No abstract available.

Process mining discovers and analyzes a process model from historical event logs. The prior art methods use the key attributes of case-id, activity, and timestamp hidden in an event log as clues to discover a process model. However, a user needs to specify them manually, and this can be an exhaustive task. In this article, we propose a two-stage key attribute identification method to avoid such a manual investigation, and thus this is a step toward fully automated process discovery. One of the challenging tasks is how to avoid exhaustive computation due to combinatorial explosion. For this, we narrow down candidates for each key attribute by using supervised machine learning in the first stage and identify the best combination of the key attributes by discovering process models and evaluating them in the second stage. Our computational complexity can be reduced from$\mathcal {O}(N^{3})$to$\mathcal {O}(k^{3})$where$N$and$k$are the numbers of columns and candidates we keep in the first stage, respectively, and usually$k$is much smaller than$N$. We evaluated our method with 14 open datasets and showed that our method could identify the key attributes even with$k = 2$for about 20 seconds for many datasets.

This paper proposes a novel approach to enhance supply chain (SC) visibility, cooperation, and performance during inventory management while effectively mitigating the risk of information leakage by leveraging machine learning techniques. The SC inventory policies are optimized using multi-agent reinforcement learning (MaRL) and SC network topological information. Furthermore, we conduct a simulation-based evaluation that demonstrates the superior performance of our method compared to alternative optimization approaches. This research effectively addresses the dual objectives of ensuring information security and achieving cost reduction in SC inventory management.

Correlated equilibrium is an established solution concept in game theory describing a situation when players condition their strategies on external signals produced by a correlation device. In recent years, the concept has begun gaining traction also in general artificial intelligence because of its suitability for studying coordinated multi-agent systems. Yet the original formulation of correlated equilibrium assumes entirely rational players and hence fails to capture the subrational behavior of human decision-makers. We investigate the analogue of quantal response for correlated equilibrium, which is among the most commonly used models of bounded rationality. We coin the solution concept the quantal correlated equilibrium and study its relation to quantal response and correlated equilibria. The definition corroborates with prior conception as every quantal response equilibrium is a quantal correlated equilibrium, and correlated equilibrium is its limit as quantal responses approach the best response. We prove the concept remains PPAD-hard but searching for an optimal correlation device is beneficial for the signaler. To this end, we introduce a homotopic algorithm that simultaneously traces the equilibrium and optimizes the signaling distribution. Empirical results on one structured and one random domain show that our approach is sufficiently precise and several orders of magnitude faster than a state-of-the-art non-convex optimization solver.

Sample-based evolutionary algorithms (EAs) are widely used for optimizing problems with multi (greater than one but less than four) or even many (greater than or equal to four) objectives of interest. In general, the difficulty of a problem exponentially increases with the number of objectives, serving as a clear example of the curse of dimensionality. The exploratory approach an EA takes in these cases has led to it being thought of as a big data generator, progressively sampling and evaluating solutions in high performing regions of a decision space to guide the search towards optimal solutions. Notably, in both multi- and many-objective EAs, the sampled data can be further utilized for building inverse generative models, mapping points in objective space back to solutions in the decision space. Such models offer immense flexibility to a decision maker in generating new target solutions on the fly, thereby facilitating real-time a posteriori preference incorporation into the search. In this paper, we show that the data distribution resulting from a many-objective formulation is in fact more conducive to building accurate inverse models than its multiobjective counterpart. Given the potential utility of these models, we in turn shed light on a rare blessing of dimensionality that is yet to be explored in the context of optimization. We first present simple theoretical arguments supporting our claim. Thereafter, experimental studies of Gaussian process-based inverse modeling for a synthetic and a real-world example are carried out to further confirm the theory.

CIA (Confidentiality, Integrity and Availability) is getting more and more important in the cloud based manufacturing and IIoT (Industrial Internet-of-Things)/Indutry 4.0 domain. Recently, blockchain, a decentralized ledger technique, has attracted considerable attention to realize secure and robust data management systems for IIoT. Although several blockchain-based data management platforms for IIoT have been proposed, sensor data are stored outside of the blockchain, meaning that the data integrity and accessibility are not improved. In this paper, we take a different blockchain-based approach for sensor data management; any important sensor data are stored in the blockchain. For realizing this, a series of sensor data is compressed-then-stored in the blockchain by leveraging a fact that many sensor data often have a certain level of periodicity and stability. We have tested our idea against the real sensor data measured at our model factory, and evaluated several performance metrics such as compression ratio, compression/decompression time, process time required to store and retrieve sensor data from our Proof-of-Concept system with Ethereum and the expected blockchain size. From the results, it can be concluded that the proposed platform is viable for small factory cases but needs more technological advancement is required for large scale cases.

Recent technological evolution enables Artificial Intelligence (AI) model training by users' mobile devices, which accelerates decentralized big data analysis. In particular, Federated Learning (FL) is a key enabler to realize decentralized AI model update without user's privacy disclosure. However, since the behaviour of workers, who are assigned a training task, cannot be monitored, the state-of-the-art methods require a special hardware and/or cryptography to force the workers behave honestly, which hinders the realization. Furthermore, although blockchain-enabled FL has been proposed to give workers reward, any rigorous reward policy design has not been discussed. In this paper, to tackle these issues, we present a novel method using mechanism design, which is an economic approach to realize desired objectives under the situation that participants act rationally. The key idea is to introduce repeated competition for FL so that any rational worker follows the protocol and maximize their profits. With mechanism design, we propose a generic full-fledged protocol design for FL on a public blockchain. We also theoretically clarify incentive compatibility based on contest theory which is an auction-based game theory in economics.

Improving forecast accuracy is a major goal of statisticians and forecast practitioners. Over the years, many advanced models have been proposed in forecasting studies in various science and engineering domains. It is no exception for the fast-moving-consumer-goods (FMCG) demand forecasting. However, in the literature of FMCG forecasting, there are many contradictory conclusions about the best model, typified by the long-lasting "war" between econometrics and neural networks. This is simply because there is no universal model. Whereas one model may outperform another over one dataset, its performance can be rather limited over other datasets. Hence, when forecasting is required for a new dataset, a forecaster is risking a suboptimal performance, if the model selection is based on other datasets. To minimize that risk, ensemble forecasting is often considered. This paper continues from the previous discussion (see Parts I-III) on UPC-level FMCG demand forecasting. Various statistical ensemble techniques are used to combine forecasts made using a collection of component models. It is found that the risk (measured by the spread of model-led error) of using ensemble is smaller than using a single component model. Furthermore, in a big data environment, automatic ensemble forecasting is preferred over the time-consuming and task-specific model tuning procedure, which is impractical if there are thousands, or even millions, of time series to be forecast.

Owing to the popularized utilization of academic search engines, such as Google Scholar or Microsoft Academic, the information that a researcher can conveniently access is unlimited. Especially in the domain of supply chain and transportation, where the academic publications are text-heavy and algorithm-driven, it is virtually impossible for one to follow the large amount of developments that is being created on a day-to-day basis. This poses a major barrier during literature review. To that end, the conventional literature-review methodology needs to be revised; text mining has strong potential in this aspect. In the earlier parts of this series of papers, technology infrastructure (Part I) and abbreviation extraction (Part II) have been studied. Both of those analyses focus on analyzing the words in a given set of documents. In Part III, moving from words to semantics, the task of automatic summarization of research papers is explored. More specifically, the TextRank algorithm is used to extract top N most important sentences from a paper, which could significantly boost the efficiency in scanning documents during literature review.

