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International audience; The research on supply chain (SC) digitalization, resilience, sustainability and leagility has remarkably progressed, most of it focused on the individual contributions of these four major determinants to the firm's profitability and competitiveness. Though, there appears to be a lack of conceptual guidance surrounding their roles and interplays as an integrity. We hypothesize that such an integrity frames a new kind of SC networks, or even a new understanding of the SCs. We theorize a new notion-a Reconfigurable SC or the X-network-that exhibits some crucial design and control characteristics for complex value-adding systems in highly vulnerable environments. Utilizing abductive approach and a systematic bibliometric co-occurrence analysis of 1383 articles along with tertiary studies, we review and investigate contextual factors of designing the X-networks. We propose respective frameworks and discuss the implementation principles and technologies at the macro and micro levels. Two novel concepts-dynamic SC meta-structures and dynamic autonomous services-are introduced and formalized. Distinctively, we go beyond the existing knowledge to predict proactively the future directions in the reconfigura-ble SCs. Alike our results can be of value for decision-makers to decipher systematically the chances and barriers in contemporary SC transformations.

The studies on supply chain (SC) disruption management frequently assume the existence of some negative scenarios and suggest ways to proactively protect and reactively recover the SC operations and performance if such scenarios occur. Though, there is a paucity of research on how to support methodologically the detection of realistic disruption scenarios, ideally of different risk aversion degrees. The contribution of our study lies in a conceptualization of a new methodical approach to the detection of disruption scenarios, ripple effect dispersal and recovery paths in supply chains on the basis of structural genomes. The objective is to integrate and expand the existing knowledge gained isolated in robustness analysis and recovery planning into a comprehensive framework for building a theory as well as for managerial purposes. The outcomes of this research constitute a useful decision-making support tool that allows detecting disruption scenarios at different risk-aversion levels based on the quantification of the structural robustness with the use of the genome method and observing the scope of disruption propagation, i.e., the ripple effect. The advantage of using a robustness computation by the genome method is that this allows detecting both the disruption scenarios of different severity, the ripple effect dispersal, and the corresponding recovery paths. Our results can be of value for decision-makers to compare different supply chain structural designs regarding the robustness and to identify disruption scenarios that interrupt the supply chain operations to different extents. The scenario detection can be further used for identifying optimal reconfiguration paths to deploy proactive contingency and reactive recovery policies. We show a correlation between the risk aversion degree of disruption scenarios and the outcomes of the reconfiguration policies.

Recently, the applications of Blockchain technology have begun to revolutionise different aspects of supply chain (SC) management. Among others, Blockchain is a platform to execute the smart contra...

This study develops a resilience control model and computational algorithm for simultaneous structural–operational design of supply chain (SC) structural dynamics and recovery policy control. Our model integrates both structural recovery control in the SC as a whole and the corresponding functional recovery control at individual firms in the SC. Such a comprehensive combination is unique in literature and affords more realistic application to SC resilience control decisions. The focus of our study is to advance insights into feedback-driven understanding of resilience within open system control context. We construct a model that allows theorizing the notion of SC resilience within a disruption dynamics profile as a product of degradation and recovery control loops and examine the conditions for changes of disruption profile states. We show that the deviations from the resilient trajectory are associated with structural and performance degradation, and the recovery operations in structural adaptation yield the performance recovery. We contribute to existing works by comprehensively modelling structural dynamics and operational dynamics within an integrated feedback-driven framework to enable proactive SC resilience control. Our approach conceptualizes a new perspective as compared to the more common closed system view where SC resilience is treated from the performance equilibrium point of view. The proposed approach can help explain and improve the firms’ operations in multiple ways. First, the combination of structural and functional dynamics can help revealing the latent supply–demand allocations which would be disrupted in case of particular changes in the SC design and suggest re-allocations of supply and demand Second, the model can be used to perform the dynamic analysis of SC disruption and recovery and to explain the reasons of SC performance degradation and restoration. This analysis can be further used to improve SC risk mitigation policies and recovery plans.

International audience; This study theorizes how the optimal control paradigm can be applied to modeling and algorithmic solutions in manufacturing systems with dynamically changing hybrid structural-logical-terminal constraints. Our study conceptualizes and operationalizes a unique class of problems with both flexible machines and flexible jobs that can be frequently encountered in manufacturing systems with individualized products when process and schedule are created simultaneously. We offer a model to schedule jobs in manufacturing systems when the structural-logical terminal constraints are changing dynamically , and an algorithm to obtain a tractable solution analytically within the proven axiomatic of the optimal program control and mathematical optimization. We develop a dynamic decomposition methodology for modeling and control of schedules in highly flexible production systems combining the advantages of continuous and discrete optimization. The findings suggest that our approach can be of value for approaching problems with a simultaneous process design (i.e., task composition) and operation sequencing (i.e., service composition). Utilizing the outcomes of this research could also support the consideration of dynamics in the operations control. The operations execution can be accurately modeled in continuous time as state variables the updates of which allow for data-driven control of machine availability and capacity disturbances. Besides, the method developed theorizes further generalized insights into decomposition methods for scheduling and is supported by an analytical analysis and an algorithmic realization.

In this chapter, we present an introduction to scheduling in Industry 4.0 and cloud manufacturing systems. We elaborate on the peculiarities of scheduling and sequencing problems in the context of Industry 4.0 and smart manufacturing. We delineate recent research streams and summarize the structure and contribution of the book.

Scheduling in Industry 4.0 systems belongs to a class of problems that have mixed structural-temporal-logical constraints. In other words, a strong coupling is considered when product and process are created simultaneously. As a result of the proven NP-hardness of such problems, solution methods have extensively utilized different decomposition principles. The known decomposition methods in discrete optimization are founded on the difficulties in deriving analytical properties. The existing solutions in continuous optimization are based on the maximum principle and yield a dynamic process decomposition using the natural logic of time. By combining the advantages of continuous and discrete optimization, this chapter develops a decomposition method for shop floor scheduling in Industry 4.0 manufacturing systems. Technically, this study proposes to decompose dynamically the large-scale assignment matrix according to the precedence relations between the operations of the jobs and considers only the operations that satisfy these precedence relations at a given time point in small-dimensional, discrete optimization models. Continuous optimization is used to generate a schedule from the assignments found in the discrete optimization models at each time point by extremizing the Hamiltonian function at this time point subject to scheduling objective(s). In addition, the execution of the operations in time can be accurately modeled in continuous time as a continuous state variable; the machine availability and capacity disturbances at the machines are also considered. The method developed provides further insights into decomposition methods for scheduling and is supported by an analytical analysis and an algorithmic realization.

International audience; The impact of digitalization and Industry 4.0 on the ripple effect and disruption risk control analytics in the supply chain (SC) is studied. The research framework combines the results from two isolated areas, i.e., the impact of digitalization on SC management (SCM) and the impact of SCM on the ripple effect control. To the best of our knowledge, this is the first study that connects business, information, engineering and analytics perspectives on digitalization and SC risks. This paper does not pretend to be encyclopedic, but rather analyzes recent literature and case-studies seeking to bring the discussion further with the help of a conceptual framework for researching the relationships between digitaliza-tion and SC disruptions risks. In addition, it emerges with a SC risk analytics framework. It analyzes perspectives and future transformations that can be expected in transition towards cyber-physical SCs. With these two frameworks, this study contributes to the literature by answering the questions of (1) what relations exist between big data analytics, Industry 4.0, additive manufacturing, advanced trace & tracking systems and SC disruption risks; (2) how digitalization can contribute to enhancing ripple effect control; and (3) what digital technology-based extensions can trigger the developments towards SC risk analytics.

