Multi-resource Constraints Research Articles

Optimization of Asynchronous Parallel Tasks Scheduling with Multi-resource Constraints

Optimization of Asynchronous Parallel Tasks Scheduling with Multi-resource Constraints

A reinforcement learning method for scheduling service function chains with multi-resource constraints

Traditional networks are usually equipped with many dedicated middleboxes to provide various network services. Though these hardware-based devices certainly improve network performance, they are usually expensive and difficult to upgrade. To overcome this shortcoming, network function virtualization (NFV), which accomplishes network services in the form of virtual network functions (VNF) has been presented. Compared to middleboxes, the VNFs are easy to deploy and migrate. Usually, multiple VNFs are chained in a specified order as a service function chain (SFC) to serve a given flow. There are many works to schedule SFCs to minimize the average flow completion time. However, they only consider single resource limitation. In this paper, we are committed to addressing the problem of multi-resource SFC scheduling (MR-SFCS) and minimizing the average flow completion time. We formulate this problem with an Integer Linear Programming (ILP) model and prove its NP-hardness. To well tackle this problem, we propose an approach based on deep reinforcement learning (DRL), which has specific reward design and state representations. Besides, we extend the offline approach to online SFC scheduling. The experiment results demonstrate that our DRL method can significantly reduce the average flow completion time and achieves a cost saving of 69.07% against the benchmark method.

Dynamic energy-efficient scheduling of multi-variety and small batch flexible job-shop: A case study for the aerospace industry

In the context of the global implementation of carbon peaking and carbon neutrality, energy-aware production scheduling has attracted world-wide attention. The optimal scheduling problem for a multi-variety and small-batch dynamic flexible job shop scheduling is challenging in aerospace industry. This paper considers an energy-efficient scheduling problem with multi-resource constraints of multi-variety and small-batch dynamic flexible job shop (α-shop for short). First, it formulates a mathematical model of energy efficient α-shop with the objectives of minimum total energy consumption (TEC), makespan and machining cost. Next, a novel multi-objective bi-population differential artificial bee colony (BDABC) algorithm is proposed to solve the model. Then, the design of experiment Taguchi method is performed to calibrate the parameter setting of the proposed algorithm. Finally, comparative experiments of the proposed algorithm with other popular algorithms such as large-scale multi-objective optimization framework (LSMOF), large-scale multi-objective competitive swarm optimizer (LMOCSO), as well as some known algorithms including multi-objective evolutionary algorithm based on decomposition (MOEA/D), multi-objective particle swarm optimization (MOPSO) and multi-objective artificial bee colony (MOABC) are carried out to assess the effectiveness of the proposed algorithm in an aerospace plant of China based on a number of well-recognized metrics. The experimental results demonstrate that our proposed algorithm is effective reported for this real-life scheduling problem.

Energy-efficient scheduling for flexible job shop under multi-resource constraints using non-dominated sorting teaching-learning-based optimization algorithm

Due to the fact that the real manufacturing processes are often constrained by many kinds of resources and the trend that the energy consumption of factories is regulated more and more strictly, this paper studies the energy-efficient multi-resource flexible job shop scheduling problem (EE-MRFJSP). The goal is to minimize the energy consumption and completion time for all of the jobs’ production. Firstly, a general mathematic model for EE-MRFJSP is set up, in which the unit energy consumptions of the main resource’s different states are varied, and a constraint formula to ensure no crossover working periods for any resource is included. Then, a non-dominated sorting teaching-learning-based optimization(NSTLBO) algorithm is proposed to solving the problem, the details of NSTLBO include the real encoding method, Giffler Thompson rule for decoding, non-dominated sorting rule to rank the pareto sets and crowding distance of solution for maintaining the population’s diversity, and the traditional two evolving stages: teacher education and student mutual study. Finally, comparative experiments are made based on some new designed instances, and the results verify our proposed NSTLBO algorithm can effectively solve the EE-MMFJSP, and has obvious advantages by comparing with NSGA-II, NRGA, and MOPSO.

