Wenjuan Fan

A novel trust management framework for multi-cloud environments based on trust service providers

In this paper, we address the problem of trust management in multi-cloud environments based on a set of distributed Trust Service Providers (TSPs). These are independent third-party providers/trust agents, trusted by Cloud Providers (CPs), Cloud Service Providers (CSPs) and Cloud Service Users (CSUs), that provide trust related services to cloud participants. TSPs are distributed over the clouds, and they elicit raw trust evidence from different sources and in different formats. This evidence is information regarding the adherence of a CSP to a Service Level Agreement (SLA) for a cloud-based service and the feedback sent by CSUs. Using this information, they evaluate an objective trust and a subjective trust of CSPs. TSPs communicate among themselves through a trust propagation network that permits a TSP to obtain trust information about a CSP from other TSPs. Experiments show that our proposed framework is effective and relatively stable in differentiating trustworthy and untrustworthy CSPs in a multi-cloud environment.

Solving a supply chain scheduling problem with non-identical job sizes and release times by applying a novel effective heuristic algorithm

Motivated by applications in manufacturing industry, we consider a supply chain scheduling problem, where each job is characterised by non-identical sizes, different release times and unequal processing times. The objective is to minimise the makespan by making batching and sequencing decisions. The problem is formalised as a mixed integer programming model and proved to be strongly NP-hard. Some structural properties are presented for both the general case and a special case. Based on these properties, a lower bound is derived, and a novel two-phase heuristic (TP-H) is developed to solve the problem, which guarantees to obtain a worst case performance ratio of . Computational experiments with a set of different sizes of random instances are conducted to evaluate the proposed approach TP-H, which is superior to another two heuristics proposed in the literature. Furthermore, the experimental results indicate that TP-H can effectively and efficiently solve large-size problems in a reasonable time.

A novel two-stage model for cloud service trustworthiness evaluation

In this paper, we address the cloud service trustworthiness evaluation problem, which in essence is a multi-attribute decision-making problem, by proposing a novel evaluation model based on the fuzzy gap measurement and the evidential reasoning approach. There are many sources of uncertainties in the process of cloud service trustworthiness evaluation. In addition to the intrinsic uncertainties, cloud service providers face the problem of discrepant evaluation information given by different users from different perspectives. To address these problems, we develop a novel fuzzy gap evaluation approach to assess cloud service trustworthiness and to provide evaluation values from different perspectives. From the evaluation values, the perception-importance, delivery-importance, and perception-delivery gaps are generated. These three gaps reflect the discrepancy evaluation of cloud service trustworthiness in terms of perception utility, delivery utility, and importance utility, respectively. Finally, the gap measurement of each perspective is represented by a belief structure and aggregated using the evidential reasoning approach to generate final evaluation results for informative and robust decision making. From this hybrid two-stage evaluation process, cloud service providers can get improvement suggestions from intermediate information derived from the gap measurement, which is the main advantage of this evaluation process.

A Reliability-Based Trust Management Mechanism for Cloud Services

With the increasing demand for cloud services, trust management has become a challenging and important issue in a cloud computing environment. In a trust management mechanism, trust feedback is used to derive trust evaluation results. However, the reliability of the trust feedback from cloud service users needs to be considered, because unreliable trust feedback can produce wrong trust results. In this paper, we first propose a trust management framework for cloud computing environments, and then we introduce an effective reliability-based filtering mechanism to ensure the reliability of trust feedback for cloud computing services. The filtering mechanism uses two important factors, namely, familiarity and consistency, to filter out unreliable trust feedback. Our experiments show that our proposed reliability-based trust management mechanism is effective.

Single-machine serial-batching scheduling with a machine availability constraint, position-dependent processing time, and time-dependent set-up time

This article considers the single-machine serial-batching scheduling problem with a machine availability constraint, position-dependent processing time, and time-dependent set-up time. The objective of this problem is to make the decision of batching jobs and sequencing batches to minimize the makespan. To solve the problem, three cases of machine non-availability periods are considered, and the structural properties of the optimal solution are derived for each case. Based on these structural properties, an optimization algorithm is developed and an example is proposed to illustrate this algorithm.

