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International audience; Industry 4.0 arrives with a multitude of technological advances which impact the different components of a product’s value chain. Most existing research investigate the application of Industry 4.0 technologies in various parts of the manufacturing process. However, there is still a lack of comprehensive research on the impact of Industry 4.0 technologies on decision-making and how it must be adapted in order to truly benefit from the advantages brought by these technologies. This paper is thus intended to give more enlightenment on the subject, with evidence brought from the textile and clothing industry. Following this objective, a systematic literature review of research articles associated with decision-making in the Industry 4.0 era is performed with a focus on the textile and clothing industry. Articles were retrieved from the Scopus database, and the selection procedure resulted in 61 research papers. Moreover, a content analysis was performed using tables and graphs featuring quantitative results which are grounded on the proposed taxonomy. This study permits to identify some decision-making trends such as data-driven, real-time, decentralized, integrated, and sustainable decision-making. Following these decision-making trends and technologies brought by Industry 4.0, we discuss the keys for a competitive textile and clothing industry of the future. Finally, challenges facing the industry are presented following two aspects including digitalization and Industry 4.0 technologies implementation as well as environmental and social responsibility.

This paper addresses the problem of scheduling different non-preemptive jobs on a single machine under time of use electricity tariffs consideration. The considered machine has three main states (OFF, ON, Idle) and two transition states (Turn-on and Turn-off). Each of these machine’s states as well as the processing jobs, consume a specific amount of energy. Moreover, a speed-scalable case of the problem is considered, in which jobs can be processed at an arbitrary speed with a trade-off between speed and energy consumption. First, a mixed-integer linear programming model with the objective of minimizing total energy consumption costs formulates this scheduling problem. Then, since this problem is strongly NP-hard, different approximate optimization methods are investigated to provide near-optimal solutions. Finally, an extensive computational study is carried out to establish the efficiency of the proposed algorithms. The obtained results show, how a well-tuned genetic algorithm combined with an adequate local search procedure constitutes an efficient method for solving this energy-aware scheduling problem.

International audience; The rapid uptake of the Wireless Body Area Networks (WBAN) and their services poses unprecedented security requirements. WBAN are evolving to support these requirements. Fulfilling these tasks is challenging as their mobile context is increasingly complex, heterogeneous, and evolving. One potential solution to meet the WBAN security requirements is trust management that helps to reach a more secure and adaptable WBAN environment. Accordingly, this article aims to serve as a brief survey of trust management approaches within intra-WBAN and inter-WBAN. For that, we first summarize trust management concepts, discuss WBAN challenges and classify the attacks on WBAN trust management models. Subsequently, we detail and compare the existing trust based approaches in a WBAN context. We pinpoint their limitations and provide a new classification of these different approaches. We also propose a set of best practices that may help the reader to build a robust and an efficient trust management framework. We complete this survey by highlighting the open future directions and perspectives for research.

International audience; The buffer allocation problem (BAP) is one of the major optimization problems considered by production systems designers. The BAP is widely studied in the literature, since buffers have a great impact on improving the efficiency of production systems especially for mass production. Nevertheless, with all upcoming changes in world’s economy, industrial requirements and ecological awareness, the necessity of considering energy efficiency in manufacturing systems design becomes crucial. This paper introduces the first variant of energy-efficient buffer allocation problem (EE-BAP). It addresses a novel integrated analytical method to find the best buffer allocation that optimizes both throughput and energy consumption for serial production lines with unreliable machines. A large experimental study, based on different instances well established in the literature, is carried out to analyze the relevance of the proposed method. The obtained results show that considerable energy savings with low or almost no productivity loss are allowed. The energy-efficient BAP is a novel problem that considers the complex trade-off between throughput and energy efficiency performance and introduces new perspectives of research in the field of sustainable manufacturing systems design.

Dynamic pricing strategies are usually adopted to dynamically adjust the products’ prices taking into account demand function characteristics to maximize the revenue. This paper addresses the problem in which a firm has to make decisions about its selling prices in each period to maximize the total profit over the whole horizon. We propose a theoretical analysis of this problem from which we show that: first, when the demand function is linear, the problem can be formulated as a quadratic programming problem. We also present the Karush-Kuhn-Tucker system, which can be used to find the optimal pricing policy when the objective function is concave. Then, when the demand is isoelastic, we also show that the problem can be reduced to the maximization of N independent functions in bounded intervals. Some numerical examples are provided to illustrate the results obtained for both the linear and isoelastic cases.

International audience; In this paper, we consider a dedicated lot sizing and scheduling problem inspired from a real-world application in tire industry. This problem consists in scheduling several products on parallel machines with eligibility constraints within a finite planning horizon. We consider several specific constraints such as the number of simultaneous scheduled products, the number of setup per period and minimum quantities to produce. A mathematical model that determines a production schedule minimizing backlogging, low and high inventory surplus is proposed and tested on real data with up to 210 products, 70 machines and 7 periods. The obtained results show the effectiveness of the proposed model which improve significantly the industrial solution.