Calculation of Buffer Size on Critical Chain Based on Duration Distribution, Multiresource Constraints, and Relay Potential

The setting of the buffer size on critical chain will impose an impact on the determination of the project duration. In order to effectively calculate the buffer size, we have comprehensively taken into account the influence of duration-related risks, multiple resource constraints, and relay potential on the buffer size of key chain in this paper, and we have enhanced the method of calculating the remaining buffer to ensure more reasonable calculation of project buffer. As a result, we can effectively calculate the buffer size in a way closer to the actual production and shorten the project duration.

INVESTIGATION ON ONLINE VNF SCALING IN A CLOUD DATACENTER USING ILP

NFV is the advanced technology in present situation. Online VNF Scaling in a cloud datacenter under multi-resource constraints were consider for formulating mathematical model. A new novel ILP Scaling algorithm works based on the regularization technique and dependent rounding.

Environmental and Social Problems and Countermeasures in Transportation System under Resource Constraints

With the rapid development of urban economy and the acceleration of urbanization, the demand for urban traffic is increasing rapidly. The single traffic-oriented planning does not take into account the requirements of traffic development on resources and the impact on the environment. The traffic construction of most cities can not fully meet the standard of ecotype. In this paper, the vehicle distribution route optimization problem under multiresource constraints such as vehicle energy capacity and vehicle loading capacity is studied, and the static and dynamic models of distribution route optimization under multiresource constraints are established. In the static distribution route optimization model, the distribution route optimization problem with subloops is solved by modifying the network structure and adding virtual resource points. In the dynamic model, the spatiotemporal network model is used to avoid the generation of subloops, and the description of the vehicle distribution route planning problem is more intuitive and accurate. The model enriches the vehicle distribution route selection scheme at the cost of expanding the model scale. And it can solve the time when the vehicle arrives and leaves the customer point. This paper provides a good countermeasure for solving the environmental and social problems in the transportation system under the condition of resource constraints.

Multi-Resource VNF Deployment in a Heterogeneous Cloud

The emerging paradigm of Network Function Virtualization (NFV) promises to shorten the renewal cycles of network functions and reduce the capital expenses by flexibly deploying virtualized network functions (VNFs) implementation on commodity servers. However, the required resource of each type (CPU, memory, etc.) for the running VNF should be provisioned to guarantee the performance when processing packets. This comes with different deployment cost, especially in a heterogeneous cloud consisting of a large number of network function platforms from various vendors. To optimally operate VNFs, it is necessary for the network operator to dynamically deploy VNFs in the expensive cloud infrastructures. In this article, we initiate the study of minimizing the deployment cost under multi-resource constraints in a heterogeneous cloud. We formulate multi-resource VNF deployment problem (MVDP) as an optimization program and prove its hardness. We propose an offline <inline-formula><tex-math notation="LaTeX">$(1, d+1)$</tex-math></inline-formula> -bicriteria approximation algorithm and an <inline-formula><tex-math notation="LaTeX">$(\mathcal {O}(1), \mathcal {O}(n \cdot \log n))$</tex-math></inline-formula> -competitive online algorithm to deploy VNFs in a scalable manner, where <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> is the number of resource types and <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of required VNFs. Large-scale simulations and DPDK-based OpenNetVM implementation show that our algorithms can reduce the overall cost by 34% and improve the performance in terms of multi-resource allocation.

Influencing parameters of productivity through value stream analysis using optimization techniques

This paper deals an uncertain environment with effective integration of various planning elements and optimization of project parameters. Time, cost and quality are the most important parameters that are optimized to fulfil the planned production and productivity. Moreover, in uncertain environments, there exist a lot of other conflicting objectives that may also need to be optimized. The proposed model aims to maximize total profit from selected products, while sequencing scheduling the products with various resource constraints with specified time intervals. Linear Programming, Variable and Separable Programming, Mathematical Model and Genetic Algorithm (GA), multiple criteria decision making technique, offers a good solution for this project planning problem. Planning and controlling several processes data simultaneously means more than simply dealing with multiple schedules with machining time. It involves the integration of resources, schedules, budgets and stakeholder needs. Productivity optimization has been studied by a number of researchers in recent years. The tools and techniques to schedule multiple processes of product with multi-resource constraints are limited and are successful in solving only small problems. Value Stream Mapping (VSM) is a lean/quality management tool which assists in establishing the current state of a process while aiding to uncover opportunities for improvement of the seven sources of waste. Productivity, customer and job satisfaction improves 5 M’s. Zero quality control and eliminating wastes.

