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This paper presents the implementation of simulated annealing (SA) method, an artificial intelligence technique, to solve the optimization problem known as the school bus routing problem (SBRP). A specific challenge in all artificial intelligence optimization techniques is the selection of appropriate value parameters. One contribution of this paper is the implementation of a design of experiments technique to provide statistical support for parameter selection. The SBRP is formulated as a 0–1 integer linear programming model, where the objective function is to minimize the total cost. Because this problem is combinatorial in nature, it is not possible to find exact solutions in an adequate time, calling for the use of an artificial intelligence optimization technique. The proposed technique is SA due to its modeling flexibility and processing speed. To demonstrate the performance of the proposed algorithm, several experiments with real instances were carried out, showing that the metaheuristic algorithm performs better in quality and time than the classic routing method.

In food services, such as cafeterias, when the servers’ capacity is less than the customers’ demand, coming with a poorly layout, the generation of large queues with long waiting times, spring out. This scenario is even worsened when the demand’s variability appears, creating problems such as the loss of customer, revenue’s decrease, and a poorly service quality. Therefore, it is important to look for solutions to revert such problems. Lean principles jointly with queuing theory might be a good approach to address them, by the application of different tools and methodologies that contribute not only to minimize or even eliminate queues but also reduce the waiting times in lines. The study presented herein includes a proposal of a methodology that merges both, Lean and queuing theory, applied to a Mexican university cafeteria, which during rush time it blows up. The methodology includes three phases: the current process analysis, the arrival analysis, and the improvement analysis. By applying different tools and using simulation scenarios in each phase, an improvement in the development of the activities in the cafeteria is reached, and several strategies are designed to enhance the behavior of the system.

Isoflavones profiles and cellular antioxidant activity (CAA) of soybeans (Glycine max) germinated for different times with several sodium selenite (Na2SeO3) concentrations were assessed. The experiments were performed using central composite design and isoflavones evaluated by HPLC–UV and HPLC-MS-TOF. Malonylated genistein and malonylated daidzein glycosides were the main isoflavones associated to the soybean sprouts. Soybeans germinated for 48 h at 20 °C with a frequent spraying of water containing less than 32 mg/L of Na2SeO3 per liter contained the optimal concentrations of selenium (Se), total isoflavones and cellular antioxidant activity. This research demonstrated that Se-enriched soybean sprouts are a good source of both dietary Se and isoflavones and potentially can be used to formulate new functional foods.

Around the world tourism industry represents economic benefits to the countries in where the tourist attractions are located. The purpose of this chapter is to search and review recent researches related to the area of tourism under supply chain management and optimization models perspectives. The main aim of this chapter is to identify and discuss how the tourism supply chain is studied when it is subject to different economic, market, and optimization strategies. Considering the period of 2005 to 2016, a systematic review was performed using research studies in the area of tourism supply chain management. The results show that game theory is used as a theoretical base in the majority of the cases, but several novel approaches are also incorporated to the analysis. This review can be used as a complement of the previous works and a valuable information source for the decision makers involved in the tourism area.

Design of personnel teams has been studied from diverse perspectives; the most common are the people and systems requirements perspectives. All these point of view are linked, which is the reason why it is necessary to study them simultaneously. Considering this gap, a decision making model is developed based on factors, models, and requirements mentioned in the literature. The model is applied to a real case. The findings indicate that the Personnel Behavior Based Lean model (PBBL) can be converted into a decision making model for the selection of team members. The study is focused not only on the individual candidates’ knowledge, skills, and aptitudes, but also on how the model considers the company requirements, conflicts, and the importance of each person to the project.

Response surface methodology and desirability function were used as tools to optimize wheat (Triticum aestivum L) germination conditions in the presence of SeO3Na2 to obtain selenium (Se)-enriched kernels. The effects of Se concentration (32.85–54.76 mg SeO3Na2/L), germination time (24–48 h) and germination temperature (18–25 °C) were investigated. The best Se-enriched wheats were selected based on selenomethionine (SeM) content, α-amylase activity (AC), and sprouting features such as kernel moisture (KM) content, germinated grains (GG) and radicle growth (RG). A second order polynomial model produced a satisfactory fitting of the experimental data considering the total kernel SeM content (R2adj = 58.8, S = 14.29), AC (R2adj = 59.80, S = 48.78), KM (R2adj = 71.30, S = 2.94), GG (R2adj = 66.70, S2 = 1.20) and RG (R2adj = 84.60, S = 23.38). Total desirability (δ) value was 67.6% in operational conditions (35 mg Se/L, 25 °C and 24 h germination). Under these conditions sprouted kernels contained 41.8 g/100 g, 95.5% and 98% for KM, GG and RG respectively, and 54 mg Se/kg as SeM and 54.8 CU of AC. In conclusion, the generated models with desirability methodology could be useful to optimize SeM content and alpha amylase activity in Se-enriched sprouted wheat kernels.