Operations Research Perspectives最新文献

This paper studies a discrete-time portfolio optimization problem, wherein the underlying risky asset follows a Lévy GARCH model. Besides a Gaussian noise, the framework allows for various jump increments, including infinite-activity jumps. Using a dynamic programming approach and exploiting the affine nature of the model, we derive a single equation satisfied by the optimal strategy, and we show numerically that this equation leads to a unique solution in all special cases. In our numerical study, we focus on the impact of jumps and evaluate the difference to investors employing a Gaussian HN-GARCH model without jumps or a homoscedastic variant. We find that both jump-free models yield insignificant values for the wealth-equivalent loss when re-calibrated to simulated returns from the jump models. The low wealth-equivalent loss values remain consistent for modified parameters in the jump models, indicating extreme market situations. We therefore conclude, in support of practitioners’ preferences, that simpler models can successfully mimic the strategy and performance of discrete-time conditional heteroscedastic jump models.

Machine learning can be effectively used to generate models capable of representing the dynamic of production processes of small and medium-sized enterprises. These models enable the estimation of key performance indicators, and are often used for optimizing production processes. However, in most industrial applications, modeling and optimization of production processes are currently carried out as separate tasks, manually in a very costly and inefficient way. Automated machine learning tools and frameworks facilitate the path for deriving models, reducing modeling time and cost. However, optimization by exploiting production models is still in infancy. This work presents a methodology for integrating a fully automated procedure that embraces automated machine learning pipelines and a multi-objective optimization algorithm for improving the production processes, with special focus on small and medium-sized enterprises. This procedure is supported on embedding the generated models as objective functions of a reference point based non-dominated sorting genetic algorithm, resulting in preference-based Pareto-optimal parametrizations of the corresponding production processes. The methodology was implemented and validated using data from a manufacturing production process of a small manufacturing enterprise, generating highly accurate machine learning-based models for the analyzed indicators. Additionally, by applying the optimization step of the proposed methodology it was possible to increase the productivity of the manufacturing process by 3.19 % and reduce its defect rate by 2.15 %, outperforming the results obtained with traditional trial and error method focused on productivity alone.

One of the main challenges of food bank warehouses in developing countries is to determine how to allocate perishable products to beneficiary agencies with different expiry dates while ensuring food safety, meeting nutritional requirements, and minimizing the shortage. The contribution of this research is to introduce a new multi-objective, multi-product, and multi-period perishable food allocation problem based on a single warehouse management system for a First Expired-First Out (FEFO) policy. Moreover, it incorporates the temporal aspect, guaranteeing the dispatch of only those perishable products that meet the prescribed minimum quality standards. A weighted sum approach converts the multi-objective problem of minimizing a vector of objective functions into a scalar problem by constructing a weighted sum of all the objectives. The problem can then be solved using a standard constrained optimization procedure. The proposed mixed integer linear model is solved by using the CPLEX solver. The solution obtained from the multi-objective problem allows us to identify days and products experiencing shortages. In such cases, when there is insufficient available inventory, the total quantity of product to be dispatched is redistributed among beneficiaries according to a pre-established prioritization. These redistributions are formulated as integer programming problems using a score-based criterion and solved by an exact method based on dynamic programming. Computational results demonstrate the applicability of the novel model for perishable items to a real-world study case.

Regulators’ increasingly stringent carbon rules to protect the environment are encouraging practitioners to modify their operational activities that are accountable for releasing emissions into the atmosphere. Thereby, practitioners dealing with product inventory planning are seeking proper management strategies not only to increase profits but also to reduce released carbons from operations. In addition, increasing uncertainty in supply operations has motivated suppliers to impose prepayment mechanisms in recent decades. This study examines the best prepayment installment policy for a practitioner for the first time, where the consumption behavior of consumers changes as a result of the combined effects of unit selling price and storage time. Moreover, to make the present inventory planning more realistic, the unit holding cost function is adopted as a power function of the inventory unit's storage period. The goal of this study is to provide the best combined installment for advance payment, price, and replenishment strategies for a practitioner under cap-and-price, cap-and-trade, and carbon tax environmental guidelines by ensuring maximum profit. For this purpose, an algorithm is created by combining all derived theoretical results from the analytical study, whereas the efficacy of the algorithm is assessed through the examination of five illustrative numerical instances. A plethora of noteworthy management insights for the practitioner are obtained by investigating the dynamic shifts in optimal strategies resulting from fluctuations in system parameters. The results reveal that if the demand is low in the nascent phases of the business cycle, then the prudent approach for the practitioner entails procuring a comparatively smaller lot-size using a modest number of payment frequencies and then setting a relatively small unit selling price to increase profits.

Home Health Care (HHC) services are essential for delivering healthcare programs to patients in their homes, with the goal of reducing hospitalization rates and improving patients’ quality of life. However, HHC organizations face significant challenges in scheduling and routing caregivers for home care visits due to complex criteria and constraints. This paper addresses these challenges by considering both caregiver assignments and transportation logistics. The objective is to minimize the total travel distance and CO${}_2$ emissions while ensuring a balanced workload for caregivers, meeting patients’ preferences, synchronization, precedence, and availability constraints. To tackle this problem, we propose a multiperiodic Green Home Health Care (GHHC) framework. In the first stage, we utilize multiobjective programming and the NSGA-II algorithm to generate Pareto front solutions that consider travel distance and CO${}_2$ emissions. In the second stage, a Mixed-Integer Linear Programming (MILP) model is proposed to balance caregivers’ workload by assigning them to the patient routes generated in the first stage. The results highlight the trade-off between shorter routes and lower emissions. Furthermore, we examine the impact of prioritizing continuity of care and patient satisfaction. This research provides valuable insights into addressing the scheduling and routing challenges in HHC services, contributing to a more efficient and environmentally friendly healthcare delivery.

