Operational Planning Problems Research Articles

A Collaborative Path Planning Method for Intelligent Agricultural Machinery Based on Unmanned Aerial Vehicles

The development of agricultural farming has evolved from traditional agricultural machinery due to its efficiency and autonomy. Intelligent agricultural machinery is capable of autonomous driving and remote control, but due to its limited perception of farmland and field obstacles, the assistance of unmanned aerial vehicles (UAVs) is required. Although existing intelligent systems have greater advantages than traditional agricultural machinery in improving the quality of operations and reducing labor costs, they also produce complex operational planning problems. Especially as agricultural products and fields become more diversified, it is necessary to develop an adaptive operation planning method that takes into account the efficiency and quality of work. However, the existing operation planning methods lack practicality and do not guarantee global optimization because traditional planners only consider the path commands and generate the path in the rectangular field without considering other factors. To overcome these drawbacks, this paper proposes a novel and practical collaborative path planning method for intelligent agricultural machinery based on unmanned aerial vehicles. First, we utilize UAVs for obstacle detection. With the field information and operation data preprocessed, automatic agricultural machinery could be assisted in avoiding obstacles in the field. Second, by considering both the historical state of the current operation and the statistics from previous operations, the real-time control of agricultural machinery is determined. Therefore, the K-means algorithm is used to extract key control parameters and discretize the state space of agricultural machinery. Finally, the dynamic operation plan is established based on the Markov chain. This plan can estimate the probability of agricultural machinery transitioning from one state to another by analyzing data, thereby dynamically determining real-time control strategies. The field test with an automatic tractor shows that the operation planner can achieve higher performance than the other two popular methods.

Energy Minimization of Dynamic Multi-UAV Communication Network for Cooperative MultiHop Data Gathering

This study proposes a minimum-energy operational planning problem for multiple unmanned aerial vehicles (UAVs) conducting a cooperative multi-hop data-gathering mission. A multi-UAV ad-hoc network collects data from multiple sources and transmits them to a ground base station. The proposed mixed-integer nonlinear programming (MINLP) formulation simultaneously determines cooperative task allocation, data gathering schedule, dynamic aerial data flow, and UAV trajectories to minimize operational energy consumption. A hierarchical two-phase solution procedure that decomposes the original problem into a series of less complex problems and solves them efficiently is developed. The effectiveness of the proposed formulation and the solution procedure is demonstrated through numerical experiments and comparison with benchmarking operational schemes.

Problems of operational planning in mechanical production: analysis of influencing factors

Problems of operational planning in mechanical production: analysis of influencing factors

Solution of a complex of problems of operational planning and management in the conditions of a mining landmark in Azerbaijan

The main direction of the economic and social development of the Republic of Azerbaijan is the intensification of agricultural production. Irrigation is a powerful means of intensifying agricultural production in the context of its specialization. In areas with insufficient moisture (especially typical for mountainous areas), irrigation is one of the decisive factors in growing high and stable crop yields. This requires the development of new technical solutions and the introduction of automated systems for low-intensity irrigation of agricultural crops that meet the requirements of ecology and environmental protection of their habitat, which makes it possible to improve the ecological state of irrigated lands and reduce water consumption. Per unit of production and increasing the yield of individual crops in irrigated fields.

Optimal sizing of residential battery energy storage systems for long-term operational planning

Appropriate battery storage capacity plays an important role in the performance and cost of residential energy systems. However, the load demand and renewable energy generation vary seasonally. To address the long-term operational planning problem of battery energy storage, two battery sizing methods are developed based on the consensus alternating direction method of multipliers (C-ADMM). The residential system layout and convex battery model considering cycling aging are first established. Then, for the one-year horizon planning, C-ADMM decomposes the original large-scale problem into 365 sub-problems. The global optimal battery size and operation strategy are obtained by coordinating these sub-problems in parallel until convergence. In addition, to obtain the optimal battery size based on receding horizon control (RHC) strategies in a year, a new bi-level optimization method is developed, which uses the particle swarm optimization (PSO) algorithm in the outer loop and C-ADMM in the inner loop. Thus the inner loop can be accelerated through parallel computing. The results show that the long-term storage planning problem of residential systems can be solved quickly through C-ADMM due to the parallel computing capability. Furthermore, the optimal battery size calculated under the RHC strategy in a year can contribute to more economic benefits than the global battery size when applied online.

