The paper studies the single-machine scheduling problem with sequence-dependent setup times under dynamic environment. In this problem, jobs arrive over time and all the information on a job is unknown in advance. The objective is to find a feasible schedule such that the maximum lateness is minimised. This problem represents a very important production scheduling problem but remains under-represented in the literature. To solve the problem, we propose a dynamic differential evolution algorithm that considers the new environment and the previous environment simultaneously. The performance of the algorithm is enhanced by two heuristics for generating efficient initial solutions, a local search procedure and a speed-up method. To evaluate its performance, the proposed algorithm is tested on 1000 instances from the literature. The computational results demonstrate that the proposed algorithm is highly effective as compared to the methods known from the literature.

With regard to the characteristics of long process, multi-units, cross-logistics and batch production mode, the order planning problem at the steelmaking shop is studied with consideration of the plant-wide process logistics balance. The problem is to allocate the orders to the continuous casters over plan periods such that the semi-finished materials produced by the steelmaking process can be delivered to the downstream hot-rolling and cold-rolling processes on time at required volume. Based on the detailed analysis of unit layout characteristics of the plant-wide process and the order composition information, a hierarchical planning method is proposed to decompose the large-scale planning problem into a set of sub-problems that are sequential solved. For the order batching subproblem, we develop an integer programming model to consolidate orders with small demand into cast-lots by considering the key batching rules. The model is efficiently solved by the optimization solver of CPLEX. For the cast-lot scheduling subproblem, a novel mixed integer programming model is developed by considering the technological constraints, the facility capacity constraints, the logistics balance constraints, the inventory constraints, and the due date constraints. An improved differential evolution algorithm is proposed to solve the cast-lot scheduling subproblem. The main improvement strategy is that the superior individuals within an external archive are further enhanced through a variable neighborhood search mechanism. Computational results on random and practical instances of different scale problems verify the effectiveness of the proposed method.

Shanghai Baoshan Iron and Steel Complex (Baosteel) is China’s largest and the world’s third-largest steel company. In 2005, our research team was tasked with developing advanced operations research-based planning tools to improve the operational efficiency of Baosteel’s Shanghai plant. In the following six years, we developed novel optimization algorithms and tailored metaheuristics, and implemented four decision support systems (DSSs) to replace the manual planning methods at the Shanghai plant. The DSSs have brought scientific operations management to Baosteel and transformed the plant’s production and final-product delivery operations. Baosteel estimates that from 2007 to 2012, they provided a cumulative economic benefit of 77 million. Based on their current usage at this plant, they also estimate that these DSSs will continue to provide an annual economic benefit of 20 million, which represents a 17 percent improvement of Baosteel’s information technology and operations management capability. They have also reduced Baosteel’s carbon dioxide emissions by 585,770 tons annually.

This paper studies a multi-stage multi-product production and inventory planning problem with random yield derived from the cold rolling process in the steel industry. The cold rolling process has multiple stages, and intermediate inventory buffers are kept between stages to ensure continuous operation. Switching products during the cold rolling process is typically very costly. Backorder costs are incurred for unsatisfied demand while inventory holding costs are incurred for excess inventory. The process also experiences random yield. The objective of the production and inventory planning problem is to minimize the total cost including the switching costs, inventory holding costs, and backorder costs. We propose a stochastic formulation with a nonlinear objective function. Two lower bounds are proposed, which are based on full information relaxation and Jensen’s inequality, respectively. Then, we develop two heuristics from the proposed lower bounds. In addition, we propose a two-stage procedure motivated by newsvendor logic. To verify the performance of the proposed bounds and heuristics, computational tests are conducted on synthetic instances. The results show the efficiency of the proposed bounds and heuristics.

Molten iron transportation in an iron and steel enterprise involve moving loaded torpedo cars with hot molten iron and empty torpedo cars by locomotives between blast furnaces and steelmaking plants. This paper considers the locomotive scheduling problem, one of the most complex routing problems where both pickup and delivery operations are included. It consists in finding one feasible schedule for each locomotive in order to satisfy all moving requests and in such a way that the concerned costs are minimized. We consider a set-partitioning model of such problem whose columns represent locomotive schedules. We then apply a column generation algorithm for solving the LP relaxed model and a branch and bound algorithm for obtaining integer solutions. Computational experiments demonstrate that the proposed algorithm is capable of solving problems with industrial size to optimality within a reasonable computation time.