International audience; In this paper, the capacitated lot-sizing and scheduling problem with sequence-dependent setups and backlogging with energy consideration is studied. A new mixed-integer programming model is proposed. The planning horizon is split into T periods where each one is characterized by a duration, an allowed maximal power, a price of electricity and power and demands of products. The objective is to find a production scheduling that minimizes the total production cost. As this problem is known to be NP-hard, a Fix-and-Relax heuristic is developed. Computational experiments demonstrate that this method provides good quality results.

This paper studies a multi-state two-machine flowshop scheduling problem with variable electricity tariffs. The objective is to process a set of jobs in the available time slots with different energy costs to minimise the total energy consumption costs. For this purpose, two different 0-1 linear programs are proposed for the problem.Computational experiments are presented and numerical results are discussed and analyzed in order to evaluate their efficiencies.

Improving the economic as well as the energetic performances of manufacturing systems has become a real challenge for both researchers and industrials. Due to the economic, legislative and environmental pressure, enhancing both economic and energetic performances arouses interest and attracts further efforts. The objective of this paper is to discuss current work done on this subject, analyze the results and propose further perspectives for research in this area towards sustainability. Furthermore, an introduction for an economic and energetic evaluation method for serial production lines is also presented.

International audience; Optimizing the performance of services and industrial systems is a real lever for creating value for companies and society [...]

The necessity of managing a production system with less cost and pollution in such a competitive world has enticed the attention of many researchers. This paper deals with a multi-state two-machine flow-shop scheduling problem under Time-Of-Use electricity tariffs. The main issue is to assign a set of jobs to available time slots with different energy costs to minimize the total energy consumption costs. For this purpose, a new linear programming (LP) model is proposed, and several numerical results are solved using CPLEX Studio 12.9.0 solver to evaluate the effciency of the proposed model for the problem. The obtained results demonstrate that CPLEX solver is not able to find the optimal solution for the problems as large as 20 jobs and 90 periods during the 3600 seconds time limitation.

International audience; This paper addresses a capacitated lot-sizing problem with pricing decisions. The considered problem consists of planning the production of different products during several time periods with setup costs. Unlike the classical version of the capacitated lot sizing problem, the demand for the products is not fixed but price-sensitive in this problem. The demand function is assumed to be nonlinear. The decisions consist of establishing the best strategy for production and inventory, and the best price policy. We propose an improved mathematical formulation by extending some previous works using new lower and upper bounds to reduce the solution space. We also introduce new heuristic methods to provide near-optimal solutions. These methods are tested on several instances from previous studies. The obtained results illustrate the efficiency of these methods.

International audience; This paper deals with a particular variant of the flowshop scheduling problem motivated by a real case configuration issued from an electro-electronic material industry. The shop floor environment is composed of two parallel semi-lines and a final synchronization operation. The jobs must follow the same technological order through the machines on each parallel semi-line. However, the operations on each semi-line are independent. The final synchronization operation, operated by a dedicated machine, can only start when the job is finished on both semi-lines. The objective is to determine a schedule that minimizes the makespan for a given set of jobs. Since this problem class is NP-hard in the strong sense, constructive heuristic procedures and metaheuristics methods are introduced to achieve optimal or near-optimal solutions. The performances of the proposed GRASP and the Simulated Annealing algorithms are evaluated and compared with the adaptation of two well-known heuristics. Computational experiments show that the proposed metaheuristics provide very good results in low computational times.

International audience; This paper deals with optimizing the location of multi-sources power plants, using renewable energies, based on a given statistical data and satisfying economic, environmental and social criteria. In the classical version of the goal-programming model, the different types of plants should be located in order to minimize the total deviations from predefined goals; keeping in mind that the parameters are place dependent. However, this paper shows that relaxing the constraints leads to more feasible solutions and improves some criteria. We introduce place-dependent parameters for placing the different power plants. Since different types of plants can be positioned in each location, we combined the goal-programming model with a flow control approach to evaluate the location frequencies distribution of power plants to each position in order to identify a characteristic energy map. The obtained results are based on numerical experiments inspired from the literature. The results show the effectiveness and the efficiency of the proposed approach.

International audience; This paper addresses a non-preemption case of a particular single machine scheduling problem with a pre-determined sequence for the jobs. The considered machine may switch between three different states (namely ON, OFF or Idle) to process all the jobs during a finite number of periods. Each state, as well as switching between the states, entails a certain energy consumption, and the objective is total energy consumption costs minimization under time varied electricity prices. This problem was already addressed in the literature, where the authors assumed it as an NP-hard problem and they introduced a meta-heuristic method to solve the problem. This study discusses the complexity of the problem to verify this fact. For this purpose, an exact polynomial method is proposed to solve the problem. In this approach, firstly the problem is modeled using a finite graph whose dimension (number of vertices and edges) is dependent on the total processing times and the total number of periods. Then, Dijkstra’s algorithm is applied to find the shortest path between the initial node and the final one, which provides the optimal solution in polynomial time. So, we can conclude that the problem is polynomial, and it does not need to use meta-heuristic approaches even for solving large instances of this problem. A numerical experiment study is also reported to show the effectiveness of the proposed approach for solving any instances of this problem in a negligible computation time.