This paper presents a survey on the applications of optimal control to scheduling in production, supply chain and Industry 4.0 systems with a focus on the deterministic maximum principle. The first objective is to derive major contributions, application areas, limitations, as well as research and application recommendations for the future research. The second objective is to explain control engineering models in terms of industrial engineering and production management. To achieve these objectives, optimal control models, qualitative methods of performance analysis and computational methods for optimal control are considered. We provide a brief historic overview and clarify major mathematical fundamentals whereby the control engineering terms are brought into correspondence with industrial engineering and management. The survey allows the grouping of models with only terminal constraints with application to master production scheduling, models with hybrid terminal–logical constraints with applications to ...

International audience; In this article, we develop an optimal control model of supply chain reconfiguration that hasbeen previously investigated with the help of mathematical programming approach. The developed modelcan be used to represent the dynamics of a continuous-flow supply chain system with a hybrid discretecontinuousstate space. Dynamics of the supply chain reconfiguration can be modelled more detailed usingthe aggregate optimal parameters determined in the mathematical programming representation. The resultsof this study allow to distribute parameters and variables between mathematical programming and optimalcontrol models in order to exploit their advantages in a joint manner.

Recent research on closed-loop supply chains (SC) and reverse logistics extensively emphasises the crucial role of reducing negative return flows such as emissions, waste, etc. In this study, we consider the return flows in the SC in light disruptive events in the SC. The objective of this study is to compare the performance impact of different recovery policies on return flows subject to the simultaneously optimized re-configuration plans for material flows. We formulate a multi-objective problem with return flow reduction function for a multi-period, multi-stage, multi-product SC. We consider a recovery problem with ripple effect, performance impact assessment and re-planning decisions. The developed multi-objective hybrid linear programming-system dynamics model allows simultaneously re-computing the material flows in a multi-stage SC after a disruption and comparing the performance impact of different recovery policies subject to return flows, gradual capacity recovery, variable recovery costs and time. The results suggest that the consideration of gradual capacity recovery leads to minimization of disruption-related return flows in both upstream and downstream SC parts. Fast and expensive recovery strategy provides the lowest return costs in the upstream SC part as compared to normal and slow recovery policies. Similar, the profits and service levels are increased. In the fast and expensive recovery policy, the performance in the upstream and downstream does not change with the introduction of the gradual recovery considerations. The effects of gradual capacity recovery introduction become evident if smaller time sub-periods are considered within the recovery period.

The quality of model-based decision-making support strongly depends on the data, its completeness, fullness, validity, consistency, and timely availability. These requirements on data are of a special importance in supply chain (SC) risk management for predicting disruptions and reacting to them. Digital technology, Industry 4.0, Blockchain, and real-time data analytics have a potential to achieve a new quality in decision-making support when managing severe disruptions, resilience, and the Ripple effect. A combination of simulation, optimization, and data analytics constitutes a digital twin: a new data-driven vision of managing the disruption risks in SC. A digital SC twin is a model that can represent the network state for any given moment in time and allow for complete end-to-end SC visibility to improve resilience and test contingency plans. This chapter proposes an SC risk analytics framework and explains the concept of digital SC twins. It analyses perspectives and future transformations to be expected in transition toward cyber-physical SCs. It demonstrates a vision of how digital technologies and smart operations can help integrate resilience and lean thinking into a resileanness framework “Low-Certainty-Need” (LCN) SC.

This chapter aims at delineating major features of the ripple effect and methodologies to mitigate the supply chain disruptions and recover in case of severe disruptions. It observes the reasons and mitigation strategies for the ripple effect in the supply chain and presents the ripple effect control framework that is comprised of redundancy, flexibility and resilience. Even though a variety of valuable insights has been developed in the given area in recent years, new research avenues and ripple effect taxonomies are identified for the near future. Two special directions are highlighted. The first direction is the supply chain risk analytics for disruption risks and the data-driven ripple effect control in supply chains. The second direction is the concept of low-certainty-need (LCN) supply chains.

