Trust and Inventory Replenishment Decision Under Continuous Review System

This thesis examines the impact of inventory manager’ trust on their replenishment decision. We conduct this study in the experimental environment and design an experiment with unknown market demand, local information, and under continuous replenishment review. We also develop a multi-round trust measurement procedure through questionnaires and administer it in the context of a laboratory experiment. To conduct the study, we take the three following steps: First we investigate inventory replenishment decision under continuous review in a decentralized supply chain. Our results show that order time intervals increase along the supply chain. Inventory managers’ replenishment decisions affect their own and the other echelons’ costs. Moreover, we find that wholesaler plays the smoothing role in the decentralized supply chain. Second, we develop a multi-round trust measurement procedure through questionnaires and conduct it in the context of a laboratory experiment. This design allows us to observe inventory managers’ trust in customer and trust in supplier over time. Our results show that trust exist in a decentralized supply chain, with local information, no communication, and no access to the market demand, and trust level varies in a continuum of intensity in a decentralized supply chain. Also, we find that trust evolves and for some echelons it grows over time. We further examine trust in customer and trust in supplier along the supply chain. Our results suggest that trust in supplier is the lowest in the middle of supply chain and that trust in customer decreases while moving upstream along a decentralized supply chain. Finally, we study the impact of trust in inventory replenishment decision and analyze data at individual and echelon level. Our results show that low trust in customer is linked to high order quantity and long order time intervals at the individual levels. Also, results on the echelon level suggest that distributor exhibits the lowest trust, highest order quantity and largest order time intervals among echelons, and retailer is the only echelon that considers trust in supplier while placing order quantities to upstream supplier. We further explore the inventory holding behavior of managers and find that inventory managers hold higher inventory level when they have lower trust in customer and trust in their upstream supplier. This research fits within the behavioral operations field

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. Three main solutions have been proposed in the literature to cope with this problem: (1) the implementation of generative algorithms to increase the amount of under-represented input samples, (2) the employment of a classifier being powerful to learn from imbalanced and noisy data, (3) the development of an efficient data pre-processing including feature extraction and data augmentation. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal-based FDD system for imbalanced conditions. Specifically, it first extracts the fault features, using Fourier and wavelet transforms to make full use of the signals. Then, it employs Wasserstein Generative Adversarial Networks (WGAN) to generate synthetic samples to populate the rare fault class and enhance the training set. Moreover, to achieve a higher performance a novel combination of Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning Machine (WELM) is proposed. To verify the effectiveness of the developed framework, different datasets settings on different imbalance severities and noise degrees were used. The comparative results demonstrate that in different scenarios GAN-CLSTM-ELM outperforms the other state-of-the-art FDD frameworks.Comment: 23 pages, 11 figure

Replenishment behaviour in sequential supply chains

Inventory managers do not predominantly follow normative optimisation models. At best, they introduce a level of bounded rationality in their inventory replenishment decisions. This paper examines the behaviour of inventory decision-makers under continuous review in a decentralised supply chain, using an experimental approach with unknown market demand and local information availability. The analysis reveals that not only the magnitude and the variability of order quantity tend to be larger, but also that the order-time intervals is lengthen and highly variable while moving upstream along the supply chain. The role of the inventory managers’ replenishment decisions on the echelon holding, backorder, and total costs, is also investigated. Finally, a normative model is designed and its solutions are compared to the experimental results. It is observed that humans do not operate in a perfectly optimal way, but are generally reluctant to risk increasing backorder costs and reducing inventory carrying cost, even if this would lead to lower total cost

Replenishment behavior in sequential supply chains

Inventory managers do not predominantly follow normative optimization models. At best, they introduce a level of bounded rationality in their inventory replenishment decisions. This paper examines the behavior of inventory decision-makers under continuous review in a decentralized supply chain, using an experimental approach with unknown market demand and local information availability. The analysis reveals that not only the magnitude and the variability of order quantity tend to be larger, but also that the order-time intervals is lengthen and highly variable while moving upstream along the supply chain. The role of the inventory managers replenishment decisions on the echelon holding, backorder, and total costs, is also investigated. Finally, a normative model is designed and its solutions are compared to the experimental results. It is observed that humans do not operate in a perfectly optimal way, but are generally reluctant to risk increasing backorder costs and reducing inventory carrying cost, even if this would lead to lower total cost

Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review

The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities. These strides have, in turn, impacted smart eco-cities, catalyzing ongoing improvements and driving solutions to address complex environmental challenges. This aligns with the visionary concept of smarter eco-cities, an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies. However, there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions. To bridge this gap, this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability. To ensure thoroughness, the study employs a unified evidence synthesis framework integrating aggregative, configurative, and narrative synthesis approaches. At the core of this study lie these subsequent research inquiries: What are the foundational underpinnings of emerging smarter eco-cities, and how do they intricately interrelate, particularly urbanism paradigms, environmental solutions, and data-driven technologies? What are the key drivers and enablers propelling the materialization of smarter eco-cities? What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities? In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices, and what potential benefits and opportunities do they offer for smarter eco-cities? What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities? The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices, as well as the formidable nature of the challenges they pose. Beyond theoretical enrichment, these findings offer invaluable insights and new perspectives poised to empower policymakers, practitioners, and researchers to advance the integration of eco-urbanism and AI- and AIoT-driven urbanism. Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions, stakeholders gain the necessary groundwork for making well-informed decisions, implementing effective strategies, and designing policies that prioritize environmental well-being

