Annals of Operations Research最新文献

I have learned from him a lot how to conduct research, but more important, I hope, how to be a better human being.

In the era of Industry 4.0, the complexity of semiconductor production is growing very fast, raising the possibility of unnoticed defective wafers and subsequent wasteful use of resources. One of the key advantages of Industry 4.0 is the accessibility to big data, which can be obtained from a number of sensors, including multiple sensor data and extensive data repositories. Recently, engineers have developed data fusion strategies for virtual metrology (VM) prediction models to effectively handle data from multiple sources. This research explores a novel approach for data-driven VM prediction model for multi-source data, namely multi-source ensemble method with random source selection. By utilizing the bagging principle for multi-source data and tree-based prediction paradigms, the proposed approach randomly selects subsets of data sources to construct each tree learner, thus reducing interdependence among the trees and minimizing the risk of overfitting, which can be a challenge faced by existing tree-based prediction models. To validate and illustrate the practical applicability of our proposed method, we use real-world data from the plasma etching process, aiming to provide potential benefits and effectiveness of our methodology.

Green firms are considering whether to open or close their new green technologies. Opening up green technology can induce imitation and transformation in traditional firms but intensify competition in the green product market. Meanwhile, green technology imitation leads to the market share transfer effect, which is a supply-side network externality that gains consumer trust and increases the market share of green products as more firms adopt the technology. However, traditional firms also face a dilemma in green technology imitation choices due to the market cannibalization problem. This study constructs a game-theoretic model with one green firm possessing proprietary green technology and one traditional firm to investigate firms’ strategic interactions among green technology opening, imitation, and investment. We find that the technology opening strategy may constitute equilibrium if the market transfer share or the market size of green products is relatively large. Accordingly, the traditional firm produces green products by imitation when the green firm opens its technology. In addition, the technology opening strategy improves social welfare compared with the technology closing strategy, thus forming a win-win situation. We further extend the analysis by considering the technology licensing contract model, consumer-side network effects, the sequential quantity game model, market demand uncertainty, and the government’s subsidy policy.

The growing share of zombie firms in developed countries puts economic growth at risk, yet understanding of these uncompetitive firms remains limited. To develop new insights and understanding of zombie firms, the current study relies on data analysis and predictive modeling and aims to establish a financial diagnosis method, based on a self-organizing map of the financial profiles of zombie firms and their pathways to zombification. This article also presents a SOM-SVM prediction model that seeks to anticipate zombie firms. The findings identify diverse profiles of zombie firms; their financial evolution from initial risky phases to zombification are not uniform. The financial deterioration that leads to zombification often cannot be observed in advance, which represents a major hurdle to efforts to differentiate zombie firms from healthy ones and restricts the effectiveness of prediction methods for identifying zombie firms in initial phases.

Vaccination is the most effective strategy for battling infectious diseases, breaking the disease transmission chain, and achieving herd immunity. Implementing vaccination for the whole population requires an integrated vaccine supply chain network that considers sustainability and resiliency in the network. For this purpose, in this research, a location-allocation-inventory-distribution problem in the sustainable and resilient vaccine supply chain network, considering mix-and-match vaccine regimens against SARS-CoV-2, is designed. The mix-and-match-based vaccination to reach robust immunization, increase vaccination effectiveness, and more resilience to cope with shortages is applied. In addition, three pillars of sustainability, to minimize distribution network costs, vaccine disposal impact, and greenhouse gas emissions, in terms of economic and environmental, and maximizing job creation, demand satisfaction, and vaccination effectiveness to ensure social sustainability, are developed. Also, scenario-based optimization is presented to meet the inevitable disruptions and breakdowns, such as the supply capacity of suppliers and uncertain amounts of vaccine demand, which depends on the previous type of vaccine injected, and robust stochastic programming is used to handle uncertainties. To solve the proposed model, efficient meta-heuristic algorithms, including the genetic algorithm (GA) and variable neighborhood search (VNS), are applied. In addition, a new hybrid algorithm called H-GAVNS based on the GA and VNS is developed in this research to discover near-optimal results. Finally, a case study of the COVID-19 vaccine in Iran’s environment is presented to confirm the accuracy of the presented model. The outcomes show that uncertainties in the real world and sustainability and resiliency aspects are well managed and responded to by the designed model.