Supply chain disruption, which is commonly known as a low-probability, high-impact risk, has become the center of attention among both practitioners and academicians. As a sheer consequence, various aspects of supply chain disruption risk have been studied. Although using strategies, such as demand management, is useful to mitigate supply risk, the disrupted supplier's operations under recovery is an important issue that needs to be analyzed at an early stage. This paper proposes a method to reduce the peak order quantity, to alleviate the production capacity issue experienced by a supplier in the value chain, who is being recovered from disruption. For this purpose, we apply a trend filtering method over a celebrated inventory management strategy, based on robust optimization. We evaluate the proposed method through numerical experimentation. The results reveal that with a little increase in operational cost, there is an opportunity for significant reduction in peak order and order quantity variance, which corresponds to business risk for the supplier who is still recovering from disruption. We propose and discuss insightful directions for future research into supply chain contracts, enabling firms to optimally share and mitigate the disruption risks across their supply chain.

As the global energy demand is increasing, the share of renewable energy and specifically wind energy in the supply is growing. While vast literature exists on the design and operation of wind turbines, there exists a gap in the literature with regards to the investigation and analysis of wind turbine accidents. This paper describes the application of text mining and machine learning techniques for discovering actionable insights and knowledge from news articles on wind turbine accidents. The applied analysis methods are text processing, clustering, and multidimensional scaling (MDS). These methods have been combined under a single analysis framework, and new insights have been discovered for the domain. The results of our research can be used by wind turbine manufacturers, engineering companies, insurance companies, and government institutions to address problem areas and enhance systems and processes throughout the wind energy value chain.

Disruptions are known to significantly affect a company's supply chain performance in today's highly volatile markets. However, most existing risk analysis tools to investigate the effects of disruptions are developed typically for specific supply chains. In this paper, we develop a mathematical model that utilizes the Time-To-Recovery (TTR) approach, through the characterization of the supply chain disruption recovery patterns. We are able to model the recovery patterns corresponding to different inherent characteristics of a supply chain, forecasting any type of supply chain's performance during the period of recovery or how long the TTR is after a disruption has occurred. The novelty of our model is that we allow a framework to capture the stochastic nature of TTR. Furthermore, the model is capable of estimating the TTR, which has tremendous commercial implication to the practitioners.

In recent years, supply chain risk management has captivated both academicians and business practitioners interest, due to increasing catastrophic events and supply chain disruptions. However, the risk management process is highly complex because of the stochastic and dynamic nature and ever growing complexity of supply chains. As the ultimate goal of most enterprises is generating and increasing revenues on the long run, it is valuable to know the effect of specific supply chain risk positions and risk management practices on company sales. In this paper, secondary data on supply chain risk is analyzed and the key risk management strategies responsible for increased company sales are revealed. The novelty of this paper lies in developing a quantifying data mining approach to provide a comprehensive understanding of supply chain risk management (SCRM) and pinpoint focus areas for revenue seeking enterprises. The results prove and showcase that our methodology is capable of providing actionable insights, which were previously unknown or unaddressed.

Realistic transportation time estimation for urban logistics is challenging due to large amount of historical spatial connections and high variability of transportation time caused by inconsistent traffic situations varying in space and time. In this paper, we propose a probability based method using temporal distribution patterns to estimate logistical transportation time among locations in an urban road network. The method explores historical logistics data including location and time data to construct temporally weighted transportation time patterns in spatial domain. It enables a point-based distributed temporal pattern to be extended to probabilistic area-based spatio-temporal pattern. The experimental results demonstrated that the estimated transportation time fell within vicinity of historical temporal records. The method can be used to generate a map of spatial distribution of transportation time which may provide support in decision making process for urban logistics planning and management.

Technology infrastructure (TechInfra) refers to metadata describing an academic field, such as journals & conferences, authors, publications and organizations. Understanding the TechInfra is often the first step in performing a literature review on a particular topic. In this paper, a study is conducted to retrieve TechInfra for a topic in supply chain management, namely, last mile logistics. Google Scholar is used as the primary tool for data collection. The first 1,000 results returned by Google Scholar are downloaded as HTML files. Subsequently, various application programming interfaces (APIs) - e.g., ScienceDirect, IEEE, CrossRef APIs - are used to enhance the data quality. Some plots are used to provide visualization of TechInfra of last mile logistics.

