Scholarly article on topic 'Simulation Techniques for Evaluating Smart Logistics Solutions for Sustainable Urban Distribution'

Simulation Techniques for Evaluating Smart Logistics Solutions for Sustainable Urban Distribution Academic research paper on "Agriculture, forestry, and fisheries"

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Abstract of research paper on Agriculture, forestry, and fisheries, author of scientific article — Ioannis Karakikes, Eftihia Nathanail

Abstract Smart logistics solutions have been developed in order to alleviate the adverse impacts of increasing goods’ transport in urban areas. However, the outcome can be questioned, unless proper assessment is conducted to compare impacts during or after implementing these solutions. Simulation has been proved as valuable tool to assess impacts of the logistics solutions, before their actual implementation in the field, and support the decision making process. This study contributes in presenting the current state of practice in modeling smart logistics solutions, provides a roadmap in simulation techniques for urban freight transportsolutionsandimprovesthe knowledge around the patterns currently followed.

Academic research paper on topic "Simulation Techniques for Evaluating Smart Logistics Solutions for Sustainable Urban Distribution"



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Procedía Engineering 178 (2017) 569 - 578

Procedía Engineering

16th Conference on Reliability and Statistics in Transportation and Communication, RelStat'2016, 19-22 October, 2016, Riga, Latvia

Simulation Techniques for Evaluating Smart Logistics Solutions for

Sustainable Urban Distribution

Ioannis Karakikes*, Eftihia Nathanail

University of Thessaly, Pedion Areos, GR-38334, Volos, Greece


Smart logistics solutions have been developed in order to alleviate the adverse impacts of increasing goods' transport in urban areas. However, the outcome can be questioned, unless proper assessment is conducted to compare impacts during or after implementing these solutions. Simulation has been proved as valuable tool to assess impacts of the logistics solutions, before their actual implementation in the field, and support the decision making process. This study contributes in presenting the current state of practice in modeling smart logistics solutions, provides a roadmap in simulation techniques for urban freight transport solutions and improves the knowledge around the patterns currently followed.

© 2017 The Authors.Publishedby Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-reviewunderresponsibilityofthescientific committee of the International Conference on Reliability and Statistics in Transportation and Communication

Keywords: city logistics, smart logistics solutions, simulation, last mile distribution, evaluation

1. Introduction

Urban distribution of goods is a main component of sustainable transport networks and one of the main contributors on traffic congestion and environmental pollution in the cities. Urbanization, consumerism, technological blooming and international competition cause a vast demand of products and services and make the

* Corresponding author. E-mail address:

1877-7058 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the scientific committee of the International Conference on Reliability and Statistics in Transportation and Communication doi:10.1016/j.proeng.2017.01.110

distribution of goods within urban areas an essential priority for public authorities. The ever growing demand in transport of goods renders the upkeep of a high traffic and living quality in cities a challenging process.

Over the last decades, many smart logistics solutions have been developed to allay cities' problems related to distribution of goods. These solutions complement conventional Urban Freight Transport (UFT) measures and policies, or replace them entirely. However, new approaches are always generated towards these smart solutions, rendering their implementation dreaded in terms of adequacy and sufficiency, due to the lack of past experience. Especially in a multi-dimensional environment as the urban environment, in combination with the multivariate nature of logistics measures, their implementation can bring adverse effects if all aspects are not considered carefully. To avoid such situations, modelling of the proposed solutions is advised, since it facilitates impact assessment and evaluation before their actual implementation in the field, and support the decision making process.

