Automatic design for shop scheduling strategies based on hyper-heuristics: A systematic review

Against the background of smart manufacturing and Industry 4.0, how to achieve real-time scheduling has become a problem to be solved. In this regard, automatic design for shop scheduling based on hyper-heuristics has been widely studied, and a number of reviews and scheduling algorithms have been presented. Few studies, however, have specifically discussed the technical points involved in algorithm development. This study, therefore, constructs a general framework for automatic design for shop scheduling strategies based on hyper-heuristics, and various state-of-the-art technical points in the development process are summarized. First, we summarize the existing types of shop scheduling strategies and classify them using a new classification method. Second, we summarize an automatic design algorithm for shop scheduling. Then, we investigate surrogate-assisted methods that are popular in the current algorithm field. Finally, current problems and challenges are discussed, and potential directions for future research are proposed.     

An improved feedrate scheduling method for NURBS interpolation in five-axis machining

Five-axis machining plays an important role in manufacturing by dint of its high efficiency and accuracy. While two rotation axes benefit the flexibility of machining, it also brings limitations and challenges. In order to further balance machining precision and efficiency, an improved feedrate scheduling method is presented considering geometric error and kinematic constraints for the Non Uniform Rational B-Spline (NURBS) interpolation in five-axis machining. A simplification method is proposed to calculate the geometric error which describes the deviation between the ideal tool path and the real tool path induced by the non-linear movement. A linear relation between geometric error and feedrate is built to limit the feedrate. The constraints determined by single axis kinematic performance and tangential kinematic performance are also considered. Under these constraints, a constrained feedrate profile is determined. Aiming to get more constant feedrate in the difficult-to-machine areas, this work proposes a scheduling method which combines morphological filtering and S-shape acceleration/deceleration (acc/dec) mode. Simulations and experiments are performed to compare the proposed feedrate scheduling method with two previous feedrate scheduling method and the results prove that the proposed feedrate scheduling method is reliable and effective.     

Optimal power flow by BAT search algorithm for generation reallocation with unified power flow controller

Optimal power flow with generation reallocation is a suitable method for better utilization of the existing system. In recent years, Flexible AC Transmission System (FACTS) devices, have led to the development of controllers that provide controllability and flexibility for power transmission. Out of the FACTS devices unified power flow controller (UPFC) is a versatile device, capable of controlling the power system parameters like voltage magnitude, phase angle and line impedance individually or simultaneously. The main aim of this paper is to minimize real power losses in a power system using BAT search algorithm without and with the presence of UPFC. Minimization of real power losses is done by considering the power generated by generator buses, voltage magnitudes at generator buses and reactive power injection from reactive power compensators. The proposed BAT algorithm based Optimal Power Flow (OPF) has been tested on a 5 bus test system and modified IEEE 30 bus system without and with UPFC. The results of the system with and without UPFC are compared in terms of active power losses in the transmission line using BAT algorithm. The obtained results are also compared with Genetic algorithm (GA).     

Control theory for stochastic distributed parameter systems,an engineering perspective

The main purpose of this paper is to survey some recent progresses on control theory for stochastic distributed parameter systems, i.e., systems governed by stochastic differential equations in infinite dimensions, typically by stochastic partial differential equations. We will explain the new phenomenon and difficulties in the study of controllability and optimal control problems for one dimensional stochastic parabolic equations and stochastic hyperbolic equations. In particular, we shall see that both the formulation of corresponding stochastic control problems and the tools to solve them may differ considerably from their deterministic/finite-dimensional counterparts. More importantly, one has to develop new tools, say, the stochastic transposition method introduced in our previous works, to solve some problems in this field.     

An optimization model for evaluating the economic impact of availability and maintenance notions during the synthesis and design of a power plant

In this paper, we introduce an optimization strategy in order to comprehensively quantify the impact of availability and maintenance notions during the early stages of synthesis and design of a new natural gas combined cycle power plant. A detailed state-space approach is thoroughly discussed, where influence of maintenance funds on each component's repair rate is directly assessed.In this context, analysis of the reliability characteristics of the system is centered at two designer-adopted parameters, which largely influence the obtained results: the number of components which may fail independently at the same time, and the number of simultaneous failure/repair events.Then, optimal solutions are evaluated as the availability-related parameters and the amount of resources assigned for maintenance actions are varied across a wide range of feasible values, which enable obtaining more accurate and detailed estimations of the expected economic performance for the project when compared with traditional economic evaluation approaches.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Global optimization for power dispatch problems based on theory of moments

Non-convex of an optimal power dispatch problem makes it difficult to guarantee the global optimum. This paper presents a convex relaxation approach, called the Moment Semidefinite Programming (MSDP) method, to facilitate the search for deterministic global optimal solutions. The method employs a sequence of moments, which can linearize polynomial functions and construct positive semidefinite moment matrices, to form an SDP convex relaxation for power dispatch problems. In particular, the rank of the moment matrix is used as a sufficient condition to ensure the global optimality. The same condition can also be leveraged to estimate the number of global optimal solution(s). This method is effectively applied to {0,1}-economic dispatch (ED) problems and optimal power flow (OPF) problems. Simulation results showed that the MSDP method is capable of solving {0,1}-ED problems with integer values directly, and is able to identify if more than one global optimal solutions exist. In additional, the method can obtain rank-1 moment matrices for OPF’s counterexamples of existing SDP method, this ensures the global solution and overcomes the problem that existing SDP method cannot meet the rank-1 condition sometimes.     

Semirigid inclusions in elastic bodies: Mechanical interplay and optimal control

The paper concerns an analysis of an equilibrium problem for 2D elastic body with two semirigid inclusions. It is assumed that inclusions have a joint point, and we investigate a junction problem for these inclusions. The existence of solutions is proved, and different equivalent formulations of the problem are proposed. We investigate a convergence to infinity of a rigidity parameter of the semirigid inclusion. It is proved that in the limit, we obtain an equilibrium problem for the elastic body with a rigid inclusion and a semirigid one. A parameter identification problem is investigated. In particular, the existence of a solution to a suitable optimal control problem is proved.     

基于组合双向拍卖的云资源调度方法

针对跨数据中心的资源调度问题，提出了一种基于组合双向拍卖（PCDA）的资源调度方案。首先，将云资源拍卖分为三个部分：云用户代理报价、云资源提供商要价、拍卖代理组织拍卖；其次，在定义用户的优先级及任务紧迫度的基础上，在拍卖过程中估算每一个工作发生的服务等级协议（SLA）违规并以此计算云提供商的收益，同时每轮竞拍允许成交多项交易；最终达到根据用户等级合理分配云资源调度的效果。仿真实验结果表明该算法保证了竞拍成功率，与传统一次拍卖成交一项的组合双向拍卖方案相比，PCDA在竞拍时间段产生的能耗降低了35.00%，拍卖云提供商的利润提高了约38.84%。