Annals of Operations Research最新文献

Many existing data protection methods (DPMs) tend to overlook the use cases of forecasting, that can result in noisy forecasts and decreased accuracy. To address this issue, we develop a novel data protection framework for data providers who prioritize data privacy with minimal loss of forecasting accuracy. Recent studies show that k-nearest Time Series (k-nTS) swapping can be applied to achieve usable forecasting accuracy while maintaining an acceptable level of privacy. However, this method is often times inefficient because it requires a few tasks for each of time series data, say, (A_j in mathbb {R}^n) for all (j=1, dots , J), where J is very large: (i) distance computing for all (J-1) vectors from (A_j); (ii) ordering all (J-1) vectors based on the calculated distances from (A_j); iii) randomly selecting one of them to swap the values; (iv) and then, finally, moving to the next time series, to start over the entire process until the last vector (A_J). This entire process is time-consuming, inefficient for practical applications, and lacks any performance guarantee. In this research, we propose a new method called the K-means Time Series (K-mTS) Shuffling, which can be applied to thousands of time series to enhance efficiency and ensure performance guarantees. The efficiency is improved by (i) clustering, which eliminates the need for iterative individual distance calculations, and (ii) simultaneous data swapping (or shuffling) within the same cluster. Furthermore, for performance guarantee, instead of using random selection as in the k-nTS approach, we apply the perfect matching scheme to find optimal vector to swap with for each (A_j) that allows data providers to systematically manage the trade-offs between data privacy and forecasting accuracy. The presented numerical results indicate that our proposed method achieves guaranteed usable forecasting accuracy than that obtained using confidential data protected by traditional methods, while maintaing an acceptable level of privacy.

This research investigates the bounded batch online-scheduling issue, specifically focusing on incompatible job families assigned to unit flowshop machines. The main criterion is to reduce the makespan. Within a unit flowshop setting, each machine standardizes the processing time of a job to one unit. The concept of linear lookahead pertains to an online algorithm’s ability to anticipate job information within the time interval ((t, lambda t +beta ]) at time t. The bound batch capacity, denoted by (b < infty), signifies a restriction on the number of jobs that can be contained within a batch. For two unit machines, we introduce the optimal online algorithm (A^{b}). In instances where multiple unit machines are employed with parameters (lambda ge 1), (0 le beta < 1), and (f ge 2), we establish a competitive ratio bounded by at most (1 + max left{ {frac{f}{{(2f - 1)(1 + alpha )}},frac{f}{{lambda (2f - 1)alpha + beta + f}}} right}) based on the online algorithm (A_{f}^{b}). Specifically, we present an optimal online algorithm when (lambda =1) and (0 le beta le displaystyle frac{3f + 1-sqrt{9 f^{2}-2f +1}}{4}). Additionally, we extend our findings to the bounded batch scheduling problem incorporating a linear lookahead interval and (f (=m)) incompatible job families on multiple unit machines.

This paper considers the study of integrated ownership in a universe of companies represented by a corporate network. The difficulty is that the system of relations is characterized by a number of cross-shareholdings (i.e., cycles). Given a source node s and a target node t, the problem is to establish whether s owns t, either directly or indirectly. In the literature, the problem is tackled by computing an ownership matrix and solving a linear system of equations. Solving the system for checking whether s owns t may cause an unnecessary computational effort which can be avoided. We propose a recursive algorithm based on the Mason’s gain formula, which is usually applied in network flow-propagation problems for analyzing the feedback effect. The number of operations needed to compute the formula grow up factorially as the number of nodes in the network increases. In fact, the solution process is hard when the network contains Strongly Connected Components of large size. Yet, in real applications corporate networks are sparse and have few Strongly Connected Components of small size. We propose a recursive algorithm based on a new simplified version of the Mason’s formula that is not computed on the whole network but only on groups of firms which form a Strongly Connected Component. This reduces the computational burden for determining the integrated ownership of a source company s in all the possible target nodes.

A model of state-contingent multi-input-output technologies, with a netput representation, is introduced to analyze different types of efficiencies. Overall efficiency allocative efficiency and technical efficiency are studied according to coalitions of firms in a firm game (being a cooperative TU-game). Different risks are investigated, inherent to either inputs or outputs, in order to measure their impacts on the different efficiencies. The risks imposed on the random direction of the directional distance function is axiomatically characterized to provide an operational functional form to measure efficiency under risks. An empirical application in portfolio management is presented, focusing on the performance evaluation of portfolios with different risk parameterization of the random direction.

In the context of global climate change and resource constraints, the sustainability of supply chain management has attracted widespread attention across various sectors. This special issue highlights the cutting-edge applications of operations research methods in sustainable supply chain management, particularly within the contexts of digitalization, low-carbon initiatives, and the circular economy. It showcases advances in supply chain resilience modeling enabled by blockchain and machine learning, low-carbon closed-loop game theory, green investment and subsidy strategies, AI-driven low-carbon transformation of small and medium-sized enterprises, and risk mitigation and performance evaluation of circular supply chains. These studies not only extend the theoretical boundaries of supply chain management amid digitalization and the circular economy but also provide practitioners with actionable decision-making tools. Future research should emphasize the integration of interdisciplinary approaches, multi-agent collaborative modeling, and the combination of AI with system dynamics to foster balanced and sustainable development of supply chains across environmental, economic, and social dimensions. Consequently, this special issue establishes a solid foundation for operations research addressing climate change and efficient resource utilization in supply chains.

We introduce the MinCovTarget+ algorithm for the problem of fair allocation of indivisible items and we study its performance with respect to some popular fairness and efficiency criteria such as minimal envy, proportionality, maximal Nash welfare and maximal total welfare. By a detailed numerical analysis we compare our newly proposed algorithm with a standard algorithm for this kind of problem: the Spliddit algorithm. Our numerical analysis shows that MinCovTarget+ provides allocations with an excellent balance between fairness and efficiency criteria. In particular, it typically yields minimal (null) envy solutions with a very high value of Nash welfare, at a fraction of the computation time used by Spliddit. Moreover, MinCovTarget+ can be applied in higher dimensions where Spliddit cannot be readily implemented. Our paper is the first in the literature to present a numerical study of these algorithms using a random uniform valuation of the goods to be allocated, as well as a novel design of the value matrix that incorporates dependent valuations. All the numerical estimates in this paper were obtained using a Macbook Air (Apple M1, 8 GB RAM). The corresponding MATLAB code is available at: https://github.com/giovannipuccetti/MinCovTarget. A user-friendly version of the algorithm is available at https://www.fair-allocation.com.

The current solutions for weight determination of conflicting stakeholder groups participating in group decisions apply subjective weight allocation to create the overall priority. This paper offers a mathematical solution for the weight allocation by preference vector-distance minimization using the Euclidean metric. The proposed technique has been tested by numerical examples and a real-world case study on an automated vehicle implementation survey. All results show that from the aspect of rank concordance, measured by Kendall W, Spearman’s Rho, and Garuti indices, the new method outperforms the equal weight and the multi-actor multi-criteria method. The proposed weight allocation technique can be beneficial for theoreticians and practitioners of multi-criteria decision-making.

Pricing a global product differently across multiple regions is a common but controversial practice. Although price differentiation helps capture unique market characteristics, it also encourages parallel trade, which may affect the overall corporate performance of a global company. We study this problem with a single global business unit (GBU) and multiple local business units (LBUs). The GBU manufactures a product and sets a transfer price for supplying the product to all LBUs, and LBUs decide retail prices for their respective regional markets. Customers can purchase products in any region by comparing LBUs’ prices and other parallel-imported factors, and we construct the demand by a mixed-multinomial logit model. Therefore, each LBU needs to consider the other LBUs’ prices when setting its price, and we formulate this problem as a two-stage game-theoretic model. We verify the existence of a pure-strategy Nash equilibrium. We then develop a learning-based algorithm to find the equilibrium prices of LBUs. Our algorithm is computationally efficient with a large scale of decision-makers. Even for cases with 100 decision-makers, the pure-strategy Nash equilibrium can be obtained within 30 minutes. Numerical studies are conducted using data from the fast-moving consumer products industries. Although parallel trade is detrimental to some LBUs, the existence of parallel trade surprisingly leads to higher overall corporate profits, which is made possible by making the product available at different price points in a market. The proposed method also enables the quantitative validation of several conjectures on parallel trading practices.