One of the important problems in moving objects control system is the calculating of goal abilities, i.e., potential of the system to perform its missions in different situations. Thus, the preliminary analysis of information and technological and goal abilities of moving objects control system is very important in practice and can be used to obtain reasonable means of the moving objects exploitation under different conditions. In the paper model-algorithmic support for abilities calculating of control system based on projection operators are proposed.

This paper introduces the structure dynamics control concept and a dynamic model to coordinate activities in cyber-physical supply chains in smart manufacturing concepts such as Industry 4.0 where a coordination of services and physical flows is needed. Continuous nature of services in the supply chain makes them similar to continuous production systems. Dynamic coordination of information services, information resources, and business process activities is considered on the scheduling level and modeled as an optimal control problem with uncertainty considerations.

International audience; Uncertainty, feedback cycles and dynamics are major challenges in production and logistics systems, supply chains, and Industry 4.0 networks. From this perspective, control theory is an important research avenue in operations and supply chain management. We briefly survey the applications of control theory to engineering and management problems in supply chains and operations areas during the period 1960-2017. Our analysis is based on bridging the fundamentals of control and systems theory to supply chains and operations management. Although operations and supply chain systems resemble control systems, they have some peculiarities which do not allow a direct application of control theoretic methods. The required modifications and possible limitations are discussed. In this setting, further development of interdisciplinary approaches to supply chain optimization is argued.

Supply chain engineering models with resilience considerations have been mostly focused on disruption impact quantification within one analysis layer, such as supply chain design or planning. Perfo...

In this paper a multiple-model description of supply chain (SC) is presented. The SC state change is described through differential equations based on a dynamic interpretation of the job execution. The problem is represented as a designing control of SC problem in presence of external actions. The approach is based on decomposition of the problem on two parts and its solution with the help of optimal control theory methods and linear programming.

International audience; Current literature presents optimal control computational algorithms with regard to state, control, and conjunctive variable spaces. This paper first analyses the advantages and limitations of different optimal control computational methods and algorithms which can be used for short-term scheduling. Second, it develops an optimal control computational algorithm that allows for the solution of short-term scheduling in an optimal manner. Moreover, qualitative and quantitative analysis of the manufacturing system scheduling problem is presented. Results highlight computer experiments with a scheduling software prototype as well as potential future research avenues.

In this paper, the existing models of supply chain resilience assessment are extended by incorporating ripple effect and structure reconfiguration. Ripple effect mitigation control is vital for supply chain risk management from positions of structural resilience and recoverability. The research approach is based on a hybrid fuzzy-probabilistic approach. The genome method is applied with the objective of including the structural properties of supply chain design into resilience assessment. A supply chain design resilience index is developed, and its computation and application are demonstrated. The results suggest a method of comparing different supply chain designs regarding the resilience both to disruption propagation and with recovery consideration. It also allows the identification of groups of critical suppliers whose failure interrupts supply chain operation.

International audience; In this study, the ripple effect in the supply chain is analysed. Ripple effect describes the impact of a disruption propagation on supply chain performance and disruption-based scope of changes in supply chain structural design and planning parameters. We delineate major features of the ripple effect as compared to the bullwhip effect. Subsequently, we review recent quantitative literature that tackled the ripple effect explicitly or implicitly and give our vision of the state of the art and perspectives. The literature is classified into mathematical optimization, simulation, control theoretic, and complexity and reliability research. We observe the reasons and mitigation strategies for the ripple effect in the supply chain and present the ripple effect control framework that includes redundancy, flexibility, and resilience analysis. Even though a variety of valuable insights has been developed in the said area in recent years, some crucial research avenues have been identified for the near future.

The comparative analysis of different types of structures of algorithms for a robot-manipulator control has been performed for the open-loop and closed-loop semi-automatic control by the gripper velocity and position vectors. Computer models have been developed for a position-velocity control system with reconfigurable structure of algorithms for a robot performing standard operations set by a human operator with a 3-degree-of-freedom handle. In the developed models an operator is represented by a vector dynamic element with dead time. Animated simulation of dynamic processes in systems with different structures of control algorithms has been performed and their precision characteristics have been evaluated. Some practical recommendations are given on the application of the achieved results for the development of training simulators for operators of robot control systems.

