Damien Lamy's Website

Peer-Reviewed International Journals:

P. Vicat-Blanc, F.R. Golra, A. Beugnard, D. Lamy, T. Coupaye, E. Abisset-Chavanne. A cross-domain functional software architecture model for industrial digital twins. Digital Twin. 2026. DOI: 10.1080/27525783.2026.2675139.

Digital twins (DTs) are collaborative software systems that combine various digital technologies to represent, analyze, and emulate the behavior of real-world entities thereby supporting decision-making. However, their development and operation remain complex and costly, limiting adoption by small and medium-sized enterprises. In addition, there is currently no widely accepted cross-industry reference architecture to guide DT engineering. This article proposes a generic functional software architecture model for DTs, that is accessible to non-specialists and adaptable to diverse industrial contexts. The framework is designed to address common functional requirements identified across a range of industrial use cases, while allowing DTs to evolve dynamically according to specific needs. The proposed approach focuses on structuring the main functional building blocks of a DT and explicitly defining the flows between these components and their environment. This contributes to clarifying system boundaries, internal organisation, and external interactions. Several applications are presented to illustrate how the framework facilitates communication among stakeholders and promotes the generalisation and reuse of Digital Twin software architectures.

S. Taguemount, D. Lamy, X. Delorme, N. Casoetto. Multi-line hybrid flow-shop scheduling problem with energy considerations. International Journal of Production Research. 2025. DOI: 10.1080/00207543.2024.2427344.

This article introduces a novel scheduling problem consisting of a multi-line hybrid flow-shop with energy considerations. The scheduling problem aims at optimising energy cost under time of use pricing structure with respect to production and energy-efficiency constraints. A 0–1 integer linear program based on a time-indexed formulation is proposed and allows to consider of variable power profiles for operations. Subsequently, a multi-start iterated local search-based heuristic is developed in order to address the resolution of large-scale instances. The performance of the proposed approaches is then assessed on randomly generated instances of various scales. Numerical experiments on an industrial case study also illustrate the economic benefits of considering a time of use pricing scheme with over 20% reduction in energy cost. Furthermore, it is demonstrated the importance of considering the system as a whole when considering energy in such a multi-line shop floor by examining three distinct optimisation strategies. Indeed, the benefits of a multi-line optimisation strategy compared to a sequential one are around 24% on average.

C. Murillo Coba, X. Boucher, D. Lamy, F. Vuillaume, A. Gay. Smart PSS modelling language for value offer prototyping: A design case study in the field of heating appliance offers. Computers in Industry. 2024. DOI: 10.1016/j.compind.2023.104041.

The recent convergence between two industrial transitions towards digitalization on the one side and servitization on the other side led to the new business strategies of digital servitization and smart PSS delivery. While inheriting from the previous scientific literature on PSS, because of the multiple impacts of digitalization in the overall system, the processes of ensuring the design and engineering of smart PSS solutions poses new challenges. This research addresses the specific needs to develop conceptual prototypes of smart PSS value offers, at early stages of the design process. The paper presents the development and experimentation of a modelling language and its associated modelling toolkit (sPS²Modeller). The application case study addresses the design of a smart PSS in the field of heating appliances, developed in collaboration with the company elm.leblanc, Bosch Group – France.

C. Murillo Coba, X. Boucher, D. Lamy, F. Vuillaume, A. Gay. Design and engineering of value-driven Smart PSS for manufacturing companies: Design risk anticipation with sPS2Risk Framework. CIRP Journal of Manufacturing Science and Technology. 2024. DOI: 10.1016/j.cirpj.2023.11.001.

Digital servitization has emerged over the last years, at the convergence between digital- and service-oriented business strategies, leading manufacturing companies to transform their business models through the adoption of so-called Smart PSS (Smart Product-Service-Systems). As a contribution to the relatively recent scientific literature on Smart PSS design, this paper presents a framework for the design and engineering of Smart PSS offers, based on a value-driven approach. The added-value of the framework is notably to integrate (i) a smart PSS prototyping approach associated with (ii) a risk management process, all along the design process. When a manufacturing company has to shift from a ‘product-centric’ to a ‘Smart PSS’ business model, sPS2Risk Framework helps decision-makers anticipating and mitigating key innovation risks. The paper presents the generic framework and its experimentation for an industrial case study, in the heating appliance sector.

