Operations Research Perspectives最新文献

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.

Despite a lot of research about predictive maintenance for onshore and offshore windmill farms, nearly no investigation has been performed to obtain the optimal sequence in which windmills are to be served in a predefined time frame. The higher fuel costs and the increasing time pressure on maintenance jobs urge the need for optimisation, so offshore windmills can be serviced at minimal costs and within a limited time frame. To minimise distance travelled, fuel consumption and average tardiness of all maintenance tasks to be carried out, a multi-objective, non-dominated sorting island model of genetic algorithms is used.

The following novel contributions are realised: (i) A multi-objective island model is used, where on each island a different genetic algorithm is used to minimise a separate cost function per island. (ii) A set of non-dominated maintenance sequences, shown as a Pareto plane, are computed and (iii) these optimal solutions can be used by the planner to select the route to be followed by the CTV when travelling from windmill to windmill during a maintenance sequence.

Tests on two of the islands have resulted in a relative improvement of around 65 to 70% on fuel consumption and distance in relation to a random sequence, while the third island has generated a relative gain of 69% in average weighed tardiness. The three islands combined have resulted in a set of Pareto optimal sequences for offshore windmill maintenance.

We analyze an integrated inventory supply chain and seek the optimal production lot, optimal production rate, and optimal (integer) number of shipments per production lot. An increasing need for higher operational efficiency, as well as growing competition among multiple products for a limited storage capacity, is driving retailers to require more frequent shipping. This imposes pressure on suppliers to share the shipping cost with retailers. The sharing ratio of the shipment cost has not previously been considered within the context of an integrated supply chain. Therefore, we contribute to the literature by investigating this entirely new parameter, assuming that the shipment cost is shared between a manufacturer and a retailer. We also consider a distributed supply chain in which each party optimizes its own cost. We analyze the problem of finding the optimal sharing ratio of the shipment cost for such a supply chain and show that there exists a specific choice of shipment cost-sharing ratio (set by the manufacturer) that results in total costs similar to those obtained in the integrated inventory model. We develop deterministic models that provides basic insights into the investigated problem. Through mathematical analysis of a nested-designs model, we provide intermediate results (which are of interest in their own right) as well as optimal analytical solutions. We show, through numerical examples, that in the scenario where each party optimizes its own cost, the manufacturer's shipment cost is a central control variable in the sense that it affects the costs of both parties.