Several models, grouped mainly according to the scope they serve, have been applied to evaluate logistics measures. For example, Ambrosini et al. (2004) addressed two families of urban freight models: (a) the operational models for improving the flow management and (b) the systematic models for evaluating the impact of interventions made in a logistic system. Hicks (1999) under a different approach, grouped the models based on their utility into: (a) simulation models, (b) optimisation models and (c) simulation-optimisation models. Accordingly, Taniguchi et al. (2012) clustered models in: (a) optimisation models and (b) simulation models. Optimisation models are associated with the process of finding the best solution out of the number of alternatives the user has in his disposal, based on the objectives need to be achieved. Such optimization models have been used into the logistics field to address issues like sourcing decisions for minimization of total costs (Farahani and Elahipanah, 2008), risk management for coming up with strategies to minimize potential disruption (Gaonkar and Viswanadham, 2014), network design for determining the best network for efficient deliveries (Melo et al., 2009) and other. Simulation models have as purpose to replicate adequately a working system in order to understand it better. Then, an adequately calibrated and validated model can be used as a test bed, where different scenarios and optimisation can be examined. Simulation models, frequently substitute optimisation models, since they can act as fancy manual calculators to test different scenarios and identify the best one, through the trial-and-error process.

In research, several models can be found that aim at the evaluation of the impacts of the concerned logistics solution in some or all four principal sustainability impact areas (economy, environment, transport, society) (Anderson et al., 2005; UK Round Table on Sustainable Development, 1996; Behrends, 2011). This paper focuses on simulation models, and presents them, considering three simulation techniques: (a) Systems dynamics, (b) Multiagent systems, (c) Traffic simulation (Taniguchi et al., 2012).

2. Simulation models

2.1. Systems dynamics

Systems Dynamics (SD) is a computer-aided approach developed by Jay W. Forrester at MIT University during the 1950's. This approach aims to analyse and solve complex problems related to policy analysis and design by applying feedback control theory to simulation models of organisations (Forrester, 2003; Angehofer and Angelides, 2000). Forrester (1969) in his publication on Urban Dynamics, proposed a new approach for analysing urban related problems, which lays the fundamental grounding for the linkage of the urban dynamics with the decision-making process of urban areas. This approach was widely adopted in the following years, since many applications took place ever since in the area of logistics. Qui et al. (2015) describe a systems dynamic model for simulating the logistics demand dynamics in the city of Beijing, China. Teimoury et al. (2013) develop a SD simulation model to study the relationships and behaviours developed in the supply chain of perishable fruits and vegetables in Tehran, Iran, as well as to analyse the supply, demand and price interactions. Tako and Robinson (2012) reviewed the Discrete Event Simulation (DES) compared to SD as decision support tools and highlighted advantages and disadvantages in applied case studies. Poles (2013) model a production and inventory system for remanufacturing activities. Shouping et al. (2015) developed an SD model to evaluate the logistics system in the city of Guangzhou. Rasjidin et al. (2012) examined the aspect of weather conditions and energy supply's fluctuation in respect to minimization of energy retailer's cost.

SD is an integrated methodology, combining scientific theory with computer simulation, focusing on the internal structure and features of a system (Zhong et al., 2013). The basic principle in SD, is modelling system's structure in order to understand the behaviour the system produces (Sterman, 2000). Through modelling it is easier to see the cause-and-effect relationships developed, resulting from the feedback loops, existing between the objects of the system. These relationships can be negative, positive or stock-and-flow, meaning that a variable's change will affect other variables in the system, including also the initial one. By fully understanding and identifying the relationships into a system, the analyst can understand the behaviour of the whole system. The main steps followed in a SD modelling process are: (a) problem structuring, (b) causal loop modelling, (c) dynamic modelling and (d) communication of results (Maani and Cavana, 2000).

In a system there are many variables connected with arrows and influence lines, forming many causal chains and loops. Directions of the influence lines express the impact of a causal chain. Analytically, the '+' sign on the upper end of an influence line, designates that the two variables on both sides of the line change in the same direction, while '-' sign stands for the opposite. Thus, feedback loops can be considered as positive or negative, based on all variables and influence lines. Negative loops tend to a balancing situation, while positive loops exhibit an unstable situation (Georgiadis et al., 2005). Causal-loop diagrams can be created once all parameters have been identified and constitute the groundwork of the SD model. The representation of the model is accomplished through a stock-and-flow diagram according to the causal loops (Egilmez and Tatari, 2012). Stock-and-flow diagrams help analysts to perform the quantitative analysis. Stock variables reflect the state of the system, while flow variables carry the diversifying stocks which express the flows in a system.