ОПТИМІЗАЦІЯ РЕСУРСІВ АЕРОПОРТУ ДЛЯ НАЗЕМНОГО ОБСЛУГОВУВАННЯ ПОВІТРЯНИХ СУДЕН В УМОВАХ НЕВИЗНАЧЕНОСТІ

Introduction. The changes that are taking place in Ukraine due to the influence of both global and regional factors lead to the fact that airports needs to review the concepts of managing the company’s activities and look for new strategies for functioning on the market. This article examines the main causes of flight delays that affect the operation of airport services and thus cause uncertainty. The activity of domestic airports is analyzed. The problem of operational planning and management of ground handling of aircraft at the airport is considered. Criteria for the effectiveness of ground handling of aircraft at the airport have been determined. Problems statement. Aviation transport in Ukraine is one of the main transport elements of the transport and logistics system of the country and the world. But it is also the most expensive mode of transport, which greatly affects the price of transportation. Transportation costs exceed 50%, and their reduction is the main goal of the transportation process. Therefore, for this, it is necessary to optimize the distribution of material and technical resources during the ground handling of aircraft at the airport. That is, regardless of flight delays, readiness to service the aircraft must be ensured by all airport services regardless of deviations from the daily flight schedule. Purpose. Analysis of the feasibility of optimizing material and technical resources during ground maintenance of aircraft, in order to increase the efficiency of the airport. The object of the study is the largest international airport «Boryspil», which, according to information from analysts, will have an even greater load after the war. The subject of the study is ground handling of aircraft at the airport. Materials and methods. Statistical analysis of ukrainian airports. Analysis of information sources regarding airline flight delays. Mathematical method of process research. Results. After studying the statistical data of ukrainian airports, it was concluded that due to the successful geographical location, the flows through them will increase. The main goal of Ukraine’s transport strategy is to integrate Ukraine’s transport complex into the global transport network, which also includes air transport. Therefore, in order to stay afloat and be competitive, it is necessary to introduce and improve the provision of services. Conclusions. Clearly organized ground handling of aircraft at the airport allows to optimize the use of material and technical resources, increase the number of serviced flights and affects the effective operation of the airport, its capacity.

ОПТИМИЗАЦИЯ ПРОИЗВОДСТВЕННОГО ПЛАНИРОВАНИЯ С НЕЧЕТКИМИ ПАРАМЕТРАМИ

This article considers the problem of operational planning of one-subject production.The organization of machine-building production is a complex set of works to determine the interrelatedindicators that characterize the activities of the enterprise. Enterprises of this type have acomplex hierarchical structure. It is also necessary to take into account that when planning theproduction process, the number of parameters is large and not all of them can be accurately determined,which affects the efficiency of the enterprise. To solve the problem of effective planning,the optimality criteria for serial one-subject production were analyzed. One-subject productionincludes those where parts of the same name are processed, that is, a production line is formed.Consequently, the task of optimizing production is to distribute the entire set of work between themachines and operators servicing this machine in such a way that the planned task is completedwithin a given time and the total cost of completing the task is minimal. The article considers theproblem of assignment under uncertainty, carried out experimental calculations and analyzed theresults obtained, which justifies the use of the proposed apparatus of fuzzy sets for solving theproblem of production planning. It is concluded that under conditions of uncertainty, when there isno exact or statistical information, the apparatus of fuzzy sets makes it possible to analyze theeffectiveness of production activities when setting parameters that reflect the possible values of thesystem. In such cases, the use of fuzzy logic mechanisms in the problems of making productiondecisions will make it possible to determine optimal or close to optimal solutions.

Optimal Distributed Energy Resource Coordination: A Decomposition Method Based on Distribution Locational Marginal Costs

In this paper, we consider the day-ahead operational planning problem of a radial distribution network hosting Distributed Energy Resources (DERs) including rooftop solar and storage-like loads, such as electric vehicles. We present a novel decomposition method that is based on a centralized AC Optimal Power Flow (AC OPF) problem interacting iteratively with self-dispatching DER problems adapting to real and reactive power Distribution Locational Marginal Costs. We illustrate the applicability and tractability of the proposed method on an actual distribution feeder, while modeling the full complexity of spatiotemporal DER capabilities and preferences, and accounting for instances of non-exact AC OPF convex relaxations. We show that the proposed method achieves optimal Grid-DER coordination, by successively improving feasible AC OPF solutions, and discovers spatiotemporally varying marginal costs in distribution networks that are key to optimal DER scheduling by modeling losses, ampacity and voltage congestion, and, most importantly, dynamic asset degradation.

