An improving agent-based engineering strategy for minimizing unproductive situations of cranes in a rail–rail transshipment yard

Abstract

Nowadays, seaports seek to achieve a better massification (massive transportation of containers) share of their hinterland transport by promoting rail and river connections in order to more rapidly evacuate increasing container traffic shipped by sea and to avoid landside congestion. The attractiveness of a seaport to shipping enterprises depends not only on its reliability and nautical qualities but also on its massified hinterland connection capacity. Contrary to what has been observed in Europe, the massification share of Le Havre seaport has stagnated in recent years. To overcome this situation, Le Havre Port Authority is putting into service a multimodal hub terminal linked only with massified modes. In this study, we focus on rail-rail transshipment of this new terminal, specifically on minimizing unproductive situations of cranes to improve crane productivity and to speed up freight train processing. To this end, an improving agent-based engineering strategy called the ''crane anti-collision strategy'' is proposed and tested using multi-method simulation software (Anylogic). In a numerical study, the simulation results reveal that our developed model is very satisfactory and outperforms other existing simulation models.

Proposal of a module-driven architecture for building simulation models devoted to container terminals: dilemmas in applying generic, flexible, and modular principles

“…In the literature of port terminals, the confidence level is fixed at 95% and the error percentage is set as 0.05. 41,55,56 In this study, the confidence level was constructed around the latest completion time of jobs. For our system, 10 replications were needed to achieve the required accuracy in the outputs.…”

“…Fotuhi et al 39 modeled yard cranes as reinforcement learning agents and used safety spacing constraint to guarantee collision avoidance. Abourraja et al 40,41 presented two agent-based strategies for gantry crane scheduling at rail–rail transshipment yard, the first one used ant-colony algorithm to model crane behavior whereas the second one used what-if rules. Garro et al 42 addressed SC scheduling issues in container terminals using MABS combined with mathematical models.…”

“…In Sun et al, 45 the authors extended the previous platform to study the extension of mega-container terminal layout. Similarly, Abourraja et al 46 adjusted the model designed in Abourraja et al 40,41 on a rail-road terminal in view of studying the layout design problem, besides the system was connected to the optimization model of Benantar et al 47 to schedule the arrival of trucks.…”

Some Issues of the Project Analysis of Options for the Implementation of European Standard Railways in Ukraine

“…Comment regarding the use of the given results: assuming a prospective volume of transportation of 1 million 20-foot containers per year (1 million TEU -twenty-foot equivalent) (Abourraja et al, 2018). If they are transported in express trains of 50 cars, 2 TEU per each, this corresponds to an average daily volume of traffic of 27.4 trains in each direction (27-28 pairs of trains per day).…”

Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

“…The solutions are then used to power the simulation model which evaluates the performance of the chosen strategy. Authors in [121] have highlighted the drawbacks of the model proposed by [119] and propose a new one to overcome the issues, applied in rail-rail transshipment for minimizing unproductive situations of cranes in Le Havre Port. And they have proposed another typical work inspired by the ant colony [122].…”