An application of Industry 4.0 methods to the production of packaging films is presented. The production planning issues that are addressed often include contrasting objectives and strategies between customer's service and production optimization. We present an Advanced Planning and Scheduling (APS) tool that allows the decision maker to automatically generate and chose among a wide range of differently optimized scenarios. The vast amount of different results is analyzed and presented to the decision maker, using advanced data analytics techniques in order to put him/her in the condition to rapidly take an aware and solidly supported decision.

In manufacturing, Machine Vision Systems are increasingly being used for their ability to collect information pertaining to the quality of a product in real time. When physical profiles are collected from images captured by Machine Vision Systems, the number and locations of the observed points can change from one item to another. The research is focused on non-parametric control charts for statistical monitoring of free-form profiles with different number and locations of observed points. The proposed method consists in extracting the shape of the monitored profile from images and in comparing it to a baseline model taken as reference. A new discrepancy metric, which consists in computing the deviation area of the monitored profile from the baseline model, is proposed. Two control charting procedures, based on univariate and a multivariate statistics are illustrated and validated through computer simulations. The automatic cutting process in leather part manufacturing (e.g. furniture, automotive interior, apparel) is the reference context.

In this work, a novel technological solution for the traceability of hides throughout the leather manufacturing process is addressed. The proposed solution relies on marking the raw hide with a permanent sub-surface tattoo, made with specific substances used as identification markers. In practical applications, the markers can be embedded so as to form a pre-established pattern, thus creating a unique identification code. To experimentally demonstrate the feasibility of the proposed solution, in this work, different types of markers were injected in a raw hide (i.e., prior to its tanning). After the tanning process, the persistence of the markers and of their pattern was verified by comparatively inspecting the hide with two different sensing technologies: microwave reflectometry and X-ray imaging. The obtained results demonstrated that the proposed traceability system is a promising solution to circumvent the age-old problem related to counterfeiting and fraudulent substitutions of raw materials in the leather manufacturing industry.

In this paper, we study an assembly job shop scheduling problem with tree-structured precedence constraints and jobs characterized by specific bills of materials. We propose a mathematical model to deal with a simplified version of the problem, as well as a fast and efficient constructive heuristic that is able to easily face real-world-sized instances. The production schedule takes into account the actual availability of materials in stock as well as the supply times and the capacity constraints, with the goal to minimize the average delay with respect to the due dates associated to the customers' orders. Computational results on data related to real-life instances show that the mathematical model is able to solve (not always to optimality) small-sized instances only. On the other hand, our heuristic approach is able to solve efficiently very large problems. Moreover, the proposed heuristic turns out to be scalable as the instance size grows.

We present an optimization heuristic to solve the operation balancing and scheduling problem on a Flexible Manufacturing Cell. The investigated problem is based on the study of an industrial case study of the Robert Bosch enterprise. The considered manufacturing system is a multi-stage CNC machining cell. Cell operations are grouped in four stages. At each stage, multiple spindles simultaneously work on a single work-piece; consequently spindle collision problem has to be considered. When, at each stage, operations are completed, a rotation mechanism simultaneously transfers each work-piece to the next stage. The innovation aspect of the present work consists in the possibility to model the geometric constraints between operations in order to avoid spindle collisions at the same stage. The proposed heuristic approach aims to maximize the system throughput, reducing the machine total cycle time and respecting complex industrial manufacturing constraints (geometrical and synchronization constraints). A Genetic Algorithm approach has been developed to solve the addressed problem. A real case study set has been solved in order to show the suitability of our approach.

Profile monitoring has been recently considered as one of the most promising areas of research in statistical process control. Often, when physical profiles are acquired by machine vision systems, the number and locations of the argument variables can change from one item to another. In this article, non-parametric approaches for statistical monitoring of free-form profiles, e.g., characterized by irregular shapes and with different argument variables, are proposed. A machine vision system is used for process control by implementing on a computer the following procedure: (i) a feature extraction phase, which extracts contour of the produced part from image and returns it in form of polygonal curve; (ii) a registration phase, for aligning the current polygonal curve to the baseline model; (iii) a matching computation phase, which is based on the deviation area, for quantifying the discrepancies of the current polygonal curve from the baseline one; (iv) a control charting phase, which is based on univariate or multivariate statistics, for process monitoring. The automatic cutting process in leather part manufacturing is used as the reference industrial case. The leather hide is cut into parts that may assume shapes different from the ‘baseline’, e.g., the reference ideal profile. Two control charting procedures are illustrated and validated through simulated test cases and a real-life case of industrial relevance.

In modern industry, the development of complex products involves engineering changes that frequently require redesigning or altering the products or their components. In an Engineering Change process, Engineering Change Requests (ECRs) are natural language written texts exchanged among process operators. ECRs describe the required change on a product or a component and the solution. After the change implementation, ECRs are stored but never consulted, missing opportunities to learn from previous projects. This paper explores the application of text clustering to natural language texts written during the Engineering Change process in industry. In detail, the use of Self Organizing Map (SOM) to the problem of unsupervised clustering of ECR texts is explored. A case study is presented in which ECRs collected during the Engineering Change process of a railways industry are analysed. The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation.

