Abstract The increasing frequency of supply chain disruptions has made resilience and digital integration essential. Indeed, firms that succeeded during the COVID-19 pandemic demonstrated advanced digital and supply chain resilience capabilities, enabling them to mitigate adverse consequences. Addressing this forefront topic in operations management, the article introduces an innovative max–min best–worst method to guide digital transformation in global supply chains. This approach aims to maximize resilience capabilities and minimize the impact of supply chain disruptions. The method is examined through an empirical case study of a highly digitalized furniture company. The findings emphasize the importance of redundancy, a robust risk management culture, and adaptability, with raw material delays and cyberattacks being the most significant threats. Moreover, prioritizing the adoption of big data, digital platforms, blockchain, and advanced control systems is crucial for addressing ongoing geopolitical disruptions. Finally, the study discusses both theoretical and managerial implications and suggests avenues for future research.

Abstract Repeat sales index methodologies are prevalent for computing indices, largely owing to their simplicity, minimal data requirements, and reliable approach to addressing real estate heterogeneity. However, this method is not without flaws, being restricted to properties sold at least twice within the sample period and demonstrating high sensitivity to the size of the dataset. In this paper, we introduce a stable and robust factorial approach that holds firm even when the data quantity is sparse due to factors such as frequent publications, thin markets, data unavailability, or a drastic drop in the number of transactions. We demonstrate that trend and volatility estimates tend to be biased with the standard repeat sales approach, but remain unbiased with our factorial method, regardless of the size of the dataset. The factorial approach exhibits greater robustness in relation to transaction numbers.

Abstract Although the mainstream literature resorts to numerical iterations for an optimization task, the research path toward exact solutions has recently become clearer; for example, the first, exact, and complete solution to quadratic programming (basis of optimization) has been reported. This paper follows the path and focuses on Problems (1) and (2). (1) We analyze the consistency between the two methodologies to the core; specifically, the analysis clarifies the common ground that supports both Numerical and Exact Optimization. The common ground is equality-constrained quadratic programming. Hence, the theoretical results help to revisit classical problems in, for instance, financial economics. (2) We selectively demonstrate the benchmark problem of Portfolio Selection and formulate the closed-form solution corresponding to positive semidefinite covariance matrices in general, which aligns with Markowitz’s vision and the diversification principle in investment. Financial insights highlight the generalization of Minimum-Variance Set (resp., Efficient Frontier), the geometric structure of which is consistently and normally a parabola (resp., the upper half of a parabola) with respect to the general risk measure. Noting the ubiquitous nature of optimization, various communities have been calling for theoretical support of Problem (1), such as consistency analysis and comparison study. In particular, the operations research community describes solving the problems “the start of a substantial study”. The contributions of this paper are: (i) solution to Problem (1) and its byproduct that helps in analyzing (2); and (ii) unified, closed-form parameterization of Minimum-Variance Set/Efficient Frontier – which theoretically complements the literature gap associated with singular covariance matrices – and its computational enhancement that enriches practical potential. Validations and evaluations are in terms of pedagogical and real-world examples, as well as numerical and hardware experiments on basic computing platforms such as microprocessor and field-programmable gate array. The practical results further strengthen the commercial potential by the computational enhancement (in time and accuracy) and low implementation cost. Overall, the proposed results achieve the research frontier and advance the field.

Abstract This investigation deals with a N-modules software reliability growth model (SRGM) wherein each module attains multiple types of faults of different severity levels. During the debugging phase, there may arise some new faults due to assumption of imperfect debugging. The reliability growth during the software testing phase governed by a non-homogeneous Poisson Process (NHPP). The proposed study is devoted to analyze the fault debugging phenomenon in all modules of software and to obtain expected number of faults observed, isolated and removed. The software reliability and optimum total cost of the software development during the testing phase are evaluated. The causes of fault occurrences in each module are different. The faults may occur with different failure rates and follow the nonlinear behavior with respect to the testing time. The NHPP model incorporates the testing-effort expenditure required for the faults debugging activities and governed by Weibull and logistic distributions which influence considerably not only the fault observation rate but also the time to isolate the faults and the time to remove the faults from all N-modules. In addition to predict the reliability of N-modular software, this study proposes optimal release policies for the SRGM by minimizing the expected cost subject to budget and testing-effort constraints, while ensuring a prescribed level of reliability and incorporating the effects of testing effort and efficiency. Some specific models are also deduced by setting the appropriate parameters so as to match with the existing models in the literature. Sensitivity results are presented through a numerical experiment to demonstrate the validity of the analytical formulations and the computational tractability of the model. An evolutionary algorithm, namely Particle Swarm Optimization (PSO) based on a dynamic penalty method, is employed to determine the optimal release time.

Considering grocery delivery optimization for the supermarket chain, this paper proposes a coordinative grocery delivery service (CGDS) strategy which is implementable by the supermarket-chain enterprise. By coordinating courier resources, the CGDS mode enhances the operational efficiency of grocery delivery for the supermarket chain adopting the individually-operated grocery delivery service (IOGDS) mode, where all stores separately run their delivery service. For the deterministic supermarket-chain CGDS problem, this study presents a mathematical programming model using the arc-flow and set-partitioning formulations, accounting for unique properties such as multiple depots and shared heterogeneous couriers. A branch-and-price-and-cut method incorporating two categories of valid inequalities is developed, along with an implicit enumeration algorithm for the pricing subproblem to meet the pickup-and-delivery requirements. The computational experiments on both the benchmark and newly-generated instance sets demonstrate the effectiveness and superiority of the proposed solution methods. The experimental results also highlight the clear advantage of the CGDS mode over the IOGDS mode, offering relevant strategic insights for the supermarket-chain operators.