1,721,010 research outputs found
Utilizzo del modulo MRP per la pianificazione della produzione nel caso di forniture in “Consignment Stock”
No full textANP/RPN: a multi criteria evaluation of the risk priority number
No full textThis paper presents an advanced version of the failure mode effects and criticality analysis (FMECA), whose capabilities
are enhanced; in that the criticality assessment takes into account possible interactions among the principal causes of
failure. This is obtained by integrating FMECA and Analytic Network Process, a multi-criteria decision making technique.
Severity, Occurrence and Detectability are split into sub-criteria and arranged in a hybrid (hierarchy/network) decisionstructure
that, at the lowest level, contains the causes of failure. Starting from this decision-structure, the Risk Priority
Number is computed making pairwise comparisons, so that qualitative judgements and reliable quantitative data can
be easily included in the analysis, without using vague and unreliable linguistic conversion tables. Pairwise comparison
also facilitates the effort of the design/maintenance team, since it is easier to place comparative rather than absolute
judgments, to quantify the importance of the causes of failure. In order to clarify and to make evident the rational
of the final results, a graphical tool, similar to the House of Quality, is also presented. At the end of the paper, a
case study, which confirms the quality of the approach and shows its capability to perform robust and comprehensive
criticality analyses, is reported
An entity embeddings deep learning approach for demand forecast of highly differentiated products
Full text linkThe paper deals with Deep Learning architectures applied to demand forecasting in a complex environment. The focus is on a famous Italian Fashion Company, which periodically performs a sales campaign, to presents its new products' line and to collect customers' orders. Although production follows an MTO strategy, fabrics must be purchased in advance and a forecasting system is required to predict the total quantity sold for each product, at the early stages of the campaign. Due to high product variability, the forecasting system must consider products' similarities and the evolution of customers taste. Additionally, customer and product data are mostly described by categorical variables (hard to reconcile with a predictive task) and, unfortunately, time-series techniques cannot be used because of a sparse dataset. Given these criticalities, we propose an end-to-end approach based on Deep Neural Networks and on Entity Embeddings. A first neural network is trained to predict the total quantity of a given product ordered by a specific customer. Different Embeddings are learned for each customer and product categorical attribute. This gives the network the ability to effectively learn the complex and evolving relationships between products characteristics and customers taste. Next, freezing the learned product's embeddings, a second Recurrent Neural Network is trained to predict the total amount ordered for a given product, incorporating real-time data of customers' orders of the ongoing sales campaign. Ten years of sales have been analyzed and the approach, tested on unseen sales campaigns, has outperformed the forecasting algorithm currently adopted by the fashion firm
Exploiting Machine Learning and Industry 4.0 traceability technologies to re-engineering the seasoning process of traditional Parma’s Ham
No full textThe work presents a Machine Learning approach for predicting the quality of the curing process of Parma ham, combined with a study of business process re-engineering, based on RFID and Deep Learning technologies for automatic recognition and tracking of the hams along the curing process. Quality management has proven to be crucial for efficient and effective processes, even more so for the food industry, both for commercial and regulatory purposes. This is even more evident in artisanal-based processes, such as the one concerning traditional Prosciutto di Parma seasoning. The work proposes and compares a Feed-Forward Neural Network and a Random Forest for predicting the distribution of the number of hams by commercial quality class of a given aging lot. Such a prediction, based on origin, process, and curing data, can provide early indications of process output, enabling strategic commercial competitive advantages. The importance of the genetic component in the determination of the final quality is also evaluated, as it is considered one of the most influential external variables. Moreover, following the AS-IS description of the current process, a redesign is proposed, to enable data collection and tracking of individual ham in order to propose a future precision prediction system that would allow even finer control of the process
Hybrid heuristic for the one-dimensional cutting stock problem with usable leftovers and additional operating constraints
Full text linkThe One-Dimensional Cutting Stock Problem consists in cutting long bars into smaller ones, to satisfy customers’ demand, minimizing waste and cost. In this paper the standard problem is extended with the inclusion of additional constraints that are generally neglected in scientific literature, although relevant in many industrial applications. We also modified the standard objective function, by assuming that bars may have a different economical value and a different processing or shipping priority. Moreover, in line with business requirements, among solutions that generate the same cutting waste, we prefer the ones that generate a low number of leftovers, especially if leftovers are long, so that the likelihood of their reuse is high. To solve the problem, we propose a Simulated Annealing based heuristic, which exploits a specific neighbor search. The heuristic is implemented in a parametric way that allows the user to set the priorities of the bars and to choose the specific sub-set of constraints he or she wants to consider. The heuristic is finally tested on many problem instances, and it is compared to three benchmarks and to one commercial software. The outcomes of this comparative analysis demonstrate both its quality and effectiveness
