The permutation flow-shop scheduling problem (PFSP) is an important problem in production industry.The problem has been a subject of many research and various algorithms to solve PFSP have been developed over the years.The newly developed algorithms are usually tested on Taillard and VRF benchmarks and their results are compared using various measures that assess the size of error made by an algorithm and the computation time.In this paper, we propose two new measures to assess the quality of results of algorithms for solving PFSP with the makespan criterion.The first ARD.NEH measure gives similar results as the well known ARPD measure but is robust to updates of the best known solutions of benchmark problems.The second ARID measure is an intervalbased measure which is able to assess whether the good quality of an algorithm results stems from its good behavior of this algorithm for a few instances or from its good behavior for most instances.The computational experiments confirm the usefulness of the proposed quality measures.

Abstract In the past decade, a lot of challenges to access, assess, and to acquire the needed technological opportunities to teach computers what naturally comes from the human brain and to understand how we naturally react when we rely on technology. The ability to document human thoughts, reactions and behavior to computers has led to the coming of NLP, AI, Dl, & ML. Aim to understand the influence of IoT on humans with the use of DL to achieve content correctness and accuracy with virtual technology. Studies show that the way we think, react, and do the things we think “Internet of thoughts” reflect our personality. The way we think determines the way we react and the way we do things are based on how we think. Technology advancement has reinforced a lot of changes in humans which makes humans vulnerable to personal content exposure misappropriation due to the continuously changing nature of humanity and language. The study uses NLP, DL and behavior-oriented drive and influential function and results show that IoT based on VR influences human psychology “Internet of Thoughts”.

The N-NEH+ algorithm is one of the most efficient construction algorithms for solving the permutation flow-shop problem with the makespan criterion.It extends the well-known NEH heuristic with the N-list technique.In this paper, we propose the Starting Point (SP) method that employs a new strategy for using the N-list technique.The SP method allows to obtain an algorithm that is a combination of NEH and an N-list-based algorithm.Extensive numerical experiments on the standard set of Taillard's and VRF benchmarks show that the SP method significantly improves the results (average relative percentage deviation) of the NEH and N-NEH+ algorithms.

Abstract Supervision of repair and diagnostic works aimed at improving the safety of maintenance crews is one of the key objectives of the distributed INRED system. Working in a real industrial environment, the INRED system includes, among others, the so-called INRED-Workflow, which provides an infrastructure for process automation. Participants of the service processes, managed by the INRED-Workflow, are controlled at each stage of the performed service procedures, both by the system and other process participants, such as quality managers and technologists. All data collected from the service processes is stored in the System Knowledge Repository (SKR) for further processing by using advanced algorithms, and the so-called Smart Procedures merge services supplied by other INRED system modules. The applicability of workflow management systems in conjunction with image recognition and machine learning methods has not yet been thoroughly explored. The presented paper shows the innovative usage of such systems in the supervision of the repair and diagnostic works.

We investigate the problem of vehicle route planning in a dynamic environment. In order to better reflect real-life situations, we assume that travel times are not known exactly, but bounded from below and from above, i.e., they are given as interval quantities. Accordingly, we develop algorithms for effective route replanning in a highly dynamic road network environment that combines traffic image processing with interval data for dynamic path optimisation. The developed algorithms are integrated into a larger system for traffic management. The efficiency of the proposed algorithms and their ability to support the dynamics of road traffic is verified using real data. The experimental research was also conducted using the microscopic, time-discrete, space-continuous traffic simulator.

Project selection is a complex multi-criteria decision making process that is influenced by multiple and often conflicting objectives.The complexity of the project selection problem is mainly due to the high number of projects from which an appropriate collection (an effective portfolio) of investment projects must be selected.This paper presents a new conception of a hybrid framework for construction of an effective portfolio of investment projects.The parameters of the considered model are described using both probability distributions and fuzzy numbers (possibility distributions).The proposed framework enables to take into account stochastic dependencies between model parameters and economic dependencies between projects.As a result, a set of Pareto optimal solutions is obtained.The performance of the proposed method is illustrated using an example from metallurgical industry.