Complex Equipment Remanufacturing Schedule Management Based on Multi-Layer Graphic Evaluation and Review Technique Network and Critical Chain Method

In order to solve project schedule delays caused by resource conflicts and uncertainty of the environment, work duration, and logical relationship, this paper proposed CCPSP (critical chain project scheduling problem) optimal scheduling model which integrates CCM (critical chain method) and multi-level GERT (graphical evaluation and review technique) network. DEACA (differential evolution ant colony algorithm) is designed to solve the problem, in which DE (differential evolution algorithm) is used to deal with the cross problem and mutation problem, and ACA (ant colony algorithm) is used to deal with each stage of the optimization process. The neighborhood structure is introduced to reduce the invalid search. By storing the non-dominated Pareto optimal solution in external files, the Pareto optimal frontier is obtained. This paper makes full use of the advantages of the two algorithms to achieve fast convergence and a global search for optimal solutions. At the same time of seeking the optimization algorithm, considering the multi-resource constraints, environmental uncertainties and other factors of the project, a buffer is set up, and a “minimum duration - maximum robustness” project schedule is formulated. Finally, through the analysis and verification of the project example, it shows that the project schedule made by this method is feasible.

A novel mathematical model and a large neighborhood search algorithm for container drayage operations with multi-resource constraints

Multi-resource constraints in container drayage operations exist widely in real-life scenarios but have been seldom reported in literature. This study mainly addresses a container drayage problem with a limited number of empty containers at depots, with the goal of minimizing both the number of trucks in operation and the total working time of trucks. The problem is firstly formulated as a bi-objective mathematical model based on a so-called determined-activities-on-vertex graph. Three schemes are proposed to handle the developed mathematical model as: (a) a nonlinear constraint is linearized; (b) a parameter that is a large enough constant in the model is analyzed and tuned deeply; and (c) the bi-objective model is converted into a single-objective model. Furthermore, a large neighborhood search algorithm is designed to solve the problem. The two solution methods are validated and evaluated based on randomly generated instances as well as instances from literature. Numerical experimental results indicate that the methods can provide optimal or near-optimal solutions for medium- and large-scale instances in a short running time.

A Robust Optimization Scheduling for Carrier Aircraft Support Operation Based on Critical Chain Method

The maintenance and service support of carrier aircraft (MSSCA) is a complex process involving many types of resources and activities that require optimisation which can be considered as a multi-resource constrained multi-project scheduling problem (MRCMPSP). It is of great significance to optimize the makespan and obtain a proactive robust schedule to be adaptive to the changes in the dynamic flight deck environment. This paper develops a critical chain method (CCM) for carrier aircraft support robust scheduling in a time-critical and resource-constrained operation environment. The CCM consists of developing a desirable deterministic schedule under multi resource constraints and time-critical issues, and adding a project buffer (PB) to the end of the schedule is used to get a robust proactive scheduling and deal with uncertainty. The triangular fuzzy number (TFN) is applied to describe the durations of each activities and calculate the PB size and obtain an appropriate proportionality between the activity duration and the buffer size. In the numerical research, different size simulations are designed to calculated the robust optimization scheduling. The computational results show that the CMM for carrier aircraft support robust scheduling can make a better decision in the robust proactive optimization scheduling about the resource allocation.

Concurrent container scheduling on heterogeneous clusters with multi-resource constraints

By effectively virtualizing operating systems and encapsulating necessary runtime contexts of software components and services, container technologies can significantly improve portability and efficiency for distributed application deployment. It flexibly extends virtual machine based cloud (Infrastructure-as-a-Service) as a much lighter virtual environment (container cluster) for agile application management. However, existing container management systems are not capable of handling concurrent requests efficiently, particularly for the underlying clusters with heterogeneous machines and the requested containers with multi-resource demands. In this paper, we propose an Enhanced Container Scheduler (ECSched) for efficiently scheduling concurrent container requests on heterogeneous clusters with multi-resource constraints. We formulate the container scheduling problem as a minimum cost flow problem (MCFP), and represent the container requirements using a specific graph data structure (flow network). ECSched affords flexibility in constructing the flow network based on a batch of concurrent requests, and performs the MCFP algorithm to schedule the concurrent requests in an online manner. We evaluate ECSched in different testbed clusters, and measure the scheduling overhead with large-scale simulations. The experimental results show that ECSched outperforms state-of-the-art container schedulers in container performance and resource efficiency, and only introduces a small and acceptable scheduling overhead in large-scale clusters.

