This paper presents an effective use of hybrid metaheuristics algorithm for solving Job Shop Scheduling Problem (JSSP). Integration of three metaheuristics algorithms: Shuffled Frog Leaping Algorithm (SFLA), Intelligent Water Drops algorithm (IWD) and Path Relinking (PR) algorithm were put together to solve JSSP. First, simulation model was developed and tested on the test data of Traveller Salesman Problem (TSP). Second, the model was tested on real world production line to solve the problem of Minimum Needed Workers (MNW) at the production line. The model enables individual test of three mentioned algorithms and calculation of new proposed Random MultiNeighbourhood based Shuffled Frog Leaping Algorithm with Path Relinking (RMN-SFLA-PR). Experiments were tested on two software environments MATLAB and Simio, which gives us reliable, robust and tangible results. Results show that the new proposed RMN-SFLA-PR algorithm converged to optimum almost ten times faster than individual algorithms. The most important thing is the successful rate of all independent runs of the proposed RMN-SFLA-PR is 100 % in low-dimensional cases of the 4 benchmarks (dj38) and in JSSP to solve MNW for the real world production line.

Multi-Objective (MO) optimization is a well-known research field with respect to the complexity of production planning and scheduling. In recent years, many different Evolutionary Computation (EC) methods have been applied successfully to MO production planning and scheduling. This paper is focused on making a review of MO production scheduling methods, starting from production scheduling presentation, notation and classification. The research field of EC methods is presented, then EC algorithms` classification is introduced for the purpose of production scheduling optimization. As a main goal, MO optimization is focused on hybrid EC methods, and presenting their advantages and limitations. Finally, a survey of five scientific databases is presented, with the analysis of the scientific publications the terminology development of the scientific field is presented. Using the citation analysis of the scientific publications, the application for the MO optimization in manufacturing scheduling is discussed.

The presented manuscript deals with the impact of manufacturing flexibility on the sustainability justification of the manufacturing system, related to manufacturing sustainable social, environmental and financial impact. Such impact is not described in the research sphere. The complexity of the optimisation parameters is reflected in the multi-objective nature that can be evaluated with the use of the simulation study method. The manuscript presents a description of manufacturing flexibility modelling, with respect to the four-level architectural model, describing an optimisation problem of high-mix low-volume production. The impact of manufacturing flexibility on the sustainability justification is presented by the new block diagram. Sustainability parameters' mathematical modelling is presented with two main optimisation parameters of energy consumption and machine scrap percentage. The impact is evaluated and described by an appropriate multi-criteria optimisation method on a sustainably justified production system.

The present study has investigated mathematical and simulation model interactivity for production system scheduling. A mathematical model of a Flexible Job Shop Scheduling Production optimisation problem (FJSSP) was used to evaluate a new evolutionary computation method of multi-objective heuristic Kalman algorithm (MOHKA). Ten Brandimarte and five Kacem benchmarks were applied for evaluation and comparison of MOHKA optimisation results with the Multi-Objective Particle Swarm Optimization algorithm (MOPSO) and Bare-Bones Multi-Objective Particle Swarm Optimization algorithm (BBMOP-SO). Benchmark data sets were divided into three groups, regarding their complexity, from low, middle to high dimensional optimisation problems. The optimisation results of MOHKA show high capability to solve complex multiobjective optimisation problems, especially with real world production systems data. A new robust method is presented of optimisation data interactivity between a mathematical optimisation algorithm and a simulation model. The results show that the presented method can overcome the integrated decision logic of commercial simulation software and transfer the optimisation results into the simulation model. Our interactive method can be used in a variety of production and service companies to ensure an optimised and sustainable cost-time profile.

The presented manuscript deals with the impact of manufacturing flexibility on cost-time investment as a function of sustainable production, which addresses the company’s sustainable social and environmental impact adequately. The impact of manufacturing flexibility on cost-time investment in the research sphere is not described, despite the fact that we know its key role in the high-mix low-volume production types. Recently, researchers have been addressing intensively the impacts of various parameters on the sustainable aspect and its dependence on manufacturing flexibility. The complexity of the influence parameters is reflected in the multi-criteria nature of optimization problems that can be solved with appropriate use of the evolutionary computation methods. The manuscript presents a new method of manufacturing flexibility modelling, with respect to the four-level architectural model, which reflected as a symmetry phenomena influence on the cost-time profile diagram. The solution to a complex optimization problem is derived using the proposed improved heuristic Kalman algorithm method. A new method is presented of optimization parameters’ evaluation with respect to the manufacturing flexibility impacts on cost-time investment. The large impact of appropriate multi-criteria optimization on a sustainably justified production system is presented, with the experimental work on benchmark datasets and an application case. The new method allows a comprehensive optimization approach, and validation of the optimization results by which we can provide more sustainable products, manufacturing processes, and increase the company’s total, social and environmental benefits.

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In the time of Industry 4.0, the dynamic adaptation of companies to global market demands plays a key role in ensuring sustainable financial and time justification. Financial accessibility, a wide range of user-friendliness, and credible results of the visual computing methods and data-driven simulation modeling enable a higher degree of usability in small, medium, and large enterprises. This paper presents an innovative method for modelling and simulating workplaces in manufacturing based on visual data captured with a spherical camera. The presented approach uses simulation scenarios to investigate the optimization of manual or collaborative workplaces. We evaluated and compared three simulated scenarios, the results of which highlight the potential for improvement regarding manufacturing productivity and cost. In addition, ergonomic analyses of a manual assembly workplace were performed using existing evaluation metrics. The results show the possibility of creating a three-dimensional model of a workplace captured with a spherical camera, which not only describes the model dimensionally but also adds terminological and other production parameters obtained through the analysis of manufacturing system videos. The confirmation of the appropriateness of introducing collaborative workstations is also confirmed by ergonomic analyses Ovaco working analyzing system (OWAS) and rapid upper limb assessment (RULA), which demonstrate the sustainable limits of manual assembly workplaces.