We present a framework for multi-objective optimization where the classical mean–variance portfolio model is extended to integrate the environmental, social and governance (ESG) criteria on the same playing field as risk and return and, at the same time, to reflect the investors’ preferences in the optimal portfolio allocation. To obtain the three–dimensional Pareto front, we apply an efficient multi-objective genetic algorithm, which is based on the concept of $\varepsilon$-dominance. We next address the issue of how to incorporate investors’ preferences to express the relative importance of each objective through a robust weighting scheme in a multicriteria ranking framework. The new proposal has been applied to real data to find optimal portfolios of socially responsible investment funds, and the main conclusion from the empirical tests is that it is possible to provide the investors with a robust solution in the mean–variance–ESG surface according to their preferences.

This paper presents a bi-level approach to support retailers in making investment decisions in renewable-based systems to provide clean electricity. The proposed model captures the strategic nature of the problem and combines capacity sizing decisions for installed technologies with pricing decisions regarding the electricity tariffs to offer to a reference end-user, representative of a class of residential prosumers. The interaction between retailer and end-user is modeled using the Stackelberg game framework, with the former acting as a leader and the latter as follower. The reaction of the follower to the electricity tariff affects the retailer’s profit, which is calculated as the difference between the revenue generated from selling electricity and the total investment, operation and management costs. To account for uncertainty in wholesale electricity prices, renewable resource availability and electricity request, the upper-level problem is formulated as a two-stage stochastic programming model. First-stage decisions refer to the sizing of installed technologies and electricity tariffs, whereas second-stage decisions refer to the operation and management of the designed system. The model also incorporates a safety measure to control the average profit that can be achieved in a given percentage of worst-case situations, thus providing a contingency against unforeseen changes. At the lower level, the follower reacts to the offered tariffs by defining the procurement plan in terms of energy to purchase from the retailer or potential competitors, with the final aim of minimizing the expected value of the electricity bill. A tailored approach that exploits the specific problem structure is designed to solve the proposed formulation and extensively tested on a realistic case study. The numerical results demonstrate the efficiency of the proposed approach and validate the significance of explicitly dealing with the uncertainty and the importance of incorporating a safety measure.

An alternative approach for the panel second stage of data envelopment analysis (DEA) is presented in this paper. Instead of efficiency scores, we propose to model rankings in the second stage using a dynamic ranking model in the score-driven framework. We argue that this approach is suitable to complement traditional panel regression as a robustness check. To demonstrate the proposed approach, we determine research efficiency of higher education systems at country level by examining scientific publications and analyze its relation to good governance. The proposed approach confirms positive relation to the Voice and Accountability indicator, as found by the standard panel linear regression, while suggesting caution regarding the Government Effectiveness indicator.

The airplane boarding process, which can have a significant impact on a flight’s turnaround time, is often viewed by researchers and airlines primarily in terms of minimizing total boarding time (TBT). Airplane capacity, number of passengers on board, amount of luggage, and boarding strategy are common factors that affect TBT. However, besides operational efficiency, airlines are also concerned with customer satisfaction, which affects customer loyalty and financial return. One factor that influences passenger experience is the individual boarding time (IBT), here defined by the time passengers stand inside the cabin. Considering these two aspects, an agent-based model is presented that compares the performance of three alternative mainstream boarding strategies in a 132-seat and a 160-seat single-aisle commercial airplane. An important characteristic of the model that differentiates it from previous work is that overhead bins have a physical limitation, which could lead to an increase in aisle interferences on full flights as passengers take longer to find a place for their carry-on luggage. Another important contribution is the analysis of how passenger seat location affects IBT. Our results show that outside-in (OI) produces shorter TBT than random and back-to-front boarding, and also shorter IBT and much shorter maximum IBT than BTF, particularly for passengers seated in the middle of the airplane. This suggests that among the three most popular boarding strategies used by airlines across the world, OI is the best when it comes to balancing airplane boarding efficiency with individual customer satisfaction.

Fast-paced markets require complex interactions from all supply-chain agents to satisfy customer demands and needs. The manufacturing industries face some difficulties in terms of production amounts and smooth delivery rates. Technical experts found that a warm-up period before a production run helps address those challenges and improves the workability of machine tools in the manufacturing process. The use of a warm-up process causes a reduction of faulty products (an adverse production outcome) and improves operational efficiency. Also, a shortage in the supply of commodities creates difficult conditions for inventory management decisions, posing the same production problems as mentioned above. Consideration of the warm-up process has recently been included in the scope of operations research, but it is necessary to study its interaction with the presence of shortage. This study presents a system where a manufacturing environment utilizes the warm-up process in its initial phase and shortages are allowed during the production period, in addition, the study takes into account carbon emissions during manufacturing to integrate environmental concerns. We assume that the company has the capability to trade the surplus carbon capacity it hasn't produced. This study offers a comprehensive framework that incorporates former research that addresses warm-up process, carbon emissions, shortages, and defective items. To solve the proposed non-linear programming problem with inequality constraints, we employ the Karush-Kuhn-Tucker (KKT) conditions method to determine the optimal solutions. Managerial insights are derived, and sensitivity analysis highlights the effects of the system parameters on the decision variables. The sensitivity analysis results indicate that the carbon trading cost has a significant impact on the overall cost, and subsequently, the company's profit.