The utilization of a specific priority rule in scheduling operations in flexible job shop systems strongly influences production goals. In a context of production control in real practice, production performance indicators are evaluated always en bloc. This paper addresses the multi-criteria evaluating five selected conflicting production objectives via scalar simulation-based optimization related to applied priority rule. It is connected to the discrete-event simulation model of a flexible job shop system with partially interchangeable workplaces, and it investigates the impact of three selected priority rules—FIFO (First In First Out), EDD (Earliest Due Date), and STR (Slack Time Remaining). In the definition of the multi-criteria objective function, two scalarization methods—Weighted Sum Method and Weighted Product Method—are employed in the optimization model. According to the observations, EDD and STR priority rules outperformed the FIFO rule regardless of the type of applied multi-criteria method for the investigated flexible job shop system. The results of the optimization experiments also indicate that the evaluation via applying multi-criteria optimization is relevant for identifying effective solutions in the design space when the specific priority rule is applied in the scheduling operations.

The article addresses an approximate solution to the multi-objective optimization problem for a black-box function of a manufacturing system. We employ the surrogate of the discrete-event simulation model of a batch production system in an analytical form. Integration of simulation, Design of Experiments methods, and Weighted Sum and Weighted Product multi-objective methods are used in an arrangement of a priori defined preferences to find a solution near the Pareto optimal solution in a criterion space. We compare the results obtained through the analytical approach to the outcomes of simulation-based optimization. The observed results indicate a possibility to apply the suitable analytical model for quickly finding the acceptable approximate solution close to the Pareto optimal front.

The paper presents the outcome of a study that dealt with scheduling of operations and in particular with the impact of selected priority rules on production goals. The impact of the priority rules was examined on a simulation model of the smart flexible manufacturing system with interchangeable workplaces. These problems are characterized as job shop scheduling problems. The study documents the influence of the five selected priority rules FIFO (First in First out), EDD (Earliest Due Date), STR (Slack Time Remaining), STR/OP (Slack Time Remaining per Operation) and SQNO (Shortest Queue at the Next Operation) on chosen production objectives. The effect of applied priority rule has been studied at a different production system loading. The evaluation of experimental results and their synthesis conduce to the formulation of knowledge for the real use of these priority rules in the scheduling of operations.

Complex manufacturing systems control, involving contradictory production goals, is a challenge in spite of many multi-objective optimization decision-making strategies. In this article we demonstrate an application of the scalarization approach with a priori articulated preferences in the multiobjective optimization via discrete-event simulation in the field of the manufacturing product control. Especially, we focus on the effect of the used scalarizing multi-criteria function form combining all considering production goals using user-supplied weights. Four key performance indicators: costs per part unit, an average flow time, machines utilization and an amount of production for studied production system were finding by simulation in simulator Witness and optimized simultaneously under specific constraints. Metaheuristics like Thermoadaptive Simulated Annealing and Random Solutions and at last a brute force algorithm were used as optimization methods for finding global minimum of scalar functions generated by different methods of scalarization.

The article aims to describe stages of development of a mobile, anthropomorphic robot. The individual phases of the bio-inspired model-based design of a two-legged robot are described, from the analysis of human walking patterns, through the creation of a simulation model and a control system to the creation and testing the physical model of such a robot. The bio-inspired design is based on an analysis of the movements of the individual parts of a lower human body during walking locomotion. Based on this analysis, the chosen motion model is described as a combination of passive and dynamic walking with a controlled, linear inverted pendulum model. When creating the simulation model, an open kinematic chain consisting of a base (the frame) and two effectors (the legs) is used. The simulation of the virtual model is realized using the software tool Matlab with its toolboxes Simulink and Simscape, and the results confirm the correctness of the design. The feasibility of the design is confirmed by creation of a physical robot skeleton using 3D printing and by a commissioning of the control system based on Atmel ATmega2560 and Raspberry Pi Zero W microcontrollers.