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In the future integrated energy market, the traditional centralized planning method cannot solve the planning problem for multi-stakeholder integrated energy systems (MSIESs). To cope with this problem, this paper proposes a synchronously decentralized adaptive robust programming (SDARP) model for MSIESs based on the analytical target cascading (ATC) algorithm. In this model, different stakeholders are planned in parallel under the source-load uncertainties, aiming at minimizing the annual investment and operation costs and fully taking into account the interactive heating/cooling power transmitted by the district heating/cooling network. In each stakeholder, the column-and-constraint generation algorithm is used to address the robustness problems in the three-layer adaptive robust programming model. In addition, a tractable alternating optimization procedure is used to solve the non-convex SDARP model with finite convergence. Case studies verify the superiority and effectiveness of the SDARP method in dealing with the planning problem of MSIESs.

In order to improve the efficiency of path planning for Unmanned Aerial Vehicles (UAV), this paper proposed a method combined of GA and A* algorithm for three-dimensional path planning, which is divided into global path planning and real-time path re-planning. Global path planning focus on safety and optimality, and provides an optimal path for the UAV that meets the constraints by using the real number gene coding method. Real-time path re-planning focuses on emergency threats and needs to ensure real-time performance. It is implemented by combining the two-dimensional A* algorithm and height planning that improves the searching efficiency compared to traditional three-dimensional A* methods. Finally, simulation results show that the proposed method in this paper can effectively reduce the search space and improve the search efficiency.

Nowadays, automated guided vehicle (AGV) systems are frequently employed in automated warehouses. Recently, a problem has emerged regarding the movement of AGVs under uncertain transportation conditions necessitated by the novel logistics required for connected industries and societies. In the present study, we attempt to develop a simultaneous planning method to determine the optimal number and dwell point of AGVs in an AGV transfer system, under uncertain transportation conditions, based on a genetic algorithm. We propose an algorithm that can determine the optimal number of AGVs as well as the dwell points for idle AGVs such that the mean response time is minimized and the amount of the work done by the AGVs is maximized, even when the transportation condition is uncertain. Moreover, we investigate the effectiveness of the proposed algorithm using numerical calculations and simulation experiments. The results show that the proposed algorithm performs better than previously used algorithms, in terms of the average matching time of products.

To solve the problem that the active management strategy is not considered in the current active distribution network expansion planning research, we propose a new active distribution network expansion planning method considering active management strategy. Combining active management with distribution network extend planning and DG locating and sizing. Considering the impact of DG active management on the distribution network, the two-layer planning model is used to optimize the line structure of the distribution network and the locating and sizing of the distributed generation. The upper model is optimized by the binary particle swarm optimization algorithm, and the lower model is optimized by the particle swarm optimization algorithm. The analysis of the example shows that the proposed method can reduce the economic cost and improve the reliability of the distribution network.

Based on “One Belt and One Road”, this paper studies the path selection of multimodal transport by using the method of multi-objective mixed integer programming. Therefore, this paper studies the factors of transportation time, transportation cost and transportation safety performance, and establishes a mathematical model. In addition, the method of multi-objective mixed integer programming is used to comprehensively consider the different emphasis and differences of customers on cargo transportation. Then we use planning tools of Microsoft Excel to solve path selection and to determine whether the chosen path is economical and reliable. Finally, a relatively complex road network is built as an example to verify the accuracy of this planning method.

The main content of this research is about the ATP (Available to promise) allocation problem based on Assemble-to-Order Supply Chain. In the case of Multiple-Suppliers, the synchronous planning method is selected to discuss the supply plan and demand plan and an ATP allocation decision model adapted to ATO supply chain production environment is established. Based on the equal-weight combination forecasting method, the forecast demand was input into the ATP distribution model. The mixed integer linear programming method was adopted to solve the calculation and analyze the cost and benefit. Finally, the optimal ATP distribution scheme was obtained.

