Search results

The target of this research is to develop a mathematical model which combines the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and the Multi-Skilled Resource-Constrained Project Scheduling Problem (MSRCPSP). Due to the importance of resource management, the proposed formulation comprises resource leveling considerations as well. The model aims to simultaneously optimize: (1) the total time to accomplish all projects and (2) the total deviation of resource consumptions from the uniform utilization levels.

The K-Means (KM) and Fuzzy C-Means (FCM) clustering methods have been separately applied to discover the clusters of activities which have the most similar resource demands. The discovered clusters are given to the scheduling process as priori knowledge. Consequently, the execution times of the activities with the most common resource requests will not overlap. The intricacy of the problem led us to incorporate the KM and FCM techniques into a meta-heuristic called the Bi-objective Symbiosis Organisms Search (BSOS) algorithm so that the real-life samples of this problem could be solved. Therefore, two clustering-based algorithms, namely, the BSOS-KM and BSOS-FCM have been developed.

Comparisons between the BSOS-KM, BSOS-FCM and the BSOS method without any clustering approach show that the clustering techniques could enhance the optimization process. Another hybrid clustering-based methodology called the NSGA-II-SPE has been added to the comparisons to evaluate the developed resource leveling framework.

The practical importance of the model and the clustering-based algorithms have been demonstrated in planning several construction projects, where multiple water supply systems are concurrently constructed.

Reviewing the literature revealed that there was a need for a hybrid formulation that embraces the characteristics of the RCMPSP and MSRCPSP with resource leveling considerations. Moreover, the application of clustering algorithms as resource leveling techniques was not studied sufficiently in the literature.

The purpose of this paper is to evaluate effects of simultaneous supplementation of milk protein concentrate (MPC) as texture modifier and microencapsulated Lactobacillus paracasei (L. paracasei) (entrapped in gellan–caseinate) on physico-chemical, sensorial and microbial characteristics of yogurt during storage time.

L. paracasei cells were encapsulated through unique pH triggered gelation technique using combination of sodium caseinate-gellan gum as protective shell material. MPC was also used to improve physico-chemical indices of probiotic yogurt at different levels (0–3 percent).

The results showed that yogurt samples containing encapsulated L. paracasei showed lower post-acidification and higher viability. Samples containing encapsulated L. paracasei showed less syneresis amount, due to possible hydration of shell material, also application of MPC could reduce this attribute during storage time. The numbers of probiotic bacteria were remained above the recommended therapeutic minimum throughout the samples.

The findings suggest a practical ingredient in probiotic dairy product. Simultaneous usage of this kind of encapsulation via MPC enhanced sensorial and physical properties of probiotic yogurt while of no reduction in viable counts survival.

This study revealed usage of microcapsules of L. paracasei prepared by the gelation of sodium caseinate-gellan gum could be a suitable manner for delivery of probiotics in fermented dairy products like yogurt.

This study examines the diffusion process of corporate sustainability (CS) in the global automotive industry. It discusses the different roles played by the automakers, as the industry’s focal firms, in diffusing CS strategies throughout their respective supply networks. Studies have explained this phenomenon as being the result of the higher levels of stakeholder exposure faced by focal firms, which generate higher levels of supplier sustainability risk. In this context, the authors examine the effects of three network-related firm characteristics – resource dominance, resource substitutability, and network centrality – in determining the effectiveness of a firm in diffusing CS in its network. For that purpose, we present a theoretical framework from which we derive a set of hypotheses and test them on a sample of the global automotive supply network containing 10,726 firms linked by 45,044 inter-firm relationships. The results lend significant support to the argument that these network-related firm attributes are crucial mechanisms to the process of diffusion of CS strategies in a supply network, thus contributing to extant literatures in strategic management, international business, and sustainable supply chain management.

The purpose of this paper is to give an overview of the design issues of permanent magnet machines for the hybrid electric and plug‐in electric vehicles, including railway traction and naval propulsion.

Focus is given on both synchronous permanent magnet and reluctance machines. An overview of the design rules are provided, covering the topics of: fractional‐slot windings, fault‐tolerant configurations, flux‐weakening capability, and torque quality.

The peculiarities of these machines and the advanced design considerations to fit the automotive requirements are analyzed.

The paper includes a wide description of innovative electrical machines for electric vehicles, including not only the traction capability, but also analysis of features as weight reduction, torque ripple reduction, increase of fault tolerance, and so on.

Despite the wide dissemination and application of current signature analysis (CSA) in general industry, CSA is not commonly used in the wind industry, where the use of vibration signals predominates. Therefore, the purpose of this paper is to review the use of generator CSA (GCSA) in the online fault detection and diagnosis of wind turbines (WTs).

This is a bibliographical investigation in which the use of GCSA for the maintenance of WTs is analyzed. A section is dedicated to each of the main components, including the theoretical foundations on which GCSA is based and the methodology, mathematical models and signal processing techniques used by the proposals that exist on this topic.

The lack of appropriate technology and mathematical models, as well as the difficulty involved in performing actual studies in the field and the lack of research projects, has prevented the expansion of the use of GCSA for fault detection of other WT components. This research area has yet to be explored, and the existing investigations mainly focus on the gearbox and the doubly fed induction generator; however, modern signal treatment and artificial intelligence techniques could offer new opportunities in this field.

Although literature on the use of GCSA for the detection and diagnosis of faults in WTs has been published, these papers address specific applications for each of the WT components, especially gearboxes and generators. For this reason, the main contribution of this study is providing a comprehensive vision for the use of GCSA in the maintenance of WTs.

This paper proposed a bi-level mathematical model for location, routing and allocation of medical centers to distribution depots during the COVID-19 pandemic outbreak. The developed model has two players including interdictor (COVID-19) and fortifier (government). Accordingly, the aim of the first player (COVID-19) is to maximize system costs and causing further damage to the system. The goal of the second player (government) is to minimize the costs of location, routing and allocation due to budget limitations.

The approach of evolutionary games with environmental feedbacks was used to develop the proposed model. Moreover, the game continues until the desired demand is satisfied. The Lagrangian relaxation method was applied to solve the proposed model.

Empirical results illustrate that with increasing demand, the values of the objective functions of the interdictor and fortifier models have increased. Also, with the raising fixed cost of the established depot, the values of the objective functions of the interdictor and fortifier models have raised. In this regard, the number of established depots in the second scenario (COVID-19 wave) is more than the first scenario (normal COVID-19 conditions).

The results of the current research can be useful for hospitals, governments, Disaster Relief Organization, Red Crescent, the Ministry of Health, etc. One of the limitations of the research is the lack of access to accurate information about transportation costs. Moreover, in this study, only the information of drivers and experts about transportation costs has been considered. In order to implement the presented solution approach for the real case study, high RAM and CPU hardware facilities and software facilities are required, which are the limitations of the proposed paper.

The main contributions of the current research are considering evolutionary games with environmental feedbacks during the COVID-19 pandemic outbreak and location, routing and allocation of the medical centers to the distribution depots during the COVID-19 outbreak. A real case study is illustrated, where the Lagrangian relaxation method is employed to solve the problem.