Modeling and solving the steelmaking and casting scheduling problem

Abstract

We propose a general model for the problem of planning and scheduling steelmaking and casting activities obtained by combining common features and constraints of the operations from a real plant and the literature. For tackling the problem, we develop a simulated annealing approach based on a solution space made of job permutations, which uses as submodule a chronological constructive procedure that assigns processing times and resources to jobs. Our technique, properly tuned in a statistically principled way, is able to find good solutions for a large range of different settings and horizons. In addition, it outperforms both a greedy procedure and a constraint‐based solver developed for comparison purposes on almost all instances. Finally, we have collected several real‐world instances that we make available on the web along with the solution validator and our best results.

Importance of the integrated manufacturing execution system for a metallurgical enterprise

“…Manufacturing Execution System functionality in various standards Source: [11] The advantages of a production system (MES) include its fast payback. Reducing the number of defects, increasing productivity without attracting additional labor and increasing production capacity, optimizing work schedules, including schedules for the continuous operation of equipment -all this ultimately helps to increase profits [7]. The integration of automated enterprise management systems (MES) allows increasing the efficiency of production processes.…”

“…Digital technologies enable large metallurgical producers to form their own ecosystems that can unite the client network and contractors [6][7][8]. The MES system most often covers a wide range of tasks: installation and configuration of rational modes of operation of the enterprise and each of its individual technological processes; maintenance of proper operation of a given mode of activity of the enterprise; provision of the growth of the quality of the manufactured product by reducing costs, emergencies, downtime, etc.…”

Multi‐level, multi‐stage lot‐sizing and scheduling in the flexible flow shop with demand information updating

“…The latter is more suitable in practice because it quickly discovers the near‐optimal solutions to the problem. Many researchers use MIP‐based heuristics such as rolling horizon (RH), relax‐and‐fix, fix‐and‐optimize to solve the problem, and some researchers combine these heuristics with meta‐heuristics such as variable neighborhood search and simulated annealing (e.g., Sahling et al., 2009; Lang and Shen, 2011; Ramezanian and Saidi‐Mehrabad, 2013; Seeanner et al., 2013; Xiao et al., 2013; Chen, 2015; Armellini et al., 2020). Araujo and Clark (2013) provide a facility location reformulation and strengthen it with the newly proposed constraints to obtain the better solution for the LSS problem.…”

Untitled

“…To increase the service life of capacities and mechanisms, it is better to build them considering the possibility of collecting data on their state. In this regard, solutions using Big Data and IoT technologies are applied, with the later integration of data with ERP (Enterprise Resource Planning) or MES systems (Manufacturing Execution System) (Armellini et al, 2018). The use of simulation modelling and end-to-end tracking allows controlling the technological process to maintain the specified product quality parameters and reduce costs.…”