With regard to the characteristics of long process, multi-units, cross-logistics and batch production mode, the order planning problem at the steelmaking shop is studied with consideration of the plant-wide process logistics balance. The problem is to allocate the orders to the continuous casters over plan periods such that the semi-finished materials produced by the steelmaking process can be delivered to the downstream hot-rolling and cold-rolling processes on time at required volume. Based on the detailed analysis of unit layout characteristics of the plant-wide process and the order composition information, a hierarchical planning method is proposed to decompose the large-scale planning problem into a set of sub-problems that are sequential solved. For the order batching subproblem, we develop an integer programming model to consolidate orders with small demand into cast-lots by considering the key batching rules. The model is efficiently solved by the optimization solver of CPLEX. For the cast-lot scheduling subproblem, a novel mixed integer programming model is developed by considering the technological constraints, the facility capacity constraints, the logistics balance constraints, the inventory constraints, and the due date constraints. An improved differential evolution algorithm is proposed to solve the cast-lot scheduling subproblem. The main improvement strategy is that the superior individuals within an external archive are further enhanced through a variable neighborhood search mechanism. Computational results on random and practical instances of different scale problems verify the effectiveness of the proposed method.

In this paper, a multiyear hybrid planning method (HPM) based on cost-benefit analysis is proposed for implementation of smart charging strategies for plug-in electric vehicles (PEVs) in a distribution system. Two different strategies which consider an integrated grid-to-vehicle and vehicle-to-grid charging framework are utilized in this paper for smart charging of PEVs. The objective functions of the two strategies, namely minimization of total daily cost and minimization of peak-to-average ratio, represent an economic and a technical perspective, respectively. The proposed HPM is based on optimally allocating two different charging strategies at different nodes of the distribution system during the planning horizon. The network impacts and the related financial impacts of the proposed HPM are assessed against two conventional planning methods (CPMs) which are developed corresponding to the two charging strategies. In a CPM, a single charging strategy is implemented on all the nodes of the distribution system for the entire planning horizon. The simulation studies performed on small and medium sized distribution systems demonstrate the effectiveness, feasibility, and scalability of the proposed HPM.

For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, and the total distance of routes were normally considered as the optimization objectives. Except for the above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, a corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, a robust dynamic multi-objective vehicle routing method with two-phase is proposed . Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii) The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii) A metric measuring the algorithms robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

This paper studies a problem in which a fleet of heterogeneous fixed-wing unmanned aerial vehicles (UAVs) must identify the optimal flyable trajectory to traverse over multiple targets and perform consecutive tasks. To obtain a fast and feasible solution, a coupled and distributed planning method is developed that integrates the task assignment and trajectory generation aspects of the problem. With specific constraints and a relaxed Dubins path, the cooperative mission-planning problem is reformulated. A distributed genetic algorithm is then proposed to search for the optimal solution, and chromosomal genes are modified to adapt to the heterogeneous characteristic of UAVs. Then, a fixed-wing UAV model with 6 degrees of freedom (DOF) and a path-following method is used to verify this proposed mission-planning method. The simulation results show that the proposed approach obtains feasible solutions and significantly improves the operating rate, with the potential for use in a real mission.

This paper presents a path planning method for partial field coverage. Therefore, a specific path planning pattern is proposed. The notion is that lighter machinery with smaller storage tanks can alleviate soil compaction because of a reduced weight, but does not enable full field coverage in a single run because of the smaller storage capacity. This is relevant for spraying applications and related in-field work. Consequently, multiple returns to a mobile or stationary depot located outside of the field are required for storage tank refilling. Therefore, a suitable path planning method is suggested that accounts for the limited turning radii of agricultural vehicles, satisfies compacted area minimisation constraints, and aims at overall path length minimisation. The benefits of the proposed method are illustrated by means of a comparison to a planning method based on the more common AB pattern. It is illustrated how the proposed path planning pattern can also be employed efficiently for single-run field coverage., Comment: - 44 pages, 14 Figures, 2 Tables

This work explores the coordinate planning methods of multiple microgrids and distribution network with mixed AC/DC interconnection method which consider the coupling influence of interconnection structure and operation mode. The structure and evaluation model of the multiple microgrids system were discussed, and then system planning models were established. In order to solve the complicated combinatorial model, the benders decomposition methods were used to divide the problem into benders master problem, reliability sub-problem and operation sub-problem. Simulation results on the multiple microgrids test system with distribution network show that the proposed methods are effective.

In distribution network management, switch reconfiguration is an important tool for reducing energy loss. Recently, a variety of annual reconfiguration planning methods considering energy loss have been studied. However, no conventional methods address the reconfiguration periods in fine granularity. Practically, switch durability does not support high-frequency switching. Therefore, this paper proposes a new optimization method for annual reconfiguration scheduling. This method determines switch configurations and their reconfiguration periods with a constraint on the permissible reconfiguration times. In addition, this paper reveals the annual energy loss reduction effect of this optimization. Our method is based on partial network optimization with exhaustive enumeration of all feasible configurations. Experiments were conducted using a standard Japanese distribution network model with 468 switches. The results show that optimizing the reconfiguration periods reduces energy loss by up to 2.1 times, relative to that in a simulated conventional operation, which considers reconfiguration at equal intervals. We believe that this is the first quantitative report to address the difference between optimal reconfiguration scheduling and conventional reconfiguration.

This paper proposes a stochastic multistage expansion planning method in order to solve the mid-term and long-term optimal feeder routing problem. Pseudo dynamic behaviour of the network pa...

The integration of energy internet (EI) and electric power system has a profound influence on electric power system planning. Therefore, this paper mainly resolves the EI-oriented multi-stage generation and transmission coordinated planning problem with a modified fireworks algorithm. Firstly, we combined with Stackelberg game theory and focused on subject game theory, system low carbon characteristic and renewable energy generation, a multi-stage generation and transmission coordinated planning model for electric power system is proposed. The model has a main objective function of maximizing the social welfare and two secondary objective functions of maximizing individual profits in the operation stage and maximizing overall profits of generation companies in the planning stage. Then, a modified fireworks algorithm (MFWA) is proposed with good expandability, convergence and adaptability to solve the problem. The MFWA is modified by introducing Gaussian Mutation to improve global search capability. The basic coding method and solution procedure of the MFWA are depicted. Finally, an improved IEEE-24 bus system is used to test the proposed planning method and MFWA. By comparing three scenarios and analysing results, we can verify the validity and feasibility of the proposed method.

Objectives In the article, problems in the optimising of machining operations, which provide end-unit production of the required quality with a minimum processing cost, are addressed. Methods Increasing the effectiveness of experimental research was achieved through the use of mathematical methods for planning experiments for optimising metal cutting tasks. The minimal processing cost model, in which the objective function is polynomial, is adopted as a criterion for the selection of optimal parameters. Results Polynomial models of the influence of angles φ, α, γ on the torque applied when cutting threads in various steels are constructed. Optimum values of the geometrical tool parameters were obtained using the criterion of minimum cutting forces during processing. The high stability of tools having optimal geometric parameters is determined. It is shown that the use of experimental planning methods allows the optimisation of cutting parameters. In optimising solutions to metal cutting problems, it is found to be expedient to use multifactor experimental planning methods and to select the cutting force as the optimisation parameter when determining tool geometry. Conclusion The joint use of geometric programming and experiment planning methods in order to optimise the parameters of cutting significantly increases the efficiency of technological metal processing approaches.

A heuristic planning method of electric vehicle (EV) fast charging station on a freeway considering the power flow constraints of the distribution network was proposed. Firstly, the spatial and temporal distribution of EV charging points was determined based on a spatial-temporal model (STM); then the charging demand of candidate fast charging stations was determined based on the Shared Nearest Neighbor (SNN) Clustering algorithm; finally, a heuristic principle was proposed to meet the power flow constraints of the distribution network and the final planning scheme (the location and capacity) was determined. A typical test system was used to verify the validity of the planning method.