Shrinking and receding horizon approaches for long-term operational planning of energy storage and supply systems

To efficiently solve long-term operational planning problems of energy storage and supply systems, a near-optimal solution method based on the shrinking and receding horizon approaches was developed. A lower bound of the original problem is first calculated by solving a simplified problem, in which the number of binary variables is reduced and the related constraints are simplified. Then, a short-term operational planning problem, with a shrunken planning horizon length, is solved by providing the simplified problem results as the terminal condition for stored energy. Within this problem, the original binary variables and constraints are considered. The short-term operational planning is updated using a receding horizon approach, in which the initial and terminal conditions are provided by the previous short-term operational planning results and simplified problem results, respectively. The solution, obtained by connecting the solutions in all the updating horizons, is regarded as a near-optimal solution of the original problem. The developed method was applied to solve a long-term operational planning problem for an energy storage and supply system, which includes a photovoltaic unit and a metal hydride tank with a water electrolyzer. The results showed that the developed method could find a better solution in a shorter computation time than the conventional methods.

The Method of Operational Planning of Group Actions of Aircraft in the "Air Taxi" Mode

The article is devoted to the development of algorithms for operational planning of routes for a group of aircraft (AC). We consider group actions of small and unmanned aircraft in the "air taxi" mode, when there is no regular flight schedule between the points of destination, and requests are received "on call" for flights to points whose composition is unknown in advance and is of a random nature. The multicriteria task of planning a group flight in the "air taxi" mode is being solved. The solution to this problem is proposed using the apparatus of the queuing theory, according to which the system under consideration belongs to the class of multichannel queuing systems with waiting. A method for solving the problem of operational planning of aircraft actions is proposed. An algorithm for group target distribution of new claims between aircraft is developed on the basis of a modified minimax criterion for assigning the nearest aircraft for an object with a maximum service time. The developed algorithm is based on the following four main operations: in the first operation, priority unserved targets are selected according to the criterion of assigning a dynamic priority; in the second operation, the formed list is ranked according to another criterion, taking into account the importance and total distance of each ground object from the aircraft group, in three this operation selects the object with the maximum rank, and for it the task of assigning "own" aircraft is solved according to the third criterion of maximum proximity, in the fourth operation the conditions of non-intersection of the group flight routes are checked. A computer model of the system for servicing requests in the air taxi mode has been developed. The developed model makes it possible to analyze various scheduling algorithms, as well as to determine at each step the number of free claims and the number of free and busy aircraft. A comparison is made between the well-known in the theory of queuing and the proposed minimax approaches. It is shown that, in comparison with the known variants of scheduling in the queuing theory, on the basis of the proposed approach, the optimal number of used aircraft is achieved.

Сравнительная оценка эффективности и обоснование выбора варианта перегрузки груза в порту

The article discusses the conditions for transshipment of cargo in the port and their impact on the efficiency of the production process. The efficiency of the port's production process for handling ships and wagons is largely determined by the options for work that are implemented in this process. The problem under consideration is due to the difference in the parameters of technological lines and the resources used for various options for handling cargo in the port. There is provided a comparative characteristic of options for handling cargo in the port. It has been established that the direct option of transshipment of cargo without additional delays of ships and wagons is the most preferable for the seaport, but its implementation in the production process of the port is very difficult and in some cases impossible. The main reasons for the impossibility of using direct options for overloading are indicated. The formula for estimating the costs of cargo passing through the ports is given and it is established in which case the preference is given to the direct transshipment option. For clarity, various schemes for the movement of goods in the port are given, which can be used for operational planning and in the allocation of port resources for handling vehicles. An express method is proposed for the operative selection of the option of cargo transshipment at a minimum of labor intensity. Restrictions have been established in the form of resources fixed at the transshipment complexes and the correspondence of the loading of the complex to its carrying capacity. The expediency of the priority allocation of resources for the processing of the vessel according to the direct option when forming preference functions for solving the problems of operational planning and regulation of the processing of vehicles in the port has been proved.

