A Paradigm for the Scheduling of a Continuous Walking Beam Reheat Furnace Using a Modified Genetic Algorithm

The paper discusses the application of a paradigm for creating scheduling systems for steel reheating furnaces. The proposed paradigm utilizes a modified version of a Genetic Algorithm (GA) to optimize such schedules via new ways of realizing the crossover and mutation operations. The work was conducted in collaboration with the Thrybergh Combination Mill owned by CORUS- Sheffield (UK). The outcome of this research work is a novel scheduling system which links together the scheduling and furnace controls and is also able to accept new and already established mill heuristics. The proposed methodology is 'flexible' as well as 'generic' as it can be augmented easily in order to suit other industrial set-ups.     

烟丝加工双机流水线生产调度与维护联合优化

首先将烟丝加工制程抽象为带缓冲的双机Flow shop,以最小化最大完工时间为目标,建立了双机Flow shop生产调度与设备维护联合优化数学模型.然后,基于烟丝加工制程的特点,给出并证明了其联合优化调度问题的若干最优解特性.在此基础上,提出了一个简洁、有效的算法用于求解该问题.最后,通过实际烟丝加工案例验证了该算法的有效性.     

Applications of Genetic Algorithm in Polymer Science and Engineering

In the last several years, genetic algorithm (GA) has gained wide acceptance as a robust optimization algorithm in almost all areas of science and engineering. Polymer science and engineering is no exception. Researchers in this field have devoted considerable attention to the optimization of polymer productionusing objective functions and constraints that lead to products having desired material properties. Multiple-objective functions have been optimized simultaneously. An example is the minimization of the reaction time in a reactor (lower costs) while simultaneously minimizing the concentration of side products (that affect the properties of the product adversely). Several end-point constraints (equality or inequality) may also be present, as, e.g., obtaining polymer of a desired molecular weight. Again, this requirement stems from producing polymer having desired strength. Solving such problems usually result in Pareto sets. A variety of adaptations of GA have been developed to obtain optimal solutions for such complex problems. These adaptations can be used to advantage in other fields too. The applications of GA in areas of polymer science and engineering other than polymerization systems are few and far between, but this field is now maturing, and it is hoped that the future will see several newer applications.     

The Optimal Scheduling of a Reversing Strip Mill: Studies Using Multipopulation Genetic Algorithms and Differential Evolution

This article addresses a problem of minimizing the hot rolling time of an ingot, from a given initial thickness to a prescribed final one, subject to a number of system constraints. The idea is to determine the minimum possible odd number of passes, so that the ingot leaves in the same direction as it entered, which would ensure the necessary degree of reduction without violating the prescribed upper limits of the available torque and roll force. A maximum rolling velocity was also prescribed and additional restrictions were imposed on the rates of acceleration and deceleration inside the mill. The problem was solved by using a number of variants of genetic algorithms, including a multipopulation island model and differential evolution, besides the simple genetic algorithms. The results are compared with some earlier work based on a discrete dynamic programming technique, and a model based on an improved formulation is also presented.     

A Genetic Algorithm to Solve Capacity Assignment Problem in a Flow Network

Computer networks and power transmission networks are treated as capacitated flow networks. A capacitated flow network may partially fail due to maintenance. Therefore, the capacity of each edge should be optimally assigned to face critical situations—i.e., to keep the network functioning normally in the case of failure at one or more edges. The robust design problem (RDP) in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge’s failure. The RDP is known as NP-hard. Thus, capacity assignment problem subject to system reliability and total capacity constraints is studied in this paper. The problem is formulated mathematically, and a genetic algorithm is proposed to determine the optimal solution. The optimal solution found by the proposed algorithm is characterized by maximum reliability and minimum total capacity. Some numerical examples are presented to illustrate the efficiency of the proposed approach.     

On the equivalence of small batch assembly line balancing and lot streaming in a flow shop

In this article we compare two apparently dissimilar scheduling problems. The Small Batch Assembly Line Balancing problem is the process of dividing operations over multiple stations so as to produce a series of parts whose processing times are characterized by learning. The Lot Streaming in a Flow Shop problem is the process of splitting a given lot or job to allow overlap of successive operations in multi-stage production systems, thereby reducing the makespan of the corresponding schedule. We show that the two problems are formally equivalent. By exploring the mathematical equivalence of the two problems, a number of unexpected new results have been obtained.     

Effects of an Inaccurate Sorting Procedure on Optimal Procurement and Production Decisions in a Remanufacturing System

Under the uncertainty of market demand and quality of returns, sorting prior to disassembly is effective for timely obtaining information about the remanufacturability of used products. In this article, we assume that the remanufacturable fraction of used products is a random variable and introduce an inaccurate sorting procedure of used products prior to disassembly. Then, three two-stage optimization models are formulated to maximize the expected profits of a remanufacturer in a single period with used products and/or new parts as inputs to meet the stochastic market demand. Moreover, the article provides a case study to explore the optimal decisions under different scenarios and analyzes the effects of parameters, such as the unit disassembly cost, unit sorting cost, and proportion of sorting errors. Finally, the results indicate that whether remanufacturing with sorting is more profitable than that without sorting mainly depends on the sorting accuracy and the relative value between disassembly cost and sorting cost. When considering or not considering a long lead time of new parts, the effects of sorting errors on procurement policies are different. A long lead time will result in lower expected profits. Finally, the diverse types of classification errors have different influences on procurement policies and corresponding expected profit.     

Application of a Solidification Mathematical Model and a Genetic Algorithm in the Optimization of Strand Thermal Profile Along the Continuous Casting of Steel

This work presents an optimization method based on a genetic algorithm applied to continuous casting process. A simple genetic algorithm was developed, which works linked to a mathematical model permitting the determination of optimum values for the water flow rates in the secondary cooling zones. First, experimental data (industrial) were compared with simulated results obtained by the solidification mathematical model, to determine the metal/cooling heat transfer coefficients along the machine by the inverse heat conduction problem method. The industrial data concerning surface strand temperature were obtained by using infrared pyrometers along a continuous caster machine during casting of both SAE 1007 and 1025 steels. In a second step, these results were used by a numerical code based on a genetic algorithm for determining optimum settings of water flow rates in the different sprays zones, which are conducive to the best quality of the solidified strand. The simulations were carried out by analyzing the solidification process during continuous casting to attain metallurgical restrictions concerning the reheating of strand surface temperature and metallurgical length.