In general, there are lots of such optimal solutions, and a set of Pareto optimal solutions are only obtained.
Decision-makers have to choose a final preferred one from among the Pareto optimal solutions based on personal
preferences or additional criteria. There have been a variety of approaches to obtain the Pareto optimal solutions as
well as to select the final preferred one, many of which are reviewed in the surveys recently collected by Ehrgott and
Gandibleux (2002) and Figueira et al. (2005), providing a comprehensive overview of multi-criteria decisionmaking and multi-objective optimisation.
Several MOP models have been proposed to facilitate decision-making for supply chain and logistics
optimisation under the conditions of risk due to the consideration of different demand risk from many different
customers (e.g., Weber and Ellram (1992); Weber and Current (1993); Ghodsypour and O’Brien (2001); Wu and
Olson (2008)). Recently, MOP has increasingly been used for a decision-making tool in the field of green supply
chain management and reverse logistics, including hazardous material transport and hazmat waste management (e.g.,
Iakovou, 2001; Zografos and Androutsopoulos, 2004; Chang et al., 2005; List et al., 2006; Yong et al., 2007; Sheu,
2008), since these have uncertainty, complexity, and potential risks for people in nature, in the process of transport,
transactions and purchasing.
In supply chain and logistics management, both costs and risks are required to be simultaneously evaluated, and
these are often conflicting. In that case, total operational costs are typically estimated, consisting of transport costs,
inventory costs, reprocessing costs and final disposal costs, while the assessment is undertaken at the same time for
the operational risks generated for transportation, storage, reprocessing and final disposal. This leads problems
associated with supply chain and logistics management when using MOP.
2.5. Multi-agent simulation
There are four major stakeholders relating to city logistics; (a) Shippers, (b) Freight carriers, (c) Residents and (d)
Administrators. These stakeholders have different objectives and different types of behaviour. Shippers try to
minimise their costs in supply chains. Freight carriers try to meet shippers’ requests to collect and deliver goods
within strict time windows. Residents want quiet, noiseless atmosphere and clean air in their community. Finally
administrators hope to activate the vitality of the city with sustainable transport systems. Understanding the
behaviour of the stakeholders and interaction among them is needed for evaluating city logistics measures before
implementing them.
Multi-agent modelling techniques allow complicated urban freight transport systems with multiple actors to be
investigated (e.g. Weiss, 1999; Ferber, 1999; Wooldridge, 2002). Multi-agent models generally deal with the
behaviour and interaction among multiple agents, which are most suitable to understand and study the behaviour of
stakeholders in urban freight transport systems and their response to policy measures. Davidsson et al. (2005)
provided a survey of existing research on agent-based approaches in freight transport and noted that agent-based
approaches seem very suitable for this domain. Duin et al. (1998) showed dynamic actor network analysis for
complex logistics problems. Ossowski et al. (2005) presented multi-agent approaches to decision support systems in
traffic management. Jiao et al. (2006) presented an agent based framework in the global manufacturing supply chain
network. The literature shows a number interesting examples of multi-agent approaches to transport logistics
problems but most of them do not directly focus on urban freight transport systems.
2.5. Multi-agent simulation
There are four major stakeholders relating to city logistics; (a) Shippers, (b) Freight carriers, (c) Residents and (d)
Administrators. These stakeholders have different objectives and different types of behaviour. Shippers try to
minimise their costs in supply chains. Freight carriers try to meet shippers’ requests to collect and deliver goods
within strict time windows. Residents want quiet, noiseless atmosphere and clean air in their community. Finally
administrators hope to activate the vitality of the city with sustainable transport systems. Understanding the
behaviour of the stakeholders and interaction among them is needed for evaluating city logistics measures before
implementing them.
Multi-agent modelling techniques allow complicated urban freight transport systems with multiple actors to be
investigated (e.g. Weiss, 1999; Ferber, 1999; Wooldridge, 2002). Multi-agent models generally deal with the
behaviour and interaction among multiple agents, which are most suitable to understand and study the behaviour of
stakeholders in urban freight transport systems and their response to policy measures. Davidsson et al. (2005)
provided a survey of existing research on agent-based approaches in freight transport and noted that agent-based
approaches seem very suitable for this domain. Duin et al. (1998) showed dynamic actor network analysis for
complex logistics problems. Ossowski et al. (2005) presented multi-agent approaches to decision support systems in
traffic management. Jiao et al. (2006) presented an agent based framework in the global manufacturing supply chain
network. The literature shows a number interesting examples of multi-agent approaches to transport logistics
problems but most of them do not directly focus on urban freight transport systems.
Taniguchi et al. (2007) presented multi-agent models for treating city logistics schemes in which shippers, freight
carriers and administrators are involved. This model included a reinforcement learning process to take better policies
into the next step based on a reward which was given as a result of the previous action of the agent. This paper
presents multi-agent models for evaluating the behaviour and interaction among stakeholders who are involved in
urban freight transport systems as well as the effects of city logistics measures. Multi-agent simulation on a small
test road network demonstrated that the VRPTW-D model which dynamically adjusted vehicle routing planning to
the current travel times generated good performance in terms of increasing profits for freight carriers and decreasing
costs for shippers. After applying multi-agent models on a large test road network, it was observed that the VRPTWD model generated a win-win situation by increasing profits for freight carriers and decreasing the costs for shippers.
The results also show that implementing road pricing can reduce NOx emissions but may increase costs for shippers.
To avoid such effects, introducing co-operative freight transport systems helps shippers reduce their costs.
Tamagawa et al. (2009) presented a multi-agent model in which five stakeholders, freight carriers, shippers,
residents, administrators, and urban motorway operators were involved. They embedded a Q-learning process in
decision making of policies made by agents taking into account the reward from the previous action. After applying
multi-agent models in an urban road network, they examined the performance of several city logistics measures
including road pricing and truck bans. In spite of implementing of city logistics measures and the increase of using
urban motorways, freight carriers could keep their transport costs at the level of the situation without any city
logistics measures or tolling, and they could keep their delivery charges at the same level. As a result, shippers could
also keep their delivery costs at the same level as before. They conclude that the implementation of these measures
had the effect of improving the environment for all stakeholders.
Donnelly (2009) modelled urban goods movement using hybrid models based on aggregate macroeconomic
interactions, discrete event micro simulation and agent-based modelling. These models were successfully applied in
Portland City, US for examining several city logistics scenarios using existing data sets.
2.6. Health
The health of persons involved in urban distribution is an important element of the vision for city logistics
(Taniguchi et al., 2004). Occupational health and safety of employees is a substantial issue and there is increasing
pressure on employers to be more proactive with respect to protecting the health of their employees. This section
outlines approaches used to model the effects of air quality and physical activity of drivers.
2.7. Air quality
Carriers are a major stakeholder in City Logistics and the drivers of trucks and vans are often exposed to high
levels of emissions for extended periods. A number of research studies have shown that there is higher air pollution
inside road vehicles compared to ambient air quality (Chertok, 2004; Fruin, 2004; Rodes et al., 1998). The level of
in-cabin exposure to air pollutants is a function of the time a person spends in the cabin in urban streets. Therefore,
drivers have an increased risk of cancer and respiratory diseases, such as asthma.
The disability adjusted life year (DALY) measurement is an indicator of the burden of disease (BoD) in the
community. One DALY represents one lost year of ‘healthy’ life and is a combination of years of life lost (YLL) as
a result of premature mortality plus an equivalent number of ‘healthy’ years of life lost as a result of disability
(YLD). DALYs are the summation of years due to premature mortality (YLL) in the population and the years lost
due to disability (YLD). DALYs are regularly determined for particular States and cities and typically calculated for
particular diseases such as diabetes mellitus and cardiovascular disease (Murray and Lopez, 1996).
Kayak and Thompson (2007) report that the possible contribution to the BoD for the population of the Melbourne
Statistical Division (MSD) jurisdictions from exposure to diesel fuel emissions, by in-vehicle diesel engine
environments is multiples greater than that to the population outside the vehicles.
Improved methods are needed to develop and identify cost effective methods to reduce the risk of health
problems for drivers from air pollution. There is a need to incorporate public health impact evaluation using tools
such as the DALY health measurement for avoidable deaths into city logistics planning.
2.8. Physical activity
There are increasing concerns relating to the rising levels of obesity in many countries with most residents
leading increasingly sedentary life-styles. Physical activity is most commonly undertaken at work, home or during
recreation and transport. Drivers of freight vehicles face increased risks of not undertaking sufficient levels of
physical activity at work.
The current Australian physical activity guidelines are, “Put together at least 30 minutes of moderate-intensity
physical activity on most, preferably all, days.” (Commonwealth Department of Health and Family Services, 1998)
A classic epidemiological study of bus conductors in London, concluded that the higher physical activity of
conductors on double-decker buses in London contributed to the lower incidence and mortality in the conductors
(Morris, 1953).
There is a need to incorporate more physical activity in urban distribution since many logistics tasks have been
mechanised in the last 50 years. A significant amount of physical activity has taken out of contemporary daily
distribution system in cities, with typical motorisation of delivery of newspapers and mail as well as the collection
of garbage. Drivers often experience long periods sitting in vehicles driving or waiting.
Accelerometers can be used to accurately measure the amount of energy expenditure undertaken due to physical
activity. Activity diaries can also be used to estimate the frequency, intensity and duration of physical activities
undertaken by individuals.
A person’s weight is largely determined by the amount of energy expenditure and food energy intake. Combining
levels of predicted energy expenditure (including that from non-motorised distribution) with dietary details the
weight of individuals can be simulated (Westerterp et al., 1995; Payne and Dugdale, 1977). The health benefits can
then be determined since many burden of disease studies relate the risk of chronic diseases to Body Mass Index
(BMI).
Daily Physical Activity Levels (PALs) can be determined by estimating the total energy expenditure, expressed
as a multiple of the basal metabolic rate (Ainsworth et al., 2000). It is recommended that average PALs should be
above 1.6 (AICR, 2007).
Estimating energy expenditure for an individual over a daily period is a complex and challenging task. There are
several methods of calculating physical activity levels. A simple method has developed for determining basal,
activity and total energy expenditure levels based on personal attributes such as age, height, weight and gender as
well the duration and metabolic rate of activities undertaken (Ainsworth, 2000; Gerrior et al., 2006).
There has been a recent trend in the delivery of mail within Australian cities towards using trolleys and bicycles
instead of motor cycles and vans. This is due to the difficulty in recruiting licensed motorcycle riders as well as the
increasing number of motorcycle riders that are becoming overweight. Distribution of mail to dwellings in
residential areas by bicycle has many health benefits for riders. In addition, there are environmental benefits of less
air pollution as well as social benefits associated with reduced safety costs associated with crashes (number and
severity).
2.9. Human security engineering
Cities can experience a range of natural and man-made disasters that can cause disruptions to urban distribution
systems. Following the emergency response and relief phases, urban logistics systems often need to be redesigned as
reconstruction and recover efforts are undertaken.
There is a need to build more resilience into urban transport systems to limit the effect of disasters (Murray-Tuite
and Mahmassani, 2004; Murray-Tuite, 2006). Traffic systems are often disrupted as a result of disasters and city
logistics schemes can provide an efficient means of continuing distribution services when the capacity of the urban
traffic system has been reduced.
Following a disaster the capacity of traffic links is lost or reduced resulting is increased travel time, delays and
delay penalties. Changes in the origin and destination patterns for freight vehicle trips can result due to links being
blocked leading to changed routes within urban areas.
Models can assist in designing appropriate city logistics schemes to operate throughout the reconstruction period
following a disaster. Models can also assist in identifying vulnerable links as well as determining the most efficient
schedule of reconstruction projects.
Economic loss modelling involves estimating the direct losses including the reconstruction of traffic links and
traffic management as well as the indirect losses such as business disruption and increase delay (Buckle, 2005).
Catastrophe models predict the financial loss from disasters (Grossi and Kunreuther, 2005). Hazard and inventory
modules provide the information to estimate the vulnerability of structures to damage from a disaster. Losses are
predicted by direct costs of repair or reconstruction as well as indirect costs such as business interruption and
evacuation.
Hazard modules involve consideration of the location, frequency and severity of disasters that is largely based on
analysis of historical data. The inventory module provides information on the type and strength of structures.
Vulnerability modelling predicts the damage to structure for given disaster events. Loss models estimate the
financial loss incurred from the physical damage.
The Risk from Earthquake Damage to Roadway Systems (REDARS) model (Werner et al., 2005; 2007) estimates
post disaster trip demands and this is used to identify vulnerable transport links (including bridges). It provides
guidance for making decisions to reduce risks and can assist in developing response and recovery strategies.
2.10. Manmade disasters
Manmade disasters is a challenging topic to be tackled in terms of City Logistics. It includes war, terrorism,
epidemics and pandemics, traffic accidents, nuclear accidents, food or water contamination, building collapses and
so on. Tansel (1995) compares the characteristics of manmade disasters with that of natural disasters and states that
manmade disasters could happen anywhere where there is human activity, while natural disasters are generally
regional. He also indicates that hazardous waste was the most serious environmental problem among manmade
disasters, confronting risk managers in the 1990s. Risk management in hazardous material transport has therefore
been a major research topic relating to manmade disasters (e.g., Gopalan et al., 1990; List et al., 1991; Beroggi and
Wallace, 1995; Nozick et al., 1997; Miller-Hooks and Mahmassani, 1998).
There is an increasing need to further secure transportation infrastructure due to terrorist threats and incidents
seen since September 11th, 2001. Sheffi (2001) points out that supply chains are particularly vulnerable to
intentional or accidental disruptions and suggest multi-supplier strategy as a possible approach for alleviating the
vulnerability.
Tang (2006) reviewed quantitative models that deal with supply chain risks and classifies supply chain risks into
two types: operational risks and disruption risks. The disruption caused by manmade disasters include disruption
risks, whilst the operational risks refer to those inherent uncertainties that inevitably exist in supply chains, such as
uncertain customer demands and uncertain costs. In addition, robust supply chain strategies are proposed for
mitigating disruption risks that would enhance efficiency and resiliency. Lau et al. (2008) proposed a real-time
supply chain management model based on multi-agent simulation with its application in the event of bird flu (avian
influenza) or terrorist attacks. Mohan et al. (2009) investigated the risks faced by poultry supply chains in the avian
influenza epidemic, identifying risk factors, losses and gains, and mitigation strategies used by different players in
the supply chain.
