Production, Manufacturing and LogisticsReal-time control of freight forwarder transportation networks by integrating multimodal transport chains
Introduction
Since the 1990s, the transport market in the European Union has been subject to various fundamental changes. As a major consequence of liberalization and deregulation, Western European freight forwarders are facing intense competition. This is mainly caused by the substantially lower labor costs of a large number of new competitors from Eastern Europe.
As a result of a decline in freight charges, the German transport market – originally characterized by a majority of moderate-sized companies – faces a growing trend towards concentration. Specifically, larger-sized forwarders often take over small competitors in order to augment their presence in local regions. Smaller-sized forwarders counteract this by cooperating with each other in order to benefit from synergy effects (cf. Krajewska and Kopfer, 2006). Cooperation between freight forwarders usually comprises the bilateral outsourcing of partial or entire transportation processes and the collaborative construction of transportation hubs. Transportation hubs are mainly used for inefficient connections. Goods that are delivered to hubs are subsequently transported by local partners to their final destinations.
In contrast to transportation planning where all decisions are based on the theoretical anticipation of future processes, a real-time-oriented control system has to ensure the feasible and efficient execution of the transportation process. Consequently, after starting the transportation processes, the control system has to react to each significant disturbance in the transportation network. Clearly, changes (e.g., traffic congestion or newly incoming transportation requests) are very likely to occur, and since they have a substantial impact on the overall efficiency of the controlled transportation processes, it is of significant importance that they are dealt with effectively. Thanks to the use of modern communication technologies, all required information is available in real-time even in a widespread transportation network (Mintsis et al., 2004, Gendreau and Potvin, 1998, Fleischmann et al., 2004b). As illustrated in Fig. 1, the current situation in the transportation network is mapped in a centralized data base. Based on this situation, a real-time control system has to adapt the transportation plan that is already in execution to the changed data. This, however, requires the integration of a sophisticated disturbance management system.
Section snippets
Literature review
Transportation planning has become a vital research area because of its practical relevance. In most literature on transportation, static approaches are distinguished from real-time (dynamic) ones. It is assumed that all parameters in static approaches are known in advance with certainty. Hence, no updates are necessary because of the absence of disturbances (Yang et al., 2004, Gendreau et al., 1999). In contrast to this, real-time concepts have to be able to handle unexpected data changes
The update handling of the new control approach
In what follows, the update handling of the new real-time control approach is introduced. Note that the update handling determines when and how the current transportation plan is updated according to the dynamically changing situation in the transportation network. It is clear that possible changes are restricted to decisions that have not yet been made. However, since plan execution and plan adaptation take place simultaneously, the set of those decisions that are alterable by plan adaptation
The mathematical model
This section introduces a new dynamic model that defines temporary optimization problems being considered at the adaptation level. These problem instances are derived from particular snapshots of the executed transportation process. As depicted in Section 2, these snapshots are generated at the end of each anticipation-horizon.
The dynamic model integrates multiple transshipments, the use of transportation hubs, partial or total outsourcing of transportation services, and several dynamic
Solution approaches
In this section, a description of the improvement procedure that is applied at the adaptation level is given. Each plan is defined as a set of vehicle and request tours that are stored as double linked lists. The examination of neighboring solutions requires efficient operations in order to modify existing vehicle/request tours. Thus, a specific storage management system is applied. By allocating a sufficient number of elements on a stack beforehand, memory allocation operations are performed
Computational results
In order to validate the efficiency of the new approach and to gain an insight into real-time control systems, alternative real-time approaches are coded in C++ and applied to various process scenarios. Three different groups of transportation processes characterized by specific settings of complexity and dynamism are controlled in real-time.
Conclusions
This paper proposes a new real-time approach for freight forwarder transportation networks. In order to enable continuous plan adaptation, a dynamic model is proposed. For the first time, this new model integrates multimodal transport chains and multiple transshipments. Furthermore, the use of transportation hubs and external services that result from cooperative agreements are also considered.
In order to continuously adapt the current transportation plan during its execution and handle
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