Employee Profile

Stéphane Dauzère-Pérès

Adjunct Professor - Department of Accounting and Operations Management


Yang, Wenhui; Chen, Lu & Dauzère-Pérès, Stéphane (2021)

A dynamic optimisation approach for a single machine scheduling problem with machine conditions and maintenance decisions

International Journal of Production Research Doi: 10.1080/00207543.2021.1910746

In modern production systems, considering machine conditions is becoming essential to achieving an overall optimisation of the production schedule. This paper studies a single machine scheduling problem, where the actual processing times of jobs depend on their position in the production sequence and maintenance is considered. Moreover, the machine is subject to an uncertain condition variation. There is a trade-off between rejecting a maintenance action, resulting in longer processing times, and accepting a maintenance action, leading to higher processing efficiency for future jobs. The problem is formulated as a finite-horizon Markov Decision Process. The objective is to minimise the makespan. Optimality properties are analysed, based on which a dynamic optimisation approach is developed. Computational experiments demonstrate the effectiveness of the proposed approach.

Bitar, Abdoul; Dauzère-Pérès, Stéphane & Yugma, Claude (2021)

Unrelated parallel machine scheduling with new criteria: Complexity and models

Computers & Operations Research, 132 Doi: 10.1016/j.cor.2021.105291

In this paper, a scheduling problem on non-identical parallel machines with auxiliary resources and sequence-dependent and machine-dependent setup times is studied. This problem can be found in various manufacturing contexts, and in particular in workshops of wafer manufacturing facilities. Three different criteria are defined and analyzed: The number of products completed before the end of a given time horizon, the weighted sum of completion times and the number of auxiliary resource moves. The first criterion is maximized, while the two others are minimized. The first and the third criteria are not classical in scheduling theory, but are justified in industrial settings. The complexity of the problem with each of the new criteria is characterized. Integer linear programming models are also proposed and numerical experiments are conducted to analyze their behavior.

Tamssaouet, Karim; Dauzère-Pérès, Stéphane, Knopp, Sebastian, Bitar, Abdoul & Yugma, Claude (2021)

Multiobjective Optimization for Complex Flexible Job-Shop Scheduling Problems

European Journal of Operational Research Doi: 10.1016/j.ejor.2021.03.069

In this paper, we are concerned with the resolution of a multiobjective complex job-shop scheduling problem stemming from semiconductor manufacturing. To produce feasible and industrially meaningful schedules, this paper extends the recently proposed batch-oblivious approach by considering unavailability periods and minimum time lags and by simultaneously optimizing multiple criteria that are relevant in the industrial context. A novel criterion on the satisfaction of production targets decided at a higher level is also proposed. Because the solution approach must be embedded in a real-time application, decision makers must express their preferences before the optimization phase. In addition, a preference model is introduced where trade-off is only allowed between some criteria. Two a priori multiobjective extensions of Simulated Annealing are proposed, which differ in how the simultaneous use of a lexicographic order and weights is handled when evaluating the fitness. A known a posteriori approach of the literature is used as a benchmark. All the metaheuristics are embedded in a Greedy Randomized Adaptive Search Procedure. The different versions of the archived GRASP approach are compared using large industrial instances. The numerical results show that the proposed approach provides good solutions regarding the preferences. Finally, the comparison of the optimized schedules with the actual factory schedules shows the significant improvements that our approach can bring.

Le Quéré, Étienne; Dauzère-Pérès, Stéphane, Tamssaouet, Karim, Maufront, Cédric & Astie, Stéphane (2020)

Dynamic Sampling for Risk Minimization in Semiconductor Manufacturing

Winter simulation conference : proceedings Doi: 10.1109/WSC48552.2020.9384001

Altazin, Estelle; Dauzère-Pérès, Stéphane, Ramond, François & Tréfond, Sabine (2020)

A multi-objective optimization-simulation approach for real time rescheduling in dense railway systems

European Journal of Operational Research, 286(2), s. 662- 672. Doi: 10.1016/j.ejor.2020.03.034

Rescheduling trains in dense railway systems to cope in real time with limited disturbances is a challenging problem with multiple conflicting objectives and various types of decisions. Based on the French railway system in the Paris region, this paper proposes an approach combining multi-objective optimization, to select rescheduling decisions, and macroscopic simulation, to compute the objectives associated to these decisions. Possible decisions include canceling or short-turning trains and skipping or adding stops. Three main objectives are optimized to propose multiple solutions to the decision makers: The recovery time, the quality of service for passengers and the number of decisions. Two greedy heuristics are presented whose results on actual data are compared with a full enumeration method. The multi- objective feature of the approach is also analyzed. The implementation and successful validation in real life of a decision-support tool, that is now implemented, is discussed.

