A multi-objective approach for intermodal train load planning

Heggen, Hilde; Braekers, Kris; Caris, An

doi:10.1007/s00291-017-0503-1

Cited by 8 publications

(7 citation statements)

References 34 publications

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“…For own long-haul services, constraints (4) and (5) impose a maximum length and weight respectively. It is assumed that a feasible assignment of containers to the specific locations on wagons can be found using a detailed train load planning algorithm as for example proposed in Heggen et al [13]. Furthermore, each container r can only be assigned to a specific rail service if its release time a r at the customer increased with the time needed to arrive at the considered terminal allows the container to arrive before the service departure (6).…”

Section: Elsementioning

confidence: 99%

“…Algorithm 1 provides the general heuristic structure. It includes a constructive heuristic (lines 2-4) as well as a framework with several removal operators (lines 9-12) and insertion operators (lines [13][14][15].…”

Section: General Structure Of the Lnsmentioning

confidence: 99%

“…The problem also includes real-time itinerary replanning and asset management [12]. Next, during train load planning, containers are assigned to specific locations on intermodal trains while including detailed capacity considerations related to the wagon and slot length, and weight restrictions [13]. The fact that these problems and decision levels are interrelated is recently stressed by several authors (e.g., van Riessen et al [14], Zhang and Pel [15]).…”

Section: Introductionmentioning

confidence: 99%

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Intermodal Container Routing: Integrating Long-Haul Routing and Local Drayage Decisions

et al. 2019

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Intermodal logistics service providers decide on the routing of demand through their service network. Long-haul routing decisions determine the selected departure and arrival terminals for containers and imply corresponding drayage tasks. Traditionally, given these long-haul routes and fixed drayage tasks, drayage operations are planned in a second phase by establishing truck routes to transport containers to and from terminals by truck. In this paper, operational decisions on local drayage routing in large-volume freight regions with multiple terminals on the one hand, and intermodal long-haul routing on the other hand are merged into an integrated intermodal routing problem. Different long-haul routing decisions imply different drayage tasks to be performed and thus impact total trucking costs. The approach aims at reducing the number of road kilometres and increases bundling opportunities by maximising the long-haul capacity utilisation. In this way, it contributes to the modal shift towards intermodal transport and a more sustainable transport system. As a weekly planning horizon is used, a maximum daily active time and a minimum overnight's rest are included for multi-day drayage routing. A large neighbourhood search heuristic is proposed to solve the integrated intermodal routing problem. This integrated planning approach provides decision support for routing customer orders throughout the intermodal network with the aim of minimising total transport costs and maximising capacity utilisation. Experiments show the added value of the integrated approach, which uses more information to make better-informed decisions and increase the capacity utilisation. The largest savings in trucking costs are obtained for clustered instances with demand characteristics closest to real-life cases. Finally, a real-life case study analyses the impact of tactical service network design decisions on the total operational costs.

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Section: Elsementioning

confidence: 99%

Section: General Structure Of the Lnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intermodal Container Routing: Integrating Long-Haul Routing and Local Drayage Decisions

et al. 2019

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“…In Tricoire (2012), MDLS has been applied to several multi-objective problems such as the multi-objective multi-dimensional knapsack problem, the bi-objective set packing problem and the bi-objective orienteering problem. It has been later applied to intermodal train loading planning (Heggen et al, 2018) and several variants of the travelling salesman problem 60 (Defryn and Sörensen, 2018) and vehicle routing problems (Kovacs et al, 2015;Molenbruch et al, 2017;Lehuédé et al, 2019). A variant of the original MDLS, called Improved Multi-Directional Local Search (IMDLS) was proposed by Lian et al (2016).…”

Section: Meta-heuristic Reference Papersmentioning

confidence: 99%

Multi-directional local search for sustainable supply chain network design

Eskandarpour

Dejax

Péton

2019

International Journal of Production Research

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In this paper, we propose a bi-objective MILP formulation to minimize logistics costs as well as CO 2 emissions in a supply chain network design problem with multiple layers of facilities, technology levels and transportation mode decisions. The proposed model aims at investigating the trade-off between cost and CO 2 emissions through supply chain activities (i.e., raw material supply, manufacturing, warehousing, and transportation). To this end, a multi-directional local search (MDLS) metaheuristic is developed. The proposed method provides a limited set of non-dominated solutions ranging from a purely cost effective solution to a purely environmentally effective one. Each iteration of the MDLS consists in performing local searches from all non-dominated solutions. To do so, a Large Neighborhood Search (LNS) algorihtm is used. Extensive experiments based on randomly generated instances of various sizes and features are described. Three classic performance measures are used to compare the set of non-dominated solutions obtained by the MDLS algorithm and by directly solving the MILP model with the epsilon-constraint approach. This paper is concluded by managerial insights about the impact of using greener technology on the supply chain * Corresponding author. Olivier Péton, IMT Atlantique,

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“…From the eleven paper submitted to this special issue, the following three papers have been selected: Heggen et al (2018) consider train load planning problems in intermodal terminals and develop a multi-objective model including several practical constraints. The problem is solved exactly by an -constraint method and heuristically by a multi-directional local search procedure.…”

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confidence: 99%

Rail terminal operations

2018

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Strengthening the railway system is among the top priorities of the long-term transport agendas issued by many national and international authorities. Rail transportation relieves the overloaded road networks and is preferred based on its reduced environmental impact. An important driver to increase the attractiveness of the railway system is to establish more efficient train handling processes in existing railway terminals. Relevant terminals are traditional shunting yards, where railcars are rearranged to trains via a system of switches and shunting tracks, and intermodal terminals, where containers are exchanged between trains and trucks by huge gantry cranes. In all these terminals, multiple stakeholders (e.g., train operators, forwarding companies and terminal operators) and various equipment types (e.g., locomotives, railcars, cranes, containers) have to be coordinated, so that the application of quantitative approaches for planning the operations in rail terminals is highly advisable.B Nils Boysen nils.boysen@uni-jena.de

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A multi-objective approach for intermodal train load planning

Cited by 8 publications

References 34 publications

Intermodal Container Routing: Integrating Long-Haul Routing and Local Drayage Decisions

Intermodal Container Routing: Integrating Long-Haul Routing and Local Drayage Decisions

Multi-directional local search for sustainable supply chain network design

Rail terminal operations

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