Tuesday 21st of May

 Peer-Reviewed International Journals:

Xavier Delorme, Gérard Fleury, Philippe Lacomme & Damien Lamy (2023): Modelling and solving approaches for scheduling problems in reconfigurable manufacturing systems, International Journal of Production Research, 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.

A. Laurent, D. Lamy, B. Dalmas and 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

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 and N. Tchernev. 
"An effective multi-start multi-level evolutionary local search for the flexible job-shop problem". Engineering Applications of Artificial Intelligence (EAAI), vol. 62, pages 80-95 (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.

S. Kemmoé, D. Lamy and N. Tchernev.
"Job-shop like manufacturing system with variable power threshold and operations with power requirements". International Journal of Production Research (IJPR), vol. 55(20), pages 6011-6032 (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 pro fi le is attached to operations that must be scheduled. This power pro fi le 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.

 Peer-Reviewed International Conferences:

A. Cerqueus, P. Gianessi, D. Lamy and X. Delorme.
"Balancing and configuration planning of RMS tominimize energy cost". 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, Julia Schulz and Michael 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 and 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 and 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. Lacomme and G. Fleury.
"Toward Scheduling for Reconfigurable Manufacturing Systems". 21st IFAC World Congress (IFAC 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.

D. Lamy, X. Delorme and P. Gianessi.
"Line Balancing and Sequencing for Peak Power Minimization". 21st IFAC World Congress (IFAC 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 pro table 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 speci c 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 and 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). IFAC-PapersOnLine, Volume 51, Issue 11, 2018, Pages 1299-1304, DOI:10.1016/j.ifacol.2018.08.353.
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 and 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), Saarbrücken, Germany.
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.

S. Kemmoé, D. Lamy and 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), Troyes, France. IFAC-PapersOnLine, Volume 49, Issue 12, 2016, Pages 1749-1754, DOI:10.1016/j.ifacol.2016.07.835.
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. 

G. Avez, P. Lacomme, D. LamyR. 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), Montréal, Québec.
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 and N. Tchernev. 
"An Optimization Approach for Job-shop with Financial Constraints in the Context of Supply Chain Scheduling Considering Payment Delay Between Members". In Proceedings of the 4th International Conference on Operations Research and Enterprise Systems (ICORES 2015), 190-198, 2015, Lisbon, Portugal, DOI:10.5220/0005271301900198.
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 and N. Tchernev. 
"A Job-shop problem considering operations with consumption peaks and energy constraints". In Proceedings of the 15th Information Control Problems in Manufacturing (INCOM 2015). IFAC-PapersOnLine, Volume 48, Issue 3, 2015, Pages 788-793, DOI:10.1016/j.ifacol.2015.06.179.
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. Kemmoé, D. Lamy and N. Tchernev. 
"An Optimization Framework for Job-shop with Energy Threshold Issue With consideration of machining operations with consumption peaks ". In Proceedings of the 7th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA 2015), 118-132, 2015, Prague, Czech Republic, here.
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 and N. Tchernev. 
"Job-shop like manufacturing system with time dependent energy threshold and operations with peak consumptions". In S. Umeda et al. (Eds.): Advances in Production Management Systems (APMS 2015), Part I, IFIP AICT 459, pp. 617-624, 2015, DOI:10.1007/978-3-319-22756-6_75.
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 and N. Tchernev. 
"A Metaheuristic based on Simulation for Stochastic Job-shop Optimization". In Proceedings of the 6th International Conference on Industrial Engineering and Systems Management (IESM 2015), Seville, Spain, ISBN:978-2-9600532-6-5.
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, D. Lamy and N. Tchernev. "A GRASPxELS for supply chain optimization considering payment delay between members" 5th International Conference on Metaheuristics and Nature Inspired Computing, (META 2014), Marrakech, Morocco, Oct 27-31, 2014.

 Peer-Reviewed National Conferences:

G. Fleury, X. Delorme, P. Lacomme and D. Lamy. "Modélisation des problèmes d'ateliers reconfigurables". 20th ROADEF Conference (ROADEF 2020), Feb 19-21, 2020.

Valeur par defaut

D. Lamy, A. Cerqueus, S. Finco, P. Gianessi. "Intégration du Rest Allowance dans l’ordonnancement d’ateliers de types Job-shop". 20th ROADEF Conference (ROADEF 2019), Feb 19-21, 2019.

P. Fenies, S. Kemmoé, D. Lamy et N. Tchernev. "Métaheuristique pour le Group-shop Scheduling Problem". 19th ROADEF Conference (ROADEF 2018), Feb 21-23, 2018.

B. Dalmas, D. Lamy et A. Laurent. "Approche de couplage optimisation - data mining pour le RCPSP". 19th ROADEF Conference (ROADEF 2018), Feb 21-23, 2018.

S. Kemmoé, D. Lamy et N. Tchernev. "Job-shop Flexible sous contrainte énergétique". 18th ROADEF Conference (ROADEF 2017), Feb 22-24, 2017.

S. Kemmoé, D. Lamy et N. Tchernev. "Metaheuristique et simulation pour le job-shop flexible stochastique". 17th ROADEF Conference (ROADEF 2016), Feb 10-12, 2016.

M. Gondran, S. Kemmoé, D. Lamy et N. Tchernev. "Une approche bi-objectif au problème du Job-shop sous contrainte de pics de consommation énergétique". 17th ROADEF Conference (ROADEF 2016), Feb 10-12, 2016.

S. Kemmoé, D. Lamy et N. Tchernev. "Job-shop sous contrainte de pics de consommation énergétique". 16th ROADEF Conference (ROADEF 2015), Feb 25-27, 2015.