Operations Research Perspectives最新文献

In distribution systems such as airlines and express package delivery, the use of hub-and-spoke networks is common, and flow consolidation at hub facilities is essential for cost reduction. While a constant discount factor is typically used to model cost reduction in interhub links, this paper explores an extension of the incomplete hub location problem with modular capacity that enables direct connections between non-hub nodes. The modified approach, called MHLPDC, aims to locate a set of hub facilities, connect each non-hub node to a hub, and activate hub facility links, access arc links, and direct links between non-hub nodes to minimize network costs. The MHLPDC integrates link activation decisions into the decision-making process and utilizes modular arc costs to model the flow dependence of transportation costs in all arcs. To solve the problem, the paper presents a mixed-integer mathematical programming formulation and heuristic algorithm based on a greedy randomized adaptive search and variable neighborhood search approach. The proposed algorithm produces high-quality solutions, as demonstrated through computational experiments on benchmark instances with up to 40 nodes. Furthermore, a sensitivity analysis of the optimal network structure indicates that increasing the discount factor, by varying hub and access arc capacities as well as the associated variable costs, results in fewer hubs being established and more direct shipments between non-hub nodes being permitted.

After a pandemic, all countries experience a shortage in vaccine supply due to limited vaccine stocks and production capacity globally. One particular problem is that it is hard to predict demands for vaccines during the global crisis. On the other hand, vaccines are usually made and packaged in different places, raising logistical issues and concerns that can further delay distribution. In this paper, we propose an optimization formulation model to link infectious disease dynamics and supply chain networks considering a one-to-one relationship between demand and supply for vaccines. We focus on designing a vaccine coordination system using government subsidy that considers the equilibrium behaviors of manufacturers under an actual demand for the vaccine. This study evaluates vaccine manufacturers and government behaviors that help the vaccine market to reach the socially optimal. Different decisions, such as vaccine demands and vaccine production and distribution are investigated. A study of the monkeypox pandemic in the U.S. is performed to validate our model and its results. The obtained results from testing the proposed system problem revealed that the vaccine coverage increased by up to 35%, while the unmet demand reduced by up to 60%, in comparison to when vaccine manufacturers act individually.

This paper studies the static economic optimization problem of a system with a single aggregator and multiple prosumers in a Real-Time Balancing Market (RTBM). The aggregator, as the agent responsible for portfolio balancing, needs to minimize the cost for imbalance satisfaction in real-time by proposing a set of optimal personalized prices to the prosumers. On the other hand, the prosumers, as price taker and self-interested agents, want to maximize their profit by changing their supplies or demands and providing flexibility based on the proposed personalized prices. We model this problem as a bilevel optimization problem. We first show that the optimal solution of this bilevel optimization problem can be found by solving an equivalent convex problem. In contrast to the state-of-the-art Mixed-Integer Programming (MIP)-based approach to solve bilevel problems, this convex equivalent has very low computation time and is appropriate for real-time applications. Next, we compare the optimal solutions of the proposed personalized scheme and a uniform pricing scheme. We prove that, under the personalized pricing scheme, more prosumers contribute to the RTBM and the aggregator’s cost is less. Finally, we verify the analytical results of this work by means of numerical case studies and simulations.

To remain competitive, companies must decide on new, desirable products. This can be achieved by integrating insights how customers use a product into the process of deciding on a new product. Currently, this process is primarily based on market research that can only reveal the intention of consumers. Through the digitization of products, companies have access to large amounts of customer data that allow the application of data analytics methods. We provide a taxonomy of artificial intelligence, machine learning and data analysis, so that the notion of data analytics can be defined. Thus, the terms customer usage data, as well as a generic, five-stage product decision process (PDP) are defined and differentiated from consumer data and the product development process. Eventually, we show which data analytics methods on customer usage data can be used in order to tackle current challenges within the PDP. We incorporate the results of our structured literature review by connecting selected examples to our concept of the PDP. Our insights help to apply the proper data analytics methods in the PDP and thereby address the interplay between product decision and product development. Finally, future research directions for data analytics methods on customer usage data are put forward.

Broiler chickens are specially raised for meat production and their supply chain is composed of breeders, hatcheries, feed mills, farms, slaughterhouses, wholesalers, and retailers. The coordination between supply chain players has great potential to streamline production and increase competitiveness in the market. A critical element in the decision-making process of this agri-system is the weight gain of broiler chickens. Variability in chicken growth patterns can affect production plans in practice. The most efficient weight estimation models require sensors in real-time, which are not available in many companies. Thus, some small farms require a simpler approach to adjust production planning based on the uncertainty of chickens’ weight while they transition to new technologies. This research proposes an optimization-based methodology for the integration of farms and slaughterhouses to plan production under chicken growth uncertainty. The methodology includes two models: (i) a two-stage stochastic model that supports lot-sizing and inventory management decisions while considering scenarios of chicken growth uncertainty; and (ii) a mixed-integer linear programming model that supports lot allocation. We present the results of implementing the methodology in a poultry company in Santa Marta (Colombia), where we improved costs by 8.6% while meeting tactical, biological, and biosecurity constraints.

A linear optimization problem which is amenable to column generation and contains a single parameter in the objective function is considered. We extend and adapt the standard linear programming column generation scheme to effectively and efficiently solve this problem for all values of the parameter. As a potential application we consider bi-objective discrete optimization and describe how the one-parametric column generation scheme can be used to form an outer approximation of the Pareto frontier for such a problem.

This paper investigates the social-welfare efficiency drivers of public university hospitals in Brazil by focusing on how the surrounding social welfare conditions may affect their performance. A novel Genetic Envelopment Analysis (GEA) approach is developed here to this end. Subsequently, LASSO regression is applied to filter the impact of social-welfare related variables –on efficiency scores. Results indicate that beds, number of employees and number of doctors are the influential factors in determining the efficiency level, while the operating scales are not relevant to the productivity level. We further find that there is a degree of difference related to the efficiency level among the hospitals in the sample. Finally, our results show that GEA estimates present higher discrimination and dispersion compared to DEA, SFA and TOPSIS, also GEA provides the most reliable and accurate results. In the second stage analysis, we find that female population ratio and high school ratio significantly affect the efficiency level in a negative manner, while the urban population ratio has a significant and positive impact. Based on these results, we provide important policy implications.

In the last decade, increasing costs and organizational concerns regarding the funding and allocation of financial resources have led to significant attention being given to financial flow and its effects on planning decisions throughout supply chain networks. This study aims to develop a simulation-optimization model to integrate the financial and physical flows in a supply chain planning problem under economic uncertainty. The simulation-optimization model includes a mixed-integer linear programming model and a simulation-based optimization model that are connected through an iterative process. The economic value added (EVA) index is used to measure the financial performance of the supply chain. This study extends the literature on two research domains namely supply chain planning and finance and simulation-optimization modelling for supply chain management. The proposed model applies a scenario approach to cope with economic uncertainty in the supply chain. To demonstrate the efficiency of the proposed model, the performance of the proposed model in solving a test problem from the recent literature is compared with the performance of a conventional simulation-based optimization and mixed-integer linear programming approaches. The results of the study show a minimum of 6% improvement in the EVA obtained from the proposed simulation-optimization model compared to the EVA obtained from the simulation-based optimization model in all the studied scenarios. Moreover, the standard deviation of the EVA obtained from the proposed simulation-optimization model is at least 69% lower than the EVA obtained from the mixed integer programming model in all the studied scenarios. This shows that the proposed simulation-optimisation approach is more robust to economic uncertainty than the mixed-integer linear programming approach.