Lecture Notes in Economics and Mathematical Systems

We consider heuristic algorithms for the resource-constrained project scheduling problem. Starting with a literature survey, we summarize the basic components of heuristic approaches. We briefly describe so-called X -pass methods which are based on priority rules as well as metaheuristic algorithms. Subsequently, we present the results of our in-depth computational study. Here, we evaluate the performance of several state-of-the-art heuristics from the literature on the basis of a standard set of test instances and point out to the most promising procedures. Moreover, we analyze the behavior of the heuristics with respect to their components such as priority rules and metaheuristic strategy. Finally, we examine the impact of problem characteristics such as project size and resource scarceness on the performance.

Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem

Systematic literature review (SLR) is a well-known research method. However, there is a paucity of detailed SLR guidelines in operations management (OM). The recent interest in SLR in OM has not been followed by the same rigour observed in disciplines as medical sciences and public policy. There are no OM-specific SLR protocols, detailed step-by-step methods and reporting procedures. Therefore, this paper provides a step-by-step approach to SLR for OM scholars and an overview of SLR’s evolution as a research method in OM and the resulting progression of themes. The step-by-step approach aims to serve as a guideline sufficiently broad to avoid skipping any significant step, but still being easy to be understood and applied. The paper describes procedures for rigourous SLR, reveals a growing use of literature review in OM, specially for qualitative SLR and traditional narrative reviews, assesses contemporary and emerging themes in OM, and provides a research agenda.

Conducting systematic literature review in operations management

Trends in transportation and logistics

Este trabajo de maestria presenta una propuesta para calcular el plan de reabastecimiento entre una empresa y sus filiales internacionales (1xm), a traves de un modelo de colaboracion basado en la estrategia de VMI y en el problema de reabastecimiento conjunto de ordenes (JRP). El objetivo del modelo es encontrar el programa de reposicion adecuado para la empresa y sus filiales tal que se minimicen los costos de reabastecimiento. 
 
La solucion del modelo se llevo a cabo a traves de un algoritmo genetico el cual es exitoso en la solucion de este tipo de problemas. Los resultados del modelo fueron validados mediante la comparacion de los enfoques colaborativo e individual y a traves del analisis de datos historicos, con lo cual se encontro que el modelo planteado permite disminuir los costos de reabastecimiento de la empresa. De esta forma se cumplen totalmente los objetivos propuestos para este trabajo de maestria.

Modelo de reabastecimiento colaborativo entre una empresa y sus filiales internacionales

In the modern supplier-customer relationship, Vendor Managed Inventory (VMI) is used to monitor the customer's inventory replenishment. Despite the large amount of literature on the subject, it is difficult to clearly define VMI and the main associated processes. Beyond the short-term pull system inventory replenishment often studied in academic works, partners have to share their vision of the demand, their requirements and their constraints in order to fix shared objectives for the medium/long-term. In other words, the integration of VMI implies consequences for the collaborative process that links each partner's different planning processes. In this article we propose a literature review of VMI. Based on the conceptual elements extracted from this analysis, we suggest a VMI macro-process that summarises both operational and collaborative elements of VMI.

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A review of Vendor Managed Inventory (VMI): from concept to processes

Cooperative subcontracting relationship within a project supply chain: A simulation approach