International audience; Modern production and logistics systems, supply chains, and Industry 4.0 networks are challenged by increased uncertainty and risks, multiple feedback cycles, and dynamics. Control theory is an interesting research avenue which contributes to further insights concerning the management of the given challenges in operations and supply chain management. In this paper, the applicability of control theory to engineering and management problems in supply chain operations is investigated. Our analysis bridges the fundamentals of control and systems theory to supply chain and operations management. This study extends our previous survey in the Annual Reviews in Control (Ivanov et al. 2012) by including new literature published in 2012–2018, identifying two new directions of control theory applications (i.e., ripple effect analysis in the supply chains and scheduling in Industry 4.0) and analysis towards the digital technology use in control theoretic models. It describes important issues and perspectives that delineate dynamics in supply chains, operations, and Industry 4.0 networks and identifies and systemizes different streams in the application of control theory to operations and supply chain management and engineering in the period from 1960–2018. It updates the existing applications and classifications, performs a critical analysis, and discusses further research avenues. Further development of interdisciplinary approaches to supply chain optimization is argued. An extended cooperation between control engineers and supply chain experts may have the potential to introduce more realism to dynamic planning and models, and improve performance in production and logistics systems, supply chains, and Industry 4.0 networks. Finally, we analyze the trends towards the intellectualization of control and its development towards supply chain control analytics.

As a result of supply chain structural dynamics, the ripple effect occurs whereby disruption propagates downstream or upstream from the initial disturbance point in the network. Since ripple effect analysis includes both dynamic and static parametrical sets, the research objective of this study is to identify recommendations on the preferable applications of simulation and optimization methods. We identify some problem classes and datasets for which optimization, simulation, and hybrid optimization-simulation methods can be recommended.

The chapter presents a holistic system that implements an advanced river flood modeling and forecasting approach. This approach extends traditional methods based on separate satellite monitoring or river physical processes modeling, by integration of different technologies such as satellite and in situ data processing, input data clustering and filtering, digital mapping of river valleys relief, data crowdsourcing, hydrodynamic modeling, inundation visualization, and also duly warning of stakeholders.

International audience; Recent research underlines the crucial role of disruption events and recovery policies in supply chains. Despite a wealth of literature on supply chain design with disruption considerations, to the best of our knowledge there is no survey on supply chain with disruptions and recovery considerations. We analyse state-of-the-art research streams on supply chain design and planning with both disruptions and recovery considerations with the aim of relating the existing quantitative methods to empirical research. The paper structures and classifies existing research streams and application areas of different quantitative methods subject to different disruption risks and recovery measures. We identify gaps in current research and delineate future research avenues. The results of this study are twofold: operations and supply chain managers can observe which quantitative tools are available for different application areas; on other hand, limitations and future research needs for decision-support methods in supply chain risk management domains can be identified.

Part 7: Operations Planning, Scheduling and Control; International audience; Industry 4.0 technology is based on the concepts of flexibility and dynamic assembly system design. This enables new production strategies and creates new challenges for job shop scheduling. In particular, manufacturing processes for different customer orders may have individual machine structures whereas the flexible stations are able to execute different functions subject to individual sets of operations within the jobs. This study develops a control approach to job shop scheduling in a customized manufacturing process and job sequencing of operations within the jobs. The developed approach presents a contribution to flexible distributed scheduling in the emerging field of Industry 4.0-based innovative production systems.

We present a new model for optimal scheduling of complex technical objects (CTO). CTO is a networked controlled system that is described through differential equations based on a dynamic interpretation of the job execution. The problem is represented as a special case of the job shop scheduling problem with dynamically distributed jobs. The approach is based on a natural dynamic decomposition of the problem and its solution with the help of a modified form of continuous maximum principle blended with combinatorial optimization.

