Agent-Based Approaches to Transport Logistics

Davidsson, Paul; Henesey, Lawrence; Ramstedt, Linda; Törnquist, Johanna; Wernstedt, Fredrik

doi:10.1007/3-7643-7363-6_1

Cited by 15 publications

(10 citation statements)

References 40 publications

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“…In this figure, we can see that the trust model is tightly integrated with social model elements that are used as a primary planning input, furthermore, most of the restrictions in the planning model that follows use trust in agents or actions as an input to ensure reasonable team selection and adaptation to local (action dependent) trust levels. The algorithm we present can be compared to abundant previous works in the MAS logistics area [21]. Enhanced Contract-Net-Protocol as defined in [28] and further extended towards practical application by [55] achieves the same result using the negotiation between coalition leader and perspective members.…”

Section: Trust-based Planning For Adversarial Domainsmentioning

confidence: 93%

Advanced Agent Methods in Adversarial Environment

Pěchouček¹,

Řehák²,

Šišlák³

et al. 2005

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Section: Trust-based Planning For Adversarial Domainsmentioning

confidence: 93%

Advanced Agent Methods in Adversarial Environment

Pěchouček¹,

Řehák²,

Šišlák³

et al. 2005

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“…Luo et al () and Moulin et al () have used ABS for crowd management. Applications of this technique have also been reported in supply chain management (Kaihara, ), in freight transportation (Davidsson et al, ), in the military (Cioppa et al, ), for emergency evacuations (Pan et al, ), in the management of the environment (Hare and Deadman, ) and in cell biology (Pogson et al, ), to cite just some further examples.…”

Section: Agent‐based Simulationmentioning

confidence: 99%

Analysing trade‐offs in container loading: combining load plan construction heuristics with agent‐based simulation

Mustafee

Bischoff

2013

Int Trans Operational Res

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In this paper we bring together two Operations Research techniques, Cutting and Packing Optimisation (CPO) and Simulation, and present a multi-methodology approach for analysing the trade-offs between loading efficiency and various important practical considerations in relation to the cargo -such as its stability, fragility, or possible cross-contamination between different types of items over time. The feasibility of this approach is demonstrated by considering a situation where the items to be loaded have differing degrees of perishability and where badly deteriorated items can affect those in their immediate vicinity (e.g. through the spread of mould). Our approach uses the output of the CPO algorithms to create agents that simulate the spread of mould through proximity-based interactions between the agents. The results show the trade-offs involved in container utilisation and the propagation of mould, without evidence of any correlation between them. The contribution of this research is the methodology and the feasibility study. KEYWORDS: SIMULATION, OPTIMISATION, METHODOLOGY, TRANSPORT INTRODUCTIONThe efficient loading of cargo into freight containers -and, more generally, the proficient packing of smaller items into larger objects -has been a subject of intensive research for at least thirty years. George and Robinson (1980) were among the first to propose an algorithm for constructing a container loading plan. Their approach was heuristic in nature and based on the idea of building a series of 'walls' of items across the width and height of the container. Since then numerous different approaches have been developed for both the knapsack version of the problem -where the space available is fixed and loading all the cargo may not be possible -and, to a lesser extent, its bin-packing form -where all of the cargo involved must be stowed and a cost-effective way of using a set of containers is sought. In this paper the term Container Loading Algorithms (CLAs) is used to describe any approach which is designed to produce container loading plans.CLAs are commonly used for a wide variety of container loading problems, for example, loading of cargo that consists of either identical items (completely homogenous cargo) or cargo consisting of a large number of different types of items relative to the total number of items (strongly heterogeneous cargo) or cargo comprising of a relatively few different types of items relative to the total number of items (weakly , 1995). There are several studies that have proposed algorithmbased approaches aimed at homogeneous cargo (Han et al, 1989; George, 1992), strongly heterogeneous cargo (Gehring et al, 1990) and weakly heterogeneous cargo (George and Robinson, 1980;Morabito and Arenales, 1994;Ngoi et al, 1994).The majority of these studies have focused on a particular category of combinatorial optimisation problem -the knapsack problem -wherein the only parameters that are known/used are the dimensions of the cargo and the container and some measure of value associa...

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“…Two research approaches are starting to be differentiated in terms of road traffic simulations using MAS, made by Meir and Rosenschein [37]. The first approach tries to offer organizational models to improve global problems, such as logistics and/or services [15,59], and the second approach offers solutions for "local" traffic congestion problems. Congestion is a deteriorated state because all agents make "optimum" local decisions a priori, far from the global optimum.…”

Section: Road Traffic Simulation Based On Multi-agent Approachesmentioning

confidence: 99%

Simulation in contexts involving an interactive table and tangible objects

Kubicki

Lebrun

Lepreux

et al. 2013

Simulation Modelling Practice and Theory

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By using an interactive table, it is possible to interact with several people (decision-makers) in a simultaneous and collaborative way, around the table, during a simulation session. Thanks to the RFID technology with which the table is fitted, it is possible to give tangible objects a unique identity to include and to consider them in the simulation. The paper describes a context model, which takes into consideration the specificities related to interactive tables. The TangiSense interactive table is presented; it is connected to a Multi-Agent System making it possible to give the table a certain level of adaptation: each tangible object can be associated to an agent which can bring roles to the object (i.e., the roles are the equivalent of a set of behaviors). The Multi-Agent System proposed in this paper is modelled according to an architecture adapted to the exploitation of tangible and virtual objects during simulation on an interactive table. A case study is presented; it concerns a simulation of road traffic management. The illustrations give an outline of the potentialities of the simulation system as regards the context-awareness aspect, following both the actions of the decision-makers implied in simulation, and the agents composing the road traffic simulation.

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Agent-Based Approaches to Transport Logistics

Cited by 15 publications

References 40 publications

Advanced Agent Methods in Adversarial Environment

Advanced Agent Methods in Adversarial Environment

Analysing trade‐offs in container loading: combining load plan construction heuristics with agent‐based simulation

Simulation in contexts involving an interactive table and tangible objects

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