Uncertain Machine Load Forecasting Based on Least Squares Support Vector Machine

Machine state is a very important constraint for job shop scheduling. For the uncertainty machine state, the paper proposes a machine load forecasting method based on support vector machine. The method reduces complexity and improves efficiency by eliminating a large number of unrelated input factors and selecting a small number of input parameters with strong correlation. The efficiency of the algorithm is verified by the production workshop instance.

Machine load balancing game with linear externalities

The Machine Load Balancing Game with linear externalities is considered. A set of jobs is to be assigned to a set of machines with different latencies depending on their own loads and also loads on other machines. Jobs choose machines to minimize their own latencies. The social cost of a schedule is the maximum delay among all machines, i.e. {\it makespan. For the case of two machines in this model an Nash equilibrium existence is proven and of the expression for the Price of Anarchy is obtained.

Hybrid Differential Evolution Algorithm and Adaptive Large Neighborhood Search to Solve Parallel Machine Scheduling to Minimize Energy Consumption in Consideration of Machine-Load Balance Problems

Environmental and economic considerations create a challenge for manufacturers. The main priorities for production planning in environmentally friendly manufacturing industries are reducing energy consumption and improving productivity by balancing machine load. This paper focuses on parallel machine scheduling to minimize energy consumption (PMS_ENER), which is an indicator of environmental sustainability when considering machine-load balance problems. A mathematical model was formulated to solve the proposed problem and tested using a set of problem groups. The findings indicated that the mathematical model could find an optimal solution within a limited calculation time for small problems. For medium and large problems, the mathematical model could also find the optimal solution within a limited calculation time, but worse than all metaheuristics. However, finding an optimal solution for a larger problem is time-consuming. Thus, a novel method, a hybrid differential evolution algorithm with adaptive large neighborhood search (HyDE-ALNS), is presented to solve large-scale PMS_ENER. The new mutation and recombination formula for the differential evolution (DE) algorithm proposed in this article obtained promising results. By using the HyDE-ALNS, we improved the solution quality by 0.22%, 7.21%, and 12.01% compared with a modified DE (MDE-3) for small, medium, and large problems respectively. In addition, five new removal methods were designed to implement in ALNS and achieve optimal solution quality.

A supervised method for scheduling multi-objective job shop systems in the presence of market uncertainties

In real industries, managers usually consider more than one objective in scheduling process. Minimizing completion time, operational costs and average of machine loads are amongst the main concerns of managers during production scheduling in practice. The purpose of this research is to develop a new scheduling method for job-shop systems in the presence of uncertain demands while optimizing completion time, operational costs and machine load average are taken into account simultaneously. In this research a new multi-objective nonlinear mixed integer programming method is developed for job-shop scheduling in the presence of product demand uncertainty. The objectives of the proposed method are minimizing cost, production time and average of machine loads index. To solve the model, a hybrid NSGA-II and Simulated Annealing algorithms is proposed where the core of the solving algorithm is set based on weighting method. In continue a Taguchi method is set for design of experiments and also estimate the best initial parameters for small, medium and large scale case studies. Then comprehensive computational experiments have been carried out to verify the effectiveness of the proposed solution approaches in terms of the quality of the solutions and the solving times. The outcomes are then compared with a classic Genetic Algorithm. The outcomes indicated that the proposed algorithm could successfully solve large-scale experiments less than 2 minutes (123 seconds) that is noticeable. While performance of the solving algorithm are taken into consideration, the proposed algorithm could improve the outcomes in a range between 9.07% and 64.96% depending on the input data. The results also showed that considering multi-objective simultaneously more reasonable results would be reached in practice. The results showed that the market demand uncertainty can significantly affect to the process of job shop scheduling and impose harms in manufacturing systems both in terms of completion time and machine load variation. Operational costs, however, did not reflect significantly to market demand changes. The algorithm is then applied for a manufacturing firm. The outcomes showed that the proposed algorithm is flexible enough to be used easily in real industries.

Model analysis and optimization of operating rates of enterprises with small-series production type

The article deals with issues surrounding the production management of enterprise with small-series production type. Specific features of small-series production are determined. Task complex defined by initial production phase comes under review. Problem of calculation, analysis and optimization of machine load is covered in depth. It is assumed that machine load calculating task will minimize potencial stress of production plan and maximize workplace capacity. Calculations are carried out for each type of work for the required period (year, month, week, etc.) for the whole enterprise, workshops, production areas, workplaces. The labor intensity for each workplace and usage coefficient are calculated. The analysis of obtained indexes is the basis for redistribution of the workload capacity.

G2-stetiges Klothoidenüberschleifen*/G2-continuous clothoid smoothing – Results of a real-time smoothing process on the basis of piecewise defined clothoids

Das Klothoidenüberschleifverfahren ermöglicht einen G2-stetigen Übergang zwischen NC (Numerical Control)-Sätzen bei linearem Krümmungsprofil. Zudem ist dieses Profil über die Verteilung der Klothoiden- und Kreissegmente steuerbar und bietet die Möglichkeit beispielsweise hinsichtlich der Vorschubgeschwindigkeit optimiert zu werden. Allgemein entstehen glattere Dynamikprofile, welche die Maschinenbelastung reduzieren oder höhere Bearbeitungsgeschwindigkeiten zulassen.   The clothoid smoothing process enables a G2-continuous transition between NC blocks with a linear curvature profile. In addition, this profile can be controlled via configuration of the clothoid and circle segments and offers the possibility to be optimized, for example, with regard to the feed rate. In general, smoother dynamic profiles are created, which reduce the machine load or allow for higher machining velocities.