Wu, Cheng-Hung; Zhou, Fang-Yi, Tsai, Chi-Kang, Yu, Cheng-Juei & Dauzère-Pérès, Stéphane (2020)

A deep learning approach for the dynamic dispatching of unreliable machines in re-entrant production systems

International Journal of Production Research, 58(9), s. 2822- 2840. Doi: 10.1080/00207543.2020.1727041

This research combines deep neural network (DNN) and Markov decision processes (MDP) for the dynamic dispatching of re-entrant production systems. In re-entrant production systems, jobs enter the same workstation multiple times and dynamic dispatching oftentimes aims to dynamically assign different priorities to various job groups to minimise weighted cycle time or maximise throughput. MDP is an effective tool for dynamic production control, but it suffers from two major challenges in dynamic control problems. First, the curse of dimensionality limits the computational performance of solving large MDP problems. Second, a different model should be built and solved after system configuration is changed. DNN is used to overcome both challenges by learning directly from optimal dispatching policies generated by MDP. Results suggest that a properly trained DNN model can instantly generate near-optimal dynamic control policies for large problems. The quality of the DNN solution is compared with the optimal dynamic control policies through the standard K-fold cross-validation test and discrete event simulation. On average, the performance of the DNN policy is within 2% of optimal in both tests. The proposed artificial intelligence algorithm illustrates the potential of machine learning methods in manufacturing applications.

Lima, Alexandre; Borodin, Valeria, Dauzère-Pérès, Stéphane & Vialletelle, Philippe (2020)

A sampling-based approach for managing lot release in time constraint tunnels in semiconductor manufacturing

International Journal of Production Research, s. 1- 25. Doi: 10.1080/00207543.2020.1711984 - Full text in research archive

For the sake of product yield and quality considerations, Time Constraints (TCs) are imposed between process operations in various multi-product manufacturing systems. Often spanning a number of operations, time constraints tend to follow each other in close succession and overlap, forming thus Time Constraint Tunnels (TCTs). The regulation problem of releasing lots in these time constraint tunnels is particularly challenging in semiconductor manufacturing systems, because of re-entrant flows, machine heterogeneity and High Mix Low Volume (HM-LV) production configurations, which are typical in many wafer fabrication facilities. In such an evolving and time-varying context, this paper proposes a sequential sampling-based approach to estimate the probability that, prior to its release, a lot leaves a given time constraint tunnel on time. The proposed approach proves to be competitive in various respects by: (i) taking into account industry specific features, (ii) being industrially tractable, and (iii) being sensitive and responsive to the current manufacturing system. Based on real-life instances, numerical experiments highlight the computational effectiveness and the industrial soundness of the proposed problem modeling together with the solution approach.

Dauzère-Pérès, Stéphane & Hassoun, Michael (2020)

On the importance of variability when managing metrology capacity

European Journal of Operational Research, 282, s. 267- 276. Doi: 10.1016/j.ejor.2019.09.014

Mohammadi, Mehrdad; Dauzère-Pérès, Stéphane, Yugma, Claude & Karimi‐Mamaghan, Maryam (2020)

A queue‐based aggregation approach for performance evaluation of a production system with an AMHS

Computers & Operations Research, 115, s. 1- 21. Doi: 10.1016/j.cor.2019.104838

Production planning optimization remains a major challenge in almost all industries, particularly in high-tech manufacturing. A critical task to support such optimization is performance evaluation, wherein an accurate estimation of the cycle time as a function of the throughput rate plays a key role. This paper develops a novel aggregation model based on a queueing network approach, so-called queue-based aggregation (QAG) model, to estimate the cycle time of a job-shop production system that consists of several processing workstations, and in which products are transferred via an Automated Material Handling System (AMHS). The proposed model aggregates both production and automated material handling systems and provides an accurate and fast estimation of the overall cycle time. The performance and superiority of the proposed model is validated by comparing its results with those of a detailed simulation model. Numerous sensitivity analyses are performed to provide valuable managerial insights on both the production and automated material handling systems.