International audience; This study aims at presenting the Ripple effect in supply chains. It develops different dimensions of the Ripple effect and summarises recent developments in the field of supply chain (SC) disruption management from a multi-disciplinary perspective. It structures and classifies existing research streams and applications areas of different quantitative methods to the Ripple effect analysis as well as identifying gaps in current research and delineating future research avenues. The analysis shows that different frameworks already exist implicitly for tackling the Ripple effect in the SC dynamics, control and disruption management domain. However, quantitative analysis tools are still rarely applied in praxis. We conclude that the Ripple effect can be the phenomenon that is able to consolidate research in SC disruption management and recovery similar to the bullwhip effect regarding demand and lead time fluctuations. This may build the agenda for future research on SC dynamics, control, continuity and disruption management, making supply chains more robust, adaptable and profitable.

Recent research underlines the crucial role of disruption events and recovery policies in supply chains. Despite a wealth of literature on supply chain design with disruption considerations, to the best of our knowledge there is no state-of-the art review on supply chain with disruptions and recovery considerations. We analyse state-of-the-art research streams on supply chain design and planning with both disruptions and recovery considerations with the aim to relate the existing quantitative methods to empirical research. The paper structures and classifies existing research streams and application areas of different quantitative methods subject to different disruption risks and recovery measures. We identify gaps in current research and delineate future research avenues. The results of this study are twofold. Operations and supply chain managers can observe which quantitative tools are available for different application areas. On other hand, limitations and future research needs for decision-support methods in supply chain risk management domains can be identified.

International audience; We consider time critical supply chains in the Australia dairy industry and recovery policies in the presence of the ripple effect. Ripple effect is the impact of a disruption on supply chain economic performance and disruption-based scope of changes needed in the supply structures and parameters to preserve the resilience. First, we describe the ripple effect in general and one example of the ripple effect in the dairy supply chain in Australia. Second, we present a model for reactive recovery policies in the dairy supply chain under conditions of the ripple effect and exemplify them on a simulation example. The results of this study can be used in future for comparing proactive and reactive approaches to tackling the ripple effect from resilience and flexibility views.

International audience; Continuous flow scheduling problems have their place in many industries such as gas, oil, chemicals, glass and fluids production as well as production of granular goods and steel details. The disruptions in processing capacities may result in schedule performance decrease. In this paper, we develop a new method for robustness analysis of those schedules that are formulated in continuous time in the state-space domain. The developed method is based on attainable sets (ASs) that allow computing a form to represent the states and performance of schedules in regard to different capacity degradation levels. Having such a form, it becomes possible to estimate the schedule robustness. The technical development and approximation of ASs are presented. A robustness index is developed on the basis of the minimax regret approach, and it can be used for decision-makers regarding the trade-off ‘performance vs. robustness’. As such, it becomes possible to compare maximal possible profits in situations without disruptions and realistic profits subject to some robustness investments and costs of protection against disruptions. With the presented results, it becomes possible to obtain ASs for interval data with no a priori information about perturbation impacts, i.e. for non-stationary perturbations. ASs permit to consider perturbations and schedule performances as time functions. Perturbation functions may be set up for different uncertainty scenarios, including interval perturbations.

International audience; In this study, an approach to re-planning the multi-stage supply chain (SC) subject to disruptions is developed. We analyze seven proactive SC structures, compute recovery policies to re-direct material flows in the case of two disruption scenarios, and assess the performance impact for both service level and costs with the help of a SC (re)planning model containing elements of system dynamics and linear programming. In the result, an explicit connection of performance impact assessment and SC plan reconfiguration issues with consideration of the duration of disruptions and the costs of recovery has been achieved.