Surface transport systems, including freight transport, are inherently very vulnerable to terrorist threats. Plant
(2004) examined the impact of the terrorist attacks on the North American rail industry and government agencies
concerned with railroad security. Okonweze and Nwagboso (2004) placed emphasis on the usage of real-time
information systems and categorised intelligent transport subsystems, which can help to protect transport
infrastructure and systems against terrorism. Murray-Tuite (2007) presented a framework for evaluating a risk (i.e.,
capacity loss between an OD) to the road transport network from direct targeting by terrorists.
2.11. Hazardous material transport
Hazardous material transport in urban areas has been an important research area for decades. Once a vehicle
carrying hazardous material is involved in a crash on a roadway it may cause a large impact on people and buildings
and other traffic by the explosion or spill of hazardous material. These risks should be assessed in advance and well
controlled for safer management of transport using ICT (Information and Communication Technology) and ITS.
Modelling techniques are required for assessing risks and evaluating measures to manage hazardous material
transport in urban traffic environments. Eiichi Taniguchi et al. / Procedia Social and Behavioral Sciences 2 (2010) 5899–5910 5907
Erkut and Verter (1998) presented an overview of modelling hazardous material transport and pointed out that
different risk models suggest different “optimal” paths for a hazmat shipments between a given origin-destination
pair. Five categories of risk models were suggested in the literature: (a) Traditional risk, (b) Population exposure, (c)
Incident probability, (d) Perceived risk, and (e) Conditional risk.
Incorporating the risks of hazardous material transport often requires multi-objective optimisation models.
Giannikos (1998) presented a multi-objective programming model for this problem taking into account total costs,
total perceived risk, individual perceived risk and individual disutility. Chang et al. (2005) described a method for
finding non-dominated paths for multiple routing objectives in networks where the routing attributes are uncertain,
and the probability distributions that describe those attributes vary by time of day. Bell (2006) discussed using a mix
of routes by determining the set of safest routes and the safest share of traffic between these routes leads to better
risk averse strategy based on a game theory approach. Beroggi (1994) proposed a real time routing model for
assessing the costs and risks in a real-time environment and pointed out that the ordinal preference model turned out
to be superior to the utility approach.
2.12. Traffic safety
There has been a lot of research relating to traffic safety and accidents. Empirical studies have been undertaken to
identify the relationship between crash frequency and some or all of the factors including vehicle-kilometres,
average hourly traffic volume per lane, average occupancy, lane occupation, average speed, its standard deviation,
curvature, road geometry, and ramp section design (e.g., Jovanis and Chang (1986); Miaou and Lum (1993);
Shankar et al. (1995); Abdel-Aty and Radwan (2000)). The focus has also been on the relationship between accident
rates and traffic characteristics, including hourly traffic volume, level of service, weather, and hourly traffic flow of
cars and lorries (e.g., Fridstrøm et al., 1995; Ivan et al., 1999; Martin, 2002). Furthermore, driving behaviour has
been considered to be a crucial factor causing traffic accidents. Sleep-related or sleepiness-related driving accidents
are investigated by Horne and Reyner (1995), and Sagberg (2001).
However, there have been few studies taking into account the effect of the flow of freight vehicles and heavy
goods vehicles (HGVs) on traffic accidents, except for Hiselius (2004), Ramírez et al. (2009). Zaloshnja and Mill
(2004) investigate the relationship of ramp design and truck accident rates. Tzamalouka et al. (2005) identify the
contributing factors to the probability of falling asleep and crash risk through the interview questionnaire survey to
professional drivers including truck drivers. The use of intelligent transport systems are also undertaken in terms of
the prevention of traffic accidents caused by freight traffic (Palkovics and Fries, 2001; Sarvi and Kuwahara, 2008).
3. Conclusion
There is a need for transport logistics models to incorporate risk so that urban distribution systems can become
more resilient with respect to natural and manmade hazards. Models that take risks into account can assist in
designing city logistics schemes to improve the health and safety of persons involved in transport logistics as well as
residents.
This paper has outlined how recently developed modelling techniques such as stochastic programming, agent
based simulation and robust optimisation methods can be applied to urban freight and supply chain networks. Links
between human security engineering and city logistics were described. The need for models to assist in the recovery
of transport infrastructure for the public sector as well as to develop plans for business continuity management for
the private sector were outlined.
Due to growing urbanisation and the increasing prevalence of extreme weather events as well the continuing
threat of terrorism, improved urban freight models are required to minimise the disruptions to urban freight systems
from natural and manmade hazards.
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