Scheduling Semiconductor Testing Facility by Using Cuckoo Search Algorithm With Reinforcement Learning and Surrogate Modeling

A semiconductor final testing scheduling problem with multiresource constraints is considered in this paper, which is proved to be NP-hard. To minimize the makespan for this scheduling problem, a cuckoo search algorithm with reinforcement learning (RL) and surrogate modeling is presented. A parameter control scheme is proposed to ensure the desired diversification and intensification of population on the basis of RL, which uses the proportion of beneficial mutation as feedback information according to Rechenberg's 1/5 criterion. To reduce computational complexity, a surrogate model is employed to evaluate the relative ranking of solutions. A heuristic approach based on the relative ranking of encoding value and a modular function is proposed to convert continuous solutions obtained from Lévy flight into discrete ones. The computational complexity and convergence analysis results are presented. The proposed algorithm is validated with benchmark and randomly generated cases. Various simulation experiments and comparison between the proposed algorithm and several popular methods are performed to validate its effectiveness.

Lexicographic Multiobjective Scatter Search for the Optimization of Sequence-Dependent Selective Disassembly Subject to Multiresource Constraints.

Industrial products' reuse, recovery, and recycling are very important because of their environmental and economic benefits. Effective product disassembly planning methods can improve their recovery efficiency and reduce their bad environmental impact. However, the existing approaches pay little attention to sequence-dependent disassembly with resource constraints, such as limited disassembly operators and tools, which makes the current planning methods ineffective in practice. This paper considers a multiobjective resource-constrained and sequence-dependent disassembly optimization problem with disassembly precedence constraints. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with traditional optimization criterion leads to a novel multiobjective optimization model such that the energy consumption and disassembly time are minimized while disassembly profit is maximized. Since the problem complexity increases with the number of components in a product, a lexicographic multiobjective scatter search (SS) method is proposed to solve the proposed multiobjective optimization problem. Its effectiveness is verified by comparing the results of linear weight SS and genetic algorithms. The results show that it is able to provide a better solution in a short execution time and fulfills the precedence requirement in a product structure and resource constraints.

Page-sharing-based virtual machine packing with multi-resource constraints to reduce network traffic in migration for clouds

Virtual machine (VM) packing plays an important role in improving resource utilization in cloud data centers. Recently, memory content similarity among VM instances has been used to speed up multiple VM migration in large clouds. Based on this, many VM packing algorithms have been proposed, which only considered the memory capacity of physical machines (PMs) as the resource constraint. However, in practice the results of such algorithms are not feasible, because thy may not satisfy the constraints of multiple resources (e.g., CPU of the PMs). Besides, the granularities of memory sharing in existing studies are very coarse, and they cannot fully leverage the benefits of memory content similarity which mainly appears at memory page level. In this paper, we study the page-sharing-based VM packing that considers constraints in multiple resources. Given a set of VM instances that share a large number of common memory pages, we pack them into the minimum number of PMs, subject to the constraints in the multiple resources on the PMs. This problem is solved in two steps. First, we pack the maximum number of VMs into a given PM, and then propose an approximation algorithm. The approximation ratio is better than that of the existing algorithm. Then, based on this approximation algorithm, we propose a heuristic algorithm to solve the general problem. Experimental results show that our heuristic algorithm outperforms existing approaches with at most 25% less required PMs and at most 40% less memory page transferring.