A bi-level joint planning model of distributed generation (DG), electric vehicle charging station (EVCS) and active distribution network (ADN) framework is proposed by considering demand side management (DSM). The upper ADN framework planning model is established by taking the lowest annual comprehensive cost as the upper level objective, which is solved by the improved partheno-genetic algorithm (IPGA). Based on the upper framework scheme, the lower DG and EVCS planning model is established to minimum the annual construction maintenance cost, and is solved by the biogeography-based optimization (BBO) algorithm. Simulation results confirm the effectiveness of the proposed joint planning method of DG, EVCS and ADN framework.

with a large number of uncertain distributed power supply access to the distribution network, increasing multiple uncertainties in distribution networks brings new challenges to its planning. Considering the uncertainty of load and wind speed, this paper proposes a robust planning method considering multiple uncertainties for distribution network. Firstly, based on life cycle cost and robust optimization, a two- layer robust planning model considering multiple uncertainties is constructed. Then the upper planning scheme is verified with the worst scenario. Finally, the IEEE33 node is used for simulation analysis. Simulation results based on standard example verify the rationality and effectiveness of the proposed method.

In this paper a methodology for distribution system expansion planning (DSEP) is presented in which in addition to network reinforcement, renewable distributed generations (RDGs) are considered as system capacity expansion options. The problem is formulated as a multistage expansion model which optimizes simultaneously the allocation of wind units as well as investment on network reinforcement over the planning horizon. The proposed optimization model considers the economic and environmental objectives, concurrently. For solving of the proposed optimization problem, an efficient mixed integer linear programming (MILP) approach is presented. The simulation results demonstrate the effectiveness of the approach in planning of environmentally friendly distribution networks.

multi-circuit direct current (MCDC) is one of the effective methods to solve the large-scale grid-connection problem of new energy. When planning the new energy access system with MCDC, the combination of site selection and constant capacity makes the planning a nondeterministic polynomial problem (NP-hard). According to the continuous output curve of new energy, this paper quantifies the annual power consumption and power discard, and puts forward the access system planning model of multi-cluster new energy through gathering stations and multi-return dc grid-connection under the economic optimal target. The profit of each cluster is discussed, and the mathematical relationship between the location of the collecting station and the new energy access capacity is found when the cost is continuous. Finally, the complex planning with capacity and coordinates is transformed into the ferma-weber (f-w) site selection problem of pure coordinate planning and solved by iteration. An example is given to demonstrate the effectiveness and rapidity of the proposed method.

The application and integration of soft open point (SOP) can benefit the operation of active distribution network a lot. This paper proposes a multi-objective SOP planning methodology in active distribution network. The multi-objective SOP planning model is established based on the bi-level programming. The upper level model is solved by the nondominated sorting genetic algorithm-II. The lower level model is transferred to a second-order cone model by using convex relaxation, and it is solved by second-order cone programming method. The modified IEEE 33-bus active distribution network is used to test and validate the effectiveness of the proposed bilevel multi-objective SOP planning method.

Energy Internet, which is regarded as the new generation energy utilization system, is an ideal solution to achieve sustainable development. In this paper, we propose a park-type Energy Local Area Network (ELAN) planning method for the planning and operation control of the ELAN. Firstly, we classify the park from its industrial structure and analyse the positioning of the park in terms of energy, environment, and load factors. Then, the physical planning architecture of the park-type ELAN is built. Subsequently, a planning method of park-type ELAN is proposed, which can incorporate the optimization problem of multi-energy cooperative scheduling into the planning model. Finally, the effectiveness of the proposed method is verified by performing calculations with a solid waste treatment park system.

Siting and sizing of energy stations (ESs) and pipeline networks (PNs) significantly influence views on the economy of regional integrated energy system (RIES). In this paper, we present a synergy planning method for siting and sizing ESs and PNs. This study describes the basic structure of ESs and PNs in RIES, clearly points out influencing factors that should be considered in the planning model. Then, with flow velocity, pipe diameter, equipment capacity, and the location of ESs and PNs as variables, the cost-based function model is built based on many constraints. The Genetic Algorithm (GA) based heuristic algorithm and the PNs planning algorithm based on “Dijkstra Algorithm (DA) + Simulated Annealing (SA)” are used to solve the problem proposed in this paper. In the results and discussion, the proposed model and method are verified by a hypothetical region from OpenStreetMap. The economic changes are studied in a basic scenario and three comparison scenarios. The results show that synergy planning can save 293,320 $ and reduce the load peak-valley difference rate by 3.24%. Moreover, optimizing the flow velocity can reduce 0–32.04% lowest annual cost (LAC).

In this article, the impacts of alternative generation integration in a power grid are discussed from the view of complex network theory. Using the improved complex network index, the structural performance of the system could be assessed in planning. Also, the distribution of load and generation are also considered in the modeling. Compared with the existing planning method, the proposed method can not only solve alternative generation units siting issues but also locate the corresponding conventional generation to be curtailed or replaced. Furthermore, as more information is obtained, e.g., related policy or cost parameters, a multi-objective comprehensive decision model is designed, the weight coefficient of which is determined by the two-tuple linguistic decision method. The proposed indices and models can effectively realize fast location and help improve the structural performance of the system with appropriate alternative generation integration. The models and methods are tested and verifie...

Electric energy substitution technology is of great significance for optimizing the final energy consumption structure, promoting energy conservation and emission reduction, and preventing air pollution. This paper aims to review the current research status of electric energy substitution technology and related fields, summarize the planning methods of electric energy substitution supporting network, and provide reliable network support for large-scale promotion of electric energy substitution. Firstly, the connotation, strategic significance and characteristics of electric energy substitution technology are summarized, and the basic structure of electric energy substitution technology is proposed. Then, the characteristics of network planning and gridding planning ideas are integrated into the gridding planning framework under the background of electric energy substitution is proposed. Finally, the development challenges of gridding planning methods in the current electric energy substitution background in terms of technology, mechanism and infrastructure are summarized, and looking forward to the development direction of the electric energy replacement supporting network plan under the background of the 14th Five-Year Plan and the new round of power system reform.

With the large-scale application of LCC-HVDC and VSC-HVDC in power systems, the mutually exclusive constraints (MES) appear in the optimal planning of hybrid AC/DC receiving end power grid. The MES increase the scale of the construction set of substations and the transmission lines and decrease the planning efficiency and effectiveness of the conventional method. This paper proposes a novel hybrid AC/DC receiving end grid planning method with MES. Constraint satisfaction problem (CSP) is utilized to model the set of mutually exclusive selected lines, in which mutually exclusive candidate lines are converted to mutually exclusive variables and then introduced into the planning model as constraints. After establishing the hybrid AC/DC receiving end grid planning model with MES, the backtracking search algorithm (BSA) is used to solve the optimal planning. The effectiveness of the proposed hybrid AC/DC power grid planning method with MES is verified by case studies.

Aiming at the problem of the optimal coordinated attack strategy of multi-UAV system against multiple moving targets in a known environment, a PMMAXQ layered reinforcement learning algorithm applied to multi-UAV cooperative attack is proposed. Firstly, the problem of multi-UAV coordinated strike, the successful strike conditions and the movement strategy of moving targets are described. Then the moving targets are allocated rationally through the 0-1 planning method. Finally, the MAXQ algorithm is improved by the Bayesian probability statistical formula. The simulation results show that, compared with the MAXQ algorithm, the PMMAXQ algorithm converges faster and the average cumulative reward value that the UAV can obtain is higher.

Smart grid refers to a modern electric energy supply system to tackle a lot of problems in grid management, such as, resource shortage, environment pollution and so on. In this paper, we propose a novel smart grid planning method using multi-objective particle swarm optimisation algorithm. The goal of smart grid plan is to calculate the minimum investment and annual operating costs, when we obtain the planning level of load distribution, substation capacity and power supply area to satisfy the load requirement and optimised substation location. Afterwards, we propose a multi-objective particle swarm optimisation algorithm which integrates the estimation of distribution algorithm. Furthermore, the propose approach divides the particle population into a lot of sub-populations and then build probability models for each population. Finally, experimental results demonstrate that the proposed method can effectively arrange new substation, which is able to make up for deficiencies of current existing substations.

