Annals of Operations Research最新文献

Social preferences for altruism and fairness are widely recognized factors in operations management. In this paper, we establish a two-echelon supply chain comprising an innovative supplier who is rational and a leading manufacturer with social preferences. We establish a game-theoretical model to examine the manufacturer’s optimal preferences, pricing strategies, and innovation levels under the wholesale price contract (WPC) and the cost-sharing contract (CSC). We find that when the manufacturer’s fairness preferences manifest as disadvantageous inequality, different levels of innovation effectiveness incline the manufacturer to different social preferences under the WPC, and fairness is consistently shown under the CSC. However, when the manufacturer’s fairness preferences involve advantageous inequality, although the innovation level increases due to social preferences, the manufacturer should remain fully rational regardless of which contract is implemented. Moreover, as the manufacturer’s altruism increases, we observe a decrease in the cost-sharing rate, indicating a greater unwillingness to bear a higher proportion of innovation costs. This is because altruism is reflected in a higher wholesale price. Surprisingly, the CSC combined with altruism neither incentivizes innovation nor increases profit for the whole channel. The CSC does not always outperform the WPC in terms of profits and innovation. Besides, we extend the analysis to incorporate the supplier’s fairness preferences, and find that such preferences not only discourage the supply chain innovation but also harm the interests of all parties involved. Our findings contribute valuable insights into the role of altruism and fairness in operations management on innovation and pricing strategies.

This paper extends Scarf’s (J Econ Theory 3:169–187, 1971) (alpha)-core result to HK-(delta) and PZ-(delta)-cores in an n-player dynamic Cournot game. It shows that HK-(delta) and PZ-(delta)-cores are identical to (gamma)-core and both non-empty in the 2-player case ((n=2)); the two cores are both proper subsets of (gamma)-core and PZ-(delta)-core is non-empty while HK-(delta)-core may be empty in the multi-player case ((nge 3)).

We consider an overseas manufacturing supply chain with long lead-times and multiple transportation modes, where orders are placed using forecasted demand. The forecast error, which is the difference between forecasted and actual demand quantity, is considered an uncertain parameter. We assume that the amount of excess inventory in the warehouse at the beginning of each period is also uncertain. Order quantities of parts must be determined for each available transportation mode. We model this problem using a two-stage stochastic programming approach to minimize the overall expected order procurement, inventory holding, and backorder costs under demand and inventory uncertainty. We use the Sample Average Approximation (SAA) method to solve our two-stage stochastic program. We run our experiments using simple random sampling as well as the stratified sampling technique called Latin Hypercube Sampling (LHS) to generate random samples from a continuous distribution in each replication of SAA. We compare the results obtained by using simple random sampling and stratified sampling methods. Further, we use a scenario decomposition based method, Progressive Hedging Algorithm (PHA), to solve the extensive form of two-stage stochastic programming problem generated in each replication of SAA. We evaluate the performance of our solution algorithm at different levels of forecast error and inventory uncertainty.

This study addresses a routing and scheduling problem related to the transport of forest tractors between harvesting sites using ballast trucks. The problem is framed as a Pickup and Delivery Vehicle Routing Problem with Time Windows (PDVRPTW), and considers factors like road conditions, legal time limits, and a diverse fleet with specific compatibility rules. To address it, we developed a mixed-integer linear programming model aimed at minimizing travel distance, fleet usage, and delays. The model was implemented in CPLEX with a Branch-and-Bound approach and tested using real data from a Brazilian forestry company. Scenarios with varying demand levels–low, medium, and high–were solved optimally in under 3 min. These results show that exact optimization methods can be practical for complex planning in forest logistics, offering valuable insights for future decision-support applications.

