Abstract At the time of commonplace automation, robotization and the rapid development of IT, high qualifications of employees have become the critical element of every industry system. This follows from their limited availability, frequently high costs of procurement and possible employee absenteeism. Moreover, the concept of Industry 4.0 will transform current industry employees into knowledge employees. This is due to the fact that hard and routine tasks will be executed by robots and computers. This constitutes change in the required employee competences. Unfortunately, the aspect of management and configuration of employee competences is often overlooked in industrial practice. In response to the existing problem, the article puts forward the original model of employee competence configuration which is a basis for responses to numerous questions of managers of production processes, both general ones, e.g., Do we have a sufficient set of competences to execute a production schedule? as well as detailed ones, e.g., Which and how many competences are missing? etc. An important novelty of the presented model is the possibility of its application with both proactive and reactive questions. Due to the discrete and combinatorial nature of the problem under consideration, the use of mathematical programming methods was limited only to small data instances. Therefore, a proprietary dedicated genetic algorithm was proposed to solve this problem, which turned out to be extremely effective. The use of this genetic algorithm has enabled finding a solution depending on the instance data up to 70 times faster than by use of the mathematical programming.

The design of logistic trains fleet oriented distributed and scalability-robust control policies that ensure deadlock-free operations is of crucial importance for efficient material handling systems. This study considers a multi-item assembly system where in-plant transport operations are organized in milk-run loops. A solution to a milk-run routing and scheduling problem subject to fuzzy pickup and delivery time constraints is developed. This type of problem can be treated in terms of a fuzzy constraint satisfaction problem, therefore, the main objective is to provide a reference model analytical formulas of which enable one to obtain solutions that do not require time-consuming computer simulations. Two versions of the model were parameterized assuming independent implementation of convex and ordered fuzzy numbers. The accuracy of both models was experimentally verified according to the results of multiple simulations. Results from this study provide an approach to avoid time consuming computer simulation-based calculations of logistic trains fleet schedules avoiding congestions while concurrently maintaining throughput at maximal achievable level.

The article presents the problem of academic teachers' competences configuration in the context of the university course timetabling problem (UCTP).Usually when solving UCTP, the set of available academic teachers and the competences they have is defined.The sets of lecture rooms, subjects (courses), student groups, time-slots, etc. are also known.Problems can emerge when it is not possible to find a satisfactory UTCP solution due to the missing sufficient number of specific academic teachers' (lecturers') competences, which is reflected in the possibility to teach specific classes (courses).In order to detect early such a situation and effectively manage the available and required lecturers' competences, a mathematical model of lecturers' competences configuration has been formulated in the form of a MILP (Mixed Integer Linear Programming) problem.Its solution has a direct impact on UTCP.The article also presents the implementation of the model in the LINGO solver environment and computational experiments.

Constraints, although ubiquitous in production and distribution planning, scheduling and control, often lead to inconsistencies in the decision-making process. The constraint-based modeling helps circumvent many organization-impacting issues. To address this, we developed a multi-level approach to the modeling and solving of combinatorial optimization problems. It is versatile and effective owing to the use of multi-level presolving and multiple paradigms, such as constraint programming, logic programming, mathematical programming and fuzzy logic, for their complementary strengths. The capability of this framework and its advantage over mathematical programming alone or over hybrid frameworks is shown in the illustrative example, in which combinatorial optimization is used as a benchmark to prove the effectiveness of the proposed approach. Knowledge of the problem is stored in the form of facts.

This study deals with modelling and optimization of handling jobs (orders) in groups.All jobs in a group should be delivered at the same time after processing.The authors present a novel hybrid method, which includes the modelling and optimization of the problem in the hybrid environment composed of MP (Mathematical Programming) and CLP (Constraint Logic Programming).Due to the large complexity of the optimization problem, dedicated heuristic is also proposed instead of MP.The paper also presents an author's model for optimization scheduling groups of jobs.The model has been implemented in several environments: Hybrid (CLP/MP), Hybrid (CLP, heuristic), MP and heuristic.The obtained results of numerical experiments confirm the high efficiency and usefulness of the hybrid approach to optimize such problems.

This paper deals with declarative decision support framework for scheduling groups of orders.All orders in a group should be delivered at the same time after processing.The authors present a novel declarative approach to modeling and solving scheduling problems as a declarative decision support framework.The proposed framework makes it possible to ask different types of questions (general, specific, logical, etc.).It also allows, scheduling emerging orders or groups of orders without changing the existing schedules.To implement was used CLP (Constraint Logic Programming) environment.To increase the efficiency of the framework, particularly in the area of optimization made its integration with MP (Mathematical Programming) environment.The paper also presents the implementation of illustrative model, using the proposed framework.In addition, an efficiency analysis of the presented solution in relation to the application of mathematical programming have been conducted.

The paper presents a concept and application of a hybrid multi-agent approach to modeling and optimization the supply chain problems. Two environments (mathematical programming (MP) and constraint logic programming (CLP)) and two types of agents were integrated. This integration and hybridization, complemented with an adequate transformation of the problem, facilitates a significant reduction of the combinatorial problem. The whole process takes place at the implementation layer, which makes it possible to use the structure of the problem being solved, implementation environments and the very data. The strengths of MP and CLP, in which constraints are treated in a different way and different methods are implemented, were combined to use the strengths of both. The proposed approach is particularly important for the decision models with an objective function and many discrete decision variables added up in multiple constraints. The presented multi-agent approach will be compared with classical mathematical programming on the same data sets and models. In addition, the proposed approach will be used for modelling and optimization of the hybrid model, which has not only linear constraints but also logical constraints.