Abstract
The imbalance of inbound and outbound containers generates large numbers of idle runs of container trucks in inland container transportation. In tradition, the trucking company acquires shipping requests from the spot market to increase profitability, with the precondition that both origin and destination locations of the acquired request are the same as the idle travel. Such precondition undoubtably leads to limited benefits and poor stability, given that shipping requests from the spot market are of high randomness. This paper proposes to adopt the idea of Physical Internet (PI) and assign an acquired request to several trucks, allowing transfers from one truck to another at logistics hubs. In this way, temporally and spatially dispersed idle resources could be consolidated to achieve the shipments of selective shipping requests, in a relay manner. To our knowledge, this paper is among the first to study the new paradigm of inland container transportation and formally model it as a PI-based selective vehicle routing problem (PI-SVRP). A mixed integer program (MIP) is presented and validated with CPLEX on small instances. In order to tackle real world instances, several novel heuristics have been designed particularly according to the special problem structure and embedded into an adaptive large neighborhood search (ALNS) framework. Finally, substantial instances are generated based on real data and a series of numerical studies are performed to derive managerial implications for practitioners.
Original language | English |
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Article number | 108403 |
Number of pages | 13 |
Journal | International Journal of Production Economics |
Volume | 245 |
DOIs | |
Publication status | Published - Mar 2022 |
Externally published | Yes |
Keywords
- Adaptive large neighborhood search (ALNS)
- Inland container transportation
- Physical Internet (PI)
- Selective vehicle routing problem (SVRP)
ASJC Scopus subject areas
- General Business,Management and Accounting
- Economics and Econometrics
- Management Science and Operations Research
- Industrial and Manufacturing Engineering