Showing papers presented at "International Conference on Logistics, Informatics and Service Sciences in 2018"

IOT Enabled Smart Logistics Using Smart Contracts

Abstract: Advancements in sensors and devices have enabled Internet of Things (IoT) adoption in various sectors, especially in domains looking to automate and increase their real-time decision making capabilities to improve efficiencies. Supply chain management in logistics is a perfect fit for adoption of IoT, since it involves shipment of assets being moved, tracked and housed by a number of machines, vehicles and people each day. Smart Contracts are terms and conditions parties can specify that assure trust in the enforceability of the contract and provide visibility at every step of a supply chain. IoT devices can write to a smart contract as a product moves from the factory floor to the store shelves, providing real-time visibility of an enterprises entire supply chain. This paper proposes a smart logistics solution encapsulating smart contracts, logistics planner and condition monitoring of the assets in the Supply Chain Management area. A prototype of the solution is implemented which demonstrates accountability, traceability and liability for asset handling across the supply chain by various parties involved in a logistics scenario.

Data Sharing and Tracing Scheme Based on Blockchain

Abstract: The existing data sharing models have some issues such as poor transparency of data transactions, data without security assurance and lacking of effective data tracking methods. This paper proposed a brand-new data sharing scheme based on blockchain technology. Firstly, a blockchain double-chain structure about blockchain was introduced, one chain was used to store the original data and another was used to store transaction data generated by transactions. This structure separated the original data storage and data transactions. Secondly, combined with proxy re-encryption technology, safe and reliable data sharing were achieved. Finally, a new design was implemented. The logical structure of data transaction records enables data to be traced. The results of correctness and security analysis showed that this scheme can provide new technical ideas and methods for big data sharing and data trace.

Unmanned Retail’s Distribution Strategy Based on Sales Forecasting

Abstract: With the increasing development of mobile technology and popularity of ‘New Retail’ concept, more and more venture capitalists have been chasing investment opportunities in the unmanned retail business, such as F5 future store, Xiaomai, Xingbianli, CityBox, also and e-commerce firms Alibaba Group Holdings and JD.com. Currently the main forms of unmanned retail are smart vending machines, unmanned convenience stores, and unmanned smart shelves in offices. Additionally, other concepts, such as unmanned cafes and bookstores, continue to emerge. These forms have a great advantage: with the help of new technologies, the cost of unmanned retail is much lower than that of traditional retail forms. However, how to formulate an effective replenishment and distribution strategy is the biggest operational problem, also is the main research question of this paper. Firstly, this paper forecasts the purchase demand of each node based on the analysis of the nodes’ historical sales data, and then according to the demand forecast results, optimizes the distribution routes to effectively reduce transportation costs and achieve supply chain optimization.

Truck and Drone in Tandem Route Scheduling under Sparse Demand Distribution

Abstract: Once the drone’s use area is focus on military area, now it is being broadened. The drone is more frequently used in logistic distribution, due to its low cost in delivery. However, the delivery range capacity of drone is limited. This problem can be solved by truck and drone complement delivery. This research is about drone truck in tandem routing modeling. We focus on solving drone and truck routing problem under sparse demand condition.

Concepts for Cargo Ground Handling of Unmanned Cargo Aircrafts and Their Influence on the Supply Chain

Abstract: The steadily growing share of air freight transport in the entire logistics industry is mainly due to the three major advantages of speed, safety and reliability. To meet the rising demands, automated transport and delivery processes are increasingly used. As part of the DLR (German Aerospace Center) research project Automated Low Altitude Delivery (ALAADy), a fully automated Unmanned Cargo Aircraft (UCA) with a payload of one ton under the precondition of the Minimum Risk Configuration is being developed. The theoretical and practical concepts of this topic were examined within the study under the premise that “No infrastructure exits at destination” in order to obtain the most automated process possible for future logistics. Our study shows that the interaction between an UCA and an automated robotic container-system solves both problems of the penultimate and the last mile within the logistics chain. Furthermore, the concepts of ground handling and the development of unmanned systems, including their present capabilities, were studied theoretically to design a model representing basic ground handling processes of Unmanned Aircraft Vehicles (UAVs). The intention was to create a base for further research on this matter by targeting the key requirements for ground handling processes of UCAs in the given concepts. We found out that our obtained findings, approaches for an automated turnaround of an UAV can therefore serve as a basis for future analyses in UCA ground handling and last miles logistics.

