Hans-Otto Günther’s research while affiliated with Pusan National University and other places

One of the important issues in container terminals for the efficient operation of a rail-based transport system, called “flat car system (FCS),” is how to determine the route and travel schedule of each flat car (FC). It is assumed that a flat car may have to reserve multiple resources simultaneously at a moment during its travel. Example of the resources are transfer points (TPs) and intersections (ISs) on guide-path network. A travel-scheduling algorithm based on Dijkstra’s algorithm is suggested using the concept of time-windows.

We propose a long-term strategic network design model for urban transport systems based on the time-to-sustainability optimization method in order to provide decision support for policy makers and city planners. The approach is based on a structural transport network representation covering most relevant data on transport demand, user groups, transport infrastructure and services including vehicle fleet sizes and powertrains used. The model minimizes the time until predefined targets for sustainability indicators such as CO2, air pollution, and costs are all met steady-state. It delivers results on capacities and usage intensity of transport modes and their infrastructure and reveals which service and technology could be essential under current technology assumptions to meet overall sustainability targets. The objective of the model is to reveal fundamental dynamics in the transition towards the sustainability steady-state and to better understand the levels of magnitude in action to be taken by city planners. Considering a long-term planning horizon of 20-30 years, the model hence indicates possible transition paths to meet the increasing demand for urban mobility in a sustainable way, i.e. balancing economic, environmental and social objectives. We present a basic model formulation from which a more detailed city-specific model can be derived.

This paper proposes a method for dispatching automated vehicles in container terminals. A bidding concept is developed which particularly considers the urgency and required travel time of a transportation task. The objectives are to minimize the task delay and the required travel time to perform all tasks. Essential part of the bidding concept is its suitability for real-time application, i.e. the dispatching results are continuously being updated based on the current state of the terminal. The proposed bidding procedure is intended for practical application in an automated container terminal. Numerical experiments show that the bidding algorithm performs well compared to a greedy heuristic, which was adapted from a standard procedure known from the literature.

Customer demand in global supply networks is highly uncertain due to unexpected global and local economic conditions and, in addition, affected by seasonal demand fluctuations for final products. Therefore, in maritime transportation the design of short-sea shipping services for containerized goods has to prove its economic efficiency under varying conditions of transportation demand. Since liner shipping involves significant capital investments and huge daily operating costs, the appropriate design of the service network is crucial for the profitability of the container feeder lines. Usually, quantitative models for shipping network design are based on deterministic forecasts, which are prone to errors caused by uncertainty factors and structural changes in the development of demand. This paper puts special emphasis on the impact of seasonal demand fluctuations on the structure of the related H&S networks, the capacity of the fleet operating within the network, the deployment of ship types as well as on the associated routes of the ships. A simulation and artificial neural network based forecasting framework is developed to support the design of service networks of short-sea shipping lines. The proposed methodology has been tested for a feeder liner shipping service in the East Mediterranean and Black Sea region. Numerical results show that seasonal demand fluctuations have vital impact on the network design of container feeder lines.

In today’s global economy, businesses in all parts of the world are interlinked by logistics chains. Particularly, maritime systems play a vital role in the intercontinental transportation of goods. In order to meet the rising global handling volumes and the customers’ demand for quick and on-time delivery, shipping liners as well as terminal operators and software companies have developed new management and technological solutions, e.g. on pricing for logistics services, the handling of containers under conditions of scarce resources, the application of advanced information technology, and the establishment of intermodal links to coastal and on-land transportation systems. A further challenge is the consideration of environmental effects which are gaining increasing importance in politics and society. As a result, global freight networks are emerging which combine maritime transport, inland waterways and onshore road and rail transportation systems.The primary objective of this specia ...

Sustainability with its multiple social, economic and environmental objectives has been approached so far with multi-objective optimization strategies. Decision makers needed to define weights discriminating one objective over the other and/or were confronted with single period trade-off results. In supply chain network design, however, decision makers need to know how to transform a supply chain network towards multiple sustainability goals over several periods of time. Companies increasingly set long-term targets for multiple sustainability goals, e.g. CO2 emission reductions, recycling rates, preservation of jobs or ensured profitability. Decision support models need to answer if, how and when these multiple sustainability goals can be all achieved. Since complex dynamics and interrelations between these goals exist in a supply chain network, simple static scenario planning is insufficient either. We propose the Time-to-Sustainability (TTS) optimization strategy as a new approach for long-term supply chain network design. TTS minimizes the time frame until pre-defined targets for multiple sustainability indicators are achieved steady state. This way, TTS delivers new insights on how supply networks transform towards a sustainability steady state and allows decision makers to validate the feasibility of long-term sustainability targets. Three variants of the TTS approach are presented and evaluated using data from the automotive industry.

In response to sustainability challenges such as global climate change or poverty in parts of the world, decision makers in corporations and politics are setting sustainability targets requiring the transformation of industry supply chains towards a more sustainable level. In our study, we address the question, if and how fast these long-term sustainability targets can be reached and how the underlying transformation path develops. We focus on the long-term design of sustainable industry supply networks. As integral part of a comprehensive linear optimization model "Minimize the Time-to-Sustainability" (TTS) is proposed as a novel optimization strategy for this problem. The TTS strategy is based on the triple bottom line sustainability concept which incorporates multiple and often incompatible objectives for the environmental, economic and social dimension of sustainability, e.g. reducing greenhouse gas emission, maintaining supply network cost competiveness, and creating new job opportunities. In contrast to conventional optimization strategies, TTS minimizes the time until pre-defined target values for all sustainability objectives are achieved and thus the supply chain reaches a sustainable steady state. With this approach, TTS delivers new insights on how supply networks transform towards a sustainability steady state and allows decision makers to validate the feasibility and impact of long-term sustainability targets. Three variants of the TTS approach are presented and evaluated using the transition towards more electrified vehicles in the automotive industry as a specific application.

Manufacturing in process industries mostly takes place in multi-level supply networks consisting of several plants which are located at different sites and linked by complex logistics relations and material flows. Thus, a key issue is the coordination of plant operations considering the supply of intermediates between various plants. Time-consuming setup and cleaning activities require production to be carried out in campaigns. We propose an application-oriented approach for supply network planning in the pharmaceutical industry which is based on a novel aggregation scheme and a disjunctive graph representation of the problem. A mixed-integer linear programming model is proposed based on a continuous representation of time. Our approach generates a comprehensive schedule which coordinates the production activities, including the campaigns in the network. Numerical results show that our approach yields solutions for small and medium-sized problem instances very quickly and is even capable of coping with the complexity of real world problems.