Wu, Cheng-Hung; Yao, Yi-Chun, Dauzère-Pérès, Stéphane & Yu, Cheng-Juei (2020)

Dynamic dispatching and preventive maintenance for parallel machines with dispatching-dependent deterioration

Computers & Operations Research, 113 Doi: 10.1016/j.cor.2019.104779 - Full text in research archive

A dynamic decision model that coordinates dispatching and preventive maintenance decisions for failure- prone parallel machines in make-to-order (MTO) production environments is developed in this research. The primary objective is to minimize the weighted long-run average waiting costs of MTO systems. Two common but seldom studied stochastic factors, namely, the dispatching-dependent deterioration of ma- chines and machine-health-dependent production rates, are explicitly modeled in the proposed dynamic dispatching and preventive maintenance (DDPM) model. Although the DDPM model is developed using Markov decision processes, it is equally effective in non-Markovian production environments. The per- formance of the DDPM model is validated in Markovian and non-Markovian production environments. Compared with several methods from the literature, simulation results show an improvement of at least 45.2% in average job waiting times and a minimum reduction of 48.9% in average machine downtimes. The comparison results between the optimal dynamic dispatching policies with and without coordinated preventive maintenance show that performance improvement can be mostly attributed to the coordina- tion between preventive maintenance and dispatching decisions.

Dauzère-Pérès, Stéphane; Hassoun, Michael & Sendon, Alejandro (2020)

A Lagrangian heuristic for minimising risk using multiple heterogeneous metrology tools

International Journal of Production Research, 58(4), s. 1222- 1238. Doi: 10.1080/00207543.2019.1614693 - Full text in research archive

Motivated by the high investment and operational metrology cost, and subsequently the limited metrology capacity, in modern semiconductor manufacturing facilities, we model and solve the problem of optimally assigning the capacity of several imperfect metrology tools to minimise the risk in terms of expected product loss on heterogeneous production machines. In this paper, metrology tools can differ in terms of reliability and speed. The resulting problem can be reduced to a variant of the Generalized Assignment Problem (GAP), the Multiple Choice, Multiple Knapsack Problem (MCMKP). A Lagrangian heuristic, including multiple feasibility heuristics, is proposed to solve the problem that are tested on randomly generated instances.

Christ, Quentin; Dauzère-Pérès, Stéphane & Lepelletier, Guillaume (2019)

An Iterated Min-Max procedure for practical workload balancing on non-identical parallel machines in manufacturing systems

European Journal of Operational Research, 279(2), s. 419- 428. Doi: 10.1016/j.ejor.2019.06.007 - Full text in research archive

This paper presents an original approach for a practical workload balancing problem on non-identical parallel machines in manufacturing systems. After showing the limitations of an initial model, in particular to support relevant decisions, the min–max fairness workload balancing problem is motivated and positioned in the literature. The Iterated Min–Max (IMM) procedure is then presented, with its properties, and illustrated. The IMM consists in solving a succession of linear programs using information from dual variables obtained at each iteration. Computational results on industrial instances show the relevance of the approach when compared to the initial model. The current use of the IMM procedure in an industrial tool is discussed.