Public Philanthropy Logistics Platform Based on Blockchain Technology for Social Welfare Maximization

Abstract: Focusing on the overlapping areas of the logistics industry with a large number of mature blockchain applications and the public welfare industry that requires high transparency and credibility, this paper designs and implements an innovative philanthropy logistics platform based on blockchain technology through the Ethereum platform. Our platform makes use of the open, transparent, and irrevocable features of the blockchain, combined with a unique Responsibility Relay System and Evaluation and Reporting Mechanism, and can achieve the consistency of the data on the chain with real-world status, as well as the authenticity and transparency of philanthropy logistics data. This paper also establishes a model for evaluating philanthropy material donations for social welfare based on the classic network maximum flow algorithm. After four months of empirical analysis, we have concluded that the blockchain platform can greatly increase the user's trust in the project, enhance the system's cleanliness coefficient and increase the quality of philanthropically raised materials, thereby improving the public welfare of charitable donations. The paper draws the conclusion that this blockchain platform is a technical solution for maximizing social welfare.

Return Freight Insurance: Implications for Online Platforms, Third-party Retailers and Consumers

Abstract: In this paper, we investigate the effect of return freight insurance (RFI) in an online platform setting where two competing third-party retailers provide RFI to consumers. First, we find that RFI generally increases the price level on the platform. Second, we find that when retailers have same base return rates, retailers’ profits are not changed while the platform’s profit is increased. Whereas, when retailers have different base return rates, RFI only benefits the retailer with high base return rate and harms the retailer with low base return rate, and RFI benefits the platform if and only if the product differentiation degree is relatively large. Third, we find RFI increases customer surplus if RFI’s return hassle cost reduction (HCR) and/or willingness-to-pay enhancement (WTPE) effect is relatively strong. Moreover, we find RFI increases social welfare if RFI’s HCR and/or WTPE effect is relatively strong and the product differentiation degree is relatively large. Such findings offer guidelines for online platforms and third-party retailers to design appropriate RFI provision strategies.

Blockchain Application for Rideshare Service

Abstract: This paper proposes a scheme to use blockchain technology for rideshare services. This paper replaces the centralized authority that matches drivers and riders, with block chain and a matching application that uses two types of coins, which encourages the drivers turning into miners. To evaluate the proposed system, this paper applies the proposed blockchain rideshare service to a case study to simulate and find the least matching probability to make drivers benefit from this system. Furthermore, this paper establishes a mathematical model of the stationary distribution of drivers and calculate the stationary profit of each driver in the blockchain rideshare system.

A Three-Stage Approach for Split Delivery Vehicle Routing Problem Solving

Abstract: This paper describes a mathematical model of the split delivery vehicle routing problem. The objective is to minimize travel distance while using the lowest number of vehicles. A clustering first and routing later strategy is used to realize this objective. A three-stage approach is proposed: first, the maximum minimum distance method is employed to cluster the customer points into the lowest number groups; second, load demands in all groups are adjusted to form all possible routes according to the maximum vehicle load capacity by adopting the “push-out” and “pull-in” tactics; and third, possible routes are optimized to minimize the total travel distance. Case studies are introduced to verify the feasibility and effectiveness of this proposed algorithm. The results show that the performance of this three-stage proposed algorithm is better than the tabu search algorithm, the k-means clustering method, the BCO method and the sweep clustering algorithm in both the optimized travel distance and the less time consumption.

Variable-Scale Clustering

Abstract: Human naturally analyze and decide a problem from different perspectives, hierarchies, and dimensions, that is referred to as scale transformation (ST). Clustering, as one of the most effective data analysis tools, should support this ST demand. Hence, this paper focuses on the ST problem among clustering analysis especially for decision making. We define the variable-scale dataset based on the rough set theory. What’s more, an algorithm of variable-scale clustering (VSC) is also proposed. A case study shows that compared to the k-modes, the clustering results of the VSC are more available and accessible to decision makers.

Hierarchical Clustering Algorithm for Binary Data Based on Cosine Similarity

Abstract: Clustering algorithm for binary data is a challenging problem in data mining and machine learning fields. While some efforts have been made to deal with clustering binary data, they lack effective methods to balance clustering quality and efficiency. To this end, we propose a hierarchical clustering algorithm for binary data based on cosine similarity (HABOC) in this paper. Firstly, we assess similarity between data objects with binary attributes using Cosine Similarity (CS). Then, the Cosine Similarity of a Set (CSS) is defined to compute similarity of a set containing multiple objects. Based on CSS, we propose the Cosine Feature Vector of a Set (CFVS) and additivity of CFVS to compress data and merge two clusters directly. We also exploit hierarchical clustering method to implement clustering, in order to avoid the sensitivity to the order of data objects and algorithm parameters. Experimental results on several UCI datasets demonstrate that HABOC outperforms existing binary data clustering algorithms.

A Bilevel Multi-Objective Model for Sustainable Road Pricing Design on A Bimodal Transport Network

Abstract: This study proposes a bilevel multi-objective model to find road tolls for sustainable urban development on a bimodal transportation network. Three objectives, including minimization of total travel time budget, total exhaust emissions and negative impacts for health, are considered in the upper level model, which correspond to economic, environmental and social sustainability, respectively. Meanwhile, the lower level is a joint model of mode split and route choice for each individual traveler in which mode choice equilibrium follows the binary logit discrete choice model and path choice equilibrium is determined by a three-objective user equilibrium that achieves the travel time budget surplus (TBS) maximization. For solving the proposed bilevel optimization model, the paper develops an algorithm that combines the multi-objective genetic algorithm (NSGA-II) and a path-based traffic assignment algorithm. Then the feasibility and effectiveness of the algorithm are well demonstrated by a numerical example.

A Maturity Model for Examination Management in University

Abstract: The examination management plays an irreplaceable role on teaching quality in university education, therefore, we analyze the process of examination management. From the perspective of project management, we introduce and establish a model from two key dimensions: maturity index system and key process area. Thus, corresponding models for the two dimensions are proposed respectively. Onwards, based on samples from universities in Beijing, we conduct an empirical analysis to measure the performance using our proposed models. The evaluation results highlight the importance and the practicality of maturity model for examination management in university.

Optimizing Production Line Balance Based on Witness Simulation

Abstract: Based on the current LCD (Liquid Crystal Display) module production line type S-S00A(SEL315V3-S00A), this paper studies the production line of liquid-crystal module with the method of industrial engineering and simulation method. Firstly, the process of the production line of the liquid crystal module is analyzed, and the cycle time of the production line is measured in 24 seconds, and the rate of the production line balance is 60.3%. With the witness software simulation platform used, it is found that the working hours are not high and the proportion of idle time is about 40%. The production process of liquid-crystal module production is analyzed by process analysis. Combining the 5W1H (What, Why, Where, When, Who, How) question technology and ECRS (Eliminate, Combine, Rearrange, Simplify) principle to optimize the production process, the processes cancel 3 times handling, cancel 5 times checks, merge several operations, and reduce the five workers. Finally, simulation model of optimized production line of liquid-crystal module is established, and the simulation results show that the workload for each station basic at 80%, improving the utilization rate of hours. The cycle time of the optimized liquid crystal module production line is 18 seconds. The production line balance rate is 85.56%, increasing by 25%.

Trustworthiness Optimization of Industrial Cluster Network Platform Based on Blockchain

Abstract: Industrial cluster is an important organization form and carrier of development of small and medium-sized enterprises, and information service platform is an important facility of industrial cluster. Improving the credibility of the network platform is conducive to eliminate the adverse effects of distrust and information asymmetry on industrial clusters. The decentralization, transparency, openness, and intangibility of block chain technology make it an inevitable choice for trustworthiness optimization of industrial cluster network platform. This paper first studied on trusted standard of industry cluster network platform and construct a new trusted framework of industry cluster network platform. Then the paper focus on trustworthiness optimization of data layer and application layer of the platform. The purpose of this paper is to build an industrial cluster network platform with data access, information trustworthiness, function availability, high-speed and low consumption, and promote the sustainable and efficient development of industrial cluster.