Chapter 1. Supply Chain Management Simulation: An Overview (Caroline THIERRY, Gerard BEL and Andre THOMAS). 1.1. Supply chain management. 1.2. Supply chain management simulation. 1.3. Supply chain management simulation types. 1.4. Decision systems and simulation models (systems). 1.5. Simulation software. 1.6. Simulation methodology. 1.7. Conclusion. 1.8. Bibliography. Chapter 2. Continuous Simulation for SCM (Daniel THIEL and Vo Thi Le HOA). 2.1. System dynamics models for SCM. 2.2. Application: recent research into the bullwhip effect. 2.3. Conclusion. 2.4. Bibliography. 2.5. Appendix 1. Chapter 3. Discrete-event Simulation for Supply Chain Management (Valerie BOTTA-GENOULAZ, Jacques LAMOTHE, Florence PICARD, Fouad RIANE and Anthony VALLA). 3.1. Discrete-event simulation and supply chain. 3.2. Discrete-event simulation for supply chain redesign. 3.3. Discrete-event simulation for cooperation process risk analysis. 3.4. Discrete-event simulation for business process reengineering. 3.5. Conclusion. 3.6. Bibliography. Chapter 4. Simulation Games (Thierry MOYAUX, Eric BALLOT, Michel GREIF and Bertrand SIMON). 4.1. Introduction. 4.2. Literature review. 4.3. Theories about the usage of games. 4.4. Examples of implementation methodologies and obtained results. 4.5. Conclusion. 4.6. Bibliography. Chapter 5. Centralized Approaches for Supply Chain Simulation: Issues and Applications (Lyes BENYOUCEF, Vipul JAIN and Patrick CHARPENTIER). 5.1. Introduction. 5.2. Supply chain centralized simulation - a literature review. 5.3. Supply chain simulation using centralized approaches. 5.4. Some industrial and practical applications. 5.5. Conclusions and perspectives. 5.6. Bibliography. Chapter 6. The Interest of Agents for Supply Chain Simulation (Thibaud MONTEIRO, Didier ANCIAUX, Bernard ESPINASSE, Alain FERRARINI, Olivier LABARTHE and Daniel ROY). 6.1. Decision problems in enterprise networks. 6.2. State of the art: modeling and simulation of supply chains with agents. 6.3. Conclusion and summary of the projects. 6.4. Bibliography. Chapter 7. Agent-based Simulation of Business Network Planning andCoordination Systems (Thibaud MONTEIRO, Didier ANCIAUX, Sophie D'AMOURS, Bernard ESPINASSE, Alain FERRARINI, Olivier LABARTHE and Daniel ROY). 7.1. Decision system in a supply chain. 7.2. Decision-making tools to supply chain control. 7.3. Simulation tools to design supply chain planning and coordination systems. 7.4. Bibliography. Chapter 8. Simulation for Product-driven Systems (Andre THOMAS, Pierre CASTAGNA, Remi PANNEQUIN, Thomas KLEIN, Hind EL HAOUZI, Pascal BLANC and Olivier CARDIN). 8.1. Introduction. 8.2. Control architectures of manufacturing systems. 8.3. Validation with simulation in HMS or product-driven systems. 8.4. Simulation: a computer-aided tool for product-driven systems. 8.5. Industrial applications. 8.6. Conclusion. 8.7. Bibliography. Chapter 9. HLA Distributed Simulation Approaches for Supply Chains (Fouzia OUNNAR, Bernard ARCHIMEDE, Philippe CHARBONNAUD and Patrick PUJO). 9.1. Introduction. 9.2. Modeling and discrete-event simulation. 9.3. Self-organized control of supply chain networks. 9.4. Reactive control by evaluation of multi-site plans. 9.5. Conclusion. 9.6. Bibliography. Chapter 10. Software Tools for Simulation (Franck FONTANILI, Pierre CASTAGNA and Bernard YANNOU). 10.1. Short history of the tools for simulation in industrial engineering. 10.2. Typology of the simulation tools for the supply chain. 10.3. Key points of the construction of a simulation model. 10.4. Limits and objectives of simulation tools. 10.5. Methodology of a simulation project. 10.6. Possibilities of coupling. 10.7. Main functionalities and criteria of selection of a tool. 10.8. Classification of the commercial tools. 10.9. Example of modeling with three tools. 10.10. Useful links. 10.11. Bibliography. List of Authors. Index.

Simulation for supply chain management

Making decisions on the base of uncertain forecasts is one of the key challenges for efficient Supply Chain Management. This article suggests the use of the theory of possibility for building a procurement plan on the base of ill-known requirements. These requirements, expressed in quantities by date, may come from various sources: forecasts or orders for instance. The possible types of imperfection pervading requirement are analysed and a unified representation model is suggested. A method is then described for calculating a plausible demand by period without loss of information; it is illustrated on an example in the last section.

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Modelling of ill-known requirements and integration in production planning

Performance improvement in supply chains, taking into account customer demand in the tactical planning process is essential. It is more and more difficult for the customers to ensure a certain level of demand over a medium term horizon as their own customers ask them for personalisation and fast adaptation. It is thus necessary to develop methods and decision support systems to reconcile the order and book processes. In this context, this paper intends firstly to relate decision under uncertainty and the industrial point of view based on the notion of risk management. This serves as a basis for the definition of an approach based on simulation and decision theory that is dedicated to the design of cooperative processes in a customer-supplier relationship. This approach includes the evaluation, in terms of risk, of different cooperative processes using a simulation-dedicated tool. The evaluation process is based on an exploitation of decision theory concepts and methods. The implementation of the approach is illustrated on an academic example typical of the aeronautics supply chain.

Caroline Thierry

Papers

A review of Vendor Managed Inventory (VMI): from concept to processes

Cooperative subcontracting relationship within a project supply chain: A simulation approach

Simulation for supply chain management

Modelling of ill-known requirements and integration in production planning

Design of Cooperative Processes in a Customer-Supplier Relationship: An Approach Based on Simulation and Decision Theory