs the punctual delivery of the goods is a key factor for mode choice in intermodal transportation networks. However, only a limited number of studies have included stochastic transportation time in Service Network Design, which refers to decisions regarding transportation mode and services, so far.

The paper on hand combines a Sample Average Approximation approach with Discrete Event Simulation for transportation network optimization with stochastic transportation times. This includes the corresponding vehicle routing problem for road vehicles. The share of orders transported by intermodal road-rail vs. unimodal road transportation in dependence of costs and delays of the trains is evaluated for a generic transportation relation in Central Europe. The data is backed by empirical data for transportation orders and delay distributions.

This study was designed to evaluate innovative last-mile delivery concepts involving autonomous parcel robots with simulation and optimization. In the proposed concept, the last mile of parcel delivery is split into a two-tiered system, where parcels are first transported to a transfer point by conventional trucks and then delivered with parcel robots on customer demand.

The purpose of this publication is to compare different time slot selection options for customers, namely due window and on demand selection, in the context of city logistics measures such as access regulations and driving bans for city centers. The researchers use AnyLogic as delivery optimization software. They build an agent-based simulation model, including a Geographic Information System environment and optimization algorithms for allocation and scheduling of delivery robots.

A common challenge in field service planning is to evaluate different design choices, related to staffing decisions, technician scheduling strategies, and technological improvements in order to make the system more efficient. This work provides a simulation-based optimization tool to support decision makers in tackling this challenging problem. The proposed framework relies on an optimization engine for the generation of the daily plans.

The study uses AnyLogic as field service planning software to evaluate the applicability of such plans by taking into account the stochastic factors. Furthermore, an interface manages the communication between these two components and allows a feedback loop between the simulator and the optimizer to achieve more robust plans. The applicability of the framework is demonstrated through a business case to evaluate different staffing decisions.

This paper presents transportation network analysis results based on data from an agent-based simulation study. The research is aimed at establishing whether a fleet of electric vans with different charging options can match the performance of a diesel fleet. The researchers describe a base model imitating the operations of a real-world retailer using agents. They then introduce electric vehicles and charging hubs into their model. After that, they evaluate how the use of electric vehicles, charging power, and charging hubs influence the retailer’s operations. The simulation experiment suggests that, though they are useful, technological interventions alone are not sufficient to match the performance of a diesel fleet. Hence, reorganization of the urban delivery system is required in order to reduce carbon emissions significantly.

There has been an increased interest in resilient supplier selection in recent years, much of it focusing on forecasting the disruption probabilities. The results of this study advance our understanding about how and when machine learning and simulation can be combined to create digital supply chain twins, and through these twins improve resilience. The proposed data-driven decision-making model for resilient supplier selection can be further exploited for design of risk mitigation strategies in supply chain disruption management models, redesigning the supplier base or investing in most important and risky suppliers.