Prioritizing the effective strategies for construction and demolition waste management using fuzzy IDOCRIW and WASPAS methods

Purpose The construction industry is a key driver of economic growth. However, the adverse impacts of construction and demolition waste (CDW) resulted from the active construction projects on the economy, environment, public health and social life necessitates an appropriate control and management of this waste stream. Developing and promoting the construction and demolition waste management (CDWM) hierarchy program at the strategic level is essential. Design/methodology/approach This study aims to propose a hybrid decision model that hybridizes the Integrated Determination of Objective Criteria Weights (IDOCRIW) and weighted aggregated sum product assessment (WASPAS) under a fuzzy environment. Findings The proposed method ranks the potential strategic alternatives by the sustainable development criteria to improve the performance of CDWM. As indicated in the results, the fuzzy approach in the decision-making process enables the transformation of linguistic variables into fuzzy numbers that show uncertainty and ambiguity in real-world systems. Moreover, the close correlation between the final ranking of the proposed methodology and the average priority order of the strategic alternatives obtained by five different multi-criteria decision-making (MCDM) methods implies the validity of the model performance. Practical implications This proposed model is an appropriate tool to effectively decide on the development of CDWM from a strategic point of view. It aims to establish an MCDM framework for the evaluation of effective strategies for CDWM according to the indices of sustainable development. Implementing proper operational plans and conducting research in CDWM has the highest priority, and enacting new and more stringent laws, rules and regulations against the production of CDW has secondary priority. This study contributes to the field by optimizing the CDWM by applying the top-priority strategies resulted from the proposed fuzzy hybrid MCDM methodology by the decision-makers or policy-makers to reach the best managerial strategic plan. Originality/value In the proposed methodology, the IDOCRIW technique is utilized and updated with the triangular fuzzy numbers for the first time in the literature to derive the weights of sustainable development criteria. The fuzzy WASPAS method is utilized for evaluation and providing a final ranking of the strategic alternatives

Comparison the effect of movento, a chemical pesticide, with chitosan, a biologic pesticide, on female reproductive system in Balb/C mice

Chemical pesticides possess toxic and destructive impacts on the reproductive system and disrupt endocrine function. In this experimental study, the effect of movento, as a chemical pesticide, was investigated in comparison with chitosan, a biological pesticide, on the female reproductive system in vivo. In this study, 56 mice were randomly dived into 7 groups including control and experimental groups treated with movento and chitosan. After a 21-day treatment, mice were killed and their ovaries and blood being collected. In addition, the samples were fixed and stained with H & E method. The results exhibited that treatment with 2.5 and 5 mg/kg chitosan had no significant effect on the number and diameter of primary, secondary and antral follicles, while these items were significantly reduced in 10 mg/kg ch-itosan group and all movento-treated groups as well. In addition, the level of sexual hormones, such as estradiol, FSH and LH, was decreased in 10 mg/kg chitosan group and all movento-treated groups in comparison with the control gr-oup. The findings showed that movento affected the sexual hormone levels, ovary and ovarian follicle structure and in-duced abnormality in female reproductive system, while chitosan, as a biological pesticide, should be used due to its minimum effects on female reproductive syste

Movento influences development of granulosa cells and ovarian follicles and FoxO1 and Vnn1 gene expression in BALB/c mice

Objective(s): Pesticides has wide range of infertility in female reproductive. This study was done to evaluate the effect of movento pesticide on development of granulosa cells and ovarian follicles and FoxO1 and Vnn1 gene expression in BALB/c mice. Materials and Methods: In this study 40 healthy BALB/c mice 5-6 weeks age were used. Animals were randomly allocated into four groups. Control (without any intervention), three experimental groups received 25, 50 and 100 mg/kg movento dissolved in PBS by gavage for 21 days. Animals scarified after three weeks. For determining the effects of movento on granulosa cells in culture, treatments were conducted to movento (125, 250, 500 μg/ml) for 24 hr. We surveyed the expression of the FoxO1 and Vnn1 in granulosa cells in vitro, and its relation to cell death by flowcytometer and DAPI. Levels of FoxO1 and Vnn1 were analyzed by real-time PCR. Results: Exposure to movento significantly decreased ovarian weight and the number of primary, secondary and antral follicles. Further, treatment with different concentration of movento induced apoptosis on granulosa cells. Gene expression analysis showed the transcriptional expression of FoxO1 and vnn1 in granulosa cells. Level of Vnn1 mRNA in granulosa cells was decreased in granulosa cells and expression of FoxO1 significantly increased in treated groups in compare to controls (P-valu