We extend the literature by representing the robust coordination approach.We consider disruptions in capacities and supply.The application of attainable sets to supply chain coordination. Coordination plays crucial role in supply chain management. In this paper, we extend the existing body of literature on supply chain coordination by representing a robust schedule coordination approach. A hybrid discrete/continuous flow shop supply chain with job shop processes at each supplier stage is studied. For this purpose, the developed scheduling model comprises operations control (for customer order fulfillment dynamics), channel control (production machine and transportation dynamics), resource control (material supply dynamics), and flow control (processing and shipment dynamics) with multiple objectives. Based on the scheduling model, we introduce a robust analysis of schedule coordination in the presence of disruptions in capacities and supply. The application of attainable sets opens a possibility to analyse schedule coordination dynamics under disruptions. The results provide insights of how to integrate the coordination issues into schedule robustness analysis. We exemplify the developed approach for the case of two-stage supply chain coordination, and derive managerial insights for both considered scheduling problem and application of dynamic control methods to supply chain coordination in general.

International audience; In recent years, remarkable advancements have been achieved in quantitative analysis methods for supply chain design (SCD). Typically, cost or service level optimisation has been included in the objective functions. At the same time, supply chain managers face the ripple effect that arises from vulnerability, instability and disruptions in supply chains. This research aimed to quantify the ripple effect in the supply chain from the structural perspective. The research agenda of this study includes issues of integrating operability objectives as new key performance indicators, e.g. resilience, stability, robustness into SCD decisions. The research is based on a simultaneous consideration of both static structural properties of SCD and execution dynamics subject to uncertainty and disruptions. Due to high dimensionality of real SCD problems, such integration can hardly be implemented in only one model. In this study, an original two-model multi-criteria approach is proposed in order to assess the potential ability of an SCD to remain stable and resilient. This modelling approach is based on a combined application of a static and a dynamic model. A multi-criteria approach relies on the analytic hierarchy process method. The results of this research can be used as an additional quantitative analysis tool in order to select an SCD. An additional application of the developed method is that it can be used at the control stage in order to adapt supply chain execution subject to the achievement of desired economic performance.

International audience; An integrated multi-stage scheduling and routing problem with alternative machines at each supply chain (SC) stage and non-preemptive operations is studied. The peculiarity of the considered problem is the consideration of both continuous flows in operations and discrete assignments. Such problems are typical in the process industry. Taking into account the basic aspects that pertain to this case, a dynamic decomposition of the problem on the basis of optimal programme control is proposed. A particular feature and novelty of the presented study is the consideration of non-preemptive operations and their execution dynamics. The solution is based on a combined application of discrete and continuous optimization. With the special properties of the proposed scheduling model, it becomes possible to develop an optimal solution to the integrated scheduling and routing of jobs. Since the schedule generation is formulated in control terms, the models of scheduling and adaptation can be integrated. From the practical point of view, such integration can be advantageous subject to further dynamic analysis of the robustness, stability, and adaptability of SC schedules.

International audience; Supply chain disruptions management aims at real-time supply chain monitoring, alerting supply chain participants about deviations and possible disruptions as well as elaborating adjustment control actions. A basis for the alerting and disruption recovery is a tolerance area of execution parameters’ admissible deviations. Two important questions still remain open: (i) how to determine the borders of the tolerance area and (ii) what adjustments steps should be taken to overcome a disruption. In this paper, we propose to use dynamic robustness analysis on the basis of attainable sets and the adaptive adjustment on the basis of positional optimization as an integrated framework to extend existing techniques. We consider supply chain robustness as a dynamic supply chain property that emerges through controlled adaptability on the basis of feedback loops. With the results of this study, the borders of deviations’ sectors within the tolerance area can be determined. In each of these sectors, certain adjustment actions at different adaptation levels can be interrelated.

International audience; Achievement of the planned supply chain (SC) performance depends not only on the excellent planning and scheduling techniques but also on execution control. SC managers spend ∼40-60% of their working time handling disruptions. The issues of disruptions management in SCs have been tackled so far from perspectives of business process frameworks, information technologies and control theory. This confirms the multi-disciplinary nature of the SC control domain. This study aims at delineating the multi-disciplinary perspective of the SC control as composed of the elements from Supply Chain Event Management (SCEM), Radio-Frequency Identification (RFID) and control theory. The objectives are to identify possible interfaces and new managerial insights emerging from the synergetic effects. With the gained results, a systematic analysis on the interfaces 'technology-process-model' is presented to improve existing practices of SC control.

