СОВЕРШЕНСТВОВАНИЕ ТЕХНОЛОГИЧЕСКОГО ПРОЦЕССА ЗАГОТОВКИ И ВЫВОЗКИ ДРЕВЕСИНЫ С ИСПОЛЬЗОВАНИЕМ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ

The application of fuzzy logic is considered in optimizing the sequence of operations that involve the use of self-propelled units in industrial railway transportation. The consequences of using constant values calculated analytically as operation priorities have been analyzed. The type of queue discipline and the specifics of constraints on the stay of requests in the queue, characteristic of railway industrial transport and technological systems, are described. The advantages and disadvantages of existing methods for queuing freight operations specified in the daily work plan in railway industrial transport and technological systems are investigated. Limitations arising from the use of classical scheduling theory methods have been identified. Finding the optimal sequence of operations in railway industrial transport and technological systems involves simultaneous movement planning for multiple mobile units, which is not considered in classical graph theory. The classical problem of finding the shortest path and its known solving algorithms only work with static graphs, while railway industrial transport and technological systems are characterized by rapid changes in the graph's state. The problems caused by the dynamic nature of the graph's state in railway industrial transport and technological systems have been analyzed. The application of fuzzy logic methods will enable the construction of an optimal sequence of operations based on incomplete and imprecise information and address several operational planning tasks for freight operations without precise calculations. The expected result of applying the method for determining dynamic operation priorities implemented in the neuro-fuzzy module is an increase in the adaptability of operational planning for freight operations and a reduction in the time of engagement and the required number of self-propelled mobile units.

Timber transportation is known to be the most important and expensive single component in timber harvesting. It is a crucial part in harvesting planning especially where difficulty terrain and long distances are involved. This paper presents a study of a skyline logging system, Mkumbara, Northeast Tanzania, where difficult terrain and distances cause concern for system balance in a multistage transport system. The stages were forest to upper landing by trucks and tractor, upper to lower landing by skyline, and eventually lower landing to the mill by other log hauling units. This study identified the situation and workplace factors influencing productivity and costs; assessed the coordination of the different harvesting activities, and determined the presence of “system balancing” required for efficiency. The study involved assessment of productivity and costs of timber harvesting for the skyline system from gross and detailed work-studies. The operations assessed include: timber cutting, log transport to the skyline upper landing, terminal operations, skyline operation, and log hauling to the processing mills. The study revealed that although the potential of the skyline stage was not achieved, log hauling by tractors from the forest to the upper landing was the major bottleneck. Log transportation from the lower landing to the mill need to be improved. The mill demanded 60m3 of logs per day while log hauling to the upper landing was 38.8m3 per day. Unit cost at the mill amounted to Tanzania Shillings 6,550/-, with the hauling from forest to the upper landing being the highest, contributing 30% of the unit cost. The daily productivity and costs were imbalanced between all subsequent stages, however, it is recommended that improvement of the forest to upper landing stage could greatly increase productivity, and decrease unit cost, even without large capital investment. TJFNC Vol. 75 2004: pp. 91-98

Automated guided vehicles (AGVs) are typical intelligent logistics equipment, and path planning plays a significant role in the efficient use of AGVs. To better utilize multi-load AGVs and enhance the sustainability of the logistics process, an energy-efficient path planning model is formulated for a multi-load AGV executing multiple transport tasks in a manufacturing workshop environment, with transport distance and energy consumption (EC) serving as optimization objectives. Furthermore, a two-stage approach is proposed to solve it. In the first stage, the optimal energy-efficient paths connecting any two different nodes are acquired based on the workshop transport network expressed as a topological map. Afterward, the non-dominated sorting genetic algorithm-II is adopted in the second stage to determine the optimal execution sequence of pickup and delivery operations related to the assigned transport tasks, as well as to select the optimal path from the first stage's output information to execute each operation simultaneously. Moreover, the experimental study validates the energy-saving effect of the established model and the effectiveness of the solution method, and the factors affecting the multi-load AGV EC are analyzed.

A machine that performs both punching and laser-cutting operations is referred as combined punch-and-laser machine. Such a machine has been in the market for about two decades. Although process-planning tools have been used on such a combined machine, the optimization of process planning dedicated to combined machines, based on our literature search results, has never been directly studied. This work addresses the process-planning problem for the combined punch-and-laser machine by integrating knowledge, quantitative analysis, and numerical optimization approaches. The proposed methodology helps making decisions on following issues: (i) which type of operation should be applied to each feature, and (ii) what is the optimal operation sequence (tool path) to achieve the maximum manufacturing efficiency. The ant colony optimization (ACO) algorithms are employed in searching the optimal tool path. Sensitivities of control parameters of ACO are also analysed. Through applications, the proposed method can significantly improve the operation efficiency for the combined punch-and-laser machine. The method can also be easily automated and integrated with the nesting and G-code generation processes. Some issues and possible future research topics have also been discussed.

