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High-quality product quality prediction is very important for iron and steel enterprises to ensure stable production. However, most existing prediction methods are manually designed learning models. These methods consider only macroscopic data while ...

Graph learning processes entire attributed graphs, posing computational and scalability challenges. This study proposes a joint entropy enhanced multi-objective Bayesian optimization approach (JE-MoBopt) for efficient graph pruning. By employing the non-...

Planning in the cold rolling is an increasingly important vital aspect in steel industry. In this study, we investigate a cold rolling planning problem derived from a steel company, which is characterized by multi-item considerations with sequence-...

The evolution of artificial intelligence has given rise to numerous machine learning (ML) models for predicting the structural properties of materials, expediting the process of new material development. However, many of these models have parameters set ...

This paper studies a crucial decision-making problem of order planning for a hot rolling process (OPHR). The OPHR problem is to determine the optimal sequence of orders, with the goals of minimizing penalties for earliness/tardiness, maximizing profits ...

In this paper, we propose a symmetry parallel search strategy that can be applied to permutation-related combinatorial optimization problems. The proposed symmetry parallel search strategy considers the parity of the permutations and decomposes the ...

This study addresses the quay crane scheduling problem (QCSP), which involves scheduling a fixed number of quay cranes to load and unload containers from ships in a maritime container terminal. The objective is to minimize the completion time while ...

Surface defect detection is an important task in the field of manufacturing, and dealing with imbalanced data is a challenge that has been addressed using methods such as anomaly detection and data augmentation. However, optical devices pose a ...

• A Generalized Well Neural Network via Template-Testing comparison is proposed.
• Dual-Attention and Recurrent Residual mechanisms proposed for noise elimination.
• Our approach generalizes to new batches via template collection in FMS.

The single-row facility layout problem (SRFLP) is concerned with arranging facilities along a straight line so as to minimize the sum of the products of the flow costs and distances among all facility pairs. SRFLP has rich practical applications and is ...

This study investigates a multi-objective batching decision problem that arises in batch annealing operations in the iron and steel industry. The problem concerns selecting coils from a set of waiting coils to be annealed to form ...

• A practical batching decision problem arising in steel industry is studied.
• A ...

MOEA/D (multi-objective evolutionary algorithm based on decomposition) decomposes a multi-objective optimization problem (MOP) into a series of single-objective sub-problems through a scalarizing function and a set of uniformly ...

• The MCDM process is incorporated into the knee-guided evolutionary algorithm framework.
• The knee and boundary regions are introduced to accelerate the convergence of the population.
• Change detection in dynamic environment is ...

Although dynamic multi-objective optimization problems dictate the evolutionary algorithms to quickly track the varying Pareto front when the environmental change occurs, the decision maker in the loop still needs to select a final optimal ...

The operation optimization problem of furnace process (OOPFP) is to obtain an optimal setting of furnace temperatures to make the reheated slabs have suitable temperature distribution with minimum energy consumption and oxide loss. Since the ...

We study integrated production- and delivery-scheduling problems that arise in practical make-to-order settings in several industries. In these problems, make-to-order products are first processed in a plant and then delivered to customer sites through ...

• Practical order rescheduling problem in steel industry is investigated.
• A MIP model is proposed considering original, deviation and equilibrium objective.
• A discrete differential evolution with new mutation and crossover is ...

Order management is a critical and complicated issue in the production process of iron and steel industry, since orders are the bridge between customers and semi-finished/final products in different units. Usually, the scheduling of orders is ...

In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this ...

We present a new analytical method for solving the problem of relative camera pose estimation. This method first calculates the homography matrix between two calibrated views using unknown coplanar points, and then, it decomposes the matrix to estimate ...

We consider several integrated production, inventory, and delivery problems that arise in a number of practical settings where customer orders have pre-specified delivery time windows. These orders are first processed in a plant and then delivered to ...

In recent years, the historical data during the search process of evolutionary algorithms has received increasing attention from many researchers, and some hybrid evolutionary algorithms with machine-learning have been proposed. However, the majority of ...

This paper deals with the energy consumption prediction in steel industry using particle swarm optimization-based back propagation Neural Network. More than ten types of energy including electricity, coal, power and gas are considered simultaneously. The ...