The ability to execute supply chains (SC) operations (in other words, to survive) despite the disturbances caused by a disruption is considered as SC design reliability. In the scope of this research is the analysis of what SC elements will survive (i.e., be in operation) after a disruption. In this study, we take the managerial perspective of the integrated SC and suggest the genome concept and its dual analogue to quantify the SC structure reliability. This method allows determining the upper bound and the approximate lower bound of the SC reliability. This result can be used by SC managers to compare different SC designs regarding the reliability. Subsequently, we extend the structure reliability analysis towards the trade-off 'reliability vs. efficiency'. In particular, it is shown that different forms of SC design structures with similar efficiency may significantly differ regarding reliability.

IFAC-PapersOnline.net (ISSN 1474-6670); International audience; The analysis of execution policies and estimation of chances to achieve the planned economic performance in a real uncertain and perturbed execution environment is a vital and up-to-date issue in many supply chains. This study is among first that addresses this issue with the help of attainable sets. It is shown that attainable sets can be used to obtain estimations of goal attainability and consider perturbations and attainability abilities of the SC as time functions in order to estimate SC robustness. With the presented results, it becomes possible to obtain attainable sets for interval data with no a priori information about perturbation impacts, i.e., for non-stationary SC perturbations

International audience; Decisions in supply chain planning and control are interconnected and depend on tackling uncertainties and dynamics. From this perspective, control theory (CT) is an interesting research avenue for the supply chain management (SCM). In this paper, the applicability of optimal CT to SCM is investigated. Our analysis is based on the fundamentals of control and systems theory and experimental modeling. The paper describes important issues and perspectives that delineate dynamics in supply chains, identifies and systemizes different streams in application of CT to production, logistics, and SCM in the period from 1960 to 2011. It derives some classifications, performs a critical analysis, and discusses further researches. Some drawbacks and missing links in the literature are pointed out. Several crucial application areas of control theory to SCM are discussed. Subsequently, optimal program control, challenges and advantages of its application in the SCM are addressed. It is shown how optimal program control can be applied to adaptive supply chain planning. In addition, it is concluded that with the help of CT, robustness, adaptability, and resilience of supply chains can be investigated in their consistency with operations planning and execution control within a conceptually and mathematically integrated framework. However, although SCs resemble control systems, they have some peculiarities which do not allow a direct application of CT methods. In this setting, further development of interdisciplinary approaches to supply chain optimization is argued. An extended co-operation between control and supply chain experts may have the potential to introduce more realism to the dynamic planning and models and improve real-time supply chain control policies.

Decisions in supply chain planning and control are interconnected and depend on tackling uncertainties and dynamics. From this perspective, control theory (CT) is an interesting research avenue for the supply chain management (SCM). In this paper, the applicability of optimal CT to SCM is investigated. Our analysis is based on the fundamentals of control and systems theory and experimental modeling. The paper describes important issues and perspectives that delineate dynamics in supply chains, identifies and systemizes different streams in application of CT to production, logistics, and SCM in the period from 1960 to 2011. It derives some classifications, performs a critical analysis, and discusses further researches. Some drawbacks and missing links in the literature are pointed out. Several crucial application areas of control theory to SCM are discussed. Subsequently, optimal program control, challenges and advantages of its application in the SCM are addressed. It is shown how optimal program control can be applied to adaptive supply chain planning. In addition, it is concluded that with the help of CT, robustness, adaptability, and resilience of supply chains can be investigated in their consistency with operations planning and execution control within a conceptually and mathematically integrated framework. However, although SCs resemble control systems, they have some peculiarities which do not allow a direct application of CT methods. In this setting, further development of interdisciplinary approaches to supply chain optimization is argued. An extended co-operation between control and supply chain experts may have the potential to introduce more realism to the dynamic planning and models and improve real-time supply chain control policies.