In recent years due to the technological development, many great simulation (computational) tools have been created to support SD. The most prevalent are the DynamO (Forrester, 1961), iThink from ISEE systems, Vensim from Ventana systems Inc. and Powersim from Powersim Software AS.

2.2. Multi-agent systems

According to Taniguchi et al. (2001) conventional modelling methods being used in urban logistics, such as optimisation or other statistical or probabilistic methods, are not sufficient to capture the heterogeneity, complexity and unpredictability of the stakeholders within the decision making process. These methods are deterministic, meaning that they cannot provide knowledge in the whole logistics process and cannot incorporate dynamics into the system. Therefore, the need of identifying the interrelationships among stakeholders and measuring their effect in sustainable urban logistics policy analysis, can be served through a Multi-Agent System (MAS). In the field of urban logistics several researches have been conducted using MASs to evaluate smart logistics solutions. Duin et al. (2012) developed a MAS to evaluate an Urban Distribution Center as well as to analyse the arising dynamic behaviour of stakeholders. Graudina and Grundspenkis (2005) evaluated the performance of intermodal terminals for urban freight, based on a detailed description of the intra-terminal processes. Teo et al. (2012)presented a MAS model for evaluating an e-commerce delivery system solution. This was achieved by combining vehicle routing and scheduling problem via time window auction theory. Tamagawa et al. (2010) built a MAS model that concerned truck ban and discounting motorway tolls, considering freight carriers, shippers, residents, administrators and motorway operators. Taniguchi et al. (2007) examined the produced financial benefits and costs for freight carriers and shippers after implementing road pricing. Finally, Wangapisit et al. (2014) explored the implementation of joint delivery system and car parking management as city logistics measures.

The functional framework of this technique consists of three stages: specification in which information related to the decision structure is gathered, validation which refers to the validation of the developed model in respect to base models and analysis in which all different scenarios and outcomes of the evaluation of the model are counted in to recommend the most appropriate solution (Anand et al., 2015). A MAS establishes every stakeholder category as an independent entity which focuses on specific aspects of a solution, by creating modular objects, the 'Agents'. Given that two or more 'Agents' are enabled in the decision making process of a policy, MASs consolidate the individual capabilities, knowledge, objectives and viewpoints of the involved agents, and through cooperation, negotiation and co-ordination help them reach their common goal (Durfee et al., 1989). The system can be very flexible in terms of actors' participation since a high number of agents can be involved. Moreover, MASs have been used in many

complex real systems, which operate in unpredictable environments and are able to measure the impact of multiagent strategies (Horling et al., 2000). In such cases, what makes a MAS more suitable over other traditional methods is mainly the distribution of the system. This distribution (decentralization) makes the system less sensitive to certain risks, but this comes in trade-off with the increase of difficulty to analyse comprehensively the overall performance of the system (Braubach et al., 2004).

MAS is a relatively new simulation technique with increasing applications in recent few years. Frequently applied methods in such systems are MASCOT, CoagenS, Agend Enterprise, TELETRUCK and VRPTW-D (Hellingrath, 2009; Graudina and Grundspenkis, 2005; Taniguchi et al., 2007).

2.3. Traffic simulation

Micro, meso and macro simulation has been proved to be a valuable tool for planning, designing and evaluating the contribution of the urban goods transport to urban mobility and environment. Simulation of different scenarios can be accessed on the basis of interaction of city's traffic related attributes e.g. transport infrastructure system, local driving regulations, modal split, traffic volumes etc., with the properties of the proposed UFT measures. On market, there are various simulators, which can evaluate directly the impacts of the concerned measure in two principal sustainability impact areas (a) environment and (b) transport.

Several software tools exist that can be used for simulation. The most prevalent for micro simulation are Vissim from PTV, AIMSUN from TSS- Transport, CORSIM by US Federal Highway Administration, SUMO from DLR, PARAMICS from Quadstone Limited, vtSim from Technical University of Munich and more. However, all models that will be designed for a specific case need calibration in order to produce credible and reliable models. Calibration is a prerequisite to replicate accurately the real traffic situations of a system. Along with the calibration process, a validation process to verify the credibility of the model under fresh field data is also needed. The process of calibration and validation should be included as principle precondition also in meso and macro simulation. Software tools for macro and meso simulation are VISUM (macro simulation) from PTV, AIMSUN (meso& macro simulation), DYNACAM (macrosimulation) by US Federal Highway Administration, MATSim (meso and macro simulation) by Technical University of Berlin, OpenTrafficSim (micro, meso and macro simulation) from Delft University of Technology, Repast (meso and macro simulation), MAINSIM (meso and macro simulation), TRANSIM (macro simulation), Carsim (meso and macro simulation), osmtraffic (meso and macro simulation) and other.

Many studies in literature dealt with the evaluation of logistics solutions through micro, meso and macro simulation. Queshi et al. (2012) presented a micro simulation-based evaluation of the soft time windows variant of the Vehicle Routing Problem (VRP) with Vissim. Gattuso et al. (2014) developed a micro simulation model to evaluate a logistics platform in the agri-food sector. Taylor (2005) examined urban freight transport with macro simulators within the City Logistics Paradigm in the city of Sydney, Australia in order to increase transport system's performance. Scroeder et al. (2012) presented a multi-agent freight transport model using the MATSim simulation software for a fictitious system. In their example, the two groups making the logistics decisions are the transport service providers and carriers. Walker and Manson (2014) showed in their study the development of a micro simulation traffic model, concluding that more telematics does not necessarily lead to more efficient urban logistics, since topography of urban street layout is an important contingency variable. Hosoya et al. (2003) developed a micro simulation model of the metropolitan area of Tokyo, Japan in order to evaluate four logistics policies in Vehicle-Kilometer-Traveled (VKT) NOx and costs terms, considering also individual firm's behaviour and their characteristics. Their evaluation concluded that the most effective measure in this simulation was road pricing.

3. Components of the urban distribution evaluation

3.1. Impact areas

Comprehensive estimation and evaluation of the effects of logistics solutions is required, whether a solution will be finally implemented or not (Hosoya et al., 2003). Since sustainability of logistics solutions is the goal to be reached, their evaluation should be projected to the four sustainability impact areas. According to Nathanail and

Papoutsis (2015), sustainability in urban distribution can be expressed through sustainability in the following core impact areas:

• Economy: Economy is considered the impact area that includes all benefits and costs deriving from the implementation of a measure. Economy encloses also the aspect of energy, i.e. energy availability, demand, price and consumption. In order to achieve a sustainable economy both the financial perspectives and the energy utilization of the concerned solution(s), should be sustainable, as well.

• Environment: Environmental impact includes the evaluation of the impact of the logistics system in terms of emissions, air quality, noise, and waste products. Ultimate goal during or after the implementation of a UFT solution is the preservation of natural resources and mitigation of the negative effects on the ecosystem. Environmental impacts may refer from local to bigger natural ecosystems (on regional scale).

• Transport: Transport area refers to the upkeep of a high quality urban freight transport system. Attractiveness, accessibility, level of service, safety, reliability are all aspects taken into account to evaluate system's transport and mobility. Individual sustainability in all the aforementioned aspects constitutes a prerequisite, for achieving overall transport sustainability.

• Society: The societal impact area considers all impacts on the liveability of the concerned urban area, i.e. public health, convenience, accidents, nuisance and living standards. Again, this impact area is considered sustainable, when all aspects compounding the liveability in a society, are considered sustainable too.

Moreover, several stakeholder categories are usually involved into the decision making and therefore, into the evaluation process. The evaluation for each stakeholder category can be realized by setting one single criterion (monetary) or several criteria (non-monetary). When several individual criteria are set by two or more stakeholder categories, then the evaluation enables multi-stakeholder multi-criteria decision making. In this case, a global index (quantified result) can be estimated by combining the individual indices per solution with the relevant weights for each stakeholder category for each impact area. A Delphi process could be used to 'assign' weights per stakeholder category, once a 70% consensus is achieved (NOVELOG, 2016a).