Operating Room Surgery Scheduling with Fuzzy Surgery Durations Using a Metaheuristic Approach

Operating room (OR) surgery scheduling is a challenging combinatorial optimization problem that determines the operation start time of every surgery to be performed in different surgical groups, as well as the resources assigned to each surgery over a schedule period. One of the main challenges in health care systems is to deliver the highest quality of care at the lowest cost. In real-life situations, there is significant uncertainty in several of the activities involved in the delivery of surgical care, including the duration of the surgical procedures. This paper tackles the operating room surgery scheduling problem with uncertain surgery durations, where uncertainty in surgery durations is represented by means of fuzzy numbers. The problem can be considered as a Fuzzy Flexible Job-shop Scheduling Problem (FFJSP) due to similarities between operating room surgery scheduling with uncertain surgery durations and a multi-resource constraint flexible job-shop scheduling problem with uncertain processing times. This research handles both the advanced and allocation scheduling problems simultaneously and provides an Ant Colony Optimization (ACO) metaheuristic algorithm which utilized a two-level ant graph to integrate sequencing jobs and allocating resources at the same time. To assess the performance of the proposed method, a computational study on five test surgery cases is presented, considering both deterministic and fuzzy surgery durations to enhance the significance of the study. The results of this experiment demonstrated the effectiveness of the proposed metaheuristic algorithm.

Dual-Objective Program and Scatter Search for the Optimization of Disassembly Sequences Subject to Multiresource Constraints

The effective dismantling of discarded products regardless being used or not is critically important to their reuse, recovery, and recycling. However, the existing product disassembly planning methods pay little or no attention to resource constraints, e.g., limited numbers of disassembly operators and tools. Thus, a resulting plan when being executed may be ineffective in practice. This paper presents a dual-objective optimization model for selective disassembly sequences by considering multiresource constraints such that disassembly profit is maximized and time is minimized. A scatter search is adopted to solve the proposed dual-objective optimization model. It embodies the generation of diverse initial solutions, global assessment of objective functions, a crossover combination operator, a local search strategy for improved solutions, and a reference set update method. To analyze the effect of different weights on its performance, simulations are conducted on different products. Its effectiveness is verified by comparing its optimization results and those of genetic local search. Note to Practitioners —This work deals with a sequence modeling and planning problem of product disassembly. It establishes a novel dual-objective optimization model for product disassembly subject to multiresource constraints. Previously, such a problem is handled through a methodology based on the optimization of a single objective, i.e., disassembly time or cost. The resultant solution is insufficient without fully considering disassembly resources, e.g., labors and tools. Also, in an actual disassembly process, a decision-maker may want to maximize disassembly profit, as well as minimize disassembly time. This work considers both objectives and proposes scatter search to solve disassembly problems. The results demonstrate that the proposed approach can solve them effectively. The obtained solutions give decision makers some desired choices to select a right disassembly process when an actual product is disassembled.

Disassembly Sequence Optimization for Large-Scale Products With Multiresource Constraints Using Scatter Search and Petri Nets.

Disassembly modeling and planning are meaningful and important to the reuse, recovery, and recycling of obsolete and discarded products. However, the existing methods pay little or no attention to resources constraints, e.g., disassembly operators and tools. Thus a resulting plan when being executed may be ineffective in actual product disassembly. This paper proposes to model and optimize selective disassembly sequences subject to multiresource constraints to maximize disassembly profit. Moreover, two scatter search algorithms with different combination operators, namely one with precedence preserved crossover combination operator and another with path-relink combination operator, are designed to solve the proposed model. Their validity is shown by comparing them with the optimization results from well-known optimization software CPLEX for different cases. The experimental results illustrate the effectiveness of the proposed method.

An ant colony optimization approach for solving an operating room surgery scheduling problem

Operating room surgery scheduling deals with determining operation start times of surgeries on hand and allocating the required resources to the scheduled surgeries, considering several constraints to ensure a complete surgery flow, the resource availability, and specialties and qualifications of human resources. This task plays a crucial role in providing timely treatments for the patients while ensuring the balance in the hospital’s resource utilization. By observing similarities between operating room surgery scheduling and a multi-resource constraint flexible job shop scheduling problem (FJSSP) in manufacturing, this article proposes an Ant Colony Optimization (ACO) approach to efficiently solve such surgery scheduling problems based on the knowledge gained in FJSSP. Numerical experiments are performed on five surgery test cases with different problem sizes and resource availability. The performance of the ACO algorithm was compared against schedules generated by a discrete event system simulation model built in SIMIO on five test cases. The results showed a superior performance of ACO in makespan, overtime, and the variation coefficient of working time.