Recently, due to the growth in machine learning and data mining, for scheduling applications in China’s industrial intelligence, we are quite fortunate to witness a paradigm of evolutionary scheduling via learning, which includes a new tool of evolutionary transfer optimization (ETO). As a new subset in ETO, single-objective to multi-objective/many-objective optimization (SMO) acts as a powerful, abstract and general framework with wide industrial applications like shop scheduling and vehicle routing. In this paper, we focus on the general mechanism of selection that selects or gathers elite and high potential solutions towards gathering/transferring strength from single-objective problems, or gathering/transferring storms of knowledge from solved tasks. Extensive studies in vehicle routing problems with time windows (VRPTW) on well-studied benchmarks validate the great universality of the SMO framework. Our investigations (1) contribute to a deep understanding of SMO, (2) enrich the classical and fundamental theory of building blocks for genetic algorithms and memetic algorithms, and (3) provide a completive and potential solution for VRPTW.

To reduce logistics scheduling costs and energy consumption, this paper studies the slab allocation and hot-rolling scheduling integrated optimization problem that arises in practical iron and steel enterprises. In this problem, slabs are first allocated to orders and then sent to heating furnaces for heating; then, they are sent to a hot-rolling mill for rolling. A 0–1 integer programming model is established to minimize the attribute difference in the allocation cost between slabs and orders, the switching cost of hot-rolling processing, and waiting times after slabs reach rolling mills. Given the problem’s characteristics, an improved differential evolution algorithm using a real-number coding method is designed to solve it. Three different heuristic algorithms are proposed to improve the quality of solutions in the initial population. Multiple parent individuals participate in the mutation operation, which increases the population diversity and prevents the algorithm from falling into the local optimum prematurely. Experiments on 14 sets of real production data from a large domestic iron and steel plant show that our improved differential evolution algorithm generates significantly better solutions in a reasonable amount of time compared with CPLEX, the simulated artificial method, and the classical differential evolution algorithm, and it can be used by practitioners.

Single-objective to multi-objective/many-objective optimization (SMO) is a new paradigm in the evolutionary transfer optimization (ETO), since there are only “1 + 4” pioneering works on SMOs so far, that is, “1” is continuous and is firstly performed by Professors L. Feng and H.D. Wang, and “4” are firstly proposed by our group for discrete cases. As a new computational paradigm, theoretical insights into SMOs are relatively rare now. Therefore, we present a proposal on the fine brushworks of SMOs for theoretical advances here, which is based on a case study of a permutation flow shop scheduling problem (PFSP) in manufacturing systems via lenses of building blocks, transferring gaps, auxiliary task and asynchronous rhythms. The empirical studies on well-studied benchmarks enrich the rough strokes of SMOs and guide future designs and practices in ETO based manufacturing scheduling, and even ETO based evolutionary processes for engineering optimization in other cases.

Steel slabs are key in-process products in the steel production process. The slab allocation problem is to allocate slabs to suitable orders by considering complicated technical restrictions and multiple management requirements. In practice, thousands of slabs in multiple production lines should be allocated to orders. Due to the complexity and large scale of the problem, it takes planners a long time to make decisions, and the obtained allocation schemes are usually ineffective because of the myopic nature of the rule-based methods. In this paper, we present a learning-based solution method for solving practical slab allocation problem in multiple hot rolling lines (SAPML). We formulate the problem as an integer programming model, then a data-based method is adopted to evaluate the mismatching cost between slabs and orders. To effectively solve the large-sized problem, a learning-based decomposition strategy is proposed to decompose the original problem into several small-sized sub-problems. Then, a branch-and-price algorithm is proposed to optimally solve the sub-problems. To further speed up the solution process, a primal heuristics based on column generation is designed to solve the large-sized sub-problems. The solution methods have been implemented in a steel company and effectively increased slab utilization and reduced production cost.

As a new abstract computational model in evolutionary transfer optimization (ETO), single-objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather than the intermediate level for specific algorithms or the microscopic level for specific operators; this method aims to develop systems with a profound grasp of evolutionary dynamic and learning mechanism similar to human intelligence via a “decomposition” style (in the abstract of the well-known “Transformer” article “Attention is All You Need”, they use “attention” instead). To the best of our knowledge, it is the first work of SMO for discrete cases because we extend our conference paper and inherit its originality status. In this paper, by implementing the abstract SMO in specialized memetic algorithms, key knowledge from single-objective problems/tasks to the multi-objective core problem/task can be transferred or “gathered” for permutation flow shop scheduling problems, which will reduce the notorious complexity in combinatorial spaces for multi-objective settings in a straight method; this is because single-objective tasks are easier to complete than their multi-objective versions. Extensive experimental studies and theoretical results on benchmarks (1) emphasize our decomposition root in mathematical programming, such as Lagrangian relaxation and column generation; (2) provide two “where to go” strategies for both SMO and ETO; and (3) contribute to the mission of building safe and beneficial artificial general intelligence for manufacturing via evolutionary computation.

In this paper, we present a novel support vector machine learning method for multi-label classification in the steelmaking process. The steelmaking process involves complicated physicochemical reactions. The end-point temperature is the key to the steelmaking process. According to the initial furnace condition information, the end-point temperature can be predicted using a data-driven method. Based on the setting value of the temperature before tapping, multi-scale predicted errors of the end-point temperature can be calculated and divided into different ranges. The quality evaluation problem can be attributed to the multi-label classification problem of molten steel quality. To solve the classification problem, considering that it is difficult to capture nonlinear relationships between the input and output in linear models, we propose a novel support vector machine with robust low-rank learning, which has the characteristics of class imbalance without label correlations; a low-rank constraint is used to deal with high-order label correlations in low-dimensional space. Furthermore, we derive an accelerated proximal gradient algorithm and then extend it to handle the nonlinear multi-label classifiers. To validate the proposed model, experiments are conducted with real data from a practical steelmaking problem. The results show that the proposed model can effectively solve the multi-label classification problem in industrial production. To evaluate the proposed approach as a general classification approach, we test it on multi-label classification benchmark datasets. The results illustrate that the proposed approach performs better than other state-of-the-art approaches across different scenarios.