This paper aims at automatic generation of optimal sequence of machining operations in setup planning by Genetic Algorithm (GA) based on minimizing the number of setup changes and tool changes, subject to various machining precedence constraints. The GA has been reconstructed as the method of representing an operation is not as simple as assigning it a binary digit as in case of a chromosome in traditional GA but it has to be a distinct real number. Accordingly, the GA operators had to be modified. At the end of each GA cycle, there might be chromosomes having high fitness values but not conforming to constraints. Moreover, due to randomness of GA, the conformable chromosomes might tend to get lost. In order to minimize such losses, the elitist model is used for selection of chromosomes. Furthermore, a special subroutine has been developed to check the chromosomes for conformability and modify/repair those that violate the constraints.

Common carrier pipelines are the best suited mode for transporting large volumes of petroleum products from production areas to long-distance terminals. In such pipelines, batches of refined petroleum products are pumped back-to-back, without any physical barrier separating them. The problem addressed in this paper deals with the scheduling of a real world multi-product, single source pipeline system that must supply the products to several offtake terminals. Most contributions on the scheduling of single-source multiproduct pipeline operations deal with the sequence deliveries in distribution terminals i.e., at any time only one terminal is connected to the pipeline. Practically, pipeline operators usually carry out simultaneous deliveries to multiple terminals to cut down the number of pipeline stoppages and pump switchings so as to reduce the energy consumed for resuming flow in idle pipeline segments, and the pump maintenance costs. This paper introduces a novel continuous mathematical approach, mixed integer linear programming to solve the short-term operational planning by allowing the execution of simultaneous deliveries to multiple receiving terminals during a pumping operation. Contrarily to previous continuous approaches that perform the pipeline input/output operations in two hierarchical stages, the new formulation aims to find both of them in a single step. The objective of this work is to find the optimal sequence of input and output operations that satisfy terminal requirements at minimum total costs. As compared to previous works, significantly improvement in solution quality has been achieved. Especially, the proposed formulation leads to better utilization of the pipeline capacity, and consequently, a substantial reduction in the amount of required time for satisfying all of the specified product deliveries, i.e., the pipeline running time. The main results are presented.

People's decisions often deviate from classical notions of rationality, incurring costs to themselves and society. One way to reduce the costs of poor decisions is to redesign the decision problems people face to encourage better choices. While often subtle, these nudges can have dramatic effects on behavior and are increasingly popular in public policy, health care, and marketing. Although nudges are often designed with psychological theories in mind, they are typically not formalized in computational terms and their effects can be hard to predict. As a result, designing nudges can be difficult and time-consuming. To address this challenge, we propose a computational framework for understanding and predicting the effects of nudges. Our approach builds on recent work modeling human decision making as adaptive use of limited cognitive resources, an approach called resource-rational analysis. In our framework, nudges change the metalevel problem the agent faces-that is, the problem of how to make a decision. This changes the optimal sequence of cognitive operations an agent should execute, which in turn influences their behavior. We show that models based on this framework can account for known effects of nudges based on default options, suggested alternatives, and information highlighting. In each case, we validate the model's predictions in an experimental process-tracing paradigm. We then show how the framework can be used to automatically construct optimal nudges, and demonstrate that these nudges improve people's decisions more than intuitive heuristic approaches. Overall, our results show that resource-rational analysis is a promising framework for formally characterizing and constructing nudges. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

With recognition of quantum computer's enormous computational ability, it is of paramount importance to develop fault-tolerant quantum computing systems for their practical use. Recently, it has been shown that fault-tolerant systems can be achieved using a small set of basic quantum operations. This, however, incurs technical difficulties in finding an optimal sequence of basic operations toward a specific target computation and may limit possible quantum computations. In this work, we aim to achieve arbitrary target quantum computations under the restriction of four universal quantum gates of Pauli-X, -Y, -Z and SWAP. We develop two gate-sequence search methods based on the fidelity measure and deep neural networks. We verify the performance of our proposed methods through numerical results comparing total search space and the number of searched nodes.

The author proposes systolic architectures for associative memories, resulting in systems whose performance parameters are realistically independent of size for long sequences of operations with proper optimization of instruction sequencing. The designs are based on well-known principles of pipelining and systolic operation using a collection of small building-block associative memories. Several alternative organizations, from a simple linear array to higher dimensional meshes and trees, are examined and evaluated with respect to cost and performance. The proposed architectures should lead to practical VLSI realizations of large associative memories, which would be impossible to implement under the 'operand-broadcasting' and 'reduction-by-wired-logic' paradigms.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In flexible manufacturing environments, the performance of a production system is often affected by the sequence of operations. While performance evaluation, improvement and lean design of production systems have been studied extensively, the joint effect of productivity and quality parameters on operations sequencing remains practically unexplored. Indeed, determining the optimal operation sequence has significant implications from both practical and theoretical perspectives. In this paper, in the framework of Bernoulli reliability and quality models, we develop effective indicators that are simple and easy to implement in practice to determine the optimal operation sequence that maximizes the system's production rate.