Deep learning and WLC: How to set realistic delivery dates in high variety manufacturing systems
Full text linkThe focus is on workload control, a production planning and control technique that reduces and stabilizes the total throughput time. In these conditions, defining realistic delivery dates should become easier, yet the use of basic techniques often proves to be ineffective. Hence, we propose using statistical and/or neural network techniques to estimate, starting from the current workload of the job shop, the expected lead time of entry jobs, and to use this estimation to define reliable delivering dates. To test the approach, we simulated a 6-machines job-shop and we make predictions using a multi-regressive linear model and a multi-layer neural network. In terms of tardy jobs, both approaches performed very well, with the neural network providing the best results
Comparison of new metaheuristics, for the solution of an integrated jobs-maintenance scheduling problem
Full text linkThis paper presents and compares new metaheuristics to solve an integrated jobs-maintenance scheduling problem, on a single machine subjected to aging and failures. The problem, introduced by Zammori et al. (2014), was originally solved using the Modified Harmony Search (MHS) metaheuristic. However, an extensive numerical analysis brought to light some structural limits of the MHS, as the analysis revealed that the MHS is outperformed by the simpler Simulated Annealing by Ishibuchi et al. (1995). Aiming to solve the problem in a more effective way, we integrated the MHS with local minima escaping procedures and we also developed a new Cuckoo Search metaheuristic, based on an innovative Levy Flight. A thorough comparison confirmed the superiority of the newly developed Cuckoo Search, which is capable to find better solutions in a smaller amount of time. This an important result, both for academics and practitioners, since the integrated job-maintenance scheduling problem has a high operational relevance, but it is known to be extremely hard to be solved, especially in a reasonable amount of time. Also, the developed Cuckoo Search has been designed in an extremely flexible way and it can be easily readapted and applied to a wide range of combinatorial problems. (C) 2018 Elsevier Ltd. All rights reserved
A new Value Stream Mapping approach for complex production systems
No full textIn this paper, an innovative framework to apply Value Stream Mapping to products with complex Bill of Materials is presented. Value Stream Mapping is one of the best tools to map a process and to identify its main criticalities. Unfortunately, it can be effectively applied to linear systems only. When the manufacturing process is complex with flows merging together, Value Stream Mapping cannot be used straightforwardly. Thus, the main objective of this work is to solve this limitation so that lean production can be enhanced in complex systems too. The proposed approach is based on seven iterative steps and integrates Value Stream Mapping with other tools typical of industrial engineering. The basic idea is to execute a preliminary analysis to identify the 'critical production path using the Temporized Bill of Material. Then, improvements are made considering all possible sharing with other secondary paths as possible constraints. Once the critical path has been optimized, a new path may become critical. Thus, the analysis proceeds iteratively until the optimum is reached and the Work In Process level has decreased under a desired value. A case study taken from a real setting environment is finally presented to assess the validity of the methodology proposed here
Development and Stress Test of a New Serious Game for Food Operations and Supply Chain Management: Exploring Students’ Responses to Difficult Game Settings
No full textSerious games (SGs) in engineering education are a proven topic, whose implementation has been significantly growing in the last decades. They are recognized as effective tools to teach and learn subjects like Operations and Supply Chain Management. The research on SGs, however, is primarily focused on displaying applications and teaching results of particular games to achieve given purposes. In this paper, we provide an exploratory research and a stress test of a new SG on a specific target group in the field of food operations and supply chain management. We provide an overview of the SG and detail its mechanics. Also, we explain how the mechanics has been implemented, by means of a set of parameters and indicators that better explain the roles available to players in the game. We conclude by reporting and discussing the results of a game session played by a class of Vocational Education and Training students under stress conditions generated by an accelerated game time
Enhancing Manual Order Picking through a New Metaheuristic, Based on Particle Swarm Optimization
Full text linkThis paper proposes a new metaheuristic algorithm called Particle Swarm-based picking time minimization (Pkt_PSO), ideated for picking time minimization in manual warehouses. As the name suggests, Pkt_PSO is inspired by Particle Swarm Optimization (PSO), and it is specifically designed to minimize the picking time in order case picking contexts. To assess the quality and the robustness of Pkt_PSO, it is compared to five alternative algorithms used as benchmarks. The comparisons are made in nine different scenarios obtained by changing the layout of the warehouse and the length of the picking list. The results of the analysis show that Pkt_PSO has a slower convergence rate and suffers less of early stagnation in local minima; this ensures a more extensive and accurate exploration of the solution space. In fact, the solutions provided by Pkt_PSO are always better (or at least comparable) to the ones found by the benchmarks, both in terms of quality (closeness to the overall best) and reliability (frequency with which the best solution is found). Clearly, as more solutions are explored, the computational time of Pkt_PSO is longer, but it remains compatible with the operational needs of most practical applications
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