A novel optimization method on logistics operation for warehouse & port enterprises based on game theory

Purpose: The following investigation aims to deal with the competitive relationship among different warehouses & ports in the same company. Design/methodology/approach: In this paper, Game Theory is used in carrying out the optimization model. Genetic Algorithm is used to solve the model. Findings: Unnecessary competition will rise up if there is little internal communication among different warehouses & ports in one company. This paper carries out a novel optimization method on warehouse & port logistics operation model. Originality/value: Warehouse logistics business is a combination of warehousing services and terminal services which is provided by port logistics through the existing port infrastructure on the basis of a port. The newly proposed method can help to optimize logistics operation model for warehouse & port enterprises effectively. We set Sinotrans Guangdong Company as an example to illustrate the newly proposed method. Finally, according to the case study, this paper gives some responses and suggestions on logistics operation in Sinotrans Guangdong warehouse & port for its future development.Peer Reviewe

Automatic Train Operation Speed Profile Optimization and Tracking with Multi-Objective in Urban Railway

Besides energy-efficiency, people also want train operation to be comfortable, punctual and parking precise. In this paper, a multi-objective model for automatic train operation in urban railway is proposed by unifying dimensions of different objectives firstly. This model is built by applying multi-objective decision with the penalty function, based on the analysis of train performance and its operation environment. Then a genetic algorithm is developed to solve this model and obtain the optimal recommended speed profiles. Thirdly, fuzzy controller is designed to achieve track recommended speed profiles. Finally, with the help of Matlab software, control effect is verified based on simulation. From the simulation results, it can be seen this strategy can meet the requirement of multi-objective, which are energy-saving, parking precisely, running punctually and comfort

Study on the project supervision system based on the principal-agent theory

Purpose: In order to solve problems in the current project management system, the paper presents the asymmetric information games existing in construction projects through information economics viewpoints. Design/methodology/approach: The owner has private information about the project profitability and he exerts an unobservable level of effort in order to increase the feasibility of successfully completing the project in terms of meeting product specifications. The paper analyzes the principal-agent relationship between the owner and supervisor with “principal-agent theory” of the game theory. In addition, the paper validates the model through two project cases. Findings: We can conclude that the incentive contract plays an important role in reducing the moral hazard. The main contribution of this studyis to examine the influence of both pre-contractual private information and the sensitivities between the interrelated performance measures on the design of an optimal incentive contract. Social implications: At last, some advices are put forward to advance the project management system in China, and some external mechanism can effectively inhibit the"moral hazard" and "adverse selection" to occur. Originality/value: A model of principal-agent relationship between the owner and supervisor is formulated. This model takes consideration of the moral hazard, which isdifferent from most existing researches in this field.Peer Reviewe

Knowledge discovery from posts in online health communities using unified medical language system

Patient-reported posts in Online Health Communities (OHCs) contain various valuable information that can help establish knowledge-based online support for online patients. However, utilizing these reports to improve online patient services in the absence of appropriate medical and healthcare expert knowledge is difficult. Thus, we propose a comprehensive knowledge discovery method that is based on the Unified Medical Language System for the analysis of narrative posts in OHCs. First, we propose a domain-knowledge support framework for OHCs to provide a basis for post analysis. Second, we develop a Knowledge-Involved Topic Modeling (KI-TM) method to extract and expand explicit knowledge within the text. We propose four metrics, namely, explicit knowledge rate, latent knowledge rate, knowledge correlation rate, and perplexity, for the evaluation of the KI-TM method. Our experimental results indicate that our proposed method outperforms existing methods in terms of providing knowledge support. Our method enhances knowledge support for online patients and can help develop intelligent OHCs in the future

Current situation and countermeasures of port logistics park information construction

Purpose: Improve work efficiency of logistics park department, and drive the economy of the park and its surrounding areas. Design/methodology/approach: Analyze the information development situation and existent questions of current national logistics park, and design proper scheme to meet the demand of port logistics park. Findings: Proposed an information construction implementation plan using technology of the Internet of things which can be applied to port logistics park. Designed a scheme for the park information construction and explained the system's implementation strategy and implementation steps. Practical implications: The proposed construction program is particularly suitable for the northwest port logistics parks in China, and also has reference function to other logistics park construction. Originality/value: Group the information construction of the logistics park into four levels, three types of users, and two requirements. The scheme is innovative and comprehensive, which can ensure the development of port logistics park.Peer Reviewe

Towards A Reference Framework For RFID-Enabled Garment SC Visibility

In the current environment organizations can no longer compete on price alone. Supply chains are becoming important, where efficiency and effectiveness play the pivotal role. RFID is a highly heralded technology in the supply chain field, which can synchronize the information flow with the material flow. However, organizations are still not widely adopting it, as the industry is missing compelling business cases to illustrate how to implement the technology and how it actually can bring benefits to the business. In this study we develop and demonstrate an innovative information infrastructure to facilitate a smooth supply chain operation. The infrastructure is designed based on an in-depth case study of a typical complete garment supply chain. SCOR model is used to ensure that the design is applicable in various supply chain set ups

A Knowledge-Constrained Role-Based Access Control model for protecting patient privacy in hospital information systems

Current access control mechanisms of the hospital information system can hardly identify the real access intention of system users. A relaxed access control increases the risk of compromise of patient privacy. To reduce unnecessary access of patient information by hospital staff, this paper proposes a Knowledge-Constrained Role-Based Access Control (KCRBAC)model in which a variety of medical domain knowledge is considered in access control. Based on the proposed Purpose Tree and knowledge-involved algorithms, the model can dynamically define the boundary of access to the patient information according to the context, which helps protect patient privacy by controlling access. Compared with the Role-Based Access Control model, KC-RBAC can effectively protectpatient information according to the results of the experiments

Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyond

Few-shot segmentation (FSS) aims to segment the novel classes with a few annotated images. Due to CLIP's advantages of aligning visual and textual information, the integration of CLIP can enhance the generalization ability of FSS model. However, even with the CLIP model, the existing CLIP-based FSS methods are still subject to the biased prediction towards base classes, which is caused by the class-specific feature level interactions. To solve this issue, we propose a visual and textual Prior Guided Mask Assemble Network (PGMA-Net). It employs a class-agnostic mask assembly process to alleviate the bias, and formulates diverse tasks into a unified manner by assembling the prior through affinity. Specifically, the class-relevant textual and visual features are first transformed to class-agnostic prior in the form of probability map. Then, a Prior-Guided Mask Assemble Module (PGMAM) including multiple General Assemble Units (GAUs) is introduced. It considers diverse and plug-and-play interactions, such as visual-textual, inter- and intra-image, training-free, and high-order ones. Lastly, to ensure the class-agnostic ability, a Hierarchical Decoder with Channel-Drop Mechanism (HDCDM) is proposed to flexibly exploit the assembled masks and low-level features, without relying on any class-specific information. It achieves new state-of-the-art results in the FSS task, with mIoU of $77.6$ on $\text{PASCAL-}5^i$ and $59.4$ on $\text{COCO-}20^i$ in 1-shot scenario. Beyond this, we show that without extra re-training, the proposed PGMA-Net can solve bbox-level and cross-domain FSS, co-segmentation, zero-shot segmentation (ZSS) tasks, leading an any-shot segmentation framework

Some q-rung orthopair fuzzy Muirhead means with their application to multi-attribute group decision making

Recently proposed q-rung orthopair fuzzy set (q-ROFS) is a powerful and effective tool to describe fuzziness, uncertainty and vagueness. The prominent feature of q-ROFS is that the sum and square sum of membership and non-membership degrees are allowed to be greater than one with the sum of qth power of the membership degree and qth power of the non-membership degree is less than or equal to one. This characteristic makes q-ROFS more powerful and useful than intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS). The aim of this paper is to develop some aggregation operators for fusing q-rung orthopair fuzzy information. As the Muirhead mean (MM) is considered as a useful aggregation technology which can capture interrelationships among all aggregated arguments, we extend the MM to q-rung orthopair fuzzy environment and propose a family of q-rung orthopair fuzzy Muirhead mean operators. Moreover, we investigate some desirable properties and special cases of the proposed operators. Further, we apply the proposed operators to solve multi-attribute group decision making (MAGDM) problems. Finally, a numerical instance as well as some comparative analysis are provided to demonstrate the validity and superiorities of the proposed method