The main task of designing a technology of metal processing by pressure is accurate prediction and ensuring the desired properties of the finished product. However, they often encounter a situation when it is impossible to simultaneously ensure the optimal value of all process parameters and the resulting properties of the finished product. In this case, it is necessary to make an informed decision regarding the assignment of certain numerical values of these parameters. This article proposes to use the mathematical apparatus of experimental planning methods to implement multi-criteria optimization of metal pressure processes. Applying of correlation analysis, function of desirability and a priori ranking by the example of optimization of parameters of the process of hydromechanical extrusion of hard-to-deform alloys is shown. Results of multi-criteria optimization of the process of hydromechanical extrusion of bar blanks from aluminum alloy AMg5 (analog of Al-5056) with the purpose of minimizing the extrusion force, deformation non-uniformity and damage index of the resulting product are presented.

Facing the discrete, multi-constrained, non-linear, multi-objective combination optimization problem of distribution network grid planning, some traditional heuristic algorithms such as genetic algorithms sometimes fall into local optimum. This paper proposes a distribution network planning method based on hybrid genetic algorithm The algorithm consists of two stages. In the first stage, the genetic algorithm is used to obtain the initial planning scheme. In the second stage, the initial planning scheme obtained in the first stage is used to form the planned route set. The improved minimum spanning tree method is used to obtain the final planning scheme. In order to make full use of the effective information obtained in the first stage, this paper proposes a transmission line classification method to assess the importance of the transmission line, provide guidance for the second stage, and improve the search efficiency and accuracy. The algorithm solves the problem that heuristic algorithms such as genetic algorithm often fall into local optimization to a certain extent, and the problem of slow convergence when the minimum spanning tree algorithm has a large number of lines to be planned.

The path planning of Multi-AGV does not especially consider the power consumption. Therefore, a new Multi-AGV scheduling planning method based on improved GA(Genetic Algorithm) is proposed. The improvement of this algorithm is showed in three aspects: first, in order to ensure that AGVs have the power to perform the task, the operation of power evaluation is added after each iteration is completed; second, the objective function will have two constraints: first, the minimum total power consumption for all AGV; second, the maximum power consumption of a single AGV is minimal; finally, by changing the mutation operator of the GA, the convergence speed and the convergence of the population are improved. The simulation results show that the improved GA scheduling results are more reasonable, and under the same conditions, AGVs power consumption will be smaller.

In this paper, a robust planning method of regional integrated energy system (IES) considering the uncertainty of cooling, heat and electrical loads on the demand side is submitted. First, the energy hub (EH) model of regional IES with Combined Cooling Heating and Power (CCHP), electric air-conditioning unit (EC) and gas boiler (GB) is established. Second, the uncertainty of load is described by the method of adjustable interval, and the robust planning model of regional IES is formed. Third, the robust programming model can be transformed into a convex mixed integer planning model, and then solved. Finally, the case study is carried out with a comprehensive area of Tianjin in China, the results are analyzed to verify the effectiveness of the proposed planning method.

In this paper, an optimal probabilistic reactive power planning method considering uncertain loads and intermittent wind generation is proposed. The objective of the probabilistic planning strategy adopted is to maximize the annual profit of the utility in face of large grid penetration of uncertainty. To maximize the profit, a modified MPSOGSA optimization technique has been used. The performance of this developed technique has been compared with those obtained from GA, GSA and PSOGSA optimization methods. Extensive simulation studies on IEEE-118 bus systems show that the with the loads and wind uncertainty included, the optimal reactive power planning can be accomplished with modified MPSOGSA.

In this paper a computationally efficient sizing method is proposed for flexible resources of grid-connected microgrids to efficiently obtain the optimal ratings of flexible resources with a large number of uncertainty scenarios considered. Initially, based on the historical data method, the scenarios are generated to represent GCMG uncertainty, which are then partitioned into the over-power scenario and under-power scenario set. The worst case of the over-power scenario set is used to determine the feasible range of grid-tied transformers and the minimum capacities of battery storage. The capacities of battery storage are optimized based on the battery storage dynamics with the under-power scenario set. Finally, simulation comparisons with a general MILP-based planning method shows the effectiveness and extraordinary computational performance of the proposed method.

