Optimization of Multi-Execution Modes and Multi-Resource-Constrained Offshore Equipment Project Scheduling Based on a Hybrid Genetic Algorithm

Offshore engineering construction projects are large and complex,having the characteristics of multiple execution modes andmultiple resource constraints.Their complex internal scheduling processes can be regarded as resourceconstrained project scheduling problems(RCPSPs).To solve RCPSP problems in offshore engineering construction more rapidly,a hybrid genetic algorithmwas established.To solve the defects of genetic algorithms,which easily fall into the local optimal solution,a local search operation was added to a genetic algorithm to defend the offspring after crossover/mutation.Then,an elitist strategy and adaptive operators were adopted to protect the generated optimal solutions,reduce the computation time and avoid premature convergence.A calibrated function method was used to cater to the roulette rules,and appropriate rules for encoding,decoding and crossover/mutation were designed.Finally,a simple network was designed and validated using the case study of a real offshore project.The performance of the genetic algorithmand a simulated annealing algorithmwas compared to validate the feasibility and effectiveness of the approach.

Scheduling Rules Based on Gene Expression Programming for Resource-Constrained Project Scheduling Problem

In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select the effective scheduling rules( SRs) which are constructed using the project status and attributes of the activities. SRs are represented by the chromosomes of GEP, and an improved parallel schedule generation scheme( IPSGS) is used to transform the SRs into explicit schedules. The framework of GEP-SR for RCPSP is designed,and the effectiveness of the GEP-SR approach is demonstrated by comparing with other methods on the same instances.

Memetic algorithm for multi-mode resource-constrained project scheduling problems

A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.

Improving resource-constrained IoT device lifetimes by mitigating redundant transmissions across heterogeneous wireless multimedia of things

In Wireless Multimedia Sensor Networks(WMSNs),nodes capable of retrieving video,audio,images,and small scale sensor data,tend to generate immense traffic of various types.The energy-efficient transmission of such a vast amount of heterogeneous multimedia content while simultaneously ensuring the quality of service and optimal energy consumption is indispensable.Therefore,we propose a Power-Efficient Wireless Multimedia of Things(PE-WMoT),a robust and energy-efficient cluster-based mechanism to improve the overall lifetime of WMSNs.In a PE-WMoT,nodes declare themselves Cluster Heads(CHs)based on available resources.Once cluster formation and CH declaration processes are completed,the Sub-Cluster(SC)formation process triggers,in which application base nodes within close vicinity of each other organize themselves under the administration of a Sub-Cluster Head(SCH).The SCH gathers data from member nodes,removes redundancies,and forwards miniaturized data to its respective CH.PE-WMoT adopts a fuzzy-based technique named the analytical hierarchical process,which enables CHs to select an optimal SCH among available SCs.A PE-WMoT also devises a robust scheduling mechanism between CH,SCH,and child nodes to enable collision-free data transmission.Simulation results revealed that a PE-WMoT significantly reduces the number of redundant packet transmissions,improves energy consumption of the network,and effectively increases network throughput.

A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic Networks

Recently,Opportunistic Networks(OppNets)are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent devices.OppNets are characterized by a rough and dynamic topology as well as unpredictable contacts and contact times.Data is forwarded and stored in intermediate nodes until the next opportunity occurs.Therefore,achieving a high delivery ratio in OppNets is a challenging issue.It is imperative that any routing protocol use network resources,as far as they are available,in order to achieve higher network performance.In this article,we introduce the Resource-Aware Routing(ReAR)protocol which dynamically controls the buffer usage with the aim of balancing the load in resource-constrained,stateless and non-social OppNets.The ReAR protocol invokes our recently introduced mutual informationbased weighting approach to estimate the impact of the buffer size on the network performance and ultimately to regulate the buffer consumption in real time.The proposed routing protocol is proofed conceptually and simulated using the Opportunistic Network Environment simulator.Experiments show that the ReAR protocol outperforms a set of well-known routing protocols such as EBR,Epidemic MaxProp,energy-aware Spray and Wait and energy-aware PRoPHETin terms of message delivery ratio and overhead ratio.