Optimization of Flatbed Loading in Picking Operations of IKEA Warehouse

Abstract: As the core operation of the warehouse operation, the optimization of the picking operation can reduce warehouse management costs, improve order response time, and improve customer satisfaction. This paper proposes a loading optimization model for analyzing the loading capacity of flatbed trucks, which is an important delivery tool in IKEA’s warehouse delivery operations. It proposes a corresponding solution using an improved genetic algorithm and the actual data for verification and quantitative analysis. It proves that the model has the validity and practical value.

Customer Consumption Preferences of B2C Website Based on Bayesian Network Model

Abstract: customer reviews are the important data source to recognize customer consumption preference of online shopping site. This paper aiming at customer consumption preferences of B2C website, customer reviews of clothing products are grabbed and preprocessed to extract feature factors. The Bayesian network model of B2C customer preferences is constructed to calculate node conditional probability distribution. Sensitive factors was recognized and dynamically adjusted. The results show that: Under the influence of the parent nodes, the customers’ evaluation of each characteristic factor variable’s subnode has a higher probability of “moderate” and “good”. The probability change of the overall evaluation node and sensitivity factors has a positive impact on the probability of other factors. Customer consumption preferences can be judged and predicted according to the probability.

Short Text Mining for Fault Diagnosis of Railway System Based on Multi-Granularity Topic Model

Abstract: Vehicle equipment is one of the core equipments of train control system in the high-speed railway, and it mainly depends on the experience of maintenance personnel to perform fault diagnosis when it fails, which is ineffective. The maintenance text data of vehicle equipment, which contains fault category information, is not fully utilized. The classification of maintenance text can well assist fault diagnosis of vehicle equipment. However, shortness and imbalanced class distribution of maintenance texts, which hinder the application of conventional text representation models and classification algorithms, pose challenges for classification task. In this paper, we propose a novel text feature selection algorithm based on multi-granularity latent Dirichlet allocation (LDA) to overcome shortness characteristic of maintenance text. To solve the problem of class imbalance, a cost-sensitive Support Vector Machine (SVM) is utilized to construct fault diagnosis model. Finally, we compare our proposed method with the state-of-the-art baseline over a vehicle maintenance text data set collected by Guangzhou railway corporation, and it outperforms traditional methods.

Intellectual Structure of Research on Data Mining Using Bibliographic Coupling Analysis

Abstract: With the rapid development of mobile Internet, the growth amount of data has exploded, and data mining has played an increasingly important role in data analyzing. More and more researches have been done on data mining. Detecting intellectual structure of data mining research is of significance in understanding its research topics and research fronts. Focusing on the method of bibliographic coupling analysis, this paper explores the intellectual structure of data mining during 2007-2016, based on 12625 cleaned bibliographic data of data mining-related articles retrieved from Web of Science. Experiments results show that there are mainly 10 research topics in the field of data mining, such as classification, frequent pattern mining and clustering, among which the first three topics are the research on domain of data mining itself, and the last seven topics are the research on data mining applications.

Tripartite Game Model of Agricultural Product Supply Chain Based on Evolutionary Game

Abstract: In the green agricultural supply chain, the farmers who are at the top of the supply chain receive low income, and consumers who are at the end of the supply chain pay agricultural products with high prices for the lengthy intermediate links. In view of this phenomenon, this paper assumes that farmers provide the green agricultural products to consumers directly. We define a new parameter: brand grade coefficient and conduct the dynamic analysis of the evolution of the game between the farmers, the government and the consumers. We assign the parameters and then analyze the dynamic graph of evolutionary game under different initial strategies drawn with MATLAB. Finally, we conclude the final evolution trend of the three-dimensional evolutionary game. That is, the role of the government in the market slowly fades finally and the stability of the market mainly relies on the participation of consumers in the market and the game between consumers and farmers. The results indicate that consumers who take the initiative to participate in the market have a certain degree of substitution to supervision of the role of government regulators. Strengthening the consumer to participate in the market is conducive to maintaining market stability.

