Takashi Hasuike

Effective portfolio diversification remains a central challenge in quantitative asset management. In this study, we propose a data-driven framework based on the predict-then-optimize (PO) paradigm, which combines return forecasting with portfolio allocation in a sequential manner. The forecasting module employs DLinear, a lightweight deep learning model, to capture temporal patterns in historical asset returns. Based on the predicted returns, a portfolio allocation strategy is constructed by optimizing the Sharpe ratio, allowing the model to generate adaptive portfolio weights under changing market conditions. This PO-based framework provides a practical way to connect predictive modeling with downstream decision-making. Empirical results across three asset universes demonstrate that the proposed approach achieves competitive performance under different settings, indicating its potential applicability in real-world portfolio management.

The DEA-R model, which integrates data envelopment analysis (DEA) with ratio analysis, allows for the evaluation of efficiency using ratio data. One main strength of DEA is its ability to provide concrete improvement targets for inefficient decision-making units (DMUs). However, identifying these targets within the DEA-R framework is particularly challenging because of the inherent characteristics of ratio data. To address this issue, this study proposes a novel approach for identifying concrete improvement targets for inputs or outputs within the DEA-R framework. Specifically, we construct the DEA-R efficient frontier based on a unique concept and develop an approach to identify improvement targets that lie explicitly on this frontier. This ensures that all the identified targets are DEA-R efficient, thereby guaranteeing their rationality and validity. Furthermore, the constructed frontier enables the setting of flexible improvement targets under various scenarios, thereby enhancing the practicality and adaptability of the proposed approach.

This study examines the relationship between a multiplicatively weighted (MW-) Voronoi diagram and the Huff model. A mathematical comparison demonstrates that the models are structurally equivalent when the Huff model is deterministic and the distance decay parameter λ takes a specific value. This theoretical finding was empirically validated using real-world e-commerce order assignment data. The experiments demonstrate the distinct strengths of each model. The Huff model enables the flexible balancing of competing objectives through parameter adjustment, whereas the MW-Voronoi diagram provides geometric clarity in the interpretation of territories. We conclude that the selection of the two models should be guided by the problem objectives, depending on whether probabilistic flexibility or deterministic spatial partitioning is required.

Cold-chain logistics plays a crucial role in maintaining the quality of temperature-sensitive products. However, it generates high energy consumption and carbon emissions due to refrigeration and complex routing operations. Therefore, this study proposes a dual-objective optimization model for low carbon cold-chain vehicle routing that simultaneously minimizes total logistics and carbon emission costs. The model comprehensively integrates transportation, refrigeration, cargo damage, and holding costs, as well as emissions from fuel consumption and refrigeration energy use. To solve the proposed model, two multi-objective evolutionary algorithms, the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO), were employed and compared under the same experimental framework. Numerical experiments based on Solomon benchmark instances demonstrate that both algorithms can effectively generate Pareto-optimal solutions. NSGA-II shows superior convergence and better diversity maintenance, whereas MOPSO achieves faster early-stage convergence and stronger global exploration. The comparative analysis, supported by quantitative performance metrics and visual results, confirms the reliability of the proposed optimization model and highlights the complementary characteristics of the two algorithms. The findings provide theoretical and practical insights for designing sustainable, cost-efficient, and environmentally friendly cold-chain logistics systems.

Improvements in return forecast accuracy do not always lead to proportional improvements in portfolio decision quality, especially under realistic trading frictions and constraints. This paper adopts the Smart Predict--then--Optimize (SPO) paradigm for portfolio optimization in real markets, which explicitly aligns the learning objective with downstream portfolio decision quality rather than pointwise prediction accuracy. Within this paradigm, predictive models are trained using an SPO-based surrogate loss that directly reflects the performance of the resulting investment decisions. To preserve interpretability and robustness, we employ linear predictors built on return-based and technical-indicator features and integrate them with portfolio optimization models that incorporate transaction fee, turnover control, and regularization. We evaluate the proposed approach on U.S. ETF data (2015--2025) using a rolling-window backtest with monthly rebalancing. Empirical results show that decision-focused training consistently improves risk-adjusted performance over predict--then--optimize baselines and classical optimization benchmarks, and yields strong robustness during adverse market regimes (e.g., the 2020 COVID-19). These findings highlight the practical value of the Smart Predict--then--Optimize paradigm for portfolio optimization in realistic and non-stationary financial environments.

Data envelopment analysis (DEA) is a powerful approach for evaluating the relative efficiency of decision-making units with multiple inputs and outputs. Integrating DEA with ratio analysis has become essential because of the increasing prevalence of ratio data (e.g., return on assets) in practical applications. This study develops a novel DEA-R model and the RAM-R model, which combines the well-established range-adjusted measure (RAM) with ratio analysis. The model effectively handles ratio data, accommodates negative values, and accounts large variations across indicators, thereby enhancing flexibility and robustness in efficiency evaluation. A case study of 93 Japanese banks compares the RAM-R model with the RAM and another slacks-based DEA-R (the slacks-based measure-R and SBM-R) models using ratio data to demonstrate its effectiveness in evaluating efficiency.

This study proposes a method for processing unordered datasets using deep learning techniques and introduces a model capable of simultaneously predicting three context effects and selections. “Context effects” refer to dramatic changes in the selection situation caused by the introduction of new options into a set of choices. Five experiments were conducted using different product-selection data. Each experiment focused on predicting context effects, selections, predictions under different context effects, predictions for different types of products, and predictions that considered context effects. The results demonstrated that the proposed model is suitable for classification prediction tasks in complex situations. The more complex the model and the larger the amount of data, the better the results. This study extends the application of neural networks to multi-attribute decision-making problems and contributes to the selection of decision-making models. It also improves the prediction accuracy and analyzes the impact of context effects on choices.

This paper proposes a concrete ticket-sales strategy in the live entertainment market by verifying the effectiveness of hybrid sales, which combine auction sales and uniform pricing methods, based on actual performances held in Japan. To conduct the social simulation of hybrid ticket-sales, we calculated the seat utility values of a particular theater and estimated the reserve price distribution of customers using transaction price data from ticket resale platforms. The results showed that while customer utility and revenue increased when auction sales seats were added, the introduction of more than a certain number of auction sales seats beyond the acceptable level of auction sales decreased customer utility. Furthermore, by allocating additional revenue from auction sales to adjust the uniform ticket price appropriately, we observed that setting approximately 40% of the total seats (700 seats) for auction is optimal for maximizing customer utility. This sales method ensures accessibility for a wide range of customers by providing premium seating to high-paying customers while maintaining affordable pricing for uniform pricing sales. Additionally, it was observed to be an effective sales system that can control illicit profits in the resale market and redistribute the profits from high resale prices to consumers and organizers.

Smallholders play a vital role in global agriculture but face challenges such as resource limitations and market uncertainties. Contract farming (CF) and cooperative markets provide opportunities to smallholders to address these challenges. In this context, this study develops mathematical planning models for fresh produce supply chains (FPSCs) in two scenarios involving CF and cooperative markets with the objective of maximizing profits of smallholders. Results of computational experiments and sensitivity analysis reveal that CF has greater potential than cooperative markets to mitigate price volatility, thereby offering a stable income source to smallholders. This study provides valuable insights into CF and cooperative market practices, contributing to the sustainability of FPSCs and improving smallholders’ livelihoods.