Airline crew scheduling: Models and algorithms

The airline crew scheduling problem has become a crucial but challenging task for commercial airlines for decades. Airlines are operating with two types of air crew: cockpit crew and cabin crew. Due to the unique operating characteristics, the scheduling problems for these two crew types are very different. Besides, according to the planning stage, the airline crew scheduling problem can be classified as tactical planning problems (traditional scheduling and robust scheduling, weeks or months before the actual operations) and operational planning problems (recovery, after disruptions have occurred during the operational stage). Realizing the significance of the airline crew scheduling problems and a lack of review on the modelling and algorithmic advancements in terms of each crew type and planning stage, we develop this paper to review the related literature from four aspects: the scheduling for cabin crew, the scheduling for both cabin crew and cockpit crew, the robust scheduling for cockpit crew, and the recovery for cockpit crew. For each stream, we examine a number of prior representative studies to review the advancements in model development and solution algorithm construction to generate insights. Finally, we conclude the review by proposing a future research agenda for the airline crew scheduling problem.

Optimization of the cold supply chain logistics network with an environmental dimension

This article is the result of generalizing a real practical problem of optimizing the operation of a cold supply logistics network. A small (up to 100 clients) logistics company solves the problem of operational planning: during the working day (fixed time window) to provide the supply of cold (food) products from distribution centers to end consumers along well-defined routes (i.e., the logistic network graph is fixed). The company has set the following conditions for the authors. In addition to minimizing the usual transport costs, take into account also the environmental costs (associated with negative consequences for the environment) that arise during the operation of the logistics supply network. And also when deciding to use software available to the company. The case is considered when the transport tariff on each route is a linearly increasing function of the total volume of all traffic on this route. When the Nash equilibrium conditions are met, the mathematical model for optimizing the total costs of the logistics network is reduced to a convex (quadratic) programming problem. Computer implementation in MS Excel environment has been carried out. This makes it possible to carry out simulation calculations for various options for transport costs on routes, taking into account environmental costs.

Multi-Alternative Operation-Planning Problem of Wind Farms Participating in Gas and Electricity Markets

Multi-Alternative Operation-Planning Problem of Wind Farms Participating in Gas and Electricity Markets

Использование искусственного интеллекта для прогнозирования электропотребления энергосбытовой компании

The existing approaches to using artificial intelligence in training the neural network using the Neurosimulator 5.0 application to predict electricity consumption according to the data of the previous period are analyzed in this article. It is also concluded that it is advisable to develop this direction of calculations for forecasting and designing power supply systems. The article is devoted to the problem of choosing a model for forecasting electricity consumption when solving the problem of operational daily planning of electricity supplies in the wholesale market. The task of forecasting electricity consumption acquired particular relevance after the emergence of the wholesale electricity market: an underestimation of the forecast leads to the need to launch expensive emergency power plants, while an overestimation leads to an increase in the costs of maintaining excess capacity. The choice of artificial neural networks for this purpose is well-founded. The most suitable architecture of an artificial neural network for solving the problem in question is a multilayer perceptron containing several layers of neurons: an input layer, one or more hidden layers and a layer of output neurons. The transmission of information usually takes place in one direction - from the input layer to the output layer. An example of power consumption prediction based on the results of the nearest measurements in the time domain is considered and an approximation error is determined. The results of approximation and prediction of power consumption showed that a root-mean-square relative error did not exceed 6.32 %, but there is an outlier at one point up to 34 %. The reserve for improving the forecast accuracy is to study the influence of additional factors such as an ambient temperature and the day factor which takes into account the load distribution by the days of the week.

Планирование поиска отделяющихся частей ракеты-носителя с помощью группы беспилотных летательных аппаратов

The paper deals with the processes of maintaining a special class of mobile objects, whose schedules are either given or require preassignment in order to maintain these objects at the right time and in the right place. The posed problem of planning the flight of a group of aerial vehicles is solved using a continuous form of dynamic programming, according to which the Bellman equation in partial derivatives corresponds to the optimality condition. An original approach to solving the problem of pre-flight and operational planning of actions of a group of unmanned aerial vehicles based on a genetic algorithm is proposed. The fundamental difference between the problem being solved and the well-known traveling salesman problem is in taking into account the required maintenance schedule. The developed planning automation tool makes it possible to increase the efficiency of measures to detect separating parts of launch vehicles using a group of unmanned aerial vehicles. Findings of research show that the developed genetic algorithm is better not only than algorithms based on one-parameter and two-parameter criteria, but even better than algorithms based on a three-parameter criterion.