Optimization of Inventory Management Process

Abstract: The paper describes an inventory control problem related to optimization of processes that occur in supply chains with multiple unreliable suppliers. Two cases are considered: (a) fixed part of the delivery cost is the same for all suppliers and (b) all suppliers have various cost components. For case (a) optimal inventory control strategy and a procedure for supplier selection are suggested. For case (b) a general scheme of optimal strategies construction is proposed. The results of computer simulation are presented.

An Evolutionary Game Model of Online-food Safety Regulation

Abstract: Because of the particularity of the trade between the online-food seller and the third-party network platform, the responsibility in the supervision process is not clear, and the gains and losses of their respective interests are difficult to guarantee the online-food safety. In view of the online-food security, the game relationship between the supervision department, the third-party network platform and the online store is studied from the point of view of evolutionary game strategy, and the game equilibrium and the game strategy under different circumstances are analyzed. The results show that the third-party network platform should be fully played in the online-food security supervision process, and the food safety supervision of the third-party network platform and the regulatory agency should be well coordinated, so as to provide theoretical basis and methods for improving the efficiency of supervision.

Recovery and Pricing Decision of Supply Chain in Mixed Competition Game

Abstract: This paper compares the traditional and closed loop supply chain in Bertrand competition. Each supply chain consists of a retailer and a supplier. The two chains compete with each other in two mixed competition models. In these two mixed competitive games, the competition intensity between the two chains and the product recovery rate of the closed loop supply chain are studied, which affect the market price, the profit of the two chains and the equalization decision of the supply chain. The research results show that the total profit of the traditional chain decreases with the increase in product recovery rate, and the total profit of the closed loop chain increases with the increase in product recovery. The profit of both chains increases as the intensity of competition increases.

Towards Improving the Knowledge Representation and Searching of Manchu Costume Culture: An Ontology-based Method with APP Implementation

Abstract: Costume culture traditionally spread through words of mouth or paper, which is not able to support systematic and extendible knowledge representation, as well as semantic searching. Hence, in order to improve the knowledge representation and searching of Manchu costume culture, an ontology-based method with semantic searching is proposed in this paper. Firstly, Manchu costume was classified and indexed according to professional view of ethnic study based on surveys and literatures. Secondly, the ontological knowledge base of Manchu costume was constructed using Protege and OWL software tool. Thirdly, the search algorithm and engine of Manchu costume was designed. Then implemented by JAVA language, integrated with Jena inference engine as well to improve the matching performance. Finally, an APP of Manchu costume was developed with HTML5 language on APICloud ECS to validate the proposed method. The contribution of this paper is threefold: a) constructing a knowledge base of Manchu costume based on ontology technique, which is extendible and generalizable to other national costume; b) proposing the semantic searching engine based on rule-based reasoning using SPARQL query language; c) implementing an APP for validating the proposed method for improving the knowledge representation and semantic searching of costume culture.

Analysis of Logistics Demand Distribution in Beijing Based on Kernel Density Estimation

Abstract: Identifying regions with high demand for urban logistics can lay the foundation for studies related to urban logistics, such as the choice of distribution routes and the layout of logistics facilities. This paper presents kernel density estimation (KDE) method to study the issues related to the spatial distribution of urban logistics demand. The paper also applies this method to the study of logistics demand distribution in Beijing. First, it selects the Gaussian kernel as the kernel function to obtain the heat map of probability density distribution of logistics demand in Beijing. Secondly, this paper finds four regions with higher distribution density throughout the year. Additionally, the paper discusses the characteristics of monthly logistics distribution in these regions and their causes. Finally, it uses the rank sum test to analyze whether the differences in demand distribution of the four regions are significant.