D.R. Harish, T. Gowtham, A. Arunachalam, M.S. Narassima, D. Lamy, M. Thenarasu. Productivity improvement by application of simulation and lean approaches in an multimodel assembly line. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2024. DOI: 10.1177/09544054231182264.

In the era where mass production is inevitable, developments in the manufacturing process have helped industries to produce goods in batches of enormous quantities. With the drastic growth in technology, it is crucial to update, and improve assembly lines continuously. This study aims to systematically understand the area where an assembly line needs improvement to enhance its process and productivity. Published articles related to assembly line improvements, lean techniques, and simulation were synthesized exhaustively. A Discrete Event Simulation model of an automated manufacturing company was developed based on the data acquired and observations. Integration of lean techniques along with simulation adds to the effectiveness of the proposed method. Crucial areas that need improvement were identified, and lean techniques such as Kaizen and Poka-Yoke were applied. The efficiency of the proposed improvements was visualized by altering the simulation model. The average WIP was reduced from 87.2 to 81.2, and the cycle time of seven stations was reduced as a result of the implementation of Lean principles. Laser Marking Station (Fixture clamping and Stopper Cylinder) and Pad Printing & Vision Inspection Station witnessed a twofold improvement in the proposed model. There was an improvement in throughput by 7.14%. The study would prove helpful for managers and policymakers to visualize the usefulness of implementing lean techniques with minimal investment.

E. Abisset-Chavanne, T. Coupaye, F.R. Golra, D. Lamy, A. Piel, O. Scart, P. Vicat-Blanc. A Digital Twin use cases classification and definition framework based on Industrial feedback. Computers In Industry. 2024. DOI: 10.1016/j.compind.2024.104113.

The Digital Twin paradigm is a very promising technology that can be applied to various fields and applications. However, it lacks a unifying framework for classifying and defining use cases. The goal of this paper is to address the identified gap. Using a field study and a bottom-up approach, it aims to categorize the various uses of the industrial Digital Twin to help formalize the concept and rationalize its adoption by a range of industrial sectors. The study is based on an iterative process of collecting use cases from a wide variety of verticals, applying grounded theory principles. The usage scenarios were extracted, synthesized, grouped and abstracted to develop an actionable use cases classification framework. This article presents the resulting taxonomy and illustrates it by detailing real industrial use cases, including their value proposition and application areas. This collection, classification and analysis of use cases led to a study of the common aspects proposed in academic and industrial definitions of the Digital Twin. The goal was to combine and generalize these aspects into a pragmatic and unifying definition, on which the Alliance for Industry of the Future (AIF) committee has converged. The main contributions of this work include proposing, from a joint industrial and academic perspective, (i) the first domain-independent and industry-focused systematic collection of Digital Twin use cases, (ii) a comprehensive framework for analyzing and classifying Digital Twin use cases and their requirements, and (iii) a consensual general definition of the industrial Digital Twin to contribute to the structuring and standardization of this very active ecosystem.

X. Delorme, G. Fleury, P. Lacomme, D. Lamy. Modelling and solving approaches for scheduling problems in reconfigurable manufacturing systems. International Journal of Production Research. 2023. DOI: 10.1080/00207543.2023.2224446.

Reconfigurable manufacturing systems (RMS) intend to bridge the gap between dedicated and flexible manufacturing systems. If the literature is mainly focused on the design step and tactical planning of such systems, few research projects have addressed scheduling at the operational level. While setup times may occur in flexible manufacturing systems, reconfiguration times considered in RMS may affect several resources at once, and hence require specific modelling and solving approaches to be considered. This paper first formalises the problem at hand through integer linear programming. An iterative search method is then provided to obtain solutions to larger-scale instances. Results obtained on generated instances show that managing even few possible configurations can yield significant improvements in solutions’ quality. Meanwhile, the extended search space implied by the increase in available configurations hinders the convergence to a good solution in a reasonable computation time, which suggests further investigations.

X. Delorme, A. Cerqueus, P. Gianessi, D. Lamy. RMS balancing and planning under uncertain demand and energy cost considerations. International Journal of Production Economics. 2023. DOI: 10.1016/j.ijpe.2023.108873.

