Multi-agent based supply chain modelling with dynamic environment

Kaihara, Toshiya

doi:10.1016/s0925-5273(03)00114-2

Cited by 151 publications

(27 citation statements)

References 2 publications

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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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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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“…The implementation of the previous SCM formulation proves to be a challenge: authors like [1], [13], [15], [17], [38], [39], [48], [49], [50], [51], and [52] recognize the fact that SC partners operate independently, with their own objectives which are often conflicting. [22], and [39] go further and states that the promises of mutual benefits are rarely realized as SC members tend to seek their own profit, by working on an opportunistic local perspective.…”

Section: The Scm Formulation Challengementioning

confidence: 99%

“…The scalerelated issues refer to the characteristics of real-life SCs-i.e. multiple end products, facilities, and capacities [49] -that make difficult the modeling effort, as there are a large number of tangible/intangible, dynamically changing variables [103], [125]. The scope-related issues refer to the existing SC models, that are limited by a number of assumptions, focus only on one certain part of the SC, or are not flexible enough to allow easy exploration of innovative policies [106], [126].…”

Section: Modeling Requirementsmentioning

confidence: 99%

Modeling the Supply Chain Management Creation of Value — A Literature Review of Relevant Concepts

Martínez-Olvera¹,

Davizon-Castillo²

2015

Applications of Contemporary Management Approaches in Supply Chains

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3. S&D elements realignment; based on the realignment of the SC partners' static and dynamic elements (i.e. network structure and decision-making processes) affecting the material, information, and cash flows… 4. C&O fulfilment; in the face of internal/external constraints and local/global objectives. SC design & modelingIn general terms, a SC can be considered as 1) composed by inbound logistics, component suppliers, final assembly plants, and outbound logistics echelons [32], [33], [34], [35], where the relevance of the physical functions (i.e. transportation and storage) to the efficient supply,

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“…72 . He then studied the concept of conjectural equilibrium in multi-agent learning systems 73 . Kaihara modeled problem of supply chain in a dynamic environment as a virtual market 74 .…”

Section: Contract Netsmentioning

confidence: 99%

Application of Artificial Capital Market in Task Allocation in Multi-robot Foraging

Akbarimajd

Simzan

2014

IJCIS

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Because of high speed, efficiency, robustness and flexibility of multi-agent systems, in recent years there has been an increasing interest in the art of these systems. Artificial market mechanisms are one of the well-known negotiation multi-agent protocols in multi-agent systems. In this paper artificial capital market as a new variant of market mechanism is introduced and employed in a multi-robot foraging problem. In this artificial capital market, the robots are going to benefit via investment on some assets, defined as doing foraging task. Each investment has a cost and an outcome. Limited initial capital of the investors constrains their investments. A negotiation protocol is proposed for decision making of the agents. Qualitative analysis reveals speed of convergence, near optimal solutions and robustness of the algorithm. Numerical analysis shows advantages of the proposed method over two previously developed heuristics in terms of four performance criteria.

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Multi-agent based supply chain modelling with dynamic environment

Cited by 151 publications

References 2 publications

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

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

Modeling the Supply Chain Management Creation of Value — A Literature Review of Relevant Concepts

Application of Artificial Capital Market in Task Allocation in Multi-robot Foraging

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