3.2. Stakeholder categories

Clarkson (1995) stated that an organization's development satisfaction derives directly from the level of satisfaction of all primary stakeholder categories involved. Urban logistics is an open and dynamically changing system where various stakeholder categories are involved, with conflicting objectives, autonomy and divergent viewpoints. The successful implementation of an urban freight solution highly depends on the eurhythmic cooperation of these stakeholders. Primary stakeholder categories in literature can be found under different naming and clustering based mainly on the activity performed. Taniguchi et al. (2012) see four stakeholder categories in urban freight transport domain: shippers, freight carriers, administrators, and residents (consumers). Under a different approach, Russo and Comi (2011) distinguish stakeholders as those who make decisions (public authorities, private companies and public-private partnerships), and those groups who have to abide by these decisions (end-consumers, receivers, shippers and wholesalers or retailers). Gonzalez-Feliu et al. (2010) assort stakeholders as loaders (senders or receivers), transporters (third-party transportation companies) and the owners and management companies (of warehouses, cross-docks and other infrastructure).

Another analysis grouped stakeholder into three broader stakeholder categories (NOVELOG, 2016b):

• Supply Chain Stakeholders (Freight Forwarders, Transport Operators, Shippers, Major Retail chains, Shop owners),

• Public Authorities (Local Government, National Government),

• Other Stakeholders (Industry and Commerce Associations, Consumer Associations, Research and Academia.

3.3. Smart Logistics Solutions

After 1990, severe logistics problems in urban areas draw the attention of researchers and policy-makers. Research activities started focusing on initiatives to alleviate cities' problems, such as restriction measures and consolidation centres (Kohler, 2004). However, over the next years, goods' increased transport deteriorated further the traffic situation in cities with clear negative impacts in other areas i.e. environment and society, too. In order to mitigate these adverse effects many methodical approaches were identified and developed by government agencies, researchers and companies.

An extended list of current logistics solutions is included in NOVELOG (2016b). These solutions are grouped in two categories: (a) cooperative logistics and (b) administrative & regulatory schemes and incentives. The logistics solutions are presented in Table 1.

Table 1. Smart Logistics Solutions (Source: NOVELOG, 2016a).

Cooperative Logistics

Administrative & regulatory schemes and incentives

Multimodality for urban freight

Urban consolidation centres

Trans-shipment facilities

ITS for freight monitoring and planning/routing

Home deliveries system

E-commerce system for small shops

Cargo bikes for B2B and B2C

Electric vehicles diffusion in businesses (zero-emission transport) Reverse logistics integration into supply chain City lockers

Loading/Unloading areas and parking Access: time windows, emission zones Access by load factor Multi-users lanes

Enforcement and ITS adoption for control and traffic management

Businesses recognition scheme

Public transport indirect promotion for shopping

Urban planning measures

Harmonization and simplification of city logistics rules Off peak deliveries Public transport for freight

4. Matching urban distribution components and simulation techniques

The review framework that follows, is based on 16 scientific studies that have been performed within the last 15 years. These studies' focus lies on the evaluation of smart logistics solutions through simulation techniques. The structure of the review is shaped in four columns which describe the logistics solution, the simulation technique, the stakeholder category and each study's information (author & year). Finally, the nomenclature of some logistics solutions has been adjusted to fit in one of the 22 logistic measures, as described in Table 1. The review summary can be seen in Table 2.

The review shows that the most frequently used simulation technique is the 'Multi-agent systems', which in combination with the 'Traffic simulation' technique is used in 13 studies (59.1%). This conclusion can be easily justified considering that the overall evaluation and decision making process in the logistics domain, cannot be examined only under a single set of criteria, established by one stakeholder category (agent). The objectives of each stakeholder category are rather divergent and in many cases even conflicting. Therefore, sustainability for a logistics solution should be checked in many aspects and under several viewpoints.

To continue, out of the 22 UFT solutions, only 12 (54.5%) have been examined in simulation models. The most frequent solution is the 'Access by load factor', while many deployed models test truck ban and road restrictions in combination with limited access in urban centres, especially during peak hours. 'Urban consolidation centers', 'ecommerce systems' and 'ITS adoption for scheduling/routing and traffic management' are also frequent solutions that are assessed in simulation modelling. Finally, the majority of the existing models (75%) have been deployed after 2010.