An Evolutionary Algorithm with Multi-Local Search for the Resource-Constrained Project Scheduling Problem

This paper introduces a hybrid evolutionary algorithm for the resource-constrained project scheduling problem (RCPSP). Given an RCPSP instance, the algorithm identifies the problem structure and selects a suitable decoding scheme. Then a multi-pass biased sampling method followed up by a multi-local search is used to generate a diverse and good quality initial population. The population then evolves through modified order-based recombination and mutation operators to perform exploration for promising solutions within the entire region. Mutation is performed only if the current population has converged or the produced offspring by recombination operator is too similar to one of his parents. Finally the algorithm performs an intensified local search on the best solution found in the evolutionary stage. Computational experiments using standard instances indicate that the proposed algorithm works well in both computational time and solution quality.

SOLVING RESOURCE-CONSTRAINED PROJECT SCHEDULING PROBLEMS WITH BI-CRITERIA HEURISTIC SEARCH TECHNIQUES

In this paper we formulate a bi-criteria search strategy of a heuristic learning algorithm forsolving multiple resource-constrained project scheduling problems. The heuristic solves problems intwo phases. In the pre-processing phase, the algorithm estimates distance between a state and the goalstate and measures complexity of problem instances. In the search phase, the algorithm uses estimatesof the pre-processing phase to further estimate distances to the goal state. The search continues in astepwise generation of a series of intermediate states through search path evaluation process withbacktracking. Developments of intermediate states are exclusively based on a bi-criteria new stateselection technique where we consider resource utilization and duration estimate to the goal state. Wealso propose a variable weighting technique based on initial problem complexity measures.Introducing this technique allows the algorithm to efficiently solve complex project schedulingproblems. A numerical example illustra

An adaptive and robust secret key extraction scheme from high noise wireless channel in IIoT

The Sixth-Generation(6G)enhances the Industrial Internet of Things(IIoT)communication efficiency and further raises security requirements.It is crucial to construct a post-quantum security communication channel between any pair of industrial equipment.Recent work shows that two legitimate devices can directly extract symmetrical secret keys in information-theoretic secure by measuring their common wireless channels.However,existing schemes may cause a high bit mismatch rate in IIoT with high noise.In addition,the physical layer key extraction scheme widely uses passive attack assumptions,which also contradicts the high-security requirements of IIoT.By analyzing and modeling IIoT systems and channels,we propose an Adaptive and Robust Secret Key Extraction scheme(ARSKE)from high noise wireless channels in 6G-enabled IIoT.To eliminate the non-reciprocity of the wireless channel,we design a smoothing method as the preprocessing module.Then,we propose a Robust Secure Reconciliation technique that can effectively resist active attacks by jointly designing the information reconciliation and privacy amplification phases.Extensive real-world experiments are conducted to test the effectiveness and robustness of our scheme.

Heuristic algorithm for RCPSP with the objective of minimizing activities' cost

Resource-constrained project scheduling problem（RCPSP） is an important problem in research on project management. But there has been little attention paid to the objective of minimizing activities＇ cost with the resource constraints that is a critical sub-problem in partner selection of construction supply chain management because the capacities of the renewable resources supplied by the partners will effect on the project scheduling. Its mathematic model is presented firstly, and analysis on the characteristic of the problem shows that the objective function is non-regular and the problem is NP-complete following which the basic idea for solution is clarified. Based on a definition of preposing activity cost matrix, a heuristic algorithm is brought forward. Analyses on the complexity of the heuristics and the result of numerical studies show that the heuristic algorithm is feasible and relatively effective.

Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

Digital surveillance systems are ubiquitous and continuously generate massive amounts of data,and manual monitoring is required in order to recognise human activities in public areas.Intelligent surveillance systems that can automatically identify normal and abnormal activities are highly desirable,as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring.This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system.The proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step framework.In the first step,the salient frames are selected from the online video stream using a frame differencing method.A lightweight 3D convolutional neural network(3DCNN)architecture is applied to extract spatio-temporal features from the salient frames in the second step.Finally,the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the camera.The proposed system performs well compared to state-of-theart violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance networks.Comprehensive experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98%with an F1-score of 0.97 on the Hockey Fight dataset,and an accuracy of 99%with an F1-score of 0.98 on the Violent Crowd dataset.

Cleft Lip Surgical Intervention in Resource Constraint Settings: A Case Report

Cleft lip and palate is a very common condition which is globally undertreated. This case report shows how cleft lip surgery can transform patient’s lives, self-perception, and their self-esteem through a visionary and adaptive leadership of a few social workers and medical doctors in resource constrained settings. The case report also shows that when treated, the benefits to the patient and their family are lifelong and disproportionately large compared to the degree of effort and cost of doing the reconstruction;and puts forth the case for educating physicians to maximize their societal benefit through work in resource-constrained settings.

