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.

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.

The efficient and timely distribution of freight goods is critical for supporting the demands of modern urban areas. Optimum freight ensures the survival and development of urban areas. In the contemporary logistic there are two main distribution strategies: direct distribution and multi-echelon distribution. In the direct distribution, means of transport, starting from the main distribution center, bring their freight directly to the delivery points, while in the multi-echelon systems, freight is delivered from the main distribution center to the delivery points through intermediate points (local warehouses, satellites). This study presents a concept and implementation of a integrated approach to modeling and optimization the MultiEchelon Systems. In the proposed approach, two methods of constraint logic programming (CLP) and mathematical programming (MP) were integrated and hybridized. The proposed hybrid approach will be compared with classical mathematical programming on the same data sets (known benchmarks) for illustrative multi-echelon model — Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP).

The author presents a novel declarative approach to modeling, solving and decision support for supply chain problems as a declarative decision support framework.The proposed framework makes it possible to ask different types of questions (general, specific, logical etc.).The implementation of the framework was performed in the 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.

This paper presents a hybrid programming framework for solving multi-objective optimization problems in supply chain.The proposed approach consists of the integration and hybridization of two modeling and solving environments, i.e., constraint logic programming and mathematical programming, to obtain a programming framework that offers significant advantages over the classical approach derived from operational research.The strongest points of both components are combined in the hybrid framework, which by introducing transformation allows a significant reduction in size of a problem and the optimal solution is found a lot faster.This is particularly important in the multi-objective optimization where problems have to be solved over and over again to find a set of Pareto-optimal solutions.An over two thousand-fold reduction in size was obtained for the illustrative examples together with a few hundred-fold reduction in the speed of finding the solution in relation to the mathematical programming method.In addition, the proposed framework allows the introduction of logical constraints that are difficult or impossible to model in operational research environments.

The paper presents a concept and implementation of a novel hybrid approach to the modelling, optimization and analysis of the supply chain problems. Two environments, mathematical programming (MP) and constraint programming (CP), in which constraints are treated in different ways and different methods are implemented, were combined to use the strengths of both.

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.