In recent years, we have observed rapid changes in the customer demand along with shorter product life cycles. In addition, sustainability concerns about production systems are growing, especially due to energy supply fluctuations in terms of either availability or cost. Among these challenges, energy efficiency is of the utmost importance, and Reconfigurable Manufacturing Systems (RMS), most notably through their scalability feature, could represent a valuable solution: production resources can be reorganized promptly to adapt throughput to external factors, such as uncertain demand or Time-Of-Use prices. Although the aforementioned challenges concern day-to-day management, they should be anticipated at the design stage of the production system, whose behavior might otherwise not meet expectations and hinder the competitiveness of the company. One possibility is to consider the expected performance of such a system from the viewpoint of different productivity and energy-efficiency criteria, through line balancing and future production planning. This can be modeled as a bi-level optimization problem, in which the line balancing of the RMS is the upper level and the configuration planning is the lower level. We consider three criteria, namely the number of workstations, the expected service level and the expected energy cost, taking into account demand uncertainty through scenarios. A three-phase matheuristic is developed and its performances on instances derived from the literature are discussed. The results show that consistent energy cost savings can be achieved, even with very few configurations.

A. Laurent, D. Lamy, B. Dalmas, V. Clerc. Pattern mining-based pruning strategies in stochastic local searches for scheduling problems. International Transactions in Operational Research. 2021. DOI: 10.1111/itor.12984.

Scheduling problems are a subclass of combinatorial problems consisting of a set of tasks/activities/jobs to be processed by a set of resources usually to minimize a time criterion. Some optimization methods used to solve these problems are hybridized with knowledge discovery techniques to extract information during the optimization process and enhance it. However, most of these hybrid techniques are custom-designed and lack generalization. In this paper, a module for knowledge extraction in Stochastic Local Searches is designed, aiming to be problem independent and plugged into optimization methods that relies on multiple Stochastic Local Search replications. The objective is to prune parts of the search space for which the exploration is likely to lead to poor solutions. This is performed through the extraction of high-quality patterns occurring in locally optimal solutions. Benchmarked on two well-known scheduling problems, the Job-shop Problem and the Resource Constrained Project Scheduling Problem, the results show both a speed up in the convergence and the reaching of better local optima solutions.

M. Gondran, S. Kemmoé-Tchomté, D. Lamy, N. Tchernev. Bi-objective optimisation approaches to Job-shop problem with power requirements. Expert Systems With Applications. 2020. DOI: 10.1016/j.eswa.2020.113753.

Nowadays, a large focus is given to mass personalisation, and multiple path shop floors are suited to such production environments. Hence, this paper deals with the Job-shop scheduling problem that is used for modelling a manufacturing system. Meanwhile, a large attention is given to energy consumption of production systems, but few works consider power requirements of the production systems in order to process operations. In order to contribute in filling this gap, this paper considers the problem where the objective is to minimise both the total completion time of all operations and the instant available power required to process these operations. The problem results in the Bi-objective Job-shop Problem with Power Requirements (Bi-JSPPR). The goal of this paper is to provide a Pareto frontier of schedules minimising both criteria, considering that operations may consume a lot of power at the beginning of the process (consumption peak), more than its consumption after a while, which allows to model power profiles of manufacturing operations. To solve the problem two metaheuristic approaches are investigated: a hybrid Non-dominated Sorting Genetic Algorithm (NSGA-II) and an iterated Greedy Randomized Adaptive Search Procedure coupled with an Evolutionary Local Search (iGRASP×ELS). An efficient local search procedure is specifically designed to improve the quality of solutions in the Pareto frontier of the hybrid NSGA-II (hNSGA-II). Computational experiments and statistical tests are conducted to demonstrate the efficiency of the approaches. Results show that both approach are complementary, having the hNSGA-II showing better average performances, while the iGRASP-ELS is better when high peak power consumption are considered.

S. Kemmoé, D. Lamy, N. Tchernev. Job-shop like manufacturing system with variable power threshold and operations with power requirements. International Journal of Production Research. 2017. DOI: 10.1080/00207543.2017.1321801.

