Annals of Operations Research最新文献

We examine how greenwashing affects the strategies and outcomes of companies and consumers. We develop a two-stage game, where a monopolist sets price and invests in environmental quality in the first stage, and competes with a new entrant in the second stage. The incumbent company is genuinely environmentally friendly, while the new entrant may use deceptive green marketing. We assume that only inexperienced consumers can be influenced by greenwashing, and consider two important dynamic factors, i.e., a change in competitive structure and a learning effect in the market. We investigate the conditions under which greenwashing is profitable for the new entrant, the ways in which the incumbent company responds to it, and the impact of greenwashing on the environment and consumers. We find that greenwashing can be mutually beneficial for both firms thanks to higher market potential and the incumbent’s first-period actions. Customers always suffer from greenwashing, and in rare cases, greenwashing can be beneficial to enhance environmental quality.

Climate change and the pressing need for green transformation represent one of the most critical challenges of global economic and environmental systems. In response, this special issue examines how operations research (OR) can drive sustainable supply chain governance through technological innovation and institutional power. Comprising 42 rigorously reviewed articles, this collection explores diverse themes such as low-carbon transformation, circular economy strategies, energy system optimization, digitalization, and socio-institutional mechanisms. These studies introduce innovative OR applications, such as AI and blockchain implementations, game-theoretic frameworks, and policy assessment mechanisms, to navigate intricate sustainability trade-offs and facilitate multi-stakeholder decision-making within green supply chains. By integrating theoretical advances with practical insights, this issue offers valuable contributions for researchers, practitioners, and policymakers aiming to achieve sustainability in supply chain operations. It underscores the imperative of interdisciplinary collaboration and systemic approaches to foster resilient, inclusive, and ecologically responsible supply chains in the era of climate urgency.

Bi-objective portfolio optimization, which simultaneously seeks to minimize risk and maximize expected return, has been a central topic in financial research for several decades. This problem is typically formulated as a parametric quadratic programming model, whose set of optimal solutions defines the efficient frontier and its construction remains a major challenge, particularly in large-scale scenarios. To address this issue, we propose the parametric direct support method (PDSM) to solve the bi-objective portfolio selection problem under linear constraints. By adapting the support concept to the special structure of the problem, the novel approach computes iteratively and efficiently all the pivot points and traces out the entire efficient frontier. In contrast to classical approaches, our PDSM method takes into account the sparsity structure of the solutions when dealing with large-scale problems. This provides a key advantage to the proposed approach, which operates on small linear systems at each iteration, thereby significantly accelerating computation and reducing CPU time. We provide a theoretical analysis of the algorithm, establish its finite termination, and evaluate its computational complexity. Extensive numerical experiments on financial datasets and randomly generated instances demonstrate the scalability and superior performance of PDSM in solving large-scale portfolio optimization problems.

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.

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.