A Discrete Multi‑Objective Artificial Bee Colony Algorithm for a Real‑World Electronic Device Testing Machine Allocation Problem

With the continuous development of science and technology,electronic devices have begun to enter all aspects of human life,becoming increasingly closely related to human life.Users have higher quality requirements for electronic devices.Electronic device testing has gradually become an irreplaceable engineering process in modern manufacturing enterprises to guarantee the quality of products while preventing inferior products from entering the market.Considering the large output of electronic devices,improving the testing efficiency while reducing the testing cost has become an urgent problem to be solved.This study investigates the electronic device testing machine allocation problem(EDTMAP),aiming to improve the production of electronic devices and reduce the scheduling distance among testing machines through reasonable machine allocation.First,a mathematical model was formulated for the EDTMAP to maximize both production and the scheduling distance among testing machines.Second,we developed a discrete multi-objective artificial bee colony(DMOABC)algorithm to solve EDTMAP.A crossover operator and local search operator were designed to improve the exploration and exploitation of the algorithm,respectively.Numerical experiments were conducted to evaluate the performance of the proposed algorithm.The experimental results demonstrate the superiority of the proposed algorithm compared with the non-dominated sorting genetic algorithm II(NSGA-II)and strength Pareto evolutionary algorithm 2(SPEA2).Finally,the mathematical model and DMOABC algorithm were applied to a real-world factory that tests radio-frequency modules.The results verify that our method can significantly improve production and reduce the scheduling distance among testing machines.

Task scheduling and virtual machine allocation policy in cloud computing environment

Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.

Machine Allocation in Semiconductor Wafer Fabrication Systems:A Simulation-Based Approach

The problem of maximizing the throughput of Semiconductor Wafer Fabrication Systems is addressed.We model the fabrication systems as a Stochastic Timed Automata and design a discrete-event simulation scheme.The simulation scheme is explicit,fast and achieves high fidelity which captures the feature of reentrant process flow and is flexible to accommodate diversified wafer lot scheduling policies.A series of Marginal Machine Allocation Algorithms are proposed to sequentially allocate machines.Numerical experiments suggest the designed methods are efficient to find good allocation solutions.

Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications.Therefore,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing environments.Effective task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog nodes.This process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource bottlenecks.In this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation.This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization algorithms.The FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response time.In relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

Efficient Multi-Tenant Virtual Machine Allocation in Cloud Data Centers

Virtual Machine（VM） allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant＇s VMs may considerably impact the tenant＇s Quality of Service（Qo S）. In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs＇ network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem（LP-MKP）. The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the Qo S among tenants, and improves tenants＇ overall Qo S in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.

An allowance allocation method based on dynamic approximation via online inspection data for deformation control of structural parts

Deformation resulting from residual stress has been a significant issue in machining.As allowance allocation can directly impact the residual stress on part deformation,it is essential for deformation control.However,it is difficult to adjust allowance allocation by traditional simulation methods based on residual stress,as the residual stress cannot be accurately measured or predicted,and many unexpected factors during machining process cannot be simulated accurately.Different from traditional methods,this paper proposes an allowance allocation method based on dynamic approximation via online inspection data for deformation control of structural parts.An Autoregressive Integrated Moving Average(ARIMA)model for dynamic allowance allocation is established so as to approach the minimum deformation,which is based on the in-process deformation inspection data during the alternative machining process of upside and downside.The effectiveness of the method is verified both by simulation cases and real machining experiments of aircraft structural parts,and the results show that part deformation can be significantly reduced.

Cost-effective resource segmentation in hierarchical mobile edge clouds

The fifth-generation(5 G)network cloudification enables third parties to deploy their applications(e.g.,edge caching and edge computing)at the network edge.Many previous works have focused on specific service strategies(e.g.,cache placement strategy and vCPU provision strategy)for edge applications from the perspective of a certain third party by maximizing its benefit.However,there is no literature that focuses on how to effciently allocate resources from the perspective of a mobile network operator,taking the different deployment requirements of all third parties into consideration.In this paper,we address the problem by formulating an optimization problem,which minimizes the total deployment cost of all third parties.To capture the deployment requirements of the third parties,the applications that they want to deploy are classified into two types,namely,computation-intensive ones and storage-intensive ones,whose requirements are considered as input parameters or constraints in the optimization.Due to the NP-hardness and non-convexity of the formulated problem,we have designed an elitist genetic algorithm that converges to the global optimum to solve it.Extensive simulations have been conducted to illustrate the feasibility and effectiveness of the proposed algorithm.