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Robust Lot-sizing and Supplier Selection under Lead Time Uncertainty
No full textAbstract book, voir url dans champ "Voir aussi"International audienceWe study the single item lot-sizing problem with supplier selectionand uncertain lead time. We consider the situation where a companyhad preselected a set of suppliers for an item, and these suppliers havedifferent prices, different lead times, but also different reliability. Weaim to provide a robust optimization approach to decide when to order,how much to order, and from which suppliers, in the context of un-certain delivery lead time. We formulate the robust optimization prob-lem with polyhedral budgeted uncertainty sets. This formulation doesnot require assumptions on order crossovers, order splitting, or on thestructure of the demand or lead times. We propose an exact row andcolumn generation algorithm to solve the considered problem, alongwith some enhancements including a fast cut generation procedure. Toimprove the scalability of the approach we propose several heuristics,including a hybrid of the robust counterpart reformulation and row andcolumn generation, and a fix-and-optimize approach in the row andcolumn generation framework. Experimental results show that the fix-and-optimize approach provides good results. Finally, we provide in-sight into the reaction of the decision-maker to unreliable suppliers.One of the conclusions is that in the considered framework, an ex-tremely risk-averse decision-maker selects a single supplier, namelythe most reliable one even if it does not offer the lowest price
Robust Optimization Approaches for Purchase Planning with Supplier Selection under Lead Time Uncertainty
No full textInternational audienceSupplier reliability is a critical issue for manufacturing companies. Delivery delays from suppliers create backlog and firefighting on the shop floors. To avoid disruption and hedge against supplier lead time uncertainty, companies rely on diversification, multi-sourcing, and safety lead times. In multi-sourcing, the buyer might order the same product (raw material) from different suppliers. The design of a robust and cost-efficient purchasing/ordering plan in multi-sourcing is a complex task which has a strong impact on the performance of the company. In this study, we investigate the use of robust optimization for the integrated lot-sizing and supplier selection problem under lead time uncertainty. More specifically, we use polyhedral budgeted uncertainty sets. The resulting model determines the ideal lot sizes to minimize the total costs taking into consideration suppliers’ reliability and prices. To solve this problem, a row and column generation approach is proposed. To alleviate scalability issues, we enhance the row and column generation through a robust counterpart formulation, and we propose an efficient fix-and-optimize approach. Our extensive computational experiments show that the fix-and-optimize approach yields good quality solutions within a reasonable amount of computational time. We provide insights into supplier diversification based on the risk profile of the decision-maker. One of the conclusions is that an extremely risk-averse decision-maker selects a single supplier, namely the most reliable one even if it does not offer the lowest price
Customer-Perceived Value Influence on Luxury Hotel Purchase Intention Among Potential Customers
No full textInternational audienceThe purpose of this study is to investigate five dimensions of customer-perceived value that may affect potential customers' intention to stay at luxury hotels. An online survey was conducted in August 2021 to collect data from 252 potential customers of luxury hotels in China. Partial least squares-based structural equation modeling was used to analyze data. The results show that financial value, hedonic value, and green value, respectively, have a positive impact on potential customers' intention to stay at luxury hotels. Neither functional value nor symbolic/expressive value is significantly associated with the potential customer's intention to stay at luxury hotels
Sustainable closed-loop supply chain with energy efficiency: Lagrangian relaxation, reformulations and heuristics
No full textInternational audienceResearch on the development of sustainable supply chain models is highly active nowadays. Merging the concept of supply chain management with sustainable development goals, leads to simultaneous consideration of all economic, environmental and social factors. This paper addresses the design of a sustainable closed-loop supply chain including suppliers, manufacturers, distribution centers, customer zones, and disposal centers considering the consumption of energy. In addition, the distribution centers play the roles of warehouse and collection centers. The problem involves three choices of remanufacturing, recycling, and disposing the returned items. The objectives are including the total profit, energy consumption and the number of created job opportunities. As far as we know, these objectives are rarely considered in a sustainable closed-loop supply chain model. The proposed model also responds to the customer demand and also addresses the real-life constraints for location, allocation and inventory decisions in a closed-loop supply chain framework. Another novelty of this research is to develop a set of efficient Lagrangian relaxation reformulations and fast heuristics for solving a real-world numerical example. The results have revealed that the obtained solution is feasible and the developed solution algorithm is highly efficient for solving supply chain models. Finally, a comprehensive discussion is provided to highlight our findings and managerial insights from our results
A Scientometric Exploration of Crowdsourcing: Research Clusters and Applications
No full textInternational audienceCrowdsourcing is a multidisciplinary research area that represents a rapidly expanding field where new applications are constantly emerging. Research in this area has investigated its use for citizen science in data gathering for research and crowdsourcing for industrial innovation. Previous studies have reviewed and categorized crowdsourcing research using qualitative methods. This has led to the limited coverage of the entire field, using smaller discrete parts of the literature and mostly reviewing the industrial aspects of crowdsourcing. This study uses a scientometric analysis of 7059 publications over the period 2006–2019 to map crowdsourcing research to identify clusters and applications. Our results are the first in the literature to map crowdsourcing research holistically. In this article, we classify its usage in the three domains of innovation, engineering, and science, where 11 categories and 26 subcategories are further developed. The results of this article reveal that the most active scientific clusters where crowdsourcing is used are environmental sciences and ecology. For the engineering domain, it is computer science, telecommunication, and operations research. In innovation, idea crowdsourcing, crowdfunding, and crowd creation are the most frequent areas. The findings of this study map crowdsourcing usage across different fields and illustrate emerging crowdsourcing applications
CEO power and corporate social responsibility decoupling
No full textInternational audienceWhile extending the scarce literature on determinants of corporate social responsibility (CSR) decoupling, we examine the impact of CEO power on CSR decoupling. Using panel data of US firms for 2002–2017, we find that CEO power increases CSR decoupling. Our results remain consistent after controlling for the endogeneity problem. Aligned with the managerial power theory, our results suggest that firms with powerful CEOs are more likely to manage CSR performance through decoupling
Family control and corporate risk-taking in China: Does working capital strategy matter?
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Sweat it for sustainability: Impact of physical activity/exercise on sustainable consumption
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Does waste management affect firm performance? International evidence
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Olive oil supply chain design with organic and conventional market segments and consumers’ preference to local products
No full textInternational audienceRecent market studies showed that the demand for organic and local agrifood products is increasing despite their higher prices. The agribusiness actors should therefore rethink the supply chain configuration to cope with new market trends characterized by the rise of the organic segment and the increase of consumers' preference to more local products. This study focuses on the olive oil sector and proposes a mixed-integer non-linear optimization model for the design of olive oil supply chains while incorporating organic and conventional market segments and considering, for each segment, a supply chain proximity- and price-sensitive demand. The model is developed with the collaboration of olive oil producers in the Mediterranean area. Thanks to this industrial collaboration, we account for real-world practices and constraints and apply the model to a realistic case study. We first linearize the model and show that it can be efficiently solved with commercial optimization softwares. Based on numerical experiments, we derive a series of managerial insights that are applicable to the considered case study, some of them are not intuitive. For instance, we show that an increase in consumers’ preference to more local products may lead the producer to offer products with a more global supply chain. The conventional product variety may be produced with a more local supply chain than the organic (premium) variety. Finally, offering a mix of organic and conventional varieties instead of only one variety would lead to implementing a more local supply chain