The article considers the solution of the traveling salesman problem using reinforcement learning. This task is to find the optimal route that passes through the specified cities at least once and then returns to the original city. The traveling salesman problem belongs to the class of NP-hard problems, and therefore finding an exact solution for sufficiently large data sets using classical mathematical methods turns out to be impossible in a reasonable time. The aim of the work is to increase the efficiency and speed of solving the traveling salesman problem with a large number of vertices by using the reinforcement learning method. A variant of solving the traveling salesman problem using the Q-learning algorithm is considered, which allows the agent to learn the optimal behavior strategy in the process of interacting with the environment, which is a set of cities and roads between them, which are modeled as a graph. Within the framework of this study, a software package was developed to solve the traveling salesman problem using reinforcement learning. A series of experiments related to the appearance of unforeseen conditions complicating the work of a traveling salesman, such as the appearance of "problem" zones or zones, the passage through which gives a reduction in time. The results of the experiments showed the capabilities of the developed algorithm for adaptation to changes and obstacles of the environment. The resulting algorithm shows itself to be more stable and reliable compared to the algorithms obtained by classical solution methods. The solution of this problem can be used in practice in a variety of real situations, including optimization of deliveries or network routing, assistance in the work of logistics departments of various transport, postal and courier services.

Optimal task sequence planning using a simulated annealing technique is developed for a high throughput chip placement machine. To minimize tact time, chips are mounted by multiple robots on moving PC boards being transferred by a precision conveyer. To avoid both geometric and dynamic interferences among multiple robots, while minimizing the tact time, the sequence of operations as well as the allocation of the multiple robots are optimized by using simulated annealing. The optimization problem is a type of traveling salesman problem, but is highly complicated because the target destinations for placing individual chips keep moving together with the PC board. First, the configuration of the new chip placement system is described. The task sequence optimization problem is then formulated, followed by the application of simulated annealing to solving the problem. An efficient algorithm for perturbing a task sequence is developed and applied to practical chip placement tasks.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Computer aided process planning (CAPP) is an important interface between computer aided design (CAD) and computer aided manufacturing (CAM) in computer integrated manufacturing (CIM). Operation sequencing in process planning is concerned with the selection of machining operations in steps that can produce each form feature of the part by satisfying relevant technological constraints specified in the part drawing. A single sequence of operations may not be the best for all the situations in a changing production environment with multiple objectives such as minimizing number of set-ups, maximizing machine utilization and minimizing number of tool changes. This paper demonstrates the application of genetic algorithms as a global search technique for a quick identification of optimal or near optimal operation sequences in a dynamic planning environment. A novel initialization scheme for representing the genetic code and a new crossover operator are designed to retain the local operation precedence for each form feature. Since sequences can be obtained quickly, this approach can actually be used by the process planner to generate alternative feasible sequences for the prevailing operating environment.

An integration approach to process design and parts-layout planning

The article deals with peculiarities of application of mathematical methods and models in management of agrarian enterprises. Mathematical modeling is found to be a universal and effective tool for studying the internal laws of certain phenomena and processes. It has been stated that since the solution of economic and mathematical models it is necessary to take into account certain system characteristics and a close connection of qualitative system parameters and how they are resolved. It is noted that a specialist who develops a particular model should have a thorough knowledge of agrarian economics and modeling theory and perfect to have mathematical tools. It is proved that the application of mathematical modeling methods economic and manufacturing processes make efficient use existing resource base in the process of achieving the agrarian strategic goals businesses and take into account significant sources of uncertainty and minimize them negative economic consequences. The arsenal of economic and mathematical methods and models has been systematized used in marketing management decisions business processes. The features by which the economic and mathematical models are classified are presented used in agricultural production. The ways of constructing the optimization models used in agriculture. The process of making managerial decisions on business processes is considered agricultural production using the optimization model fertilizer application. It is noted that, from a mathematical point of view, some economic processes in agribusinesses are the same and can be described using typical models. It is established that by means of economic-mathematical modeling it is possible to solve specific economic problems and practical problems in the field management of agricultural production and economic activity enterprises. It is concluded that the application of mathematical tools methods and models make it possible to substantiate the adoption process better management decisions when planning and finding promotion reserves efficiency of agricultural enterprises in crisis conditions and dynamic changes in the environment.