Lima, Alexandre; Borodin, Valeria, Dauzère-Pérès, Stéphane & Vialletelle, Philippe (2019)

Sampling-based release control of multiple lots in time constraint tunnels

Computers in industry (Print), 110, s. 3- 11. Doi: 10.1016/j.compind.2019.04.014 - Full text in research archive

Semiconductor wafer fabrication probably includes the most complex and constrained manufacturing processes due to its intricate and time-varying environment. This paper focuses on time constraint tunnels (TCTs), which can have a very high impact on the yield and reliability of final products. More precisely, the original problem faced by managers of controlling the release of multiple lots in a TCT is addressed in the context of a wafer facility operating in a High-Mix Medium-Volume manufacturing environment. To support the management of TCTs in an industrially acceptable context, a scheduling based sampling method is proposed to estimate the probability that multiple lots released at the entrance of a given TCT leave this TCT on time. In order to investigate the industrial viability and identify the limitations of the probability-estimation approach, numerical experiments are conducted on real-life data and analyzed through the prism of several relevant performance criteria. Insights gathered from this numerical analysis are then used to discuss the specific management requirements that stem from the criticality of TCTs in semiconductor manufacturing facilities.

García-León, Andrés Alberto; Dauzère-Pérès, Stéphane & Mati, Yazid (2019)

An efficient Pareto approach for solving the multi-objective flexible job-shop scheduling problem with regular criteria

Computers & Operations Research, 108, s. 187- 200. Doi: 10.1016/j.cor.2019.04.012 - Full text in research archive

In this paper, a general local search approach for the Multi-Objective Flexible Job-shop Scheduling Problem (MOFJSP) is proposed to determine a Pareto front for any combination of regular criteria. The approach is based on a disjunctive graph, a fast estimation function to evaluate moves and a hierarchical test to efficiently control the set of non-dominated solutions. Four search strategies using two neighborhood structures are developed. Numerical experiments are conducted on test instances of the literature with three sets of criteria to minimize and using metrics to evaluate and compare Pareto fronts. The results show that our approach provides sets of non-dominated solutions of good quality.

Nattaf, Margaux; Dauzère-Pérès, Stéphane, Yugma, Claude & Wu, Cheng-Hung (2019)

Parallel Machine Scheduling with Time Constraints on Machine Qualifications

Computers & Operations Research, 107, s. 61- 76. Doi: 10.1016/j.cor.2019.03.004 - Full text in research archive

This paper studies the scheduling of jobs of different families on parallel machines, where not all machines are qualified (eligible) to process all job families. Originating from semiconductor manufacturing, an important constraint imposes that the time between the processing of two consecutive jobs of the same family on a machine does not exceed a given time limit. Otherwise, the machine becomes disqualified for this family. The goal is to minimize both the flow time and the number of disqualifications of job families on machines. To solve this problem, an integer linear programming model and a constraint programming model are proposed, as well as two improvement procedures of existing heuristics: A Recursive Heuristic and a Simulated Annealing algorithm. Numerical experiments on randomly generated instances compare the performances of each method.

Mohammadi, Mehrdad; Dauzère-Pérès, Stéphane & Yugma, Claude (2019)

Performance evaluation of single and multi-class production systems using an approximating queuing network

International Journal of Production Research, 57(5), s. 1497- 1523. Doi: 10.1080/00207543.2018.1492163 - Full text in research archive

Performance evaluation, and in particular cycle time estimation, is critical to optimise production plans in high-tech manufacturing industries. This paper develops a new aggregation model based on queuing network, so-called queue-based aggregation (QAG) model, to estimate the cycle time in a production system. Multiple workstations in serial and job-shop configurations are aggregated into a single-step workstation. The parameters of the aggregated workstation are approximated based on the parameters of the original workstations. Numerical experiments indicate that the proposed QAG model is computationally efficient and yields fairly accurate results when compared to other aggregation approaches in the literature.

Engebrethsen, Erna S. & Dauzère-Pérès, Stéphane (2019)

Transportation mode selection in inventory models: A literature review

European Journal of Operational Research, 279, s. 1- 25. Doi: 10.1016/j.ejor.2018.11.067 - Full text in research archive

Despite the significant share of transportation costs in logistics costs and the importance of considering transportation in inventory models, the majority of the existing models either neglect or simplify transportation costs and capacities, often assuming that only one transportation option is available. The complexity of modeling and choosing the optimal transportation mode or combination of modes has increased due to the increased variety of transportation options and pricing schedules after deregulation. In this paper, we review and classify inventory models with multiple transportation modes focusing on the freight cost functions, mode characteristics and the methods for modeling multiple modes. To our knowledge, no such review has previously been published. We discuss the benefits and weaknesses of each modeling method and, based on industrial practices, identify new areas for research.

