This paper investigates a constrained excavator procurement problem (RCEPP), in which the budget is restricted and the performance of the excavators in collaboration with the pre-purchased mining trucks is considered. The RCEPP consists of deciding the purchase quantity of excavators to maximize the profit from the mining system composed of the imported excavators and the existing mining trucks. A mixed integer programming model (MIP) is established to address the RCEPP. In line with the specifications of quantum computers, a quadratic unconstrained binary optimization model is developed via reformulating the MIP. The scheduling scheme is coded into the ground state of the Hamiltonian operator, and the problem is solved using an optical coherent Ising machine (CIM). The experimental results indicate that, compared to traditional computation methods, quantum computers are capable of achieving better global optimal solutions. It has great application potential.
This paper investigates a constrained excavator procurement problem (RCEPP), in which the budget is restricted and the performance of the excavators in collaboration with the pre-purchased mining trucks is considered. The RCEPP consists of deciding the purchase quantity of excavators to maximize the profit from the mining system composed of the imported excavators and the existing mining trucks. A mixed integer programming model (MIP) is established to address the RCEPP. In line with the specifications of quantum computers, a quadratic unconstrained binary optimization model is developed via reformulating the MIP. The scheduling scheme is coded into the ground state of the Hamiltonian operator, and the problem is solved using an optical coherent Ising machine (CIM). The experimental results indicate that, compared to traditional computation methods, quantum computers are capable of achieving better global optimal solutions. It has great application potential.
With the development of quantum computing, traditional combinatorial optimization problems have found new solving methods. This paper studies the excavator and mining truck matching optimization problem based on the QUBO model, aiming to optimize equipment configuration and operational plans by maximizing long-term profit. An integer optimization model is first established and converted into a QUBO model, which is solved using the simulated annealing algorithm. Furthermore, quantum computing is explored through the Coherent Ising Machine (CIM) simulator to examine its application in large-scale combinatorial optimization. The results show that the CIM simulator outperforms traditional algorithms in both accuracy and speed, significantly enhancing computational efficiency. Based on simulated annealing and quantum computing, an optimized procurement plan is provided, yielding a maximum total profit of 500 million yuan. The experiments also demonstrate the potential of quantum computing in optimizing equipment configuration in smart mining, driving the intelligent evolution of the field.
Quantum annealing emerges as a promising approach for tackling complex scheduling problems such as the resource-constrained project scheduling problem (RCPSP). This study represents the first application of quantum annealing to solve the RCPSP, analyzing 12 well-known mixed integer linear programming (MILP) formulations and converting the most qubit-efficient one into a quadratic unconstrained binary optimization (QUBO) model. We then solve this model using the D-wave advantage 6.3 quantum annealer, comparing its performance against classical computer solvers. Our results indicate significant potential, particularly for small to medium-sized instances. Further, we introduce time-to-target and Atos Q-score metrics to evaluate the effectiveness of quantum annealing and reverse quantum annealing. The paper also explores advanced quantum optimization techniques, such as customized anneal schedules, enhancing our understanding and application of quantum computing in operations research.
Efficient production planning is essential in modern manufacturing to improve performance indicators such as lead time and to reduce reliance on human intuition. While mathematical optimization approaches, formulated as job shop scheduling problems, have been applied to automate this process, solving large-scale production planning problems remains computationally demanding. Moreover, many practical scenarios involve conflicting objectives, making traditional scalarization techniques ineffective in finding diverse and useful Pareto-optimal solutions. To address these challenges, we developed a quantum-classical hybrid algorithm that decomposes the problem into two subproblems: resource allocation and task scheduling. Resource allocation is formulated as a quadratic unconstrained binary optimization problem and solved using annealing-based methods that efficiently explore complex solutions. Task scheduling is modeled as a mixed-integer linear programming problem and solved using conventional solvers to satisfy detailed scheduling constraints. We validated the proposed method using benchmark instances based on foundry production scenarios. Experimental results demonstrate that our hybrid approach achieves superior solution quality and computational efficiency compared to traditional monolithic methods. This work offers a promising direction for high-speed, multi-objective scheduling in industrial applications.
This paper analyzes the equipment configuration of the smart mine, builds the corresponding Quadratic Unconstrained Binary Optimization (QUBO) model according to different application scenarios, and analyzes and solves the QUBO model by using the simulated annealing solver and the CIM simulator, to explore the optimal operation scheme of the smart mine under different scenarios. Firstly, under the budget constraint, the objective function is to maximize the sum of the discounted long-term profits of various excavators, and at the same time to meet the constraints such as budget limitation and type limitation, the mathematical model is established and transformed into the QUBO model, and then analytically solved to derive the optimal operation plan of the smart mine under this scenario. Subsequently, the mathematical model is reconstructed to derive the optimal operation scenario and expected profit, taking into account the factor of years of life. In terms of the solution method, kaiwu sdk shows good performance for small-scale problems, but it is limited by memory and less effective when facing large-scale problems. Therefore, this paper proposes the subQUBO method, which combines quantum and classical computing, as a potential improvement path. This study not only provides an optimization solution for the operation of smart mines, but also provides new ideas for solving similar large-scale optimization problems.
Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.
Flexible job-shop scheduling problems (FJSPs) represent one of the most complex combinatorial optimization challenges. Modern production systems and control processes demand rapid decision-making in scheduling. To address this challenge, we propose a quantum computing approach for solving FJSPs. We propose a quadratic unconstrained binary optimization (QUBO) model to minimize the makespan of FJSPs, with the scheduling scheme encoded in the ground state of the Hamiltonian operator. The model is solved using a coherent Ising machine (CIM). Numerical experiments are conducted to evaluate and validate the performance and effectiveness of the CIM. The results demonstrate that quantum computing holds significant potential for solving FJSPs more efficiently than traditional computational methods.