This paper mainly studies the problem of network planning in distribution network planning. Based on the research on distribution network planning problems, this paper proposes a distribution network planning method based on disaster map, solving the problem of introducing intermediate nodes into the planning and optimizing in feasible areas which was not considered in the previous research. Firstly, this paper establishes a mathematical model of distribution network planning considering the disaster risk of the area where the network is located, then checks the disaster prevention construction cost of the network on disaster map, finally solves the problem by using the simulated annealing algorithm, and it generates the optimal network structure and determines the constraint conditions. Finally, verifies the performance and effectiveness of the algorithm by an example analysis. The method proposed in this paper breaks through the bottleneck of searching the optimal solution within the limited point set by the traditional distribution network planning method, which makes the distribution network planning method more flexible and expands the selection of intermediate nodes. It also increases the probability of searching for a global optimal solution and provides a new idea for the distribution network planning.

Single user based distributed energy system limits the economical use of energy, but this limitation can be solved with the energy network. This planning method sets up the model, which aims to reduce the annual cost (includes the costs of equipment, operation, maintenance, fuels and carbon tax) to complete the optimization of device configuration. A mixed integer linear programming model is established to determine the energy system combination, develop running strategy and build the energy transmission network. Compared with nonlinear model, under the premise that various factors are considered, the difficulty of solving the programming problem is reduced in this paper. Finally, this paper solves the model with actual data. By comparing the planning results with traditional energy supply mode, the feasibility of the method and the economy of the regional energy network are verified.

The access of distributed generations (DG) such as wind power and photovoltaics, and the volatility of the load will cause complex uncertainties in the transmission network, which can make certain influence on the transmission performance of the power network. This paper proposed a transmission passage expansion planning method considering both uncertainties and clusters of multi-scenarios. Firstly construct a multi-scenario model with LHS algorithm to deal with the impact of fluctuation of load and uncertainties of DG, and reduce the numbers of operation scenes with Affinity propagation (AP) clustering algorithm. Then a two-layer optimization model of transmission passage expansion is established, aiming at improving the economy and DG capacity of the transmission network. The case study results show that the planning method proposed in this paper can effectively improve the DG consumption capacity and ensure the economic benefits and stability of the transmission network.

In the integrated energy system (IES), the sites of energy supply stations and layout of pipelines have a significant impact on the economy and efficiency of the system. This paper proposes a collaborative optimization method of station sites and pipelines layout considering the electricity and heating supply. Firstly, basing on P-median theory, this paper proposes a co-optimization planning model. The objective of the model is minimizing the sum of the product of the energy demand at the load node and the shortest weight distance between the load node and the corresponding energy supply station. Then, basing on enumeration method, this paper proposes an improved solving method which overcomes the drawbacks of the traditional solving method. Finally, a case study is presented to validate the proposed model and solving method. The results show that the proposed planning method based on the P-median model decreases the system operating cost effectively and realizes the collaborative optimization of the station sites and pipelines layout.

This study presents a fast transmission network expansion planning method incorporating the N-2 security criterion. Robust uncertainty set is used to characterize the N-2 security criterion where the combined failures of generators, existing lines., and candidate lines are explicitly modeled., and hence a trilevel robust optimization model is derived. The upper level minimizes the construction cost., the middle level selects the worst-case N-2 contingency that maximizes load shedding., and the lower level minimizes the operation cost under the realization of the worst-case contingency. A duality-based column-and-constraint generation algorithm is applied to solve the proposed model. The computational efficiency of the proposed method is illustrated by a numeric study on the IEEE RTS 24-bus and IEEE 118-bus test systems.

Project Evaluation and Review Technique (PERT) is one of the main project management methods used in electrical engineering construction. Due to indeterminacy of the criteria of the time parameter estimation and neglect of the project resource constraints, scheduling error often occurs. This paper introduces a new PERT schedule planning method based on the improvement of the time parameter estimation. Given that the planned duration of a single activity obeys distribution, the principle that time parameter estimation should follow is discussed, and then a method how to instead the most optimistic time by the least time with resource constraints is proposed, therefore the criteria of time parameter estimation are determined. Next, the distribution is fitted according to the given time parameters. Finally, cost-time linear optimization model is established, including probabilistic constraint and the buffer constraint of the distribution. The model is solved, results in the optimal planned schedule. Simulation analysis verifies the effectiveness of the improved method.

