This paper presents a model of scheduling of multi unit construction project based on an NP-hard permutation flow shop problem, in which the considered criterion is the sum of the costs of the works' execution of the project considering the time of the project as a constraint. It is also assumed that each job in the units constituting the project may be realized in up to three different ways with specific time and cost of execution. The optimization task relies on solving the problem with two different decision variables: the order of execution of units (permutation) and a set of ways to carry out the works in units. The task presented in the paper is performed with the use of a created algorithm which searches the space of solutions in which metaheuristic simulated annealing algorithm is used. The paper presents a calculation example showing the applicability of the model in the optimization of sub-contractors' work in the construction project.

The paper present the concept of stability assessing the of solutions which are construction schedules. Rank have been obtained through the use of task scheduling rules and the application of the KASS software. The aim of the work is the choice of the equivalent solution in terms of the total time of the project. The selected solution optimization task should be characterized by the highest resistance to harmful environmental risk factors. To asses the stability of schedule simulation technique was used.

The paper concerns the two-machine non-preemptive flow shop scheduling problem with a total late work criterion

and a common due date (F2|dj = d|Y ). The late work performance measure estimates the quality of a solution with regard

to the duration of late parts of activities performed in the system, not taking into account the quantity of this delay. In the

paper, a few theorems are formulated and proven, describing features of an optimal solution for the problem mentioned, which is

NP-hard. These theorems can be used in exact exponential algorithms (as dominance relations reducing the number of solutions

enumerated explicitly), as well as in heuristic and metaheuristic methods (supporting the construction of sub-optimal schedules

of a good quality).

This paper explores selected heuristics methods, namely CDS, Palmer’s slope index, Gupta’s

algorithm, and concurrent heuristic algorithm for minimizing the makespan in permutation

flow shop scheduling problem. Its main scope is to explore how different instances sizes

impact on performance variability. The computational experiment includes 12 of available

benchmark data sets of 10 problems proposed by Taillard. The results are computed and

presented in the form of relative percentage deviation, while outputs of the NEH algorithm

were used as reference solutions for comparison purposes. Finally, pertinent findings are

commented.

The paper considers the production scheduling problem in a hybrid flow shop environment with sequence-dependent setup times and the objectives of minimizing both the makespan and the total tardiness. The multi-objective genetic algorithm is applied to solve this problem, which belongs to the non-deterministic polynomial-time (NP)-hard class. In the structure of the proposed algorithm, the initial population, neighborhood search structures and dispatching rules are studied to achieve more efficient solutions. The performance of the proposed algorithm compared to the efficient algorithm available in literature (known as NSGA-II) is expressed in terms of the data envelopment analysis method. The computational results confirm that the set of efficient solutions of the proposed algorithm is more efficient than the other algorithm.

The Bulletin of the Polish Academy of Sciences: Technical Sciences (Bull.Pol. Ac.: Tech.) is published bimonthly by the Division IV Engineering Sciences of the Polish Academy of Sciences, since the beginning of the existence of the PAS in 1952. The journal is peer‐reviewed and is published both in printed and electronic form. It is established for the publication of original high quality papers from multidisciplinary Engineering sciences with the following topics preferred: Artificial and Computational Intelligence, Biomedical Engineering and Biotechnology, Civil Engineering, Control, Informatics and Robotics, Electronics, Telecommunication and Optoelectronics, Mechanical and Aeronautical Engineering, Thermodynamics, Material Science and Nanotechnology, Power Systems and Power Electronics.

Journal Metrics: JCR Impact Factor 2018: 1.361, 5 Year Impact Factor: 1.323, SCImago Journal Rank (SJR) 2017: 0.319, Source Normalized Impact per Paper (SNIP) 2017: 1.005, CiteScore 2017: 1.27, The Polish Ministry of Science and Higher Education 2017: 25 points.

Abbreviations/Acronym: Journal citation: Bull. Pol. Ac.: Tech., ISO: Bull. Pol. Acad. Sci.-Tech. Sci., JCR Abbrev: B POL ACAD SCI-TECH Acronym in the Editorial System: BPASTS.

The paper discusses a two-machine flow shop problem with minimization of the sum of tardiness costs, being a generalization of the popular NP-hard single-machine problem with this criterion. We propose the introduction of new elimination block properties allowing for accelerating the operation of approximate algorithms of local searches, solving this problem and improving the quality of solutions determined by them.

The presented method is constructed for optimum scheduling in production lines with parallel

machines and without intermediate buffers. The production system simultaneously

performs operations on various types of products. Multi-option products were taken into

account – products of a given type may differ in terms of details. This allows providing for

individual requirements of the customers. The one-level approach to scheduling for multioption

products is presented. The integer programming is used in the method – optimum

solutions are determined: the shortest schedules for multi-option products. Due to the lack

of the intermediate buffers, two possibilities are taken into account: no-wait scheduling,

possibility of the machines being blocked by products awaiting further operations. These two

types of organizing the flow through the production line were compared using computational

experiments, the results of which are presented in the paper.

Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied.