Laboratoire des Sciences pour la Conception, l'Optimisation et la Production

This paper presents an optimal power management mechanism for grid connected photovoltaic (PV) systems with storage. The objective is to help intensive penetration of PV production into the grid by proposing peak shaving service at the lowest cost. The structure of a power supervisor based on an optimal predictive power scheduling algorithm is proposed. Optimization is performed using Dynamic Programming and is compared with a simple ruled-based management. The particularity of this study remains first in the consideration of batteries ageing into the optimization process and second in the “day-ahead” approach of power management. Simulations and real conditions application are carried out over one exemplary day. In simulation, it points out that peak shaving is realized with the minimal cost, but especially that power fluctuations on the grid are reduced which matches with the initial objective of helping PV penetration into the grid. In real conditions, efficiency of the predictive schedule depends on accuracy of the forecasts, which leads to future works about optimal reactive power management.

Product–service systems (PSS), motivated to fulfil customers’ needs, are seen as good strategies to face today's competitive business environment. The field of PSS research is however not fully mature and many different methodologies are proposed for the PSS design. This paper seeks to understand the directions taken in eight state-of-the-art methodologies so as to identify common needs in future research. The methodologies are studied across their authors’ views and definitions of services, PSS and their objectives and challenges, along with the tools that have been developed. A maturity model is built to access the current PSS design across 20 dimensions. The model highlights that only three dimensions are strongly treated: design processes for integrating products and services, definitions of new terminologies and considerations concerning planning and designing life-cycle phases. To enhance the industrial application, collaboration between researchers and practitioners can be spurred through two challenges: common ontology and models for representation of PSS. Particular attention must also be placed on sustainability as current models do not support the generation of sustainable PSS. As a whole, the review shows that the PSS design is still in initial stages of development and substantial research is required to develop a practical PSS design methodology.

Additive manufacturing technologies can now be used to manufacture metallic parts. This breakthrough in manufacturing technology makes possible the fabrication of new shapes and geometrical features. Although the manufacturing feasibility of sample parts with these processes has been the subject of several studies, the breakthrough in manufacturing is yet to be followed by a breakthrough in designing process. In this paper, after reviewing the principle of additive manufacturing of metallic parts, the manufacturing capabilities and constraints of these processes will be examined. A designing methodology will then be suggested and illustrated with the redesign of an example part.

Since electricity plays a crucial role in countries' industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply are matched. This article discusses various algorithms and a new hybrid deep learning model which combines long short-term memory networks (LSTM) and convolutional neural network (CNN) model to analyze their performance for short-term load forecasting. The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely “hourly load consumption of Malaysia ” as well as “daily power electric consumption of Germany”, are used to test and compare the presented models. To evaluate the tested models' performance, root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared were used. In total, this article is divided into two parts. In the first part, different machine learning models, including the PLCNet, predict the next time step load. In the second part, the model's performance, which has shown the most accurate results in the first part, is discussed in different time horizons. The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools. PLCNet improved the accuracy from 83.17% to 91.18% for the German data and achieved 98.23% accuracy in Malaysian data, which is an excellent result in load forecasting.

Additive manufacturing processes, used for more than 25 years, are no longer confined to rapid prototyping applications. Mostly used nowadays in niche markets (medical applications, aerospace...) to manufacture metallic parts, they should provide improvements in terms of time-to-market, ecological impact and design compared to traditional industrial processes. Current metallic additive manufacturing studied in this paper are Selective Laser Sintering, Direct Metal Laser Sintering, Selective Laser Melting, Electron Beam Melting and Direct Metal Deposition. The performances of these processes are investigated through criteria derived from the time cost quality triangle and some prospects concerning these processes are given.

Most of the time, engineers focus on the design of physical products and on their interactions with others objects, and this is why technical services are not considered very early during the design process. On the other hand, some product-service system (PSS) methodologies still exist but are focused on the system and do not sufficiently specify engineering product criteria. Indeed, to achieve the development of consistent PSSs, a methodology is required to support engineering designers during the development process. PSSs are composed of physical objects and service units that relate each other. To have a competitive PSS, the designers must consider carefully and early in the design phase the interactions between those elements. The aim of the proposed methodology is to provide engineering designers with technical engineering specifications in relation with the whole system's requirements as precise as possible for the development of the physical objects involved in those systems. The paper describes the context of PSS development and the current methods used to develop such systems. Then, the tools and formalism used in the proposed methodology based on a function-oriented description and an activities related description are explained. Finally, an industrial example of a helium-based refrigeration unit illustrates the proposed methodology.

As the supply chains get more complex, the variety of indicators and tools to measure warehouse performance has also increased. Furthermore, the metrics that are used for performance evaluation are assessed in different manners and hence there is not clear definition for some of these metrics. To address these issues, this literature review focuses on operational warehouse performance measurement, for which the warehouse managers need to carry out periodic analysis. Using the content analysis method, performance indicators are acquired from selected papers and are classified according to time, cost, quality and productivity dimensions. The contributions of this literature review are as follows: we present a synthesis of the literature on operational warehouse performance, we provide the definitions for the performance indicators and a framework to demonstrate their boundaries and, finally, based on the literature analysis, we also provide some discussions on current trends in warehouses and propose future research directions on warehouse performance evaluation.

Circular Economy (CE) aims to maintain the value of products, components, materials, and resources in the economy for as long as possible. Current end of life (EoL) product circularity decision-making methods are focused on technical and economic factors neglecting other crucial areas such as legislative pressure and customer demand, which are pertinent in the decision-making process. This paper presents a decision-making method to evaluate end of life product circularity alternatives at strategic level. A Product Recovery Multi-Criteria Decision Tool (PR-MCDT) is proposed to evaluate product circularity strategies from an integrated point of view, i.e. by simultaneously taking into account technical, economic, environmental, business, and societal aspects. The paper also identifies key end of life decision-making factors to assess product recovery strategies. An illustrative example is presented and discussed to show the applicability of the tool for the selection of product recovery options. A PR-MCDT is used at the senior/middle management level to ensure strategic decisions, which then promote success of the company.

City logistics is one of the significant branches of supply chain management, dealing with the logistics and transportation activities in urban areas. This research area has recently appropriated an exponential growth of publications. This paper presents a bibliometric analysis along with a systematic literature review to organise the results of surveying more than 370 papers and research works published since 2010. We identify the top contributing research topics. The most common keywords used in the city logistics literature are referred to in order to propose six research categories identifying the main innovative research perspectives.

In the current study, wire and arc additive manufacturing (WAAM) of thin-walled 308L stainless steel components was reported. Firstly, the influence of the welding current, the voltage, and the travel speed in the WAAM process on the geometry of single weld beads was investigated. These parameters were also optimized for the deposition of 308L steel walls. Secondly, the microstructure and the mechanical characteristic of WAAM 308L steel walls have been explored. The obtained results reveal the optimized that parameters giving the desirable geometry of single weld beads for building 308L walls by the WAAM process. The microstructure of WAAM 308L steel walls mainly features the dendrites of austenite growing vertically and residual ferrite existing in grain boundaries of the austenite matrix. The microhardness of the built material is about 163 HV0.1. The UTS, YS and elongation of WAAM 308L walls are 532–553 MPa, 344–353 MPa, and 40–54%, respectively, which are relatively similar to those of wrought 308L stainless steel (UTS: 530–650 MPa, YS: 360–480 MPa, and elongation: 35–45%). Thus, the mechanical properties of WAAM 308L steel walls are considered to be adequate for industrial applications.

We consider a make-to-stock supplier that operates a production facility with limited capacity. The supplier receives orders from customers belonging to several demand classes. Some of the customer classes share advance demand information with the supplier by announcing their orders ahead of their due date. However, this advance demand information is not perfect because the customer may decide to order prior to or later than the expected due date or may decide to cancel the order altogether. Customer classes vary in their demand rates, expected due dates, cancellation probabilities, and shortage costs. The supplier must decide when to produce and, whenever an order becomes due, whether or not to satisfy it from on-hand inventory. Hence, the supplier is faced with a joint production-control and inventory-allocation problem. We formulate the problem as a Markov decision process and characterize the structure of the optimal policy. We show that the optimal production policy is a state-dependent base-stock policy with a base-stock level that is nondecreasing in the number of announced orders. We show that the optimal inventory-allocation policy is a state-dependent multilevel rationing policy, with the rationing level for each class nondecreasing in the number of announced orders (regardless of whether the class provides advance information). From numerical results, we obtain several insights into the value of advance demand information for both supplier and customers.

We present several fully printed organic complementary circuits using n- and p-type organic thin-film transistors. n-Type and p-type devices are developed on a flexible polyethylene-naphthalate substrate. All organic layers are deposited using a low-cost screen-printing technique. The inverters show a high gain and a switching point at exactly <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VDD</i> /2. A seven-stage voltage-controlled oscillator (VCO) is designed with an organic output buffer, using the n- and p-type organic transistors. This VCO oscillates at a frequency of 186 Hz. Finally, two complementary differential amplifiers with high gain and large bandwidth are presented. The amplifiers only draw a 1-μA current from a 40-V power supply.

This review paper provides the operations management (OM) community with an exhaustive analysis of the mathematical models developed for the problem of low-carbon supply chain management (LCSCM). Our paper belongs to the green supply chain management (GSCM) reviews but is distinguished by its specific interest in analysing research works on supply chain (SC) management regarding the reduction of carbon emissions and its related constraints. To facilitate our benchmarking of the 83 selected papers, we adopt a literature classification based on the logistic decisions studied within the developed models. We distinguish three categories of logistic decisions: operational management, technology investment and SC design coordination. Companies are currently facing great external pressures from governments and their conscientious customers to reduce their overall emissions. We analyse how these environmental constraints, which we believe are key drivers for low-carbon emissions management, have been incorporated into mathematical models. Analysing these external pressures in terms of concern about carbon emissions constitutes our main contribution through this literature review. In addition, companies are facing a challenge to reduce their carbon emissions, which are mainly generated from production, transport and storage activities. Consequently, the modelling of carbon emissions remains a crucial task when addressing the LCSCM problem. We suggest analysing the techniques used thus far to approximate those carbon emissions. Furthermore, to illustrate our literature classification and the features of the LCSCM problem, we provide the framework on which we based our analysis of the selected literature. We discuss the modelling aspects of this problem to highlight the limits of the existing literature and consequently suggest recommendations for future research. We believe that this issue will continue to be one of the top concerns of the OM community within the GSCM field as it continues to gain importance among business leaders, and political and social actors.

International audience

Circular Economy (CE) is an economic system that closes material and energy loops in production and consumption systems. In this context, digital technologies (DT) are seen as solutions for Circular Economy implementation. However, while the use of digital technologies in industry is growing, their specific effect on Circular Economy is not widely explored. Hence, this paper aims to identify the roles of digital technologies supporting Circular Economy. Based on a literature review as well as three case studies, we proposed to evaluate the relationship between Circular Economy and digital technologies by using Business Model Canvas integrating the R-principles such as reuse, remanufacture and recycle.

The circular economy (CE) can drive sustainability. For companies to select and implement circularity strategies, they need to evaluate and compare the performance of these strategies both in terms of progress towards CE but also based on their feasibility and business outcomes. However, evaluation methods for circularity strategies at the product level are lacking. Therefore, this research proposes a multi-criteria evaluation method of circularity strategies at the product level which can be used by business decision-makers to evaluate and compare the initial business of the company, transformative and future circularity strategies. This multi-criteria evaluation method aims to assist business decision-makers to identify a preferred strategy by linking together a wide variety of criteria, i.e., environmental, economic, social, legislative, technical, and business, as well as by proposing relevant indicators that take into consideration, where possible, the life cycle perspective. It also allows for flexibility so that criteria, sub-criteria, and weighing factors can be altered by the business decision-makers to fit the needs of their specific case or product. Two illustrative examples based on case companies are presented to verify and illustrate the proposed method.

Additive manufacturing (AM) consists in building parts from scratch, usually by stacking layers onto one another. Mostly used for rapid prototyping purposes, several AM processes can use metallic alloys which makes the rapid manufacture of end-use parts possible. Many researches are conducted to improve the manufacturing rate, to assess the environmental impact or to study the mechanical properties of test parts manufactured by such processes. In spite of the large number of studies, there is yet to be a designing methodology to take advantage of these processes. The formalization of the manufacturing capabilities and manufacturing constraints that these processes have has especially hardly been conducted. In this paper we investigate the manufacturing constraints of the Electron Beam Melting process, a metallic additive manufacturing process in order to issue recommendations to the designers. We will review the principle of Electron Beam Melting and look at the manufacturing capabilities designers are offered. Then we will focus on some key manufacturing constraints, the powder removing and the necessity of supporting structures. At last, we will give some recommendations regarding these two topics to take advantage of this process from the designing stage of a part.

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

Product–service systems (PSS) that create value by sharing or extending the use of products are expected to improve environmental performances of offerings. However, life-cycle analysis (LCA) which provides a comprehensive view and assesses the environmental impacts (EIs) of products is not well adapted to PSS. In this paper, an approach to help the environmental assessment of PSS using LCA during the design process is presented. It compares the environmental consequences of different PSS design alternatives and compares them to those in a hypothetical case of classical product sale replacing the PSS. The paper highlights characteristic requirements of PSS providing intensified use of products for multiple users. Next, a PSS life-cycle model is proposed to perform LCA during the design process. The parametric model constructed will help designers calculate and compare the EIs related to various scenarios of alternative PSS offers. This will facilitate decision-making during the design phase because the PSS life-cycle parameters are identified and linked to PSS design characteristics. With this approach, actions can be easily identified and engaged to improve the PSS solution. A case study on bicycle sharing in the city of Lyon illustrates the approach.

Abstract\n The assemble-to-order strategy delays the final assembly operations of a product until a customer order is received. The modules used in the final assembly operation result in large product diversity. This production strategy reduces the customer waiting time for the product. As the lead-time is short, any product rework may violate the delivery time. Since quality tests can be performed on the stocked modules without impacting the assembly schedule, the quality of the final assembly operations should be the focus. The data mining approach presented in this paper uses the production data to determine the sequence of assemblies that minimizes the risk of producing faulty products. The extracted knowledge plays important role in sequencing modules and forming product families that minimize the cost of production faults. The concepts introduced in the paper are illustrated with numerical examples.