Tamssaouet, Karim; Dauzère-Pérès, Stéphane, Yugma, Claude, Knopp, Sebastian & Pinaton, Jacques (2018)

A Study on the Integration of Complex Machines in Complex Job Shop Scheduling

Winter simulation conference : proceedings

Tamssaouet, Karim; Dauzère-Pérès, Stéphane & Yugma, Claude (2018)

Metaheuristics for the job-shop scheduling problem with machine availability constraints

Computers & industrial engineering Doi: 10.1016/j.cie.2018.08.008

Tamssaouet, Karim; Dauzère-Pérès, Stéphane & Yugma, Claude (2018)

Metaheuristics for the job-shop scheduling problem with machine availability constraints

Computers & industrial engineering, 125, s. 1- 8. Doi: 10.1016/j.cie.2018.08.008

This paper addresses the job-shop scheduling problem in which the machines are not available during the whole planning horizon and with the objective of minimizing the makespan. The disjunctive graph model is used to represent job sequences and to adapt and extend known structural properties of the classical job-shop scheduling problem to the problem at hand. These results have been included in two metaheuristics (Simulated Annealing and Tabu Search) with specific neighborhood functions and diversification structures. Computational experiments on problem instances of the literature show that our Tabu Search approach outperforms Simulated Annealing and existing approaches.

Senoussi, Ahmed; Dauzère-Pérès, Stéphane, Brahimi, Nadjib, Penz, Bernard & Mouss, Nadia Kinza (2018)

Heuristics Based on Genetic Algorithms for the Capacitated Multi Vehicle Production Distribution Problem

Computers & Operations Research, 96(August), s. 108- 119. Doi: 10.1016/j.cor.2018.04.010

In this paper, we consider the integration of production, inventory and distribution decisions in a sup- ply chain composed of one production facility supplying several retailers located in the same region. The supplier is far from the retailers compared to the distance between retailers. Thus, the traveling cost of each vehicle from the supplier to the region is assumed to be fixed and there is a fixed delivery (service) cost for each visited retailer. The objective is to minimize the sum of the costs at the production facility and at the retailers. The problem is more general than the One-Warehouse Multi-Retailer problem and is a special case of the Production Routing Problem. Five heuristics based on a Genetic Algorithm are proposed to solve the problem. In particular, three of them include the resolution of a Mixed Integer Pro- gram as subproblem to generate new individuals in the population. The results show that the heuristics can find optimal solutions for small and medium size instances. On large instances, the gaps obtained by the heuristics in less than 300 s are better than the ones obtained by a standard solver in two hours.

Kao, Yu-Ting; Dauzère-Pérès, Stéphane, Blue, Jakey & Chang, Shi-Chung (2018)

Impact of integrating equipment health in production scheduling for semiconductor fabrication

Computers & industrial engineering, 120(June), s. 450- 459. Doi: 10.1016/j.cie.2018.04.053

Monitoring the Equipment Health Indicator (EHI) of critical machines helps effectively to maintain process quality and reduce wafer scrap, rework, and machine breakdowns. To model and illustrate the integration of EHI in scheduling decisions to balance between productivity and quality risk, this paper presents two mixed integer linear programs to schedule jobs on heterogeneous parallel batching machines. The capability of a machine to process a job is categorized as preferred, acceptable, and unfavorable based on the job requirements. The quality risk of processing a job by a machine is a function of its EHI and the capability level of the machine for the job, which is modeled as a penalty in the objective function of trading-off between productivity and quality risk. The first model is static and assumes constant EHI of machines on the scheduling horizon, whereas the second model considers the EHI dynamics, i.e., the machine condition deteriorates over time based on the scheduled jobs. Numerical experiments indicate the potential applications of using EHI-integrated scheduling approaches to analyze and optimize the trade-off between productivity and quality risk.