A schedule-based system is a system that operates on or contains within a schedule of events and breaks at particular time intervals. Entities within the system show presence or absence in these events by entering or exiting the locations of the events. Given radio frequency identification (RFID) data from a schedule-based system, what can we learn about the system (the events and entities) through data mining? Which data mining methods can be applied so that one can obtain rich actionable insights regarding the system and the domain? The research goal of this paper is to answer these posed research questions, through the development of a framework that systematically produces actionable insights for a given schedule-based system. We show that through integrating appropriate data mining methodologies as a unified framework, one can obtain many insights from even a very simple RFID dataset, which contains only very few fields. The developed framework is general, and is applicable to any schedule-based system, as long as it operates under certain basic assumptions. The types of insights are also general, and are formulated in this paper in the most abstract way. The applicability of the developed framework is illustrated through a case study, where real world data from a schedule-based system is analyzed using the introduced framework. Insights obtained include the profiling of entities and events, the interactions between entity and events, and the relations between events.

Traditional manual design of analytical processes is challenging as it requires a general analyst to have good grasping of numerous algorithms and the interaction effects between each technique and the data across multiple domains. Especially in an increasingly high data variety/multi-domain environment today, this design process can be very laborious/challenging. In this paper, we describe a design optimization approach using design of experiments to determine a suitable design in a standardized text classification process with high classification performance. We focus on sentiment analysis as a use case for this approach, as standard analytical methods in each phase of the sentiment analysis process have been established; from data pre-processing, feature selection and classification. In our proposed approach, we present an automatic and domain-free technique of using design of experiments to this design process, with the sentiment classification evaluation metrics as the performance criteria for optimization. In addition, we show that several interpretable analyses can be made to better understand the complex interaction effects of various analytical techniques with the data, which then can guide a general analyst to select more appropriate process design parameters for better text classification performance.

Supplier Selection is a problem that supply chain managers have been facing for many years. Selecting the appropriate suppliers is no longer as simple as choosing only based on the price they offer. There are many criteria, which can be either quantitative or qualitative, to be considered. There is thus a need for an approach that can handle these criteria. Besides, as supply chains are becoming more complex these days, it is also important to incorporate supply risks in the evaluation of the suppliers. This paper offers an approach that mainly focuses on Data Envelopment Analysis (DEA) to analyze and compare the relative efficiencies of the suppliers. Since DEA can only handle quantitative attributes, Analytic Hierarchy Process (AHP) is being utilized to help to analyze qualitative analysis. Risks are also being considered in the evaluation of the suppliers. The proposed approach seeks to offer a holistic approach to tackle the Supplier Selection problem.

Big Data revolution has transformed business models of many organizations to include the usage of big data analytics. Big Data are believed to be the key basis of competition and growth in today's world whereby huge amounts of data are created daily. One of the main challenges of Big Data is not mainly about the storage of the data but how to blend the different varieties or sources of data together and turn them into values. As the nature of supply chain is complex and dynamic, data are stored in various forms or managed independently. The data have their own naming convention as the data from the different nodes in the supply chain seldom communicate with each other. Some of the challenges of data blending are the lack of unique identifiers to merge the data together and the lack of training data or domain knowledge to understand the criteria to blend the data. In this paper, an automatic filtering and sorting similarity metric, Term Frequency-Inverse Document Frequency (TF-IDF) Ratcliff/Obershelp is proposed. The method is able to handle the issue of same entity with different naming conventions and allow word filtering. The experiment results show that the proposed TF-IDF Ratcliff/Obershelp is able to improve the performance of the data blending.

Optimal logistics hub deployment is a strategic challenge in logistics planning and management. Selecting a proper location for the logistics hub could be significantly impacted by long-term geospatial characteristics of logistics operations including spatial distribution of target customers, convenience of traffic access and operational cost. This paper describes a method using clustering of weighted spatial patterns to find optimal locations for logistics hubs deployment. The underlying concept of this method is that an optimal location of the hub could be determined by logistics operation patterns mined from logistical spatial and temporal data. A logistics spatial pattern can be produced by spatial association rules mining and clustering. The spatial patterns weighted by characteristics of logistics operations are then be clustered to generate the final hub location. In this study, the method is validated with a real data sets of pick-up and delivery business. The experimental results demonstrated that the method was able to generate an optimal location for logistics hub deployment with reduced travel distance to frequent customers' locations.

Automatic identification system (AIS) has been widely equipped on vessels for maritime communication, positioning and traffic monitoring. The comprehensive data obtained by AIS provides spatio-temporal traces depicting the vessels' trajectories and can be used as a coherent source of information for vessels' behavior and the overall maritime traffic analysis, in supporting of the better traffic planning and service optimization. However, it is challenging to process and analysis such a large amount of AIS data that is associated with a great variety of vessels. In this paper, we propose an unsupervised data mining method using density-based strategy to analyze vessels' trajectories and extract the traffic patterns from historical AIS data. It starts with stops and moves identification from vessels' trajectories, followed by the extraction of stationary areas of interest from the stops and the detection of the main traffic routes from the moves using density-based clustering method, which takes both the speed and direction into consideration. Experiments on the real AIS data demonstrate the effectiveness of this work.

Satellite data is discrete in both space and time; it can be considered as temporal snapshots (time series) of lattice processes. As the raw datasets are often too large to host publicly, processed datasets with a coarse spatial resolution are often hosted as an alternative. Nevertheless, with a regular grid, the inhomogeneous variability in the lattice processes cannot be captured effectively. In this paper, a quadtree-based spatial data dimension reduction algorithm is demonstrated. Based on the stratum variance, this algorithm iteratively divides lattice data into strata of fours. In this way, the number of strata in an area can be correlated to the variability of that area. A satellite-derived surface solar radiation (SSR) dataset is used for the case study. Using parallel computing, the quadtree algorithm is applied on each temporal snapshot of SSR in the dataset. The processed data is then saved in a list structure. Finally, a solar resource assessment application, namely, optimizing the orientation of a photovoltaic array, is considered to demonstrate the effectiveness and efficiency of the dimension-reduced dataset.

Coordination across a supply chain creates win-win situation for all players in that supply chain; we address the benefits, in terms of forecast accuracy, of reconciling demand forecasts across a supply chain. In Part III of this three-part paper, we continue our discussion on optimal reconciliation of forecasts. Two contributions are made in this paper: 1) the grouped reconciliation technique is used to address the forecast inconsistency in situations when more than one hierarchy can be defined in a supply chain, and 2) minimum trace (MinT) estimator is used to further improve the reconciliation accuracy on top of the weighted least square (WLS) approach, which was used in the earlier parts of this three-part paper. Following the earlier works, the same set of fast moving consumer goods data is used here. The current results are compared to the previous ones. It is shown that the MinT reconciliation technique outperforms the WLS approach, which has been previously identified as the best reconciliation technique for the data from the bottled juice category in the Dominick's Finer Food dataset.

Indicator-based evolutionary algorithm (IBEA1) is a fast and effective approach for solving multiobjective optimization problems (MOPs). In the classical IBEA1, the parameter κ is predefined to amplify or shrink the indicator differences on pairwise solutions. However, the value of κ in IBEA1 needs to be carefully calibrated based on the selected indicator (e.g., hypervolume or additive e-indicator) and the encountered MOPs. In this paper, a new version of IBEA1 (labeled as IBEA2 hereafter) is proposed to adaptively adjust parameter κ for solving various MOPs. The core idea of IBEA2 is to adapt parameter κ for the purpose of selecting the subset of offspring solutions with the maximum hypervolume into the next population. Experimental studies on 44 benchmark MOPs with 2-5 objectives in jMetal verified that IBEA2 is able to find higher hypervolumes against the four classical MOEAs, which are NSGAII, SPEA2, MOEA/D and IBEA1, in the literature.

The decomposition method in multiobjective evolutionary algorithms (MOEA/D) is an effective approach to evolve solutions along predefined weight vectors for solving multiobjective optimization problems (MOPs). However, obtaining evenly distributed weight vectors for different types of MOPs is a challenge problem especially when the true Pareto fronts (PFs) are unknown before a MOEA/D starts. In this paper, a new MOEA/D with a fast hypervolume archive (called FV-MOEA/D) is proposed to adaptively adjust the weight vectors for various shapes of PFs. The core idea of FV-MOEA/D is to periodically adjust weight vectors based on solutions in the proposed archive, in which convergence and diversity are maintained by maximizing hypervolume. Experimental studies on 58 benchmark MOPs in jMetal demonstrate that the proposed FV-MOEA/D not only reached higher hypervolumes when compare to five classical MOEAs i.e., NSGAII, SPEA2, IBEA, FV-MOEA and MOEA/D, but also obtained well distributed weight vectors on PFs with different geometrical characteristics.

Collaboration between logistics service providers (LSPs) becomes increasingly crucial in urban last-mile logistics as it not only helps LSPs to reduce the transportation costs and increase the truck load factor but also improve customer service levels. However, LSPs are usually against sharing of customer data and delivery orders with their competitors. This makes most existing collaboration strategies in literature impractical in reality. To deal with this issue, we proposed a new collaboration strategy that requires less coordination efforts between the LSPs involved. In addition, we carried out a numerical experiment with the data collected from the local LSPs in Singapore to illustrate our proposed strategy. The results and the analysis highlighted shows that the proposed collaboration strategy is not only more practical than other conventional strategies, it also helps to reduce the total transportation costs and increase truckload in overall.

Route mining for busy waterways is a challenging task. Complicated shipping routes may be generated due to vessels of different types congesting in a narrow water way, frequently changing navigational direction and weaving through multiple crossing traffic. The traditional way using visual bearing and ship-stationed techniques may mitigate hazards of ship collision but lack macroscopic information for safe and efficient shipping navigation. In this paper, we proposed a spatio-temporal mining method to explore vessels' shipping patterns in Singapore Strait. The frequent shipping routes can be automatically extracted using a local polynomial regression based algorithm. Time series clustering across spatial areas is used to associate spatial pattern with temporal pattern. The aim of this study is to provide support for decision-making process in optimal shipping route planning and maritime traffic management. Mapping the pattern information to a virtual geographical information platform enables users to intuitively acquire the knowledge of vessels' shipping patterns.

In a big data enabled environment, manufacturers and distributors may have access to previously unobserved retailer-level demand related information. This additional information can be considered in demand forecasting to produce more accurate forecasts, and thus enable better stock-outs management. In Part II of this two-part paper, we explore the hierarchical nature of fast moving consumer goods (FMCG) demand (represented by sales) time series and produce one week ahead rolling forecasts on universal product code (UPC) level (or distributor level, as per our definition below). We show that the hierarchical forecasting framework has significant accuracy improvement over the conventional univariate forecasting methods. The main reason of the observed improvements is due to the price and promotion information available at the retailer level, which is assumed to be unknown to the distributor. To reconcile forecasts according to the hierarchy, only the forecast values at retailer level are needed, the business strategies of individual retailers remain proprietary. A freely available dataset is considered to encourage further exploration. Data exploratory analysis and visualization tools are discussed in Part I of the paper.

We are interested in forecasting a large collection of FMCG demand time series. As the demand of FMCG exists in a hierarchy (from manufacturers to distributors to retailers), the bottom level of the hierarchy may contain thousands or even millions of time series. Producing aggregate consistent forecasts while utilizing the unique features from each time series thus become a technical challenge. To achieve better forecasting results, exploratory analysis is often necessary to obtain insights on the underlying demand generating mechanism for each time series. Exploratory analysis aims at discovering those so-called "exogenous factors", such as price, demand of the complementary/substitutive goods and calendar events, which can help explain some of the demand fluctuation. During forecast accuracy evaluation, outlier detection is also important; a single anomalous time series can contribute much to the overall error. However, in a big data (such as retailing scanner data) enabled environment, exploratory analysis and visualization need much attention, because of the non-scalable nature of the existing methods. Scalability is essential for exogenous factor selection and outlier detection in big time series data. In Part I of this two-part paper, we introduce some exploratory analytics and visualization methods (from not scalable to very scalable) for big retailing time series. Forecasting of the hierarchical FMCG demand is addressed in Part II.

