Dans cette thèse, nous étudions deux problèmes de planification de production motivés par des applications du monde réel. Tout d'abord, un problème de planification de production pour un projet de fabrication de vêtements est étudié et un outil d'optimisation est développé pour le résoudre. Deuxièmement, une version restreinte du problème de dimensionnement du lot de capacité avec des configurations dépendantes de la séquence est explorée. Diverses formulations mathématiques sont développées et une analyse de complexité est effectuée pour donner une première analyse du problème. / In this thesis, we study two production planning problems motivated by challenging real-world applications. First, a production planning problem for an apparel manufacturing project is studied and an optimization tool is developed to tackle it. Second, a restricted version of the capacitated lot sizing problem with sequence dependent setups is explored. Various mathematical formulations are developed and complexity analysis is performed to offer a first glance to the problem.

Les travaux de cette thèse s'inscrivent dans le contexte de la coordination des décisions de planification survenant dans une chaîne logistique à deux acteurs: un fournisseur et un producteur souhaitant chacun diminuer leur propre coût. Les décisions de planification prises de manière indépendante par chaque acteur peuvent amener à une mauvaise performance de la chaîne logistique en terme de coûts, d'où la nécessité d'une coordination. Nous étudions des mécanismes de partage de coûts entre des acteurs en définissant des stratégies de coordination entre les acteurs par la mise en place de contrats. Nous considérons le cas où le producteur (resp. fournisseur) peut imposer son plan de production optimal au fournisseur (resp. distributeur). Différentes hypothèses de partage de coûts, ainsi que la problématique d'asymétrie d'information sont prises en compte dans ces travaux. Nous effectuons également des analyses expérimentales mesurant la diminution du coût de la chaîne logistique obtenue quand les acteurs coopèrent. Ce contexte nous amène à étudier de nouveaux problèmes de lot-sizing pour lesquels nous proposons une analyse de complexité et des algorithmes de programmation dynamique pour les résoudre. Nous proposons également une étude théorique des problèmes de lot-sizing à deux niveaux avec une capacité de stockage limitée. / This thesis focus on the coordination of planning decisions in a two-level supply chain composed of one supplier and one retailer. Each actor wants to minimize his own cost. The planning decisions independently took by the actors can lead to a poor performance in terms of costs, hence the necessity of coordination. We study cost sharing mechanisms between the actors by designing contracts. In this work, we consider the case where the retailer (resp. supplier) can impose his optimal production plan to the supplier (resp. retailer). Different cost sharing hypothesis, as well as the asymmetric information problem are taking into account in this thesis. We also perform an experimental analysis in order to evaluate the decrease of the supply chain cost obtained when the actors cooperate. This context leads us to study new lot-sizing problems for which we propose a complexity analysis and dynamic programming algorithms in order to solve them. We also propose a theoritical study of two-level lot-sizing problems with inventory bounds.

Cette thèse développe des méthodes d'optimisation pour la gestion de la Supply Chain et a pour thème central la flexibilité définie comme la capacité à fournir un service ou un produit au consommateur dans un environnement incertain. La recherche a été menée dans le cadre d'un partenariat entre Argon Consulting, une société indépendante de conseil en Supply Chain et l'École des Ponts ParisTech. Dans cette thèse, nous développons trois sujets rencontrés par Argon Consulting et ses clients et qui correspondent à trois différents niveaux de décision (long terme, moyen terme et court terme).Lorsque les entreprises élargissent leur portefeuille de produits, elles doivent décider dans quelles usines produire chaque article. Il s'agit d'une décision à long terme, car une fois qu'elle est prise, elle ne peut être facilement modifiée. Plus qu'un problème d'affectation où un article est produit par une seule usine, ce problème consiste à décider si certains articles doivent être produits par plusieurs usines et par lesquelles. Cette interrogation est motivée par la grande incertitude de la demande. En effet, pour satisfaire la demande, l'affectation doit pouvoir équilibrer la charge de travail entre les usines. Nous appelons ce problème le multi-sourcing de la production. Comme il ne s'agit pas d'un problème récurrent, il est essentiel de tenir compte du risque au moment de décider le niveau de multi-sourcing. Nous proposons un modèle générique qui inclut les contraintes techniques du problème et une contrainte d'aversion au risque basée sur des mesures de risque issues de la théorie financière. Nous développons un algorithme et une heuristique basés sur les outils standards de la Recherche Opérationnelle et de l'Optimisation Stochastique pour résoudre le problème du multi-sourcing et nous testons leur efficacité sur des données réelles.Avant de planifier la production, certains indicateurs macroscopiques doivent être décidés à horizon moyen terme tels la quantité de matières premières à commander ou la taille des lots produits. Certaines entreprises utilisent des modèles de stock en temps continu, mais ces modèles reposent souvent sur un compromis entre les coûts de stock et les coûts de lancement. Ces derniers sont des coûts fixes payés au lancement de la production et sont difficiles à estimer en pratique. En revanche, à horizon moyen terme, la flexibilité des moyens de production est déjà fixée et les entreprises estiment facilement le nombre maximal de lancements. Poussés par cette observation, nous proposons des extensions de certains modèles classiques de stock en temps continu, sans coût de lancement et avec une limite sur le nombre d'installations. Nous avons utilisé les outils standard de l'Optimisation Continue pour calculer les indicateurs macroscopiques optimaux.Enfin, la planification de la production est une décision à court terme qui consiste à décider quels articles doivent être produits par la ligne de production pendant la période en cours. Ce problème appartient à la classe bien étudiée des problèmes de Lot-Sizing. Comme pour les décisions à moyen terme, ces problèmes reposent souvent sur un compromis entre les coûts de stock et les coûts de lancement. Fondant notre modèle sur ces considérations industrielles, nous gardons le même point de vue (aucun coût de lancement et une borne supérieure sur le nombre de lancement) et proposons un nouveau modèle.Bien qu'il s'agisse de décisions à court terme, les décisions de production doivent tenir compte de la demande future, qui demeure incertaine. Nous résolvons notre problème de planification de la production à l'aide d'outils standard de Recherche Opérationnelle et d'Optimisation Stochastique, nous testons l'efficacité sur des données réelles et nous la comparons aux heuristiques utilisées par les clients d'Argon Consulting / This thesis develops optimization methods for Supply Chain Management and is focused on the flexibility defined as the ability to deliver a service or a product to a costumer in an uncertain environment. The research was conducted throughout a partnership between Argon Consulting, which is an independent consulting firm in Supply Chain Operations and the École des Ponts ParisTech. In this thesis, we explore three topics that are encountered by Argon Consulting and its clients and that correspond to three different levels of decision (long-term, mid-term and short-term).When companies expand their product portfolio, they must decide in which plants to produce each item. This is a long-term decision since once it is decided, it cannot be easily changed. More than a assignment problem where one item is produced by a single plant, this problem consists in deciding if some items should be produced on several plants and by which plants. This is motivated by a highly uncertain demand. So, in order to satisfy the demand, the assignment must be able to balance the workload between plants. We call this problem the multi-sourcing of production. Since it is not a repeated problem, it is essential to take into account the risk when making the multi-sourcing decision. We propose a generic model that includes the technical constraints of the assignment and a risk-averse constraint based on risk measures from financial theory. We develop an algorithm and a heuristic based on standard tools from Operations Research and Stochastic Optimization to solve the multi-sourcing problem and we test their efficiency on real datasets.Before planning the production, some macroscopic indicators must be decided at mid-term level such as the quantity of raw materials to order or the size of produced lots. Continuous-time inventory models are used by some companies but these models often rely on a trade-off between holding costs and setups costs. These latters are fixed costs paid when production is launched and are hard to estimate in practice. On the other hand, at mid-term level, flexibility of the means of production is already fixed and companies easily estimate the maximal number of setups. