Multi-agent Based Simulation Model Research Articles

Evaluation of operation cost and energy consumption of ports: comparative study on different container terminal layouts

Automatic container terminals (ACTs) have emerged as the development direction of upgrading and transformation for traditional manual container terminals. A number of terminal layouts are devised and implemented. However, it is the lack of a comprehensive comparison of different container terminal layouts from aspects of operation cost and energy consumption. In view of this, a Time-Driven Activity-Based Costing approach is used to identify the ACT profitability bottlenecks. A multi-agent based simulation model considering four main terminal layouts is established. The multi-level handling of ACTs is simulated and the corresponding operation time and energy consumption are calculated. Simulation results show that automated guided vehicles (AGVs) are the key to improving the cost-effectiveness of ACTs. The cost-effectiveness of ACTs with non-cantilever automated rail-mounted gantries (ARMGs) is more vulnerable than that of ACTs with bilateral cantilever ARMGs. On average, ACTs with bilateral-cantilever ARMGs consume less energy per container task than ACTs with non-cantilever ARMGs. This study provides management insights into resource allocation, layout design, and sustainable development.

A multi-agent based simulation model of consumer behaviour in the health and wellness food market

The main scientific goal of the article is to present the results of research which aimed to build a multi-agent simulation model of consumer behaviour on the health and wellness market. A multi-agent based modelling and simulation (MABS) method was used to build the model. The analysis of the collected data allowed for a typology of a group of consumers in the health and wellness food market. The results of the simulation experiments led to the identification of key strengths and weaknesses and indicate which opportunities and threats have the strongest impact on the development of the health and wellness food market. The findings could be useful in the development of tools and strategies that could be adapted to encourage healthy eating habits. The study can increase consumer awareness of the importance of healthy eating, sustainable consumption and lead to more healthy buying behaviour.

Study on Distributed Consistent Cooperative Control of Multi-ART in Automated Container Terminals

In order to address the congestion problem of vehicles in quay of automated container terminal with parallel layout and side-loading operations, the study regards the terminal operation system as a multi-agent system (MAS), and proposes a dynamic cooperative speed control schema of multi-ART(Artificial Intelligence Robot of Transportation, ) for the purpose of smart and distributed traffic control of container terminals. Via the real-time data interchange and message passing with ART group, the individual ART implements the collaborative speed regulation with the combination of the dynamic speed regulation strategy, so as to adjust the sequence of ARTs arriving at the quay which can decrease the waiting time of ARTs at the quay. Considering the consistency problem of individual and group agent among the dynamic speed regulation of ARTs, a novel distributed consensus protocol is established to assure that the individual status of ART will tend to converge basically during the collaborative speed regulation process, and the convergence of system can be guaranteed. Based on the case of automated container terminal of Tianjin Port, the paper establishes a multi-agent based simulation model, and simulates the decision-making process, and validates the effectiveness of mentioned model and strategy. The results demonstrate that the strategy can decrease the waiting time of ARTs under uncertainty environment of port and improve the operation efficiency of system.

A Multi-Agent Based Simulation Model of Consumer Behaviour - Food Choice and Cooking Attitudes During the Coronavirous (Covid-19) Outbreak

Purpose: The primary scientific goal of the article is to present the results of research which aimed to build a multi-agent simulation model of consumer behavior during the coronavirus Covid-19 epidemic. The research investigation concerned finding an answer to the question: to what extent did consumer eating behavior change during the pandemic, and how did the risk of becoming overweight and obesity increase? Design/Methodology/Approach: To gain deeper insight into consumer eating behavior amid the Covid-19 outbreak, a survey on food choice and cooking attitudes was carried out in the West Pomeranian region of Poland. Four hundred twenty respondents took part in the survey. The data obtained was used to elaborate a multi-agent model created by the author and the execution of simulation experiments. A multi-agent-based modeling and simulation (MABS) method was used to build the model. Findings: The research shows that among the individuals participating in the study, three types of eating attitudes were distinguished, restrained eaters (41.9%), emotional eaters (34.8%), and external eaters (23.3%). These three groups reported different behavior, highlighting, for example, an increase in ready-made and ultra-processed food consumption within the non-emotionally responsive group. Empirical data was used to develop an agent-based simulation model that combined different eating styles represented by consumers with their food and cooking habits to simulate consumer behavior during the Covid-19 epidemic. Practical Implications: The findings could be helpful in support of practical and social solutions for the identification of excessive weight and obesity risk factors and the preparation of strategies aimed at reducing the influence of pandemics on consumer eating behavior and increasing consumer awareness on this issue. Originality/Value: The study can stimulate sustainable consumption and consumer behavior, especially in light of the current Covid-19 pandemic.

Assessment of the Physical Internet Enabled Urban Logistics Using Agent-Based Simulation

The physical internet (PI) is a new paradigm in logistics to aim for more efficiency and sustainability via the shared use of hubs and transports as well as the consolidation of shipments. This study investigates the potential of an open mobility web, designed on the principles of PI, in an urban area with e-commerce warehouses. Our motivation is to understand benefits gained by incorporating PI into existing logistics networks. To that aim, a multi-agent based simulation model is developed to demonstrate the efficacy of our proposed solution approach in this modelling framework. Four transportation scenarios simulate a range of advancements from the traditional logistics to PI. The computational results using real data indicate that the PI-enabled mobility web outperforms the traditional logistics on most performance measures of time and cost. We conclude with implications for logistics stakeholders, and discuss limitations and extensions of this study.

Effects of block lengths and stacking policies on overall performance at non-transshipment container terminals

The ever-increasing worldwide container throughput and the intensified port competition have demanded better terminal operational performance, which is usually measured by the gross crane rate (GCR). In this paper, a multiagent-based simulation model is proposed to investigate how the block length of the storage yard and the tactical-level stacking policy affect the operational performance of non-transshipment container terminals over the long term. Experiments consider seven block lengths, two yard truck fleet sizes and two stacking policies. The results demonstrate that the best block length yielding the highest GCR is dependent on the stacking policy and the yard truck fleet size, and the separate stacking policy is essentially superior to the scattered stacking policy. Specifically, only when 9-yard trucks are deployed per quay crane under the separate stacking policy, can the typical 42-slot block length achieve the highest GCR. Although the experiments are not exhaustive, they do provide the first fundamental insights, with respect to the effects of block lengths and stacking policies, on the long-term performance at a manually controlled non-transshipment container terminal.

Freight Transportation Resilience Enabled by Physical Internet

This paper investigates the resilience of Physical Internet (PI) enabled freight transportation system confronted to random disruptions. As a metaphor of the digital internet, the concept of PI aims to integrate independent logistics networks into a global, open, interconnected system. Prior research shows that the new organization can reduce transportation cost through flow consolidation. Continuing along these lines, this paper examines how PI deal with disruption problems at hubs as well as the resilience of such an open logistics system. To attain this, a multi-agent based simulation model is developed. Disruptions at hubs are considered and formulated by a two-state Markov process, with a probability of breakdown αi and a probability of repair βi. Faced with the disruptions, two PI-based transportation planning strategies are proposed and investigated: risk avoidance and risk-taking. Results of experimentation on fast-moving consumer goods (FMCGs) chains in France suggest that PI can provide better performance on resilience in freight transportation. This paper also indicates a novel approach to build a resilient distribution system.

A Multi-Agent Based Simulation Model for Rail–Rail Transshipment: An Engineering Approach for Gantry Crane Scheduling

Le Havre Port Authority is putting into service a multimodal hub terminal with massified hinterland links (trains and barges) in order to restrict the intensive use of roads, to achieve a more attractive massification share of hinterland transportation and to provide a river connection to its maritime terminals that do not currently have one. This paper focuses on the rail–rail transshipment yard of this new terminal. In the current organizational policy, this yard is divided into two equal operating areas, and, in each one, a crane is placed, and it is equipped with reach stackers to enable container moves across both operating areas. However, this policy causes poor scheduling of crane moves, because it gives rise to many crane interference situations. For the sake of minimizing the occurrence of these undesirable situations, this paper proposes a multi-agent simulation model including an improved strategy for crane scheduling. This strategy is inspired by the ant colony approach and it is governed by a new configuration for the rail yard’s working area that eliminates the use of reach stackers. The proposed simulation model is based on two planner agents, to each of which a time-horizon planning is assigned. The simulation results show that the model developed here is very successful in significantly reducing unproductive times and moves (undesirable situations), and it outperforms other existing simulation models based on the current organizational policy.

MULTI-AGENT BASED SIMULATION OF OPTIMAL URBAN LAND USE ALLOCATION IN THE MIDDLE REACHES OF THE YANGTZE RIVER, CHINA

The optimization of land-use allocation is one of important approaches to achieve regional sustainable development. This study selects Chang-Zhu-Tan agglomeration as study area and proposed a new land use optimization allocation model. Using multi-agent based simulation model, the future urban land use optimization allocation was simulated in 2020 and 2030 under three different scenarios. This kind of quantitative information about urban land use optimization allocation and urban expansions in future would be of great interest to urban planning, water and land resource management, and climate change research.

MULTI-AGENT BASED SIMULATION OF OPTIMAL URBAN LAND USE ALLOCATION IN THE MIDDLE REACHES OF THE YANGTZE RIVER, CHINA

Abstract. The optimization of land-use allocation is one of important approaches to achieve regional sustainable development. This study selects Chang-Zhu-Tan agglomeration as study area and proposed a new land use optimization allocation model. Using multi-agent based simulation model, the future urban land use optimization allocation was simulated in 2020 and 2030 under three different scenarios. This kind of quantitative information about urban land use optimization allocation and urban expansions in future would be of great interest to urban planning, water and land resource management, and climate change research.

A Multi-agent Based Decision –Making Approach for Field Service Delivery of IPS2

There is a trend increasing number of manufacturing companies offer field service to customers rather than only provide products for higher revenue and stronger competitiveness. However, these companies are experiencing a lack of proper and efficient decision support in maintenance policy selection and service resource planning of service delivery. In this paper, a multi-agent based approach to service decision-making of IPS2 is presented and the performance evaluation result is given. Multi-agent based approach is utilized to model the service delivery network and different agents are generated to present the elements of field service network. Each agent has a distinct state transition and processing mechanism which can be highly coincidence with the actual situation owing to the flexibility and operability of multi-agent based simulation model. Furthermore, negotiation mechanism among the agents is introduced into the multi-agent based simulation model to map the dynamic characteristics in the actual service delivery context. A case study scenario of agricultural machinery maintenance is presented which clearly demonstrated that the proposed approach is efficient and can provide reasonable suggestions to the IPS2 providers.

Multi-Agent Based Simulation Model for Rail Transit Evacuation Process

In order to analyze the evacuation behaviors and optimize evacuation strategies for rail transit system, an evacuation agent centered simulation model was proposed. Firstly, by considering the attributes, status and decision-making behaviors of evacuation personnel, the evacuation agent model was established, and the running principle as well as construction process of multi-agent simulation model was discussed. Then, the specific definition and design for the agent attributes and evacuation behavior protocol were provided. Finally, based on the simulation model proposed, an evacuation simulation platform for the military museum station of Beijing subway line 9 was established by using REPAST and JAVA, several evacuation strategies were tested and optimized.

Study on a Pedestrian Simulation Model of Natural Movement

The purpose of this study is to construct a pedestrian simulation model of natural movement by applying rules that influence pedestrian route selection from previous experimental studies. Based on wall vector field, the multi agent-based simulation model which evolves through the relationship between vision-driven agents and configuration of environment was programmed by NetLogo. It considered visual field angle, visual distance, visual noise (looking around), moving speed, attractive factor, and other agents. The results of the simulation in several environments including a real architectural space such as the Tate Gallery produced better movement patterns than a previous model (Turner′s EVA model) both locally and globally. Since the simulation model included variables related to visual factors which affect natural movement, it could help towards understanding the relationship between visibility and movement, its effects on spatial usage, and the affordance of configuration in a built environment for natural movement.

Task Allocation Model for Rescue Disabled Persons in Disaster Area with Help of Volunteers

In this paper, we present a task allocation model for search and rescue persons with disabilities in case of disaster. The multi agent-based simulation model is used to simulate the rescue process. Volunteers and disabled persons are modeled as agents, which each have their own attributes and behaviors. The task of volunteers is to help disabled persons in emergency situations. This task allocation problem is solved by using combinatorial auction mechanism to decide which volunteers should help which disabled persons. The disaster space, road network, and rescue process are also described in detail. The RoboCup Rescue simulation platform is used to present proposed model with different scenarios.

Investigating Efficient Traffic Management Schemes for Urban Expressway Companies with a Multi-Agent Simulation Model

Efficient traffic management schemes can lead to increased traffic volumes by preventing from traffic accidents as well as to improved traffic flows, less congested traffic flows and higher convenience of users. We develop a multiagent-based simulation model composed of two agents: urban expressway company and traffic management company. These two agents can sense their circumstances and aim to increase their own income through the interaction of their behavior resulting in improved traffic flows. This model allows to find out what traffic management policy can provide improved traffic flows and increased amount of income of urban expressway company. The results of the simulation would offer interesting suggestion to the highway company which tries cost reduction at the time of privatization.

Impact of reputation information sharing in an auction-based job allocation model for small and medium-sized enterprises

This research investigates the impact of reputation information sharing in an auction-based job allocation system using a multi-agent based simulation model based on three performance measures: economic efficiency, throughput index, and penalty index. Two forms of reputation information sharing systems, dyadic and public, were implemented and studied. Dyadic reputation systems deal with one-to-one information sharing, while public reputation systems deal with many-to-many information sharing. The paper proposes a hybrid auction mechanism, which is a combination of the sealed bid and reverse auction mechanisms. Analysis of the simulation results shows that reputation information sharing benefits the system tremendously by increasing the overall economic efficiency and throughput index, while decreasing the penalty index of the system. The results also show that as the significance of reputation in the market increases, the benefits realised in terms of economic efficiency and throughput index also increases.

Global versus local sourcing for different supply chain networks: an analysis of order unfulfilment rates

Global sourcing is currently the most popular method for improving performance throughout the Supply Chain (SC). Because of this, we want to assess whether performance improvement because of global sourcing is true for all SC networks. In this paper, we assess the relationship of SC network types, global versus local sourcing, different levels of supply variation amplification and order unfulfilment rates. A multi-agent based simulation model is used for assessment. Results show that global sourcing alone cannot ensure SC performance improvement. Results also show that there is no best practice for all SCs. A simple trade-off analysis between costs and order fulfilment rates is also taken. This analysis helps diagnose the potential value of global sourcing and provides a framework for making sourcing strategy decisions.

Assessment for estimated evacuees behavior using multi agent based simulation model

Assessment for estimated evacuees behavior using multi agent based simulation model