Mathematical models for operations management

Purpose – The purpose of the doctoral thesis is to support and to facilitate the introduction of lean concepts, in the industry. Design/methodology/approach – Starting from the operating techniques included in the lean toolbox, a comprehensive set of twelve mathematical models for operations management is developed. Since Lean Thinking encompasses the whole organization, the models cover several processes performed by an organization. In particular there are: (i) three models dealing with logistic issues, (ii) five models concerning manufacturing issues, and (iii) four models concerning Total Predictive Maintenance. Findings – The models extend the capabilities of the classical lean tools by means of advanced mathematical techniques such as: fuzzy logic, multi criteria decision making, multivariate statistic and Markov processes. Practical implications – To assure the possibility to adopt the models in real industrial situations, a great effort has been made to maintain all of them as simple and straightforward as possible. Furthermore, all of them have been designed to be easily implemented in industrial information systems, and have been validated by means of industrial applications of relevance. Originality/value – The twelve models here presented provide practitioners with innovative operating tools, which integrate different techniques and overcomes most of the limits of the classical lean tools

an entity embeddings deep learning approach for demand forecast of highly differentiated products

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A new perspective on Workload Control by measuring operating performances through an economic valorization

Workload Control (WLC) is a production planning and control system conceived to reduce queuing times of job-shop systems, and to offer a solution to the lead time syndrome; a critical issue that often bewilders make-to-order manufacturers. Nowadays, advantages of WLC are unanimously acknowledged, but real successful stories are still limited. This paper starts from the lack of a consistent way to assess performance of WLC, an important burden for its acceptance in the industry. As researchers often put more focus on the performance measures that better confirm their hypotheses, many measures, related to different WLC features, have emerged over years. However, this excess of measures may even mislead practitioners, in the evaluation of alternative production planning and control systems. To close this gap, we propose quantifying the main benefit of WLC in economic terms, as this is the easiest, and probably only way, to compare different and even conflicting performance measures. Costs and incomes are identified and used to develop an overall economic measure that can be used to evaluate, or even to fine tune, the operating features of WLC. The quality of our approach is finally demonstrated via simulation, considering the 6-machines job-shop scenario typically adopted as benchmark in technical literature

Using SCRUMBAN methodology for production planning and control in luxury/fashion manufacturing companies

Operations planning and control has long been dealt with in many ways, mostly with mathematical and analytical approaches. These approaches work properly at a high level, but they have not proved suitable at the shop floor level, where, in order to cope with frequently emerging problems, what managers need is detailed information concerning the level of Work In Process (WIP) and the actual load of both equipment and resources. This issue is particularly evident in the luxury/fashion market, where, due to the continuous and rapid changes in fashions’ trends and to the need to interface with dozen of suppliers, reactivity is vital and managers are urged for planning and analysis tools that are extremely simple and easy to use. In this regard, SCRUMBAN, a recently introduced Visual Management technique, which integrates SCRUM (a widely adopted project planning technique in the software field) with Kanban, is emerging as a powerful way to control WIP, visualize loads, synchronise internal and external flows and, lastly, to motivate people. In response to a gap in the technical literature the paper focuses on this topic with the following objectives: (i) analyse the main characteristics of the SCRUMBAN technique, (ii) propose an original methodology and a structured framework for the development of this technique in the manufacturing field and (iii) provide a guideline for the application of the instrument in the luxury/fashion sector. The last point is addressed through a real case-study, developed in an Italian fashion firm, which gave outstanding preliminary results

A measurement method of routing flexibility in manufacturing systems

Article history: Received 27 January 2011 Received in revised form 25 February 2011 Accepted 2 March 2011 Available online 3 March 2011 This paper focuses on routing flexibility, which is the ability to manufacture a part type via several routes and/or to perform different operations on more than one machine. Specifically, the paper presents a comprehensive method for the measurement of routing flexibility, in a generic manufacturing system. The problem is approached in a modular way, starting from a basic set of flexibility indexes. These are progressively extended to include more comprehensive and complex routing attributes, such as: the average efficiency, the range and the homogeneous distribution of the alternative routes. Two procedures are finally proposed to compare manufacturing systems in terms of routing flexibility. The first one uses a vectorial representation of the previously defined indexes and the second one is based on data envelopment analysis, a multi-criteria decision making approach. The paper concludes with a numerical example, supported by discrete event simulation, which validates the proposed approach. © 2011 Growing Science Ltd. All rights reserve

Comparison of new metaheuristics, for the solution of an integrated jobs-maintenance scheduling problem

This 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 fuzzy multi-dimensional risk assessment framework for Integrated Management Systems (MIS)

Quality, environmental and safety management systems are typically combined into an Integrated Management Systems (MIS), for easier management and improved performance. If, as usual, the MIS is developed in accordance to ISO standards, then Process Control becomes the key element regulating the functioning of the system. Indeed, to meet requirements in terms of quality, environment and safety, all processes must be known, standardized and, lastly, controlled. In this regard, risk management plays a critical role because, unless risks and criticality are known, it is not possible to make a process stable, repeatable and controllable. The present paper focuses on the above mentioned topics and proposes a multi-dimensional risk assessment framework purposely developed for MIS. Due to the uncertain nature of the data needed for the analysis, the framework is based on fuzzy-logic rather than on probabilistic models. Specifically, for each process, a set of criteria (related to quality, environment and safety) are defined and their risk level is quantified using fuzzy linguistic variables. Criteria are then aggregated ad different levels of detail, up to a single indicator of global risk. Also, in order to take into account cause and effect relationships (among risk criteria) that cannot be easily measured, but that can only be judged by the experts, the aggregation is performed using a comprehensive set of If-Then rules combined using a Mamdani Fuzzy Inference System. Lastly, a prototype application was coded in Matlab and many tests were performed; preliminary outcomes are encouraging as they indicate framework’s robustness and stability

Defining accurate delivery dates in make to order job-shops managed by workload control

Workload control (WLC) is a lean oriented system that reduces queues and waiting times, by imposing a cap to the workload released to the shop floor. Unfortunately, WLC performance does not systematically outperform that of push operating systems, with undersaturated utilizations levels and optimized dispatching rules. To address this issue, many scientific works made use of complex job-release mechanisms and sophisticated dispatching rules, but this makes WLC too complicated for industrial applications. So, in this study, we propose a complementary approach. At first, to reduce queuing time variability, we introduce a simple WLC system; next we integrate it with a predictive tool that, based on the system state, can accurately forecast the total time needed to manufacture and deliver a job. Due to the non-linearity among dependent and independent variables, forecasts are made using a multi-layer-perceptron; yet, to have a comparison, the effectiveness of both linear and non-linear multi regression model has been tested too. Anyhow, if due dates are endogenous (i.e. set by the manufacturer), they can be directly bound to this internal estimate. Conversely, if they are exogenous (i.e. set by the customer), this approach may not be enough to minimize the percentage of tardy jobs. So, we also propose a negotiation scheme, which can be used to extend exogenous due dates considered too tight, with respect to the internal estimate. This is the main contribution of the paper, as it makes the forecasting approach truly useful in many industrial applications. To test our approach, we simulated a 6-machines job-shop controlled with WLC and equipped with the proposed forecasting system. Obtained performances, namely WIP levels, percentage of tardy jobs and negotiated due dates, were compared with those of a set classical benchmark, and demonstrated the robustness and the quality of our approach, which ensures minimal delays

Project scheduling with multi skilled resources: a conceptual framework

Projects’ success depends, mostly, on people’s motivation and competences. A good plan is essential, but it is insufficient if the project manager is incapable to dynamically reassign people to project’s tasks, so as to create multi-skilled teams and to avoid multi-tasking and over-allocation. In this regard, several models dealing with the “Multi Skilled Work Force Scheduling Problem” have been proposed, but unfortunately, most of the works produced so far has not yet found its way into practice. This is mainly because project scheduling and resources allocation are jointly considered, a fact that leads to complex and rigid mathematical formulations and that poses serious constraints on the precision of the input data. Since projects are, by their very nature, uncertain entities, we believe that it is preferable to abandon the over optimistic idea of a global optimum, in favour of a suboptimal but stable and feasible solution. To this aim the paper proposes a heuristic framework that extends the well-known “Dynamic Scheduling” approach. Specifically, the problem is tackled in a hierarchical way: project scheduling is solved first and resource allocation is solved next, considering tasks durations as fixed constraints. In doing so, our focus is on the resources allocation phase, and the objective is to assure an almost perfect matching between resources’ skills and tasks requirements, so as to assure project quality and, also, a harmonious development of the workforce. Possible approaches, based on mathematical programming, which could be easily implemented in project management software, are presented and discussed