Project Scheduling Using Hybrid Genetic Algorithm with Fuzzy Logic Controller in SCM Environment

In supply chain management (SCM) environment, we consider a resource-constrained project scheduling problem (rcPSP) model as one of advanced scheduling problems considered by a constraint programming technique. We develop a hybrid genetic algorithm (hGA) with a fuzzy logic controller (FLC) to solve the rcPSP which is the well known NP-hard problem. This new approach is based on the design of genetic operators with FLC through initializing the serial method which is superior for a large rcPSP scale. For solving these rcPSP problems, we first demonstrate that our hGA with FLC (flc-hGA) yields better results than several heuristic procedures presented in the literature. We have revealed a fact that flc-hGA has the evolutionary behaviors of average fitness better than hGA without FLC.

Hybrid genetic algorithm for bi-objective resourceconstrained project scheduling

In this study,we considered a bi-objective,multi-project,multi-mode resource-constrained project scheduling problem.We adopted three objective pairs as combinations of the net present value(NPV)as a financial performance measure with one of the time-based performance measures,namely,makespan(Cmax),mean completion time(MCT),and mean flow time(MFT)(i.e.,minCmax/maxA^PF,minA/Cr/max7VPF,and min MFTI mdixNPV).We developed a hybrid non-dominated sorting genetic algorithm Ⅱ(hybrid-NSGA-Ⅱ)as a solution method by introducing a backward-forward pass(BFP)procedure and an injection procedure into NSGA-Ⅱ.The BFP was proposed for new population generation and post-processing.Then,an injection procedure was introduced to increase diversity.The BFP and injection procedures led to improved objective functional values.The injection procedure generated a significantly high number of non-dominated solutions,thereby resulting in great diversity.An extensive computational study was performed.Results showed that hybrid-NSGA-Ⅱ surpassed NSGA-Ⅱ in terms of the performance metrics hypervolume,maximum spread,and the number of nondominated solutions.Solutions were obtained for the objective pairs using hybrid-NSGA-Ⅱ and three different test problem sets with specific properties.Further analysis was performed by employing cash balance,which was another financial performance measure of practical importance.Several managerial insights and extensions for further research were presented.

Implicit memory-based technique in solving dynamic scheduling problems through Response Surface Methodology–Part I Model and method

Purpose–This is the first part of a two-part paper.The purpose of this paper is to report on methods that use the Response Surface Methodology(RSM)to investigate an Evolutionary Algorithm(EA)and memory-based approach referred to as McBAR–the Mapping of Task IDs for Centroid-Based Adaptation with Random Immigrants.Some of the methods are useful for investigating the performance(solution-search abilities)of techniques(comprised of McBAR and other selected EAbased techniques)for solving some multi-objective dynamic resource-constrained project scheduling problems with time-varying number of tasks.Design/methodology/approach–The RSM is applied to:determine some EA parameters of the techniques,develop models of the performance of each technique,legitimize some algorithmic components of McBAR,manifest the relative performance of McBAR over the other techniques and determine the resiliency of McBAR against changes in the environment.Findings–The results of applying the methods are explored in the second part of this work.Originality/value–The models are composite and characterize an EA memory-based technique.Further,the resiliency of techniques is determined by applying Lagrange optimization that involves the models.

Implicit memory-based technique in solving dynamic scheduling problems through Response Surface Methodology–PartⅡExperiments and analysis

Purpose–This is the second part of a two-part paper.The purpose of this paper is to report the results on the application of the methods that use the Response Surface Methodology to investigate an evolutionary algorithm(EA)and memory-based approach referred to as McBAR–the Mapping of Task IDs for Centroid-Based Adaptation with Random Immigrants.Design/methodology/approach–The methods applied in this paper are fully explained in the first part.They are utilized to investigate the performances(ability to determine solutions to problems)of techniques composed of McBAR and some EA-based techniques for solving some multi-objective dynamic resource-constrained project scheduling problems with a variable number of tasks.Findings–The main results include the following:first,some algorithmic components of McBAR are legitimate;second,the performance of McBAR is generally superior to those of the other techniques after increase in the number of tasks in each of the above-mentioned problems;and third,McBAR has the most resilient performance among the techniques against changes in the environment that set the problems.Originality/value–This paper is novel for investigating the enumerated results.