The Internet of Things (IoT), a modern technology, and machine learning (ML) are used to make immediate decisions. Due to the massive development of roadside infrastructure and increasing digitalization, current procurement planning is based on primary data, and there are several paths connecting markets and cities for travel. Integrating physical and cyber systems within the framework of Industry 4.0 through intelligent metaheuristic methods is more useful. Accordingly, we propose IoT-enabled and ML-based multipath traveling purchaser problems (IoT-ML-MPTPPs) for minimum cost or time and develop an ML-based variable-length genetic algorithm (ML-VLGA) to solve the proposed problems. To purchase an item, a purchaser starts from the depot with a vehicle, visits the markets for purchase until the prespecified demand is satisfied, and returns to the depot. Thus, the present investigation aims to select the appropriate markets and optimal routing route design for minimum cost or time. In developing tropical countries, travel costs and time depend on weather and key road features such as road surfaces and congestion. In real-life scenarios, the proposed IoT-ML-MPTPPs provide insights for optimizing procurement planning and transportation logistics amid dynamic factors such as weather conditions, congestion, and road surfaces. Here, the IoT supplies the above real-time parameters during the purchaser’s journey, which are used to predict the vehicle’s velocity and per unit travel and transportation costs by applying an ML method, which enhances the intelligent decision-making process. To solve the above IoT-ML-MPTPPs, an efficient problem-specific ML-VLGA with probabilistic selection and ML-based crossover is developed and applied. Comprehensive numerical experiments are performed rigorously evaluate and validate the performance of the developed ML-VLGA. These experiments demonstrate its effectiveness in both simulated scenarios and real-world applications. Managerial insights are drawn that support the use of the model.

In marine container drayage (CD), which refers to the transport of goods over a short distance, empty containers (ECs) are required to be transported long distances between a terminal and a shipper/consignee by conventional transportation. This causes truck traffic congestion in harbor districts that increases CO₂ emissions (CO₂EM). This study addresses the vehicle dispatch problem with respect to the environmental impact of marine CD, considering the tractor dwell time is long at the customer site due to cumbersome container loading/unloading (L/U). We model this problem as an extended version of the vehicle routing problem with precedence constraints to minimize the total CO₂ weighted travel distance. To improve CD operation in long L/U time, we propose a new practice for CD operations. Additionally, we develop a mixed integer programing model (MIP) for the new practice representation, and propose a Simulated Annealing (SA) based heuristic approach to solving the new practice instance. By implementing our proposed new operation, in a long L/U time, the CO₂EM, EC move and number of tractors required can also be reduced by allowing uncoupled tractor move.

In recent years, new smart technologies like electric vehicles and active demand management have been introduced to reduce carbon emissions in electricity distribution networks, resulting in altered electricity consumption patterns. However, the impact of the technologies on electricity consumption remains uncertain due to a lack of rigorous evaluation methods and planning techniques accounting for these changes. Addressing these gaps, this paper contributes three key elements to information systems literature. First, this paper presents a policy to plan multi-period electricity distribution networks that are able to take into account changing electricity consumption patterns. This paper uses a policy to determine the impact of new technologies on distribution network investments. Second, this paper determines the effect of network topologies on distribution network investments with altered consumption patterns. Third, this paper develops and solves a novel optimization problem that formalizes the task of long-term distribution network planning under variable consumption patterns. This work holds implications for distribution network management, policy, and research. It offers recommendations for integrating our policies into practical procedures, emphasizing the need for more rigorous planning incentives for policymakers. Furthermore, the large effect of altered consumption patterns shows that actions are needed to design policies that work towards lowering load coincidence. For researchers, the presented problem and solution methods are generalizable and can help researchers in other domains (e.g. parcel delivery, water distribution) to solve comparable planning problems.