To find diversified solutions converging to true Pareto fronts (PFs), hypervolume (HV) indicator-based algorithms have been established as effective approaches in multiobjective evolutionary algorithms (MOEAs). However, the bottleneck of HV indicator-based MOEAs is the high time complexity for measuring the exact HV contributions of different solutions. To cope with this problem, in this paper, a simple and fast hypervolume indicator-based MOEA (FV-MOEA) is proposed to quickly update the exact HV contributions of different solutions. The core idea of FV-MOEA is that the HV contribution of a solution is only associated with partial solutions rather than the whole solution set. Thus, the time cost of FV-MOEA can be greatly reduced by deleting irrelevant solutions. Experimental studies on 44 benchmark multiobjective optimization problems with 2-5 objectives in platform jMetal demonstrate that FV-MOEA not only reports higher hypervolumes than the five classical MOEAs (nondominated sorting genetic algorithm II (NSGAII), strength Pareto evolutionary algorithm 2 (SPEA2), multiobjective evolutionary algorithm based on decomposition (MOEA/D), indicator-based evolutionary algorithm, and S-metric selection based evolutionary multiobjective optimization algorithm (SMS-EMOA)), but also obtains significant speedup compared to other HV indicator-based MOEAs.

Lately, the Vehicle Routing Problem (VRP) in the city, known as City VRP, has gained popularity with its importance in city logistics. Similar to city logistics, City VRP mainly differs from conventional VRP in terms of the stakeholders involved, namely the shipper, carrier, resident, and administrator. Accordingly, this paper surveys the City VRP literature categorized by stakeholders and summarizes the constraints, models, and solution methods for VRP in urban cities. City VRPs are also analyzed based on the problem of interest considered by the stakeholders and the corresponding models that have been proposed in response. Through this review, we identify the state of the art of City VRP, highlight the core challenging issues, and suggest some potential research area in this field that have remained underexplored.

Due to the complex nature and numerous interacting factors that contributes to the increased vulnerability of supply chains, traditional methods have been found to be inadequate for Supply Chain Risk Management (SCRM). Agent-Based Modeling and Simulation (ABMS), an agent-oriented approach to model and simulate complex adaptive systems, represents a recent development in supply chain planning that has been regarded highly appropriate for studying risk management [1-4]. The objective of this paper is to provide a multi-perspective survey of the state-of-the-art agent based modeling and simulation approaches for SCRM.

Supply chains are becoming more vulnerable because of harsher and more frequent natural and man-made disasters. Supply chain disruptions now seem to occur more frequently and with more serious consequences. During and after supply chain disruptions, companies may lose revenue and incur high recovery costs. Therefore, if supply chain managers were able to better measure and manage supply chain vulnerability, they might be able to reduce the number of disruptions and their impacts. However, how to measure such risk is still an emerging topic for both research and practice. This paper presents a new interactive decision support method for measuring such risk using Value at Risk (VaR) and Conditional Value at Risk (CVaR). The proposed method, based on a disruption recovery model consisting of abrupt, linear and exponential modes, aims to help supply chain managers conduct "what-if" analyses, in order to tackle such vulnerability and other risk factors that would affect their business continuity.

In an RFID-assisted track and trace information network, the same set of data may be stored in distributed locations. Data conflicts occur when the values of distributed data copies are modified locally with different values causing data inconsistency. In order to restore data consistency, the values of distributed data copies have to be synchronized to the same value by resolving the conflicts. The conventional conflict resolutions do not consider the unique characteristics of RFID data and therefore are not able to maximize the benefit of information users or may even provide erroneous resolution result. In this paper, a data synchronization method with conflict resolution accommodated to RFID applications is proposed. The method resolves the conflicts based on multiple RFID data attributes and takes the dependent relationship between data into account. Simulations confirm the efficiency of the algorithm.