Table 2. Review of city logistics model techniques.

Logistics Solutions

Simulation Technique Stakeholder Category


Cargo bikes for B2B and B2C, Home deliveries system

ITS for freight monitoring and planning/routing(Route-based guidance for delivery/pick up vehicles)

Access by load factor (Truck ban and tolling of urban expressway)

E-commerce system, Access by load factor, Access: time windows, emission zones, Enforcement and ITS adoption for control and traffic management

E-commerce system (vehicle routing and scheduling)

Urban Consolidation Center (Dynamic Usage of UCC)

Urban Consolidation Center &Loading/Unloading areas and parking (Joint delivery systems)

Intermodal terminals for urban freight

Access by load factor and time windows(Truck ban and discounting motorway tolls)

Access by load factor

Access: Time windows

Loading/Unloading areas and parking

Access by load factor, Urban Consolidation Centre

Reverse logistics integration into supply chain Business recognition scheme

Access by load factor, Access: time windows, emission zones, Off peak deliveries, Urban consolidation centres

Traffic simulation Traffic simulation

Multi-agent systems Systems Dynamics Multi-agent systems

Multi-agent systems

Multi-agent systems Multi-agent systems

Multi-agent systems

Multi-agent systems in combination with Traffic simulation (MATSim) (Freight micro-models)

Traffic simulation

Traffic simulation

Traffic simulation

Systems Dynamics Systems Dynamics

Multi-agent systems

Public Authorities

Other stakeholders

Supply Chain Stakeholders, Other stakeholders

Public Authorities

Supply Chain Stakeholders, Other stakeholders

Supply Chain Stakeholders, Public Authorities

Supply Chain Stakeholders

Supply Chain Stakeholders

Supply Chain Stakeholders, Other stakeholders

Supply Chain Stakeholders

Supply Chain Stakeholders, Public Authorities, Other stakeholders

Supply Chain Stakeholders

Supply Chain Stakeholders, Public Authorities

Supply Chain Stakeholders

Supply Chain Stakeholders

Supply Chain Stakeholders, Public Authorities

Munuzuri et al. (2010)

Walker and Manson, (2014)

Taniguchi and Tamagawa (2005)

Qiu et al. (2015)

Teo et al. (2012)

Van Duin et al. (2012)

Wangapisit et al. (2014)

Graudina and Grundspenkis (2005)

Tamagawa et al. (2010)

Schroeder et al. (2012)

Qureshi et al. (2012)

Gattuso et al. (2014)

Hosoya et al. (2003)

Poles (2013)

Tan and Blanco (2009)

Teo et al. (2014)

5. Concluding discussion

This paper analysed the emerging simulation techniques currently used for the evaluation of smart logistics solutions. Apart from the state-of-art, the authors made an effort to enhance knowledge around modelling in the urban freight domain and provide guidance to future decision makers, based on the stakeholder category they belong to and the type of solution they want to adopt. Furthermore, this review constitutes a roadmap for stakeholders

willing to evaluate a logistics solution. Stakeholders as decision makers for the implementation of a logistics solution, can be guided from similar studies regarding the simulation technique that has been applied, the results, potential complications and other information that might be proven as useful for the decision making process.

5.1. Further Research

Based on the knowledge gained through this research, authors believe that the incorporation of optimization models in this review, may formulate a compact data source, where a decision maker will be able to trace back developed models for respective logistics solutions. This compact data source could be further enriched over the following years, when more simulation models will have been deployed and additional simulation and optimization techniques may have been developed. Based on the knowledge gained through this paper, authors believe that the incorporation of optimization models in this review, may formulate a compact data source, where a decision maker will be able to trace back developed models for respective logistics solutions. This compact data source could be further enriched over the following years, when more simulation models will have been deployed and additional simulation and optimization techniques may have been developed.


This work has been supported by the ALLIANCE project ( and has been funded within the European Commission's H2020Programme under contract number 692426. This paper expresses the opinions of the authors and not necessarily those of the European Commission. The European Commission is not liable for any use that may be made of the information contained in this paper.


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