Compartmental models have gained significant attention not only in public health studies but also in fields such as Operations Research (OR), social sciences, and logistics, particularly following the COVID-19 pandemic. Their broad applicability in epidemiology and their utility in understanding, predicting, and controlling the global spread of infectious diseases have made them indispensable across various disciplines. The appeal of these models lies in their simplicity yet effectiveness in capturing the essential dynamics of disease transmission. This paper provides a comprehensive review of compartmental models, focusing on the Susceptible-Infectious-Recovered (SIR) models and the key aspects of their structure. The primary objective of this review is to enhance the ability of researchers and practitioners to understand and manage infectious disease outbreaks through a twofold approach: (1) an evaluation of the assumptions, equations, and methodologies used for estimating critical parameters in SIR models, and (2) an exploration of the relationship between SIR models and optimization models. Additionally, a systematic micro-level review has identified the most significant research gaps in the literature on compartmental models, leading to recommendations for future research. A key finding emphasizes the need to revisit various assumptions to clarify the connection between SIR models and optimization approaches, which is expected to offer valuable insights for epidemic disease modeling.

Accurate forecasting of electricity consumption in residential buildings is essential for effective energy planning, real-time load balancing, and demand-side management. As residential demand becomes increasingly variable due to the proliferation of smart appliances and renewable energy systems, robust and adaptive forecasting models are crucial. To address this need, this study introduces a hybrid forecasting model that combines a deep Temporal Convolutional Network (TCN) with a Fuzzy Wavelet Neural Network (FWNN). The TCN component captures long-range temporal dependencies in time-series data, while the FWNN integrates fuzzy logic and wavelet transforms within neural network architecture, allowing the model to effectively handle uncertainty, imprecision, and nonlinearity commonly observed in electricity consumption behaviors. To further improve predictive accuracy, the model’s hyperparameters are fine-tuned using the Aquila Optimization metaheuristic algorithm. The proposed model is evaluated on two real-world datasets containing minute-level and hourly electricity consumption records. Comparative experiments with several established baseline models show that the suggested hybrid approach consistently outperforms its rivals across key performance metrics. These results underscore the model’s accuracy and robustness, positioning it as a promising candidate for integration into modern residential energy management systems.

Dendrograms are graphical representations of hierarchical clustering. Different definitions of the distance between two clusters in hierarchical clustering lead to different dendrograms. In this paper we focus on the dendrograms obtained when this distance is assumed to be the maximum of the distances between the elements of one cluster and the elements of the other cluster, known as complete-linkage dendrograms. For initial data where some elements are at the same distance as others, the number of different complete linkage dendrograms, which corresponds to the number of different ways to break ties for equal distances, can be large. We propose a system of linear inequalities whose set of solutions is the entire set of complete linkage dendrograms. Such a system of inequalities allows the inclusion of an objective function to select the best dendrogram among all these dendrograms according to some criteria, which may not be possible with classical dendrogram computation algorithms. We also adapt the system of inequalities to single-linkage dendrograms, where the distance between two clusters is the minimum distance between an element of one cluster and an element of the other cluster. The benefits of describing complete-linkage dendrograms through a system of inequalities are illustrated in a computational study in which five different objective functions are proposed.

Facility location problems on networks deal with locating facilities (e.g., parks, schools, or health facilities) to serve a set of clients (e.g., citizens). Many existing facility location studies assume that it is possible to (re-)locate predetermined or build new facilities. However, this assumption is not realistic for situations when relocating or building facilities would be too expensive or impossible. Recognizing this challenge, Berman et al. (Ann Oper Res 40:1–16, 1992) first propose the facility network addition modification problems (FNAMPs) on networks that aim to add a given number of new edges (e.g., constructing new roadways or bridges) to improve the client accessibility to the facilities by minimizing the total accessibility or maximum accessibility cost objectives based on clients’ distances to the facility. Yet, all existing approaches for FNAMPs are only heuristics, provide no solution quality guarantees, and fail to scale to even moderate-sized network instances. In this paper, we revisit a special case of FNAMPs with binary demands. We develop approximation algorithms and efficient heuristics for this special case under the two cost objectives. We then consider the strategic aspects of the special case of FNAMPs in which clients’ locations are private information. We design scalable strategyproof mechanisms to address FNAMPs under the two cost objectives while incentivizing clients to report their locations truthfully. We conduct extensive experiments on synthetic and real-world networks to demonstrate the effectiveness and efficiency of all proposed methods against various baselines as well as the strategyproofness of the proposed methods.