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.

Scheduling Multi-Mode Resource-Constrained Projects Using Heuristic Rules Under Uncertainty Environment

Project scheduling is a key objective of many models and is the proposed method for project planning and management.Project scheduling problems depend on precedence relationships and resource constraints,in addition to some other limitations for achieving a subset of goals.Project scheduling problems are dependent on many limitations,including limitations of precedence relationships,resource constraints,and some other limitations for achieving a subset of goals.Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required,which are known and stable during the implementation process.The concept of deterministic project scheduling conflicts with real situations,in which in many cases,some data on the activity’s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity durations.Scheduling the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to us.We use the multi-mode resource allocation and schedulingmodel that takes into account the dynamicity features of all parameters,that is,the scheduling process must be flexible to dynamic environment features.In this paper,we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations,in which we apply the proposed heuristic rules(PHR)for scheduling multi-mode resource-constrained projects.Five projects are considered test problems for the PHR.The obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for the same test problems.The results in many cases of the proposed priority rules are very promising,where they achieve better scheduling dates in many test case problems and the same results for the others.The proposed model is based on the dynamic features for project topography.

Efficient Genetic Algorithm for Resource-Constrained Project Scheduling Problem

This paper presents a new genetic algorithm for the resource-constrained project scheduling problem(RCPSP).The algorithm employs a standardized random key(SRK) vector representation with an additional gene that determines whether the serial or parallel schedule generation scheme(SGS) is to be used as the decoding procedure.The iterative forward-backward improvement as the local search procedure is applied upon all generated solutions to schedule the project three times and obtain an SRK vector,which is rese...

Multi-Mode Resource Constrained Project Scheduling Models for Progress and Equal Time Interval Payments

This study utilizes a time-precedence network technique to construct two models of multi-mode resource constrained project scheduling problem with discounted cash flows （MRCPSPDCF）, individually including the progress payment （PP） and the payment at an equal time interval （ETI）. The objective of each model is to maximize the net present value （NPV） for all cash flows in the project, subject to the related operational constraints. The models are characterized as NP-hard. A heuristic algorithm, coupled with two upper bound solutions, is proposed to efficiently solve the models and evaluate the heuristic algorithm performance which was not performed in past studies. The results show that the performance of proposed models and heuristic algorithm is good.

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.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

A New Algorithm for Resource Constraint Project Scheduling Problem Based on Multi-Agent Systems

The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocation decision involved in RCPSP has also been developed. And this algorithm can be used in the multi-project scheduling field as well.Finally, an illustration is given.

The Coupling Coordination Evolution Research of Economy - Ecology - Society System in Xinjiang：Based on the Interaction Perspective of Water Resources

Under the water resources perspective,coupling coordination of economy-ecology-society system in Xinjiang was taken as the research object. By building the coupling evaluation index system of the water resources-economy-ecology-society system,quantitative evaluation and analysis of the coupling coordination of the water resources-economy-ecology-society system from 2001 to 2014 were conducted. The results showed that benefit indexes of economic system and social system grew rapidly,while benefit indexes of water resources system and ecosystem fluctuated smoothly from 2001 to 2014; coupling state of the whole system was at rival stage,and coupling coordination was reluctant coordination type,and the coupling index was slightly higher than the coupling coordination index. Moreover,it showed that the more the coupling systems,the lower indices of coupling and the coupling coordination.

A user-oriented resource scheduling method for improving agile software pattern in cloud environment

The crowdsourcing, as a service pattern in cloud environment, usually aims at the cross-disciplinary cooperation and creating value together with customers and becomes increasingly prevalent. Software process, as a kind of software development and management strategy, is defined as a series of activities implemented by software life cycle and provides a set of rules for various phases of the software engineering to achieve the desired objectives. With the current software development cycle getting shorter, facing more frequent needs change and fierce competition, a new resource management pattern is proposed to respond to these issues agilely by introducing the crowdsourcing service to agile software development for pushing the agility of software process. Then, a user-oriented resource scheduling method is proposed for rational use of various resources in the process and maximizing the benefits of all parties. From the experimental results, the proposed pattern and resources scheduling method reduces greatly the resource of project resource manager and increases the team resource utilization rate, which greatly improves the agility of software process and delivers software products quickly in crowdsourcing pattern.

Utilization Survey of Livestock Manure Resources in Large-scale Farms of Yangzhou

Based on surveying the conditions of large -scale farms and commercial manure in the each county of Yangzhou city, the situations and problems for utilization of livestock manure resources were grasped. After an analysis of the potential value of livestock manure, the suggestion and strategy for utilization of livestock manure resources were proposed based on the actual conditions in Yangzhou city.

2015 IWA Alternative Water Resources Conference Nanjing,China(May 26-28,2015)

The IWA Alternative Water Resources（AWR）Conference is organized by the International Water Association,Hohai University,the IWA China Chapter of the AWR Cluster,and the Seawater Desalination and AWR Utilization Research Center of Hohai University.

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.

AUCTIONING METHOD FOR AIRSPACE CONGESTING RESOURCE ALLOCATION AND GAME EQUILIBRIUM ANALYSIS

The airspace congestion is becoming more and more severe.Although there are traffic flow management（TFM）initiatives based on CDM widely applied,how to reschedule these disrupted flights of different airlines integrating TFM initiatives and allocate the limited airspace resources to these airlines equitably and efficiently is still a problem.The air traffic management（ATM）authority aims to minimizing the systemic costs of congested airspaces.And the airlines are self-interested and profit-oriented.Being incorporated into the collaborative decision making（CDM）process,the airlines can influence the rescheduling decisions to profit themselves.The airlines maybe hide the flight information that is disadvantageous to them,but is necessary to the optimal system decision.To realize the coincidence goal between the ATM authority and airlines for the efficient,and equitable allocation of airspace resources,this paper provides an auction-based market method to solve the congestion airspace problem under the pre-tactic and tactic stage of air traffic flow management.Through a simulation experiment,the rationing results show that the auction method can decrease the total delay costs of flights in the congested airspace compared with both the first schedule first service（FSFS）tactic and the ration by schedule（RBS）tactic.Finally,the analysis results indicate that if reallocate the charges from the auction to the airlines according to the proportion of their disrupted flights,the auction mechanism can allocate the airspace resource in economy equitably and decrease the delay losses of the airlines compared with the results of the FSFS tactic.

Improved Differential Evolution with Shrinking Space Technique for Constrained Optimization

Most of the current evolutionary algorithms for constrained optimization algorithm are low computational efficiency. In order to improve efficiency, an improved differential evolution with shrinking space technique and adaptive trade-off model, named ATMDE, is proposed to solve constrained optimization problems. The proposed ATMDE algorithm employs an improved differential evolution as the search optimizer to generate new offspring individuals into evolutionary population. For the con- straints, the adaptive trade-off model as one of the most important constraint-handling techniques is employed to select better individuals to retain into the next population, which could effectively handle multiple constraints. Then the shrinking space technique is designed to shrink the search region according to feedback information in order to improve computational efficiency without losing accuracy. The improved DE algorithm introduces three different mutant strategies to generate different offspring into evo- lutionary population. Moreover, a new mutant strategy called ＂DE/rand/best/l＂ is constructed to generate new individuals according to the feasibility proportion ofcurrent population. Finally, the effectiveness of the pro- posed method is verified by a suite of benchmark functions and practical engineering problems. This research presents a constrained evolutionary algorithm with high efficiency and accuracy for constrained optimization problems.

Economic appraisal of shale gas resources, an example from the Horn River shale gas play, Canada

Development of unconventional shale gas resources involves intensive capital investment accompa- nying large commercial production uncertainties. Eco- nomic appraisal, bringing together multidisciplinary project data and information and providing likely economic outcomes for various development scenarios, forms the core of business decision-making. This paper uses a dis- counted cash flow （DCF） model to evaluate the economic outcome of shale gas development in the Horn River Basin, northeastern British Columbia, Canada. Through numerical examples, this study demonstrates that the use of a single average decline curve for the whole shale gas play is the equivalent of the results from a random drilling process. Business decision based on a DCF model using a single decline curve could be vulnerable to drastic changes of shale gas productivity across the play region. A random drilling model takes those drastic changes in well estimated ultimate recovery （EUR） and decline rates into account in the economic appraisal, providing more information useful for business decisions. Assuming a natural gas well-head price of S4/MCF and using a 10 % discount rate, the results from this study suggest that a random drilling strategy （e.g., one that does not regard well EURs）, could lead to a negative net present value （NPV）; whereas a drilling sequence that gives priority to developing those wells with larger EURs earlier in the drilling history could result in a positive NPV with various payback time and internal rate of return （IRR）. Under a random drilling assumption, the breakeven price is S4.2/MCF with more than 10 years of payout time. In contrast, if the drilling order is strictly proportional to well EURs, the result is a much better economic outcome with a breakeven price below the assumed well-head price accompanied by a higher IRR.

The concept and the accumulation characteristics of unconventional hydrocarbon resources

Unconventional hydrocarbon resources, which are only marginally economically explored and developed by traditional methods and techniques, are different from conventional hydrocarbon resources in their accumulation mechanisms, occurrence states, distribution models, and exploration and development manners. The types of unconventional hydrocarbon are controlled by the evolu- tion of the source rocks and the combinations of different types of unconventional reservoirs. The fundamental dis- tinction between unconventional hydrocarbon resources and conventional hydrocarbon resources is their non- buoyancy-driven migration. The development of the micro- to nano-scale pores results in rather high capillary resis- tance. The accumulation mechanisms of the unconven- tional and the conventional hydrocarbon resources are also greatly different. In conventional hydrocarbon resources, oil and gas entrapment is controlled by reservoir-forming factors and geological events, which is a dynamic balance process; while for unconventional hydrocarbon resources, the gas content is affected by the temperature and pressure fields, and their preservation is crucial. Unconventional and conventional hydrocarbons are distributed in an orderly manner in subsurface space, having three distribution models of intra-source rock, basin-centered, and source rock interlayer. These results will be of great significance to unconventional hydrocarbon exploration.

A Hybrid Dynamic Programming Method for Concave Resource Allocation Problems

Concave resource allocation problem is an integer programming problem of minimizing a nonincreasing concave function subject to a convex nondecreasing constraint and bounded integer variables. This class of problems are encountered in optimization models involving economies of scale. In this paper, a new hybrid dynamic programming method was proposed for solving concave resource allocation problems. A convex underestimating function was used to approximate the objective function and the resulting convex subproblem was solved with dynamic programming technique after transforming it into a 0-1 linear knapsack problem. To ensure the convergence, monotonicity and domain cut technique was employed to remove certain integer boxes and partition the revised domain into a union of integer boxes. Computational results were given to show the efficiency of the algorithm.

Curve25519 based lightweight end-to-end encryption in resource constrained autonomous 8-bit IoT devices

Robust encryption techniques require heavy computational capability and consume large amount of memory which are unaffordable for resource constrained IoT devices and Cyber-Physical Systems with an inclusion of general-purpose data manipulation tasks.Many encryption techniques have been introduced to address the inability of such devices,lacking in robust security provision at low cost.This article presents an encryption technique,implemented on a resource constrained IoT device(AVR ATmega2560)through utilizing fast execution and less memory consumption properties of curve25519 in a novel and efficient lightweight hash function.The hash function utilizes GMP library for multi-precision arithmetic calculations and pre-calculated curve points to devise a good cipher block using ECDH based key exchange protocols and large random prime number generator function.

基于蚁群算法的Job-Shop多资源约束车间作业调度

针对多资源约束的车间调度问题,考虑资源种类变化的情况及资源在可用时间上的约束影响,建立了该类问题的通用调度模型。提出了一种基于蚁群算法的作业优化调度算法,在Job-Shop问题图形化定义的基础上,设计了状态转移规则、轨迹强度更新规则以及工序时间的决策规则,借鉴精英策略的思路,对多资源约束车间的具体调度进行了分析与实现。实例仿真表明,该方法对多资源的车间调度问题是可行的。