Absi, Nabil; Archetti, Claudia, Dauzère-Pérès, Stéphane, Feillet, Dominique & Speranza, M. Grazia (2018)

Comparing sequential and integrated approaches for the production routing problem

European Journal of Operational Research, 269(2), s. 633- 646. Doi: 10.1016/j.ejor.2018.01.052

We consider the Production Routing Problem where production planning, inventory management and distribution planning decisions must be taken. We compare two sequential approaches, one in which production decisions are optimized first and one in which distribution decisions are optimized first, with an integrated approach where all decisions are simultaneously optimized. Some properties of the solutions obtained with the different approaches are shown. Computational experiments are performed on instances of different size which are generated using two critical parameters. The numerical results illustrate the properties and show that the benefits of the integrated approach over the two sequential ones depend on the trade-off between production and distribution costs and on the trade-off between setup and inventory costs in production.

Rowshannahad, Mehdi; Absi, Nabil, Dauzère-Pérès, Stéphane & Cassini, Bernard (2018)

Multi-item bi-level supply chain planning with multiple remanufacturing of reusable by-products

International Journal of Production Economics, 198, s. 25- 37. Doi: 10.1016/j.ijpe.2018.01.014

In this paper, we investigate a multi-item production planning problem in which remanufacturable raw materials are used for manufacturing the final products. The used raw material (considered as a kind of by-product) needs to be remanufactured before being suitable to be used again to manufacture other final products. However, byproducts can be remanufactured only a given number of times. The manufacturing and remanufacturing processes are performed in separated production processes with limited capacity. Each product may be produced using specific raw material references (newly purchased and/or remanufactured). The original industrial case study is based on the supply chain of Silicon-On-Insulator fabrication units. The problem is modeled as a mixed integer linear mathematical program. Using numerical examples based on industrial data, the model is validated and its behavior is analyzed. Finally, some industrial and academic perspectives to this study are proposed.

Shen, Liji; Dauzère-Pérès, Stéphane & Neufeld, Janis S. (2017)

Solving the flexible job shop scheduling problem with sequence-dependent setup times

European Journal of Operational Research, 265(2), s. 503- 516. Doi: 10.1016/j.ejor.2017.08.021

This paper addresses the flexible job shop scheduling problem with sequence-dependent setup times and where the objective is to minimize the makespan. We first present a mathematical model which can solve small instances to optimality, and also serves as a problem representation. After studying structural properties of the problem using a disjunctive graph model, we develop a tabu search algorithm with specific neighborhood functions and a diversification structure. Extensive experiments are conducted on benchmark instances. Test results first show that our algorithm outperforms most existing approaches for the classical flexible job shop scheduling problem. Additional experiments also confirm the significant improvement achieved by integrating the propositions introduced in this study.

Brahimi, Nadjib; Absi, Nabil, Dauzère-Pérès, Stéphane & Nordli, Atle (2017)

Single-item dynamic lot-sizing problems: An updated survey

European Journal of Operational Research, 263(3), s. 838- 863. Doi: 10.1016/j.ejor.2017.05.008

Knopp, Sebastian; Dauzère-Pérès, Stéphane & Yugma, Claude (2017)

A batch-oblivious approach for Complex Job-Shop scheduling problems

European Journal of Operational Research, 263(1), s. 50- 61. Doi: 10.1016/j.ejor.2017.04.050

Altazin, Estelle; Dauzère-Pérès, Stéphane, Ramond, François & Trefond, Sabine (2017)

Rescheduling through stop-skipping in dense railway systems

Transportation Research Part C: Emerging Technologies, 79, s. 73- 84. Doi: 10.1016/j.trc.2017.03.012

Dauzère-Pérès, Stéphane; Hassoun, Michael & Sendon, Alejandro (2016)

Allocating metrology capacity to multiple heterogeneous machines

International Journal of Production Research, 54(20), s. 6082- 6091. Doi: 10.1080/00207543.2016.1187775

Chien, Chen-Fu; Dauzère-Pérès, Stéphane, Huh, Woonghee Tim, Jang, Young Jae & Morrison, James R. (1)

Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies

International Journal of Production Research [Kronikk]

Mönch, Lars; Chien, Chen-Fu, Dauzère-Pérès, Stéphane, Ehm, Hans & Fowler, John (1)

Modelling and analysis of semiconductor supply chains

International Journal of Production Research [Kronikk]

Tamssaouet, Karim; Dauzère-Pérès, Stéphane & Yugma, Claude (2018)

Minimizing makespan on parallel batch processing machines

[Academic lecture]. International Conference on Project Management and Scheduling.

