As global demand for logistics continues to grow, improving the efficiency of warehouse operations has become increasingly important. While many processes in logistics warehouses have been automated, the receiving area still relies heavily on manual work. Therefore, optimizing this area is essential. When optimizing operations in a logistics warehouse, various problems must be considered, such as layout design and worker assignment. Previous research has typically focused on these problems individually. However, because they are closely related, jointly optimizing them can further improve overall efficiency. In this study, we focus on the receiving area and conduct joint optimization of layout design and worker assignment. Specifically, we extend and apply a black-box optimization method called FMQA, which combines Factorization Machines (FM) with Quantum Annealing (QA). By using Factorization Machine regression, we build an objective function from simulator data. We then use Quantum Annealing to minimize it and improve the receiving area. A comparison with an actual warehouse environment, using the multi-agent simulator, shows that our approach can reduce mean package processing time by up to 22.1%. This result demonstrates the effectiveness of joint optimization of layout design and worker assignment.
As global demand for logistics continues to grow, improving the efficiency of warehouse operations has become increasingly important. While many processes in logistics warehouses have been automated, the receiving area still relies heavily on manual work. Therefore, optimizing this area is essential. When optimizing operations in a logistics warehouse, various problems must be considered, such as layout design and worker assignment. Previous research has typically focused on these problems individually. However, because they are closely related, jointly optimizing them can further improve overall efficiency. In this study, we focus on the receiving area and conduct joint optimization of layout design and worker assignment. Specifically, we extend and apply a black-box optimization method called FMQA, which combines Factorization Machines (FM) with Quantum Annealing (QA). By using Factorization Machine regression, we build an objective function from simulator data. We then use Quantum Annealing to minimize it and improve the receiving area. A comparison with an actual warehouse environment, using the multi-agent simulator, shows that our approach can reduce mean package processing time by up to 22.1%. This result demonstrates the effectiveness of joint optimization of layout design and worker assignment.
The manufacturing industry encounters numerous optimization problems, one of which is the optimization of storage location assignment (OSLA) problem in logistics. OSLA is a combinatorial optimization problem focused on improving the efficiency of picking operations in logistics centers. We explore quantum annealing (QA) as a potential solution to combinatorial optimization problems and investigate its applicability to the OSLA. The objective function for this optimization is the average travel distance of workers to their assigned destinations. However, this value is derived by solving the Traveling Salesman Problem for multiple orders, which is itself a combinatorial optimization problem. Therefore, it cannot be analytically represented in a quadratic unconstrained binary optimization form. To address this limitation, we employed black-box optimization with annealing (BBO-Annealing), which combines a surrogate model with an annealing algorithm, an approach that has recently gained attention in applied research involving QA. To evaluate the effectiveness of quantum computing, we compared results obtained using simulated annealing (SA) with those obtained using QA. Additionally, to assess the optimization performance of our proposed method, we compared it with a genetic algorithm (GA) that did not utilize a surrogate model of the objective function. QA demonstrated a higher probability of finding the optimal solution (33.3% vs 26.7% with SA). However, the optimization performance of GA surpassed that of the proposed method. Our analysis suggests that the relatively lower performance of our method was primarily attributable to the strong influence of constraints. The optimization performance can be improved by incorporating methods that consider the uncertainty of surrogate model predictions such as the lower confidence bound.
Black-box optimization (BBO) is used in materials design, drug discovery, and hyperparameter tuning in machine learning. The world is experiencing several of these problems. In this review, a factorization machine with quantum annealing or with quadratic-optimization annealing (FMQA) algorithm to realize fast computations of BBO using Ising machines (IMs) is discussed. The FMQA algorithm uses a factorization machine (FM) as a surrogate model for BBO. The FM model can be directly transformed into a quadratic unconstrained binary optimization model that can be solved using IMs. This makes it possible to optimize the acquisition function in BBO, which is a difficult task using conventional methods without IMs. Consequently, it has the advantage of handling large BBO problems. To be able to perform BBO with the FMQA algorithm immediately, we introduce the FMQA algorithm along with Python packages to run it. In addition, we review examples of applications of the FMQA algorithm in various fields, including physics, chemistry, materials science, and social sciences. These successful examples include binary and integer optimization problems, as well as more general optimization problems involving graphs, networks, and strings, using a binary variational autoencoder. We believe that BBO using the FMQA algorithm will become a key technology in IMs, including quantum annealers.