Trust-Enhanced Cloud Service Selection Model Based on QoS Analysis

Cloud computing technology plays a very important role in many areas, such as in the construction and development of the smart city. Meanwhile, numerous cloud services appear on the cloud-based platform. Therefore how to how to select trustworthy cloud services remains a significant problem in such platforms, and extensively investigated owing to the ever-growing needs of users. However, trust relationship in social network has not been taken into account in existing methods of cloud service selection and recommendation. In this paper, we propose a cloud service selection model based on the trust-enhanced similarity. Firstly, the direct, indirect, and hybrid trust degrees are measured based on the interaction frequencies among users. Secondly, we estimate the overall similarity by combining the experience usability measured based on Jaccard’s Coefficient and the numerical distance computed by Pearson Correlation Coefficient. Then through using the trust degree to modify the basic similarity, we obtain a trust-enhanced similarity. Finally, we utilize the trust-enhanced similarity to find similar trusted neighbors and predict the missing QoS values as the basis of cloud service selection and recommendation. The experimental results show that our approach is able to obtain optimal results via adjusting parameters and exhibits high effectiveness. The cloud services ranking by our model also have better QoS properties than other methods in the comparison experiments.

A study on decision making of cutting stock with frustum of cone bars

This paper considers the cutting stock problem with frustum of cone bars. A multiple objective optimization model is established by taking into account trim loss, the number of cutting patterns and usable leftovers. A decision-making method for solving this cutting stock problem is designed. First, an improved non-dominated sorting heuristic evolutionary algorithm is developed for generating the Pareto non-dominated solutions. Then the weights of the objectives are calculated by combining the subjective methods (subjectively determined by the decision maker) and objective methods (objectively determined by numerical computing). Finally, a multi-attribute decision making method is used for choosing a cutting plan from the Pareto non-dominated solutions. Computational results indicate that the method proposed is feasible.

Optimal production planning in a hybrid manufacturing and recovering system based on the internet of things with closed loop supply chains

During the last decades many companies have to retrieve and treat their end-of-use products when products leave their end users in order to contribute to environmental protection and avoid defiance of relevant legislations. The utilization of returned products in a proper way is the best choice to conform to the above requirement, and save the cost in the production and maintenance process as well. With the development of information technologies, especially the internet of things used in product life cycle data management, the product life cycle information can be tracked, detected, stored and used in the returned product process. In this paper, an integer linear programming model is presented based on the detail product information for the optimization of procurement, manufacturing, recovering and disposal decisions. The model considers three recovery options, several value levels of returns and the value deterioration during the processing time period in order to satisfy the products and components demand in the production planning. A numerical example and sensitivity analysis are used to illustrate the performance and applicability of the model.

Scheduling jobs on a single serial-batching machine with dynamic job arrivals and multiple job types

This paper investigates a scheduling model with certain co-existing features of serial-batching, dynamic job arrival, multi-types of job, and setup time. In this proposed model, the jobs of all types are first partitioned into serial batches, which are then processed on a single serial-batching machine with an independent constant setup time for each new batch. In order to solve this scheduling problem, we divide it into two phases based on job arrival times, and we also derive and prove certain constructive properties for these two phases. Relying on these properties, we develop a two-phase hybrid algorithm (TPHA). In addition, a valid lower bound of the problem is also derived. This is used to validate the quality of the proposed algorithm. Computational experiments, both with small- and large-scale problems, are performed in order to evaluate the performance of TPHA. The computational results indicate that TPHA outperforms seven other heuristic algorithms. For all test problems of different job sizes, the average gap percentage between the makespan, obtained using TPHA, and the lower bound does not exceed 5.41 %.

Scheduling step-deteriorating jobs on bounded parallel-batching machines to maximise the total net revenue

Abstract This paper addresses a parallel-batching scheduling problem considering processing cost and revenue, with the objective of maximising the total net revenue. Specifically, the actual processing time of a job is assumed to be a step function of its starting time and the common due date. This problem involves assigning jobs to different machines, batching jobs, and sequencing batches on each machine. Some key structural properties are proposed for the scheduling problem, based on which an optimal scheduling scheme is developed for any given machine. Then, an effective hybrid VNS–IRG algorithm which combines Variable Neighborhood Search (VNS) and Iterated Reference Greedy algorithm (IRG) is proposed to solve this problem. Finally, the effectiveness and stability of the proposed VNS–IRG are demonstrated and compared with VNS, IRG, and Particle Swarm Optimization through computational experiments.