Tamssaouet, Karim; Dauzère-Pérès, Stéphane, Yugma, Claude, Knopp, Sebastian & Pinaton, Jacques (2018)

A Study on the Integration of Complex Machines in Complex Job Shop Scheduling

[Academic lecture]. 2018 Winter Simulation Conference.

In this paper, we study the problem of considering the internal behavior of complex machines when solving complex job-shop scheduling problems encountered in semiconductor manufacturing. The scheduling problem in the diffusion area is presented, and the complex structures and behaviors of the different machine types in this area are described. Previous related research is reviewed, and our approach to consider the complexity of these machines with batching constraints when scheduling the diffusion area is described. The main part of the paper presents and discusses numerical experiments on industrial instances to show the benefit of choosing a suitable modeling for complex machines.

Beraudy, Sébastien; Absi, Nabil & Dauzère-Pérès, Stéphane (2018)

Production Planning Models with Productivity and Financial Objective Functions in Semiconductor Manufacturing

[Academic lecture]. 2018 Winter Simulation Conference.

In the semiconductor manufacturing literature, production planning models mainly aim at minimizing total production, inventory and backlog costs. Solving these models may lead to a poor utilization of the production capacity when there are not enough demands. In this paper, after presenting a first generic linear programming model with fixed lead times when total costs are minimized, a model where productivity is maximized is introduced. Then, a model is proposed that includes the maximization of profit and considers the net present value of the financial objective function. These models are then compared using a data set from the literature. The numerical results show that, although the model with productivity maximization is performing as expected, the model with profit maximization is more relevant since it also helps to increase productivity. The impact of the actualization rate is analyzed, and also the limitations of the production of some products.

Tamssaouet, Karim; Dauzère-Pérès, Stéphane, Yugma, Claude & Pinaton, Jacques (2017)

A Batch-oblivious Approach For Scheduling Complex Job-Shops with Batching Machines: From Non-delay to Active Scheduling

[Academic lecture]. Multidisciplinary International Conference on Scheduling: Theory and Applications.

Dauzère-Pérès, Stéphane (2017)

Achievements and Lessons Learned from a Long-Term Academic-Industrial Collaboration

[Academic lecture]. 2017 Winter Simulation Conference.

I had the opportunity to work for about 14 years on many different projects with two manufacturing sites of the French-Italian semiconductor company STMicroelectronics. Supported by European, national and industrial projects, this still active long-term academic-industrial collaboration led to many scientific and industrial achievements, spreading to other companies. Through regular exchanges, engineers, researchers, PhD and Master students were able to present their problems, their advances and generate new research projects. After some history of the collaboration, the presentation will survey some of the main research and industrial results in qualification and flexibility management, production and capacity planning, scheduling, automated transportation, dynamic sampling and time constraint management. Challenges faced and lessons learned when applying Operations Research and Industrial Engineering in practice, and in particular in semiconductor manufacturing, will be discussed. Benefits for both practitioners and researchers will be emphasized, such as the opportunity to propose and study new relevant problems and develop and apply novel approaches using actual industrial data.

Academic Degrees
Year Academic Department Degree
1992 Paul Sabatier University (Toulouse III), France. PhD
Work Experience
Year Employer Job Title
2016 - Present BI Norwegian Business School Adjunct Professor, Department of Accounting, Auditing and Business Analytics
2004 - Present Ecole des Mines de Saint-Etienne Professor, Center of Microelectronics in Provence, Georges Charpak Campus
2013 - 2014 Ecole des Mines de Saint-Etienne Director of the Center of Microelectronics in Provence
2004 - 2013 Ecole des Mines de Saint-Etienne Head of the Department of Manufacturing Sciences and Logistics
2003 - 2009 Molde University College Invited Professor
2000 - 2004 Ecole des Mines de Nantes Professor, Department of Automatic Control and Industrial Engineering