Abstract The warehouse layout problem (WLP), also known as warehouse slotting problem, aims to determine the assignment of products to storage locations while minimizing the total travel distance for picking operations. It is combinatorial in nature and well-known to be NP-hard. To obtain high-quality solutions to medium and large WLP instances efficiently remains a computational challenge, which has motivated our work on applying quantum optimization algorithms for this problem. We reformulate the classical quadratic assignment programming (QAP) model of the WLP to a quadratic unconstrained binary optimization (QUBO) model. Our QUBO model avoids adding slack variables and reduces additional overheads as in the typical QUBO reformulation approach, which facilitates the application of the D-Wave Leap Hybrid solver. Computational study is performed on a use case with 30 stock-keeping-units (SKUs) and 32 storage slots, for which the QUBO formulation has 960 binary decision variables. We show that the D-Wave Leap Hybrid solver finds better solution to the base scenario than the exact IBM CPLEX mixed-integer quadratic programming (MIQP) solver operating under a 60-second wall-clock limit, one-tenth of the 600-second budget allocated to CPLEX in this experimental design. A sensitivity analysis across 30 scenarios, generated by crossing five annual pick volume profiles with six co-picking matrix density levels (0.5 to 1.0), shows that the D-Wave hybrid solver outperforms CPLEX MIQP solver in 26 of 30 cases, with solution quality improvements of 0.4 to 12.1%. D-Wave’s advantage generally increases as the density of co-picking matrix decreases. These results demonstrate quantum utility for WLP and motivate further investigation of hybrid quantum-classical methods for combinatorial logistics optimization at industrial strength scales.
The earlystage of factory layout planning has a direct effect on the operating costs, efficiency, and productivity of manufacturing systems. Conventional automated planning approaches often face a trade-off between solution quality and computational effort. Recent studies have investigated the potential of quantum annealing (QA) for solving factory layout planning problems. However, existing studies have mainly focused on individual QA-based formulations, whereas current hybrid QA solvers support multiple model types that combine QA and classical methods. To explore the potential of QA for factory layout planning, this paper benchmarks four QA based approaches implemented with the corresponding solvers, including an iterative binary quadratic model (IBQM), a hybrid discrete quadratic model (HDQM), a hybrid constrained quadratic model (HCQM), and a hybrid nonlinear model (HNLM). To improve scalability, an iterative decomposition strategy is developed for IBQM, while a candidate selection procedure is introduced for HDQM. The approaches are benchmarked against genetic algorithm, particle swarm optimization, and a hybrid tabu search and simulated annealing approach on 14 layout instances with different layout sizes, geometric complexity, spatial restrictions, and transport route configurations. The comparative analysis considers solution quality, computation time, and scalability. The results show that QA-based approaches achieve competitive solution quality and substantial computational advantages, reducing computation time by up to 94.51% relative to the classical reference methods. Among them, IBQM obtains the best solution quality in 78.6% of the instances, with a maximum improvement of 17.41% associated with an 81.34% reduction in computation time. These findings provide a basis for their further application in industrial layout planning.
Quantum annealing (QA) is a heuristic optimization method for efficiently solving complex combinatorial problems with discrete variables. Thus, quantum annealing is proper for optical designs with multiple materials. The possible designs with multiple materials and various geometries form a huge solution space that is challenging for optimization. Due to their wave-like behavior, optical systems are typically coupled over long distances and can interfere with one another. Mathematically, this leads to high-order couplings in quantum annealing that are not supported by current quantum hardware. Factorization machines (FMs) can reduce high-order terms and provide an effective quadratic form suitable for quantum hardware. In this work, we adopt the previously proposed factorization machines with quantum annealing (FMQA) to design selective thermal absorbers. The FQMA, consisting of iterations between QA and FMs, has many hyperparameters, such as the initial inputs, latent factors, and selection of trained data. We study how these hyperparameters improve the convergence of the FMQA. We find that a balance between the initial random samplings and the sampling by QA is crucial for optimization. The principle underlying the entire optimization process of the FMQA requires a deliberate balance between depth and breadth, alongside an adiabatic approach. We design selective thermal absorbers to only absorb light with wavelengths ranging from 8 to 13 microns. We design the absorbers with silicon dioxide, PMMA, and silver. The FMQA with the optimized hyperparameters shows stably better figures of merit.
In planning and scheduling, large-scale optimization problems often have complex limits and a lot of factors, which makes them hard to answer with standard methods. Quantum annealing has become a hopeful way to deal with these problems because it can quickly look into a lot of possible solutions. This essay looks into how quantum annealing might be used to solve optimization problems in transportation and scheduling, with a focus on how it could be used for route planning, inventory management, and arranging workers. To do these optimization jobs, quantum annealing is used. This is a quantum computing method that gets the global minimum of a cost function. We use the D-Wave quantum annealer in this method. It turns the problem's limits and objective function into a quadratic unconstrained binary optimization (QUBO) model. The quantum annealer then works on this model to find the best answers. The suggested method combines traditional preparation methods with quantum annealing to get the best results for big schedule and transportation issues. We use quantum annealing to solve difficult multi-objective optimization problems, like reducing shipping time, cost, and resource use all at the same time. Preliminary results show that quantum annealing can make solutions much better and use computers much more efficiently than traditional methods for optimizing supplies and schedules. Quantum annealing works especially well with problems that have a lot of dimensions. It can provide scalable answers that sometimes work better than standard methods.