1,721,298 research outputs found
A Hierarchical Framework for Spares-Driven Maintenance Tasks' Reviewing, Planning and Scheduling
Full text linkThis paper explores the impacts of reviewing the time to replace of spare parts under the lenses of reliability- and cost-based optimization with the attempt to reduce the cost of a maintenance plan within a harshly constrained maintenance tasks' scheduling problem. A hierarchical procedure for a robust reliability prediction and scheduling of maintenance tasks is introduced to this purpose. This study takes inspiration from real applications of the preventive maintenance (PM) scheduling process on high-throughput packing and manufacturing lines counting hundreds of functional groups and thousands of components subject to failures and replacements. We investigate the role and the criticality of data entry in the not polynomial (NP)-hard cost-based and reliability-based scheduling problem proposed by Manzini et al. (R. Manzini, R. Accorsi, T. Cennerazzo, E. Ferrari and F. Maranesi, The scheduling of maintenance. A resource-constraints mixed-integer linear programming (MILP) model, Comput. Ind. Eng. 87 (2015) 561-568). The proposed reliability-based analysis aids the setting of proper time to failures for spare parts and combines with an analytical single-component and cost-based model to set which maintenance tasks are the most sensitive to the overall cost reduction of a maintenance plan
Sustainable food supply chain: Planning, design, and control through interdisciplinary methodologies
No full textSustainable Food Supply Chains: Planning, Design, and Control through Interdisciplinary Methodologies provides integrated and practicable solutions that aid planners and entrepreneurs in the design and optimization of food production-distribution systems and operations and drives change toward sustainable food ecosystems.
With synthesized coverage of the academic literature, this book integrates the quantitative models and tools that address each step of food supply chain operations to provide readers with easy access to support-decision quantitative and practicable methods.
Broken into three parts, the book begins with an introduction and problem statement. The second part presents quantitative models and tools as an integrated framework for the food supply chain system and operations design. The book concludes with the presentation of case studies and applications focused on specific food chains.
Sustainable Food Supply Chains: Planning, Design, and Control through Interdisciplinary Methodologies will be an indispensable resource for food scientists, practitioners and graduate students studying food systems and other related disciplines
Quick and dirty technology assessment:The case of an Italian Research Centre
No full textTechnology assessment has an increasingly critical role to play, in light of the fact that technology is the ultimate source of competitive advantage for companies, and of economic and social development for nations. In this paper, we present a methodology for a "quick and dirty" technology assessment, developed with reference to a real-word case study. The case in question is that of a large Italian Research Centre, whose top management required a rapid assessment of the technological competences, resources and results of the organisation. Because technology assessment is inherently highly complex, the problem consisted in reducing the complexity to obtain a simple methodology that could be rapidly deployed in practice. In summary, this paper: (i) describes a methodology for a "quick and dirty" technology assessment, effectively implemented in practice; (ii) points out the main problems/difficulties encountered and the possible solutions; (iii) discusses the results and the validity/applicability of the formulated methodology
Analytical and Numerical Modeling of AS/RS Cycle Time in Class-Based Storage Warehousing
No full textThis work presents an analytical model for the computation of travel time for Automated Storage and Retrieval Systems (AS/RS) with a three (ABC) class based storage in a rectangular in time warehouse. In particular, the authors provide a method for the analytical closed form evaluation of the expected mean travel time for the single/dual command cycles in the configuration with the input/output located in the bottom/left corner of the warehouse and varying the ABC curve.
A numerical simulation analysis adopting a numerical modeling has been developed, in order to validate the proposed model accordingly with a multi scenario analysis. The performance of the system obtained by the adoption of the proposed analytical travel time models under different configurations of the warehousing system (shape and dimension of the classes, ABC curve), have been evaluated and discussed
Planning low carbon urban-rural ecosystems: An integrated transport land-use model
No full textAs urbanization gradually modifies natural ecosystems and affects environmental sustainability, urban spatial planning can be used as a tool to address to Urban Metabolism and meet sustainable development targets. The concentration of people in urban areas makes these increasingly requiring for primary products and services as food and energy, and the fulfilment of such needs result in significant carbon emissions. The inclusion of spatial functions as agriculture and renewables in the urban planning can address to this environmental impact, but would require support-planning tools able to explore new land-use allocation strategies within an integrated urban-rural ecosystem. In this paper, we propose an optimization framework for the planning of low carbon urban-rural ecosystems that integrates transport and land-use planning and cope with urban metabolism, involving urban mobility, food transportation, energy supplies. This framework contributes to the literature as it formulates a network between urban, agricultural, energy, and carbon mitigation land-covers and optimizes the horizontal carbon fluxes within an integrated urban-rural environment. In order to minimize carbon emissions by mobility and resources (i.e. food) transportation, the framework aids identifying trade-offs between accessibility and density over the spatial distribution of resource-generating and resource-consuming land-covers. Proof of concept is provided with a realistic numerical example, propelled by real-world data from an Italian region. The land-use allocation solution makes the exemplifying urban-rural ecosystem behaving as carbon sink due to the established green areas and the configuration of the spatial uses. A sensitivity analysis is finally carried out to assess the impacts of mobility and resources transportation on the spatial urban-rural structure and associated carbon emissions. It comes out that the optimal urban configuration to mitigate carbon emissions from transportation integrates urban and rural uses and guarantees accessibility to several functions as cultivated areas, renewables and green covers, responsible to provide food, energy and air cleaning respectively to dwellers
A closed-loop packaging network design model to foster infinitely reusable and recyclable containers in food industry
Full text linkThe current public and private policies pursuing environmental sustainability targets mandate incisive management of packaging waste, starting with those sectors that use virgin materials most. Food industries and food supply chains adopt huge volumes of plastic crates, cardboard boxes, and wooden boxes as transport packaging, thereby representing a hotspot and an urgent call for scholars and practitioners to address. Whilst wooden and cardboard boxes are disposable solutions, plastic containers can be employed as infinitely reusable and recyclable packages but require complex logistic systems to manage their life cycle. Optimization techniques can be exploited to aid the design and profitability of such complex packaging networks. This paper falls within the scarce literature on the design of pooling networks for reusable containers in the food industry. It proposes a strategic mixed-integer linear programming model to design a closed-loop system from the perspective of the packaging maker responsible for serving a food supply chain. The container's lifespan, i.e. the number of cycles a package can be reused before recycling, represents a crucial aspect to consider when modeling such networks. Incorporating lifespan constraints within the proposed closed-loop network design model is the main novel contribution we provide to the literature. This model is applied to a real-world instance of an Italian package pooler operating with a consortium of large-scale retailers for the distribution of fruits, vegetables, bakery, and meat products. A multi-scenario what-if analysis showcases how the optimal network evolves according to potential variations in the packaging demand, as well as in the container lifespan, demonstrating how to lead packaging makers to the profitability and the long-term sustainability of the closed-loop network
A machine learning approach for predictive warehouse design
Full text linkWarehouse management systems (WMS) track warehousing and picking operations, generating a huge volumes of data quantified in millions to billions of records. Logistic operators incur significant costs to maintain these IT systems, without actively mining the collected data to monitor their business processes, smooth the warehousing flows, and support the strategic decisions. This study explores the impact of tracing data beyond the simple traceability purpose. We aim at supporting the strategic design of a warehousing system by training classifiers that can predict the storage technology (ST), the material handling system (MHS), the storage allocation strategy (SAS), and the picking policy (PP) of a storage system. We introduce the definition of a learning table, whose attributes are benchmarking metrics applicable to any storage system. Then, we investigate how the availability of data in the warehouse management system (i.e. varying the number of attributes of the learning table) affects the accuracy of the predictions. To validate the approach, we illustrate a generalisable case study which collects data from sixteen different real companies belonging to different industrial sectors (automotive, manufacturing, food and beverage, cosmetics and publishing) and different players (distribution centres and third-party logistic providers). The benchmarking metrics are applied and used to generate learning tables with varying number of attributes. A bunch of classifiers is used to identify the crucial input data attributes in the prediction of ST, MHS, SAS, and PP. The managerial relevance of the data-driven methodology for warehouse design is showcased for 3PL providers experiencing a fast rotation of the SKUs stored in their storage systems
Machine learning methods to improve the operations of 3PL logistics
Full text linkNowadays, the variety in the product mix, unpredictable customer demand and the need for a high level of service are crucial challenges in the management of a supply chain. Flexible processes are needed to gain competitive advantage and economic edges. This paper presents a data-driven application of unsupervised machine learning clustering algorithms to a real-world case study in the automotive industry. The clustering input dataset collects the data available to a third-party logistics (3PL) provider. Clustering algorithms are used to define product families for the assignment of the workload to the processing resources. Several clustering algorithms (k-means, Gaussian mixture models and hierarchical clustering) define different product families scenarios using different tuning parameters. The impact of each clustering scenario on the operations is assessed via a dashboard of logistics KPIs to identify the best performing clustering algorithm. The performance of each clustering is, then, compared to a logistic benchmark given by a capacitated clustering to identify the best compromise between a logistic-constrained algorithm with a long runtime and fast data-driven uncapacitated algorithm
A measure of innovation performance: the Innovation Patent Index
No full textPurpose: The measure of companies' Innovation Performance is fundamental for enhancing the value and decision-making processes of firms. The purpose of this paper is to present a new measure of Innovation Performance, called Innovation Patent Index (IPI), which makes it possible to quantitatively summarize different aspects of firms' innovation. Design/methodology/approach: In order to define the IPI, a secondary source, i.e. patent data, has been used. The five dimensions of IPI, i.e. efficiency, time, diversification, quality and internationalization have been defined both analyzing the literature and applying three different machine learning algorithms (regularized least squares, deep neural networks and decision trees), considering patent forward citations as a proxy of the innovation performance. Findings: Results show that the IPI index is a very useful tool, simple to use and very promptly. In fact, it is possible to get important results without making time consuming analysis with primary sources. It is a tool that can be used by managers, businessmen, policymakers, organizations, patent experts and financiers to evaluate and plan future activities, to enhance the innovation capability, to find financing and to support and improve innovation. Research limitations/implications: Patent data are not widely used in all the sectors. Moreover, the pure number of forward citations is not the only forward looking indicator suggested by the literature. Originality/value: The demand for a useable Innovation Performance tool, as well as the lack of tools able to grasp different aspects of the innovation, highlight the need to develop new instruments. In fact, although previous studies provide several measures of Innovation Performance, these are often difficult for managers to use, do not appreciate different aspects of the innovation and are not forward looking
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