Multiscale Multiresolution Generation Maintenance Scheduling: A Stochastic Affinely Adjustable Robust Approach

Generation maintenance scheduling (GMS), as a medium-term operational planning problem in power system, encounters both midterm and short-term uncertainty sources. This article presents a multiscale multiresolution uncertainty model to characterize midterm and short-term uncertainties distinctively in GMS problem. In the proposed multi-scale multi-resolution GMS (MMGMS) model, the midterm uncertainty of weekly peak loads in the scheduling horizon is characterized via plausible scenarios while short-term uncertainty of hourly loads is addressed through polyhedral uncertainty sets. To make the MMGMS approach tractable, affine policies are incorporated into the proposed model. The resulting MMGMS model, considering midterm as well as short-term uncertainties, is formulated as a stochastic affinely adjustable robust optimization (SAARO) problem. To solve this problem, a new solution methodology including stochastic optimization and probabilistic dual cut is presented. Numerical results on two test systems corroborate the effectiveness of the proposed model and solution approach.

Integrated order batching and vehicle routing operations in grocery retail – A General Adaptive Large Neighborhood Search algorithm

In recent years, established and well-known grocery retailers have increasingly been investing in the business of micro stores and petrol station shops. Supplying these stores with perishable and durable goods leads to noticeable logistics challenges for the retailers. Since the total sales volumes of these shops are typically low and the respective sales areas are very limited, highly frequent deliveries of small sizes are required. These noticeably affect a number of operational planning problems. In the warehouse, the items requested have to be collected in small order sizes. In order to achieve efficient picking operations, orders are therefore combined into larger picking orders, i.e., batches. Afterwards the orders have to be delivered to the stores at high frequency. In practice, all the planning problems mentioned are heavily interconnected due to the short planning horizon.Despite the practical relevance, order batching, order picking and delivery operations have not so far been investigated as an integrative planning problem. This paper therefore presents a novel modeling and solution approach to solve practically relevant problem sizes. The combinatorial complexity of the problem requires a heuristic solution approach. We propose an extension of the well-known Adaptive Large Neighborhood Search (ALNS) metaheuristic that we call General ALNS (GALNS), and show that a GALNS approach outperforms a similar ALNS algorithm in 96.35% of the problem instances generated. Managerial insights from general problem data and a case study with a large German grocery retailer support the applicability of the modeling and solution approach suggested in retail practice.

Distribution Network Marginal Costs: Enhanced AC OPF Including Transformer Degradation

This paper considers the day-ahead operational planning problem of radial distribution networks hosting Distributed Energy Resources, such as Solar Photovoltaic (PV) and Electric Vehicles (EVs). We present an enhanced AC Optimal Power Flow (OPF) model that estimates dynamic Distribution nodal Location Marginal Costs (DLMCs) encompassing transformer degradation as a short-run network variable cost. We decompose real/reactive power DLMCs into additive marginal cost components representing respectively the costs of real/reactive power transactions at the T\&D interface, real/reactive power marginal losses, voltage and ampacity congestion, and transformer degradation, capturing intertemporal coupling. Decomposition is useful in identifying sources of marginal costs and facilitating the employment of distributed AC OPF algorithms. DLMCs convey sufficient information to represent the benefit of shifting real/reactive power across time and achieve optimal Distribution Network and DER operation. We present and analyze actual distribution feeder based numerical results involving a wealth of future EV/PV adoption scenarios and illustrate the superiority of our approach relative to reasonable conventional scheduling alternatives. Overwhelming evidence from extensive numerical results supports the significant benefits of internalizing short-run marginal asset - primarily transformer - degradation.

A near-optimal solution method for coordinated operation planning problem of power- and heat-interchange networks using column generation-based decomposition

A near-optimal solution method for coordinated operation-planning problems of power- and heat-interchange networks using column generation-based decomposition was developed to enhance computational efficiency and scalability. The coordinated operation-planning problem, based on a mixed-integer linear programming (MILP) approach, was decomposed into a master problem concerning power and heat interchanges and subproblems for energy-supply systems on the basis of the Dantzig-Wolfe reformulation. To determine a near-optimal solution, heuristic finalization was developed, in which a final MILP problem is solved after fixing part of binary variables based on the lower bound result obtained through two-stage iterative column generation. The developed method was then applied to the coordinated operation planning of power- and heat-interchange networks consisting of 5–100 cogeneration systems using a 150-kWe gas engine and a 45-kWe polymer electrolyte fuel cell on a winter representative day. In the case of using 100 cogeneration systems, 93% of the binary variables expressing the on/off status of the cogeneration units and heat-interchange pumps were fixed in the heuristic finalization. The near-optimal solution, which has a lower daily energy cost than in the conventional solution method, can be obtained without optimization termination due to limits of computation time and memory usage.