Annals of Operations Research最新文献

This paper presents a bi-objective solution approach to address the production scheduling challenge encountered by manufacturers in a shared manufacturing environment. In such scenarios, manufacturers are required to manage orders received through a cloud platform (referred to as cloud orders) while simultaneously fulfilling orders from their long-term and regular clients (local orders). The problem is to efficiently coordinate the production of both types of orders within shared manufacturing facilities. We formulate the problem into a bi-objective mixed integer programming model aimed at simultaneously minimizing the delivery time of cloud orders and mitigating the disruptions to local order production caused by cloud orders. This solution approach comprises three key components: computation of cloud orders’ starting times, construction of available time intervals of manufacturing facilities, and a bi-objective heuristic. This heuristic combines an enhanced hybrid discrete differential evolution with a modified forward–backward earliest starting time algorithm. We introduce an advanced population initialization technique, a novel individual update strategy, and an adaptive local search mechanism based on Pareto-dominance principles to improve the search capabilities of the algorithm towards discovering Pareto non-dominated solutions. Computational results show that the proposed approach outperforms the existing algorithm in most test instances in terms of five common metrics. Insights are discussed, highlighting the practical implications and potential benefits of the proposed approach for shared manufacturing scheduling.

This paper developed a closed-loop supply chain network (CLSCN) optimization model with corporate social responsibility (CSR), recycling, remanufacturing, refurbishing, and transaction cost on a supply chain consisting of manufacturers, retailers, a new product demand market, and a retailer-refurbished product demand market. The manufacturers produce substitutable products using raw materials from two sources, new and recycled. The retailers, in turn, face the demands of new products and retailer-refurbished products, and recycle from the new product demand market. We investigated a number of cases, and our results indicate that CSR can enhance CLSCN performance but more to the party closest to the demand market. It also shows that consumer behavior, competition, CSR investment, and sustainable practices (new material procurement, recycling, refurbishing, re-manufacturing, and environmental costs), play an essential role in CSR and closing the loop. With higher landfill cost, more willingness to return from consumers, less disutility for consumers to return, higher level of recycling and refurbishing, and higher re-manufacturing rate, CSR level tends to improve. Policy-makers can use these findings to encourage firms to improve CSR in CLSCN.

This contribution observes that plant capacity notions based on traditional radial efficiency measures may leave substantial amounts of slacks or unmeasured inefficiency. These unmeasured inefficiencies can result in inaccurate assessments of production capabilities, potentially leading to suboptimal operational and strategic decisions. To remedy this problem, we define new nonradial output-oriented and input-oriented plant capacity concepts based on nonradial Färe-Lovell efficiency measures. By leveraging nonradial measures, our approach captures multidimensional inefficiencies, providing a more nuanced and accurate evaluation of production performance across various input and output dimensions. Furthermore, we also explore how the introduction of nonradial attainability levels can render the attainable output-oriented plant capacity concept more flexible. This flexibility allows for the incorporation of realistic operational constraints, ensuring that capacity assessments are both practical and adaptable to diverse production environments. An empirical illustration on a secondary data set illustrates the pertinent differences between radial and nonradial plant capacity notions. Our empirical analysis demonstrates that nonradial measures offer a more detailed understanding of capacity utilization. In particular, it shows that nonradial plant capacity concepts are especially important on a nonconvex technology.

We introduce this volume of the Annals of Operations Research that collects updated versions of the invited surveys that appeared in the journal 4OR—A Quarterly Journal of Operations Research.

Retailers play a crucial role in the economy, and their ability to effectively manage supply disruptions can significantly impact their performance and competitiveness. This study delves into the complex dynamics of retail markets, particularly in duopoly settings, where two major retailers interact strategically to maximize their profits. The goal is to understand how supply disruptions influence not only pricing and advertising strategies but also sourcing decisions. Three game structures are presented to analyze interactions between retailers, aiming to maximize total profit by determining the optimal sale price and advertising investment. The paper introduces a mathematical formulation for these games and a methodology to find the optimal solution based on the sourcing strategy (single, dual, or multiple sourcing strategy).2

An extensive numerical example with discussion and sensitivity analysis is provided. The study's results can assist decision-makers in selecting the most appropriate sourcing strategy in the presence of disruptions. The optimal pricing and advertising investment in both disruption and non-disruption scenarios for retailers are identified. The study concludes that acting as the leader in the Stackelberg game rather than Nash leads to higher profits. Moreover, dual sourcing is more advantageous than multiple sourcing for retailers. The managerial insights offer useful recommendations for retailers to improve their position in the competitive market.

The behavior of small caps in emerging markets has not received much attention in the academic literature. For international investors, this asset class is exposed to local and global market risk, default risk, political risks, and fiscal and monetary instability that give rise to inflationary pressures. This paper looks at the diversification benefits of small cap returns across emerging market countries, taking into account their exposure to inflation. The paper finds that for many of the countries with lower-than-average inflation, adding their small cap stocks to a US large-cap portfolio benchmark expands the mean variance frontier of the benchmark portfolio over the period 1999–2023. Diversification benefits of small cap stocks are rare for emerging countries with higher inflation, especially in the post GFC period.

In this paper, we investigate the mixed regular-singular control non-zero sum stochastic differential games problem under partial information where both the state dynamics and the performance functional contain time delay and mean field. We prove the existence and uniqueness of the solution of singular mean-field stochastic differential delayed equations and general reflected anticipated mean-field backward stochastic differential equations. By using Pontryagin’s maximum principle and Malliavin calculus, we establish sufficient maximum principles and necessary maximum principles about the non-zero sum game. Consequently, we find corresponding Nash equilibrium points and saddle points. Furthermore, we apply the results to study an optimal investment and dividend problem under model uncertainty.

There has been confusion between the Mean-Variance (MV) model and the Capital Asset Pricing Model (CAPM) due to the former providing the foundation for the latter. As the validity of the CAPM was challenged, the validity of the MV model suffered by association. A core implication of the CAPM is the mean-variance efficiency of the market portfolio. This property does not hold once some CAPM assumptions are relaxed. We focus on the limitations of the CAPM’s budget constraint. Markowitz never recommended portfolio selection without using realistic constraints - like bounds on short sales and portfolio weights. When such constraints are imposed, the market portfolio loses its efficiency, and the exact linearity of asset expected returns in beta fails. We use simulation analysis to examine whether beta is predictive when the market portfolio is inefficient. We also examine the irrelevance of residual risk in asset pricing. We conclude that neither beta nor the CAPM is dead.