Abstract

Abstract
Tourism plays a very important role in society. However, it has many negative impacts, such as the increasing problem of food wasting in hotel restaurants, especially when providing all-inclusive/all-you-can-eat breakfasts and dinners. This study aims to propose some activities which could lead to food wasting reduction. Through a questionnaire (with 132 respondents, from various countries), the authors wanted to find out respondents' attitudes towards suggested solutions and this problem in general. As part of the questionnaire, four possible activities were proposed in order to help to reduce food wasting and contribute to better food security in today's world. The questionnaire results proved that respondents would appreciate such opportunities, therefore answering the research question. Fridges (in which people could put their untouched leftovers), samples (before people take the whole portion of food), special cards (containing the ingredients used for the preparation of the meal), and daily announcements (of food wasting in kilograms) are four possible ways to reduce food wasting in hotel restaurants.

Abstract
Up to one third of the global food production depends on the pollination of honey bees, making them vital. This study defines a methodology to create a bee hive health monitoring system through image processing techniques. The approach consists of two models, where one performs the detection of bees in an image and the other classifies the detected bee’s health. The main contribution of the defined methodology is the increased efficacy of the models, whilst maintaining the same efficiency found in the state of the art. Two databases were used to create models based on Convolutional Neural Network (CNN). The best results consist of 95% accuracy for health classification of a bee and 82% accuracy in detecting the presence of bees in an image, higher than those found in the state-of-the-art.

Abstract

Abstract
The fast-moving consumer goods sector relies on economies of scale. However, its assortments have been overextended as a means of market share appropriation and top-line growth. This paper studies the se-lection of the optimal set of products for fast-moving consumer goods producers to offer, as there is no previous model for product line selection that satisfies the requirements of the sector. Our mixed -integer programming model combines a multi-category attraction model with a capacitated lot-sizing problem, shared setups and safety stock. The multi-category attraction model predicts how the demand for each product responds to changes within the assortment. The capacitated lot-sizing problem allows us to account for the indirect production costs associated with different assortments. As seasonality is prevalent in consumer goods sales, the production plan optimally weights the trade-off between stocking finished goods from a long run with performing shorter runs with additional setups. Finally, the safety stock extension addresses the effect of the demand uncertainty associated with each assortment. With the computational experiments, we assess the value of our approach using data based on a real case. Our findings suggest that the benefits of a tailored approach are at their highest in scenarios typical fast-moving consumer goods industry: when capacity is tight, demand exhibits seasonal patterns and high service levels are required. This also occurs when the firm has a strong competitive position and consumer price-sensitivity is low. By testing the approach in two real-world instances, we show that this decision should not be made based on the current myopic industry practices. Lastly, our approach obtains profits of up to 9.4% higher than the current state-of-the-art models for product line selection.

Abstract
This work presents the mapping of the traveling salesperson problem (TSP) based in pseudo-Boolean constraints to a graph of the D-Wave Systems Inc. We first formulate the problem as a set of constraints represented in propositional logic and then resort to the SATyrus approach to convert the set of constraints to an energy minimization problem. Next, we transform the formulation to a quadratic unconstrained binary optimization problem (QUBO) and solve the problem using different approaches: (a) classical QUBO using simulated annealing in a von Neumann machine, (b) QUBO in a simulated quantum environment, (c) QUBO using the D-Wave quantum machine. Moreover, we study the amount of time and execution time reduction we can achieve by exploring approximate solutions using the three approaches. Results show that for every graph size tested with the number of nodes less than or equal to 7, we can always obtain at least one optimal solution. In addition, the D-Wave machine can find optimal solutions more often than its classical counterpart for the same number of iterations and number of repetitions. Execution times, however, can be some orders of magnitude higher than the classical or simulated approaches for small graphs. For a higher number of nodes, the average execution time to find the first optimal solution in the quantum machine is 26% (n = 6) and 47% (n = 7) better than the classical.