Over the last decades, the production of “sustainable energy” has provided a very fertile research field, involving aspects that are traditionally considered in an independent manner, namely renewable energy production, energy storage and efficient usage of available energy. A combined analysis of these three aspects within an industrial context is the main focus of this work. We provide an insight on the problems that a small or medium manufacturing firm can expect to address when it decides to move from traditional energy suppliers to an as much as possible autonomous energy production. In particular, we consider the contribution that ICT can offer in order to allow the firm to decide the best size and composition of its own “energy production plant”, based on data regarding its production needs and weather-related data. We propose an open source framework aimed at making it possible to model various systems including both energy production, storage and consumption elements. The framework also allows the use of different approaches to optimize and fine tune the system in terms of both design and usage costs. We show how the framework can be specialized in order to be used for a typical industrial test case representing a small medium manufacturing firm that decides to change its position from a pure energy consumer to an energy combined producer, storer and user.

Accurate cost prediction during a new product development process is an important factor influencing the ability of manufacturing firms to survive in today’s competitive markets. Various methods have been proposed to predict costs during new product development processes. Although most of these methods produce point estimates, in practice, it is more realistic and useful for a method to provide interval predictions. In this paper, the application of Ordinary Least Squares (OLS) regression is considered in order to model available past data of production cost, and thus to compute interval predictions as well as point estimates for similar new products. With reference to a real case study in manufacturing, the issue of model selection is considered and a comparison of some commonly used criteria (functions of model fit penalized for model complexity) for selecting the best regression equation is analyzed. The objective of this study is to discuss the effect that the criterion used for selecting the best regression equation could have on the precision and accuracy of the prediction. Labor time data concerned the development of new sofa models were exploited as reference test case. The OLS regression is applied to several sets of labor time data and validated with respect to its fitting and predictive accuracy. The results of this study give insight into the efficacy of OLS regression for production cost estimation, and evidence on which criterion appears suitable to be used for model selection.

In this paper, we deal with single machine scheduling problems subject to time dependent effects. The main point in our models is that we do not assume a constant processing rate during job processing time. Rather, processing rate changes according to a fixed schedule of activities, such as replacing a human operator by a less skilled operator. The contribution of this paper is threefold. First, we devise a time-dependent piecewise constant processing rate model and show how to compute processing time for a resumable job. Second, we prove that any time-dependent continuous piecewise linear processing time model can be generated by the proposed rate model. Finally, we propose polynomial-time algorithms for some single machine problems with job independent rate function. In these procedures the job-independent rate effect does not imply any restriction on the number of breakpoints for the corresponding continuous piecewise linear processing time model. This is a clear element of novelty with respect to the polynomial-time algorithms proposed in previous contributions for time-dependent scheduling problems.

In the Job Sequencing and Tool Switching Problem (SSP) a number of part types, each requiring a set of tools, are to be manufactured on a single flexible machine with a capacitated tool magazine. The SSP aims at determining the processing sequence as well as the tools present in the magazine for each part, in order to minimize the total number of tools switches. In this paper, we cast the SSP as a nonlinear least cost Hamiltonian cycle problem. A branch-and-cut algorithm is proposed and compared to previous exact procedures. Computational results indicate that the algorithm is able to solve much larger instances than those reported in the literature.

From the early 1990s, the introduction of high-throughput clinical analyzers has significantly changed the workflow of In-Vitro-Diagnostics (IVD) tests. These high-tech instruments have helped and keep helping clinical laboratories both to increase quality diagnostic responses and to get more for every dollar they spend. Nevertheless, IVD industrial research has been up to now largely hardware-driven with the introduction in the market of many sophisticated technologies. The software component, models and decision support systems in particular, has lagged behind. To reach the full potential of diagnostic automation, it must be addressed the challenge of making the most intelligent use of the hardware that is deployed. Focusing on time efficiency, the authors have devised an operations research-based method for a class of high-throughput clinical analyzers. To demonstrate the validity of the research, the proposed method has been coded and integrated into the Laboratory Information System of the Laboratorio di Analisi Cliniche Dr. P. Pignatelli, one of the most important clinical laboratories in Southern Italy. Siemens Immulite ®; 2000 has been the reference case. The enhanced operating planning procedure provides a monetary benefit of 52,000 USD/year per instruments and a trade-off between clinical benefits and operating costs equivalent to the one provided by the current hardware-driven research at Siemens. Despite the proposed approach has the potential to determine guidelines for enhancing a wide range of current high-throughput clinical analyzers, we have to register a failure in trying to convince technology providers to invest in embedding such new models in their hardware. Some possible causes for such failure are highlighted, trying to find possible improvements for future developments.

This paper deals with an exact algorithm for the Time-Dependent Traveling Salesman Problem with Time Windows (TDTSPTW) with continuous piecewise linear cost functions. There are two main research streams that can benefit from efficient exact algorithms for TDTSPTW. The first concerns determining optimal vehicle route planning taking traffic congestion into account explicitly. The latter deals with sequence dependent set-up single machine scheduling problems minimizing total completion times or total tardiness. The contribution of this paper is twofold. First, it is proved that the Asymmetric Traveling Salesman Problem with Time Windows is optimal for the TDTSPTW, if all the arcs share a common congestion pattern. Second, an integer linear programming model is formulated for TDTSPTW, and valid inequalities are then embedded into a branch-and-cut algorithm. Preliminary results show that the proposed algorithm is able to solve instances with up to 67 vertices.

The present invention pertains to a methodology for the identification and traceability of materials in industrial process, with particular focus on the traceability of leather and leather-like materials throughout the manufacturing process of leather or leather-like products. Said methodology comprises, at least, the employment of the following: identification markers (3); a method for permanently embedding the markers inside the item (2) to be traced; a device for generating an electromagnetic signal to be used as stimulus for the marked material; a device for acquiring the response of the marked material to the applied stimulus; and a device for recording the aforementioned response.