Multi-day Travel Planning Using Ising Machines for Real-world Applications

Bao, Siya; Tawada, Masashi; Tanaka, Shu; Togawa, Nozomu

doi:10.1109/itsc48978.2021.9564593

Cited by 7 publications

(16 citation statements)

References 13 publications

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“…In this section, we apply the proposed subQUBO annealing to the TPP. We compare our method with the existing methods [27], [30], proposed recently: 1) Bao et al [30] is a one-step annealing method for the TPP. If the annealing machine has enough spins and…”

Section: Methodsmentioning

confidence: 99%

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Trip Planning Based on subQUBO Annealing

Noguchi,

Fukada,

Bao

et al. 2023

IEEE Access

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The trip planning problem (TPP) can be formulated as a combinatorial optimization problem that searches for the best route to visit a series of landmarks and hotels. Meanwhile, Ising machines have attracted attention due to their efficiency in solving combinatorial optimization problems. The Ising machines solve the combinatorial optimization problems by transforming the problems into quadratic unconstrained binary optimization (QUBO) models. However, the possible input QUBO size of current Ising machines is quite limited. Thus, it is hard to directly embed a large-scale TPP onto the current Ising machines. In this paper, we propose a novel subQUBO annealing method based on the combined variable selection method to solve the TPP. The proposed method finds a quasi-optimal solution to a large problem by repeatedly partitioning the original QUBO model into small subQUBOs that can be embedded onto the Ising machine. Specifically, to construct a subQUBO, we select variables from the original QUBO model, which have small deviation values. Further, we select variables randomly from the original QUBO model, so as not to fall into the local optimum. We have conducted an evaluation experiment using Ising machines on TPP and confirmed that the proposed method outperforms the state-of-the-art methods in terms of POI satisfaction and POI cost.

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Section: Methodsmentioning

confidence: 99%

“…In this section, we describe how to map the TPP to the QUBO model based on [30]. We define two type of binary variables x i,j,k and y i,k as Eq.…”

Section: B Qubo Model Mapping Of Tppmentioning

confidence: 99%

Trip Planning Based on subQUBO Annealing

Noguchi,

Fukada,

Bao

et al. 2023

IEEE Access

Self Cite

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“…Trip planning studies nowadays aim at constructing routes that optimize almost any user's requirements such as point-ofinterest (POI) satisfaction and POI cost, where POI refers to a point location that a user wants to visit or he/she considers useful. For example, Bao et al [3] considered POIs' features including satisfaction, expense, and duration time. On the other hand, besides users' preferences and trip time, Isoda et al [4] additionally evaluated a POI through its static features such as geographical positions and business hours.…”

Section: A Trip Planning Problemmentioning

confidence: 99%

“…Bao et al [3] and Isoda et al [4] picked up in Section I-A fulfilled the first condition by simultaneously optimizing multiple objectives, but they were insufficient for fulfilling the second condition because there was no direct use of past-trip records in planning. Also, Kasahara et al [5] and Lu et al [9] fulfilled the second condition, but they did not satisfy the first condition due to the lack of consideration of multi-objective functions such as minimizing POI costs and maximizing POI satisfaction.…”

Section: B Related Workmentioning

confidence: 99%

Multi-Objective Trip Planning Based on Ant Colony Optimization Utilizing Trip Records

Saeki

Bao

Takayama³

et al. 2022

IEEE Access

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Trip planning services have been developed along with tourism promotion and information technology evolutions, where we must construct trip routes that simultaneously optimize multi-objective functions such as trip expenses and user satisfaction. Moreover, utilization of past-trip records is essential, because similarities to past-trip records well reflect users' general preferences and tendencies during trip planning. In this paper, we propose a multi-objective trip planning method using ant colony optimization (ACO). By effectively using the pheromones in ACO, we can construct trip routes similar to trip records stored before and the constructed route can reflect users' general preferences. In addition, we vary ants' behaviors in ACO corresponding to various objective functions and hence we can obtain multi-objective trip routes naturally. Experimental results demonstrated that our method outperforms the baseline methods in terms of point-of-interest (POI) satisfaction, POI cost, and past-trip similarity. We also conducted a user study, which clearly indicates that our method obtains high scores through various user questionnaires.INDEX TERMS multi-objective trip planning problem, ant colony optimization, past-trip records, pheromone updating, point-of-interest (POI)

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“…Introduction: Oscillator Ising Machines (OIMs) offer a markedly different paradigm for solving combinatorial optimization-a problem class that finds increasing importance in areas from machine learning (e.g. training of Boltzmann machines [1]) to applications in logistics such as route planning [2]. Despite the ample use cases of such problems, solving them efficiently using digital computers and algorithms remains a challenge [3].…”

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confidence: 99%

A note on analyzing the stability of oscillator Ising machines

Bashar,

Lin,

Shukla

2023

Electronics Letters

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The rich non‐linear dynamics of the coupled oscillators (under second harmonic injection) can be leveraged to solve computationally hard problems in combinatorial optimization such as finding the ground state of the Ising Hamiltonian. While prior work on the stability of the so‐called Oscillator Ising Machines (OIMs) has used the linearization method, in this letter, the authors present a complementary method to analyze stability using the second‐order derivative test of the energy/cost function. The authors establish the equivalence between the two methods, thus augmenting the tool kit for the design and implementation of OIMs.

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Multi-day Travel Planning Using Ising Machines for Real-world Applications

Cited by 7 publications

References 13 publications

Trip Planning Based on subQUBO Annealing

Trip Planning Based on subQUBO Annealing

Multi-Objective Trip Planning Based on Ant Colony Optimization Utilizing Trip Records

A note on analyzing the stability of oscillator Ising machines

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