Stereotactic radiosurgery is an effective technique to treat brain tumors for which several inverse planning methods may be appropriate. We propose an integer programming model to simultaneous sector duration and isocenter optimization (SDIO) problem for Leksell Gamma Knife{\textregistered} Icon{\texttrademark} (Elekta, Stockholm, Sweden) to tractably incorporate treatment time. We devise a Benders decomposition scheme to solve the SDIO problem to optimality. The performances of our approaches are assessed using anonymized data from eight previously treated cases, and obtained treatment plans are compared against each other and against the clinical plans. The plans generated by our SDIO model all meet or exceed clinical guidelines while demonstrating high conformity.

This paper considers the problem of search and size estimation of local underwater heterogeneities in a given area. The authors introduce an approach allowing to localize such heterogeneities and to estimate their dimensions and the mass of a substance dissolved. Moreover, a task planning method is suggested for a group of autonomous underwater vehicles (AUVs) solving this problem. The corresponding discrete optimization problem is stated and two solution algorithms are compared. The authors also discuss replanning of group actions in case of emergencies during an operation. And finally, the results of simulation experiments for the proposed algorithms are provided.

Part 3: Factory Planning; International audience; Facility layout planning (FLP) is an important stage for optimal design of manufacturing systems. A major approach is to define an evaluation index based on distance and find a layout which minimizes it. Temporal efficiency is not considered in this stage but in later stages. The resultant temporal efficiency may not be optimal enough, since decision and optimization in those stages are performed under the fixed layout. For this reason, the authors have developed FLP methods considering temporal efficiency. Those methods provide the optimal layout plan for a fixed production scenario. However, production environments change dynamically in actual manufacturing, and the layout plan is no longer optimal after the changes. In this paper, the conventional method is enhanced considering robustness against the changes.

Limitations of the existing methods and models for uneven energy consumption forecast are defined. The energy consumption planning method based on informational technologies, the probability theory, the game theory, hour energy prices and volumes relations is offered. All possible variants of prices and volumes relations are identified. Two utility functions for positive and negative correction of the preliminary calculated planned hour energy volume are derived. The positive tests of the method are presented. The role of the offered method for solving the main problem of uneven energy consumption planning is shown. Environmental, scientific and economical aspects of the method are disclosed.

The goal of radiation therapy is to deliver tumoricidal dose to clinical target volume (CTV) while sparing organs-at-risk (OAR). We hypothesize that the joint use of proton and photon radiation therapy via appropriate hybrid proton-photon inverse planning method will be more favorable than proton or photon therapy alone, in terms of optimized combination of CTV coverage and OAR sparing. This work develops hybrid proton-photon inverse optimization method that simultaneously optimizes proton and photon variables. To account for delivery uncertainty, proton dose is targeted at CTV using robust optimization, and photon dose is targeted at either CTV using robust optimization or planning target volume (PTV) using the same setup shifts. The optimization objectives enforce OAR sparing and uniform CTV coverage for the total dose, while imposing uniform-dose regularization at targets for both the proton and photon component in order for both components to be individually deliverable. The hybrid problem with dose-volume-histogram (DVH) constraints is nonconvex and solved by iterative convex relaxations of DVH constraints and alternating direction method of multipliers (ADMM). Preliminary results suggest the hybrid proton-photon planning potentially improves proton or photon planning in terms of optimized combination of CTV coverage and OAR sparing.

Currently, neither the efficiency nor the effectiveness is sufficient in the area of the assemble optimization that commonly involves the genetic algorithm. A novel method to solve the cumbersome problem in the optimization of assembly sequences was proposed. On the basis of the assembly constraint matrix, the optimized assembly sequence is obtained with the proposed evaluating factors of the process requirement. That is the evolution of the original genetic algorithm to a certain extent. The effectiveness of the proposed method was proved by the comparison with the ant colony algorithm.