Simulation of Knowledge Transfer within the Project Team

Abstract: This paper analyzes the impact of knowledge transfer subjects’ motivation, capacity and cultural difference on project knowledge transfer from the perspective of the knowledge sender and knowledge receiver. Based on this, we construct a system dynamics model of project knowledge transfer and conduct the validity test and sensitivity analysis of the model by Vensim PLE software. The simulation result shows that the knowledge transfer motivation and expression capacity of the knowledge sender can promote knowledge transmission and reception in the project team, while knowledge transfer motivation and absorptive capacity of the knowledge receiver only affect knowledge reception. Meanwhile, the positive effect of knowledge innovation capacity of the both sides and the negative effect of cultural difference on project knowledge transmission and reception are also proved.

A Data-driven Fault Prediction Method for LNG Engine City Buses

Abstract: Traditional methods of fault detection and diagnosis for vehicles are mainly based on expert knowledge, signal analytics and chemical experiments, which lead to subjectivity, uncertainty and hysteresis somewhat. Motivated by this problem, we propose a novel data-driven method using Rough Set theory and Random Forest algorithm for constructing the predictive model from CAN-BUS data, bus maintenance system, bus daily schedule system and relevant weather data, which is able to reduce fault detection deviation resulting from excessive reliance on expert knowledge. More specifically, we utilize the Rough Set theory for extracting key attributes relevant to LNG bus faults. Then, Random Forest algorithm is applied for model construction of the fault prediction. This method provides the opportunity to predict bus faults during the bus operating. Moreover, the effectiveness of model constructed has been validated using real-world data showing very promising results with precision, recall and F1- score all above 0.8. In addition, key attributes extracted are useful for monitoring the bus fault. This predictive model is able to pre-identify LNG engine city buses with potential fault risks, which is important information for improving bus maintenance processes to avoid extra costs.

THUS: An Efficient Two-stage Hierarchical Algorithm for Categorical Data Clustering

Abstract: The pursuit of both quality and efficiency in the clustering analysis is a long-existed paradox. In real-world applications, a controllable method of the quality-efficiency trade-off might be more practical. The hierarchical algorithms usually perform better on the clustering quality but are much more computationally expensive than partitioning algorithms. In this paper, we proposed an efficient two-stage hierarchical algorithm for categorical data clustering (THUS) to improve the efficiency while maintaining acceptable quality. In the first stage, several efficient methods are used to generate intermediate clusters to reduce the complexity of the hierarchical stage two. Experimental results show that the proposed algorithm reduces the computational time considerably, and the clustering quality can be equivalent to the original hierarchical algorithm. By manipulating the pre-clustering level, a controllable trade-off between clustering quality and efficiency can be conducted based on application purpose.

The Reasonable Cycle Length for Periodic Train Line Planning Problem

Abstract: The application of cyclic train line plan has found an increasingly wide utilization in high-speed railway system while reasonable length of cycle also becomes an essential element of it. In order to apply the cyclic train line plan to Chinese high-speed rail system, the idea of candidate set is introduced and a Mixed Integer Programmer (MIP) model of periodic train line planning based on candidate set is established in this paper. The periodic train line planning problem is considered involving many general constraints, such as the passenger demand, the service frequency, the railway track capacity and variable constraints. The maximum service frequency is determined by specifying the maximum number of trains in every cycle. Besides, according to the data from Beijing-Shanghai high-speed railway, different cycle length of 1 hour, 2 hours and 4 hours is set to generate different cyclic train line plan. By comparing the service quality of the periodic train line plan and the existing one, the effectiveness of the proposed model and relevant method are verified.

Choice of Customer Return Policies in Opaque Selling

Abstract: No-refund policy is strictly enforced in industries such as airlines and hotels, which offers opaque sales firstly. In this paper, however, we study whether opaque sellers can apply full return policy as an effective tool to win competitive advantage in retailing markets. We consider the channel structure which has an opaque seller, a regular seller and a manufacturer as members. For this structure, we construct the Stackelberg game model, provide the unique equilibrium, and bring up the conditions of offering full return policy for the opaque seller. Furthermore, we investigate how the market differentiates in the equilibrium results. We theoretically analyze the impact of customer return policy on profits, demand and pricing determination of supply chain members. We also provide numerical examples to illuminate the influence of net salvage value on profits, demand and pricing determination of the channel members. Our study provides theoretical support for opaque sellers in customer return policy and pricing decision.