This paper addresses an important issue in manufacturing by considering the scheduling of a Job-shop like manufacturing system involving a power threshold that must not be exceeded over time. A power proﬁle is attached to operations that must be scheduled. This power proﬁle presents a consumption peak at the start of process in order to model most of real-world machining operations. These operations must be scheduled according to the instantly available power threshold. A mathematical formulation of the problem is proposed; its main goal is to minimise the total completion time of all operations. A set of instances is built based on classical format of instances for the Job-shop problem. As it is time-consuming to obtain exact solutions on these instances with the CPLEX solver, a Greedy Randomised Adaptive Search Procedure hybridised with an Evolutionary Local Search (GRASP×ELS) metaheuristic is designed. The GRASP×ELS is compared with two other metaheuristics: a Variable Neighbourhood Search and a Memetic Algorithm. The GRASP×ELS is also compared with several algorithms developed in the literature for the classical job-shop problem. Results show the relevancy of the metaheuristic approaches both in terms of computational time and quality of solutions.

S. Kemmoé, D. Lamy, N. Tchernev. An effective multi-start multi-level evolutionary local search for the flexible job-shop problem. Engineering Applications of Artificial Intelligence. 2017. DOI: 10.1016/j.engappai.2017.04.002.

In this paper, an improved greedy randomized adaptive search procedure (GRASP) with a multi-level evolutionary local search (mELS) paradigm is proposed to solve the Flexible Job-shop Problem (FJSP). The FJSP is a generalisation of the well-known Job-Shop Problem with the speciﬁcity of allowing an operation to be processed by any machine from a given set. The GRASP metaheuristic is used for diversiﬁcation and the mELS is used for intensiﬁcation. Four diﬀerent neighbourhood structures are formalised. A procedure for fast estimation of the neighbourhood quality is also proposed to accelerate local search phases. The metaheuristic has been tested on several datasets from the literature. The experimental results demonstrate that the proposed GRASP-mELS has achieved signiﬁcant improvements for solving FJSP from the viewpoint of both quality of solutions and computation time. A comparison among the proposed GRASP-mELS and other state-of-the-art algorithms is also provided in order to show the eﬀectiveness and eﬃciency of the proposed metaheuristic.

Peer-Reviewed International Conferences:

X. Delorme, P. Gianessi, D. Lamy. A new decoder for permutation-based heuristics to minimize power peak in the assembly line balancing. 22nd IFAC World Congress (IFAC WC). 2023.

We consider the Simple Assembly Line Balancing Problem with Power Peak Minimization and Earliest Starting Dates (SALB3PM-ESD), a problem of balancing an assembly line and suitably sequencing its tasks so as to minimize the peak of the electric power consumption associated with them. We propose an ILP-based decoder to optimally split (w.r.t. the power peak) a sequence of tasks over the workstations of the line. The decoder is plugged into a simple local search algorithm to test its effectiveness in quickly computing the optimal split for the solutions encountered in the space of task sequences. Preliminary tests on instances from literature show that the decoder is efficient, and that it seems indeed promising to use it to take advantage of the numerous sequence-based optimization algorithms in the scheduling literature to develop more competitive methods to efficiently tackle the SALB3PM-ESD.

X. Boucher, D. Lamy, C.M. Coba. Economic assessment of smart PSS multi-actor delivery networks: case study in the heating appliance sector. Working Conference on Virtual Enterprises (PRO-VE). 2023.

The comparative assessment of alternative Product-Service-Systems (PSS) economic models is an important objective to be integrated along the design process of smart PSS. Economic assessment of smart PSS delivery networks can support economic risk mitigation and help decision-makers in establishing collaborative mechanisms for financial risk sharing, within the enterprise networks. The paper proposes a generic and replicable approach for the development of smart PSS simulator for Economic assessment & risk analysis. A feasibility application is developed in an industrial case study (heating appliance sector).

E. Yadegari, D. Lamy, X. Delorme. Reactive Flexible Job Shop Problem with Stress Level Consideration. Advances in Production Management Systems (APMS). 2023.

Taking into account a real-world issue, the present study focuses on a flexible job shop scheduling problem (FJSSP) that deals with new job arrivals. This problem is very common in real-world manufacturing operations. On the other hand, Industry 5.0 environment pays more attention to human resources, and it is shown that well-being of workers including the stress level of them has a great impact on shop scheduling performance performance. However, with the arrival of a new job, the initial planning needs a rescheduling and these changes on the initial schedule may increase the stress level of workers. Still, given the real-world problem, we want to minimize the stress level of different rescheduling strategies. Three types of changes will be imposed to the shop floor schedule, which could lead to an increased stress level on human resources. These changes are as follows: 1 - Shifting an old operation on the same machine when the new job arrives; 2 - changing the machine assigned to an operation; 3 - altering the sequence of the operations. The procedure for calculating the different kinds of changes affected by the new job arrivals is illustrated in order to find the level of imposed stress. To solve such an NP-Hard problem, a Genetic Algorithm (GA) is investigated to solve it. At first an initial schedule is built based on benchmark instances from FJSSP literature. Then, at different times, new jobs will arrive, with routes taken from the same instance. The instances are used to validate the proposed algorithm. Behavior of the algorithm on very large problems indicates that obtained schedules remain as compact as expected while considering the stress criterion.

X. Delorme, A. Cerqueus, P. Gianessi, D. Lamy. Design and planning of configurations in RMS to minimize the energy cost facing uncertain demand. International Working Seminar on Production Economics 2022 (IWSPE). 2022.

P. Gianessi, A. Cerqueus, D. Lamy, X. Delorme. Using reconfigurable manufacturing systems to minimize energy cost: a two-phase algorithm. 17th IFAC Symposium on Information Control Problems in Manufacturing (INCOM). 2021.

Energy consumption has become a major concern for society, and since the industrial sector is the largest consumer, companies are urged to improve energy efficiency of their production systems. This paper investigates how Reconfigurable Manufacturing Systems (RMS), and particularly their scalability feature, can be exploited in order to minimize the energy cost in production systems w.r.t. a Time-Of-Use pricing scheme. In the case of RMS, the resulting energy cost optimization problem is a Bilevel Optimization problem, as it jointly considers both the line balancing and the production planning. After introducing the problem and its features, a solving approach based on a simulated annealing algorithm and a linear program is proposed. The approach is then validated on designed instances based on classical test problems taken from the literature. Results show that considerable savings in terms of energy cost can be achieved w.r.t. dedicated lines, even when optimally designed, ultimately showing the great potential of RMS towards energy efficiency.

S. Ashwin, V. Shankaranarayanan, D. Lamy, S.P. Anbuudayasankar, M. Thenarasu. Development and Analysis of Efficient Dispatching Rules for Minimizing Flow Time and Tardiness-Based Performance Measures in a Job Shop Scheduling. International Conference on Intelligent Manufacturing and Energy Sustainability (ICIMES). 2021.

Scheduling of jobs has a significant contribution to the improved performance of the job shop production system. This study is aimed at developing efficient priority dispatching rules (PDRs) for scheduling in a job shop to improve the tardiness and flow time performance measures. The selection of PDRs depends on the scheduling criteria and job shop conditions. LEKIN software can be used for a variety of well known job shop conditions and varying flexibility. Different schedules are derived using the LEKIN scheduling software for different PDRs such as earliest due date and shortest processing time. In this work, seven static dispatching rules have been taken from the literature study, and four new priority dispatching rules have been proposed and compared to schedule two formal job shop problems under four objective functions, whose aim is to improve the performance measures. The results show that the proposed PDRs are very effective in minimizing various performance measures.

D. Lamy, X. Delorme, P. Lacomme, G. Fleury. Toward Scheduling for Reconfigurable Manufacturing Systems. 21st IFAC World Congress (IFAC WC). 2020.

Reconfigurable Manufacturing Systems have been introduced in the mid 1990s as an alternative to classical dedicated or flexibles production systems. They are supposed to be more reactive and capable of evolving depending on unpredictable and high-frequency market changes induced by global market competition. While this concept has received a lot of attention in the literature, mainly at the design and conception phase of the production system, only few works are addressing the operational management of such production systems. One of the key features of reconfigurable manufacturing system is the possibility to use different configurations. The objective is to schedule operations efficiently while considering the different configurations of the system that are available. Switching from one configuration to another requires setup times. However, contrary to classical setup times that can be found in literature on scheduling problems, switching from a configuration 𝒊 to 𝒋 may require that some machines are stopped, and then reconfiguration goes beyond classical setups. This paper intends to formalise such a problem in the context of Flow-shop and Job-shop production systems. First results on small case instances are introduced.

A. Cerqueus, P. Gianessi, D. Lamy, X. Delorme. Balancing and configuration planning of RMS to minimize energy cost. IFIP International Conference on Advances in Production Management Systems (APMS). 2020.

In this paper, we investigate the use of the scalability property of RMS to reduce the energy cost during the production. The corresponding optimization problem is a new Bilevel Optimization problem which combines a line balancing problem with a planning problem. Aheuristic based on a simulated annealing algorithm and a linear programis proposed. An illustrative example is presented to highlight the poten-tial of this new approach compared to the cost obtained with a classicproduction line.

D. Lamy, J. Schulz, M. F. Zaeh. Energy-aware scheduling in reconfigurable multiple path shop floors. 53rd CIRP Conference on Manufacturing Systems (CMS). 2020.

Individualization as a major driver in societal change forces companies to adapt to continuously changing customer-specific requirements. Therefore, companies incorporate novel enabling technologies such as digitalization, servitization, and reconfigurability in manufacturing systems. Even though the research on reconfigurable systems has continued over the last 20 years, the energy management of such systems has barely been considered. This work represents recent trends connected with energy efficiency and energy flexibility in reconfigurable manufacturing systems. A suitable attempt to model an energy-related system is proposed using scheduling operations which are subject to power requirements, i.e. variable power thresholds. The results of the optimization show that reconfigurable systems support the adaptation of energy consumption to variable thresholds.

D. Lamy, S. Thévenin. The Group Shop Scheduling Problem with power requirements. 17th International Workshop on Project Management and Scheduling (PMS). 2020.

G. Fleury, X. Delorme, P. Lacomme, D. Lamy. A Conjunctive-disjunctive Graph Modeling Approach for Job-shop Scheduling Problem with Changing Modes. 17th International Workshop on Project Management and Scheduling (PMS). 2020.

D. Lamy, X. Delorme, P. Gianessi. Line Balancing and Sequencing for Peak Power Minimization. 21st IFAC World Congress (IFAC WC). 2020.

In the past years, environmental awareness started to bring new production paradigms based on energy efficiency. If it is possible to improve energy efficiency of existing production systems, it should be even more protable to consider this objective at the design stage. In the context of Paced Production Lines, and given power requirements for operations, it becomes possible to assign more efficiently these operations to stations while respecting other constraints such as maximum takt time and number of workstations. The repetitive nature of paced lines implies that misconceptions implying a high peak power consumption will see this peak power repeated over and over without having large possibilities to correct it. In order to tackle peak power minimization objectives, this implies to consider sequencing of operations in addition to their assignment to workstation which is not classical in line balancing. In this paper, the problem under study is presented with a new specic feature that allows to consider semi-active sequence of operations at each station. In order to address large scale instances, a first metaheuristic approach is implemented and evaluated on an extended dataset from the literature. Results show that it is possible to improve energy efficiency at the design stage of production systems.

B. Dalmas, D. Lamy, A. Laurent, V. Clerc. An optimization and pattern mining based approach for solving the RCPSP. In Proceedings of the 13th Metaheuristics International Conference (MIC). 2019.

In this paper, we introduce a pattern-based module to improve the efficiency of local search-based optimizations. The objective of this module is to extract frequent dependencies between tasks from good solutions, then to use them to guide the search phase. We aim at showing that knowledge can be extracted from solutions built to generate either better solutions or to generate similar solutions but faster. The first results obtained tend to validate our hypothesis.

P. Fenies, S. Kemmoé, D. Lamy, N. Tchernev. A Multi-start Multi-level ELS for the Group-Shop Scheduling Problem. In Proceedings of the 16th Information Control Problems in Manufacturing (INCOM). 2018.

In this paper, the use of a metaheuristic based on a Multi-start Multi-level Evolutionary Local Search is studied for Group-Shop Scheduling Problem solving. It appears that only a few works exist in the literature on this problem. The proposed metaheuristic approach consists in a combination of neighbour generations and local searches. Different neighbourhoods are explored during the search process. The metaheuristic is evaluated on several instances from the literature for which it proves to be able to find state of the art solutions. Results are compared with the two main contributions from the literature. Considering the instances, several new best known solutions are obtained.

S. Kemmoé, D. Lamy, N. Tchernev. Modelling flexible manufacturing systems with power constraints and machine switch on/off. 7th International Conference on Industrial Engineering and Systems Management (IESM). 2017.

This paper deals with Flexible Manufacturing System in the context of the future’s industry. The problem under study is the Flexible Job-shop which models various production systems. This study aims at scheduling operations efficiently by considering a power limitation. To this purpose, each operation has a power profile depending on the machine it is assigned to. Furthermore, in industry, machines are often left idle. This practice could lead to loss in the available power for the manufacturing system, especially when a machine requires a lot of power when it is not processing any operation. Hence, it is proposed to address the benefits of switch on/off in the Flexible Job-shop problem with power limitations. A mathematical formulation for this problem is presented in this paper. The results are promising and show that it is possible to schedule efficiently operations with power requirements in a production system.

G. Avez, P. Lacomme, D. Lamy, R. Phan and N. Tchernev. First experiment of Storm for design of efficient optimization methods : application to the job-shop with time lags. 11th International Conference on Modeling, Optimization and SIMulation (MOSIM). 2016.

Taking advantage of a cloud infrastructure requires design of algorithms based on frameworks which should gain benefit in cloud resources. MapReduce is a paradigm which contributes to the design of new algorithms in operational research since it allows job parallelization in a set of heterogeneous computers linked into a cluster via the Internet. Several significant competitors are active in line with the first Hadoop implementation of the MapReduce framework. Our contribution consists in investigating how Storm can define new promising approaches for operational research algorithms. The proposed MapReduce-based approach is experienced on the resolution of the Job-Shop with time lags i.e. a well-known NP-Complete combinatorial problem.

S. Kemmoé, D. Lamy, N. Tchernev. A GRASP Embedding a Bi-Level ELS for Solving Flexible Job-Shop Problems. 8th IFAC Conference on Manufacturing Modelling, Management & Control (MIM). 2016.

In this paper, an evolution of the GRASPxELS for the Flexible Job-shop problem is proposed, resulting in a GRASP with a bi-level ELS paradigm. As ELS is based on neighborhood search, a random neighborhood is developed, including two different neighborhood structures. This metaheuristic allowed to find state of the art solutions for Barnes and Chambers' instances, and sound solutions for the famous instances of Dauzère-Pérès while still being competitive on all this set of instances. Results are compared with recent papers focusing on this challenging problem demonstrating that this metaheuristic approach is effective.

Sylverin Kemmoé-Tchomté, Damien Lamy, Nikolay Tchernev. A metaheuristic based on simulation for stochastic Job-shop optimization. International Conference on Industrial Engineering and Systems Management (IESM). 2015.

This paper deals with stochastic Job-shop with random processing times where the objective is to find schedules robust enough in order to minimize the total completion time of all the operations. The problem is handled by use of a multi-start metaheuristic and a simulation software. At each iteration of the metaheuristic the best deterministic schedule is tested using the SIMAN simulation language; at this given schedule is then adjoined the average simulated makespan. The metaheuristic is then searching for another different schedule in the deterministic space – which could be worse than the previous one. The proposed approach is applied to a small instance for demonstration. A set of instances for job-shop problems is then adapted for stochastic use in order to validate this work. The results, both in term of computational time and of quality, show the relevance of this study.

S. Kemmoé-Tchomté, D. Lamy, N. Tchernev. Job-shop like manufacturing system with time dependent energy threshold and operations with peak consumption. IFIP International Conference on Advances in Production Management Systems (APMS). 2015.

In this study the Job-shop scheduling problem with energy considerations is considered. At each moment of the schedule an energy threshold must not be exceeded. This energy threshold is not fixed all along the schedule and can vary. The variation of energy is handled by inclusion of dummy operations. Furthermore, the operations that must be scheduled have a power profile presenting a high energy consumption (peak) at the beginning and a lower consumption after the peak’s end. A mathematical formulation of the problem is proposed. This model is experimented on a short example with the CPLEX 12.4 solver. The schedules obtained show the relevance of the model. This study shows that new approaches for scheduling are no longer avoidable and that it is possible for enterprises to schedule efficiently their tasks according to energy constraints.

S. Kemmoé, D. Lamy, N. Tchernev. An Optimization Framework for Job-shop with Energy Threshold Issue with Consideration of Machining Operations with Consumption Peaks. Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA). 2015.

In this paper the problem of the Job-shop is extended to support energy constraints. The objective is to propose scheduling tools for manufacturing systems considering consumption threshold that must not be exceeded. The operations are supposed to consume more energy at beginning and thus representing a consumption peak that is often present in machine tools. This assumption results in considering that an operation is divided into two sub-operations. The goal is then to propose the best schedule considering the energy threshold, the consumptions of operations and duration of consumption peaks as given data. A Mixed Integer Linear Model (MILP) for the problem solving is proposed; it is based on flow approach to take into account the energy threshold. Since it is difficult to find exact solutions for medium and large size problems, a metaheuristic based on a GRASPxELS is proposed. Small scale instances for the problem have been generated, and results expose the relevance of the metaheuristic approach.

S. Kemmoé, D. Lamy, N. Tchernev. A Job-shop with an Energy Threshold Issue Considering Operations with Consumption Peaks. 15th IFAC Symposium onInformation Control Problems inManufacturing (INCOM). 2015.

In this paper the Job-shop problem is addressed as a support for a production system considering an energy consumption threshold that must not be exceeded. It is considered that an operation may consume a lot of energy at the beginning of the process (consumption peak), more than its consumption after a while, resulting in the consideration of an operation as two sub-operations. The goal is then to propose the best schedule considering the energy threshold, the consumptions of operations and duration of consumption peaks as given data. A linear model based on a flow solved simultaneously with the Job-shop problem is proposed. An example of the improvements in scheduling considering consumption peaks rather than global consumption is given.

S Tchomte, D Lamy, N Tchernev. An Optimization Approach for Job-shop with Financial Constraints-In the Context of Supply Chain Scheduling Considering Payment Delay between Members. Proceedings of the International Conference on Operations Research and Enterprise Systems (ICORES). 2015.

In this paper the use of Job-Shop Scheduling Problem (JSSP) is addressed as a support for a supply chain scheduling considering financial exchange between different supply chain partners. The financial exchange is considered as the cash flow exchanges between different upstream and downstream partners. Moreover, several suppliers are involved in operations. The problem under study can be viewed as an extension of the classical JSSP. Machines are considered as business or logistic units with their own treasury and financial exchanges happen between the different partners. The goal then is to propose the best schedule considering initial cash flows in treasuries as given data. The problem is formulated as integer linear programming model, and then a powerful GRASPxELS algorithm is developed to solve large scale instances of the problem. The experiments on instances with financial constraints proved the methods addressed the problem efficiently in a short amount of time, which is less than a second in average.

S. Kemmoé, D. Lamy, N. Tchernev. A GRASPxELS for supply chain optimization considering payment delay between members. 5th International Conference on Metaheuristics and Nature Inspired Computing (META). 2014.

Peer-Reviewed National Conferences:

S. Taguemount, D. Lamy, X. Delorme. Affectation et Ordonnancement énergétiquement efficient pour le problème de flow shop hybride multi-lignes. 24th ROADEF Conference. 2023.

P Gianessi, A Cerqueus, D Lamy, X Delorme. Une heuristique en deux phases pour un problème d'équilibrage et de planification minimisant le coût énergétique d'un système reconfigurable. 22nd ROADEF Conference. 2021.

G. Fleury, X. Delorme, P. Lacomme, D. Lamy. Modélisation des problèmes d'ateliers reconfigurables. 21st ROADEF Conference. 2020.

D. Lamy, A. Cerqueus, S. Finco, P. Gianessi. Intégration du Rest Allowance dans l’ordonnancement d’ateliers de types Job-shop. 20th ROADEF Conference. 2019.

B. Dalmas, D. Lamy, A. Laurent. Approche de couplage optimisation - data mining pour le RCPSP. 19th ROADEF Conference. 2018.

P. Fenies, S. Kemmoé, D. Lamy, N. Tchernev. Métaheuristique pour le Group-shop Scheduling Problem. 19th ROADEF Conference. 2018.

S. Kemmoé, D. Lamy, N. Tchernev.. Job-shop Flexible sous contrainte énergétique. 18th ROADEF Conference. 2017.

S. Kemmoé, D. Lamy, N. Tchernev. Metaheuristique et simulation pour le job-shop flexible stochastique. 17th ROADEF Conference. 2016.

M. Gondran, S. Kemmoé, D. Lamy, N. Tchernev. Une approche bi-objectif au problème du Job-shop sous contrainte de pics de consommation énergétique. 17th ROADEF Conference. 2016.

S. Kemmoé, D. Lamy, N. Tchernev. Job-shop sous contrainte de pics de consommation énergétique. 16th ROADEF Conference. 2015.