Standardization is a key element in the effective use of lean manufacturing methodologies and tools for achieving process sustainability. Their combination is conducive to eliminating waste and improving the efficiency of production processes and guarantees the company that employees use the most efficient tools and do not waste time on unnecessary activities. These activities can be further improved by using digital solutions, in accordance with the concept of Industry 4.0. Therefore, the authors have developed the e-Lean system, whose task is to digitize selected lean manufacturing tools. The subject of this work is analysis of the functionality and effectiveness of the essential part of the e-Lean system in the form of specialized TPM (Total Productive Maintenance) software as an application. During implementation in a construction production company, the TPM application was tested by lean manufacturing and maintenance specialists. The research consisted of assessing the functionality and efficiency of processes in relation to conventional TPM solutions. Additional functionalities of the e-Lean system have been confirmed, such as systemic approval of machinery inspection, which requires passing all necessary steps at individual inspection points, direct access for supervisors to the results of inspection activities and their status, direct and easy access to photographic documentation of machines added during inspection both in optimization of working time and its course (e.g., the optimal number of steps taken by the employee during the inspection), as well as an efficient system of motivating employees (collecting points). The improvement in the effectiveness of processes was determined by measuring the control times for three control points (polymerization furnace, packing area, and defibering machines). The average control time was reduced from 16,200 to 13,923 s. Thus, thanks to the use of the application, it was found that the efficiency of using the TPM tool was increased by approx. 15% compared to previously used non-digital solutions.

The article presents the profit optimization model for multi-unit construction projects. Such projects constitute a special case of repetitive projects and are common in residential, commercial, and industrial construction projects. Due to the specific character of construction works, schedules of such projects should take into account many different aspects, including durations and costs of construction works, the possibility of selecting alternative execution modes, and specific restrictions (e.g., deadlines for the completion of units imposed by the investor). To solve the NP-hard problem of choosing the order of units’ construction and the best variants of works, the authors used metaheuristic algorithms (simulated annealing and genetic search). The objective function in the presented optimization model was the total profit of the contractor determined on the basis of the mathematical programming model. This model takes into account monthly cash flows subject to direct and indirect costs, penalties for missing deadlines, costs of work group discontinuities, and borrowing losses. The presented problem is very important for maintaining a good financial condition of the enterprise carrying out construction projects. In the article, an experimental analysis of the proposed method of solving the optimization task was carried out in a model that showed high efficiency in obtaining suboptimal solutions. In addition, the operation of the proposed model has been presented on a calculation example. The results obtained in it are fully satisfying.

Conveyor belts are vital for material transportation in coal mines due to their cost-effectiveness and versatility. These belts endure significant wear from harsh operating conditions, risking substantial financial losses if they fail. This study develops five artificial neural network (ANN) models to predict conveyor belt damage using 11 parameters from the Belchatow brown coal mine in Poland. The models target five outputs: number of repairs and cable cuts, cumulative number of repairs and cable cuts, and their ages. Various optimizers (Adam, Nadam, RMSprop, Adamax, and stochastic gradient descent or SGD) and activation functions (ReLU, Swish, sigmoid, tanh, Leaky ReLU, and softmax) were tested to find the optimal configurations. The predictive performance was evaluated using three error indicators against actual mine data. Superior models can forecast belt behavior under specific conditions, aiding proactive maintenance. The study also advocates for the Diagbelt+ system over human inspections for failure detection. This modeling approach enhances proactive maintenance, preventing total system breakdowns due to belt wear.

Distribution companies, in order to maintain a competitive advantage, must demonstrate not only the quality of the offered goods, but also the speed of execution of orders. This article deals with the allocation of the available capacity of transport during the transportation of goods between companies. Solving the problem of optimization is offered by the chosen methods of an artificial intelligence, such as Tabu Search algorithm, greedy algorithm and Tabu Search using the results of the greedy algorithm. The results were compared with the actual results of one of the Dutch distribution companies.

The efficient management of complex production systems is a challenge in today’s logistics. In the field of intelligent and sustainable logistics, the optimization of production batches, especially in the context of a rapidly changing product range, requires fast and precise computational solutions. This paper explores the potential of quantum computers for solving these problems. Traditional computational methods are often limited when it comes to optimizing complex logistics systems. In response to these challenges, the paper proposes the use of a hybrid algorithm that combines quantum technologies with classical computational methods. Such integration allows the computational power of both types of technologies to be harnessed, leading to faster and more efficient identification of optimal solutions. In this work, we consider the knapsack problem, a classic NP-hard optimization problem that is commonly used to verify the effectiveness of new algorithm construction methods. The algorithm presented is based on the Branch and Bound method and aims to ensure solution optimality in the context of the non-determinism of quantum computers. Within the algorithm, computations are performed alternately on a classical processor and a quantum processor. In addition, the lower and upper bounds of the objective function are computed in constant time using the D-Wave quantum machine.