Warehouse Storage Layout Using Hybrid Quantum Optimization
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Solution Overview
Problem
Conventional computing methods struggle to efficiently optimize warehouse space utilization and storage time due to the complexity of warehouse optimization problems, which require significant computational resources and time to generate feasible solutions.
Innovation Solution
A hybrid quantum-classical method is employed to solve the warehouse optimization problem (WOP) by using quantum computing to maximize the number of items stored at ground level and minimize storage time and space, utilizing a constrained quadratic model (CQM) problem solved by a hybrid solver with quantum annealers and classical heuristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional computing methods are used to solve warehouse optimization problems, then the problem can be solved using existing technology, but the computational cost and time required become prohibitively high
Solution Approach 1:
The patent transforms the warehouse optimization problem into a Quadratic Unconstrained Binary Optimization (QUBO) formulation, changing the mathematical parameters and representation of the problem to make it suitable for quantum annealing. This parameter transformation allows the problem to be solved more efficiently by quantum computers, reducing computational time while maintaining solution reliability
Solution Approach 2:
The patent replaces conventional digital computing mechanisms with quantum computing mechanisms. By using quantum annealers to solve the QUBO-formulated warehouse optimization problem, the system achieves faster computation times compared to traditional classical computing methods, directly addressing the time loss issue
2Productivity
If quantum computing is used to solve the warehouse optimization problem, then computational time is reduced, but the complexity of implementing and operating quantum systems increases
Solution Approach 1:
The patent introduces a hybrid quantum-classical computing architecture where a quantum annealer serves as an intermediary component. The system includes a quantum processing unit that solves the QUBO formulation and a classical processing unit that handles problem formulation, result interpretation, and integration with warehouse management systems. This intermediary structure enables productivity improvement while managing device complexity through modular design
3Speed
If items are stored horizontally to minimize storage time, then storage speed is improved, but the occupied space increases
Solution Approach 1:
The patent implements dynamic optimization that adapts storage decisions based on real-time parameters such as item characteristics, location availability, and storage urgency. The quantum annealing solver dynamically determines the optimal configuration, allowing the system to switch between horizontal and vertical storage strategies to balance storage speed and space utilization according to current operational conditions
Data Source
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AI summary
A computer-implemented method for optimizing the disposition of items in one or more locations of a physical environment is provided. The method comprises: providing (132) a plurality of available locations in which items can be disposed; providing (131) a plurality of items to be located; defining (141) a problem associated to a WOP for storing as many items as possible at ground level, said problem being defined as a constrained quadratic model problem (CQM problem) to decide the location I in which each item i should be stored; solving (143) the CQM model problem by maximizing the amount of items i stored at the ground level of the available set of locations, thus obtaining partial solutions; completing (145) each partial solution (144) by stacking, if required, all items that have been left unstacked; eliminating (149) repeated and unfeasible solutions, thus obtaining a set of N feasible solutions to the WOP; selecting (16) one or more initial solutions of the N feasible solutions and optimizing (18) them, providing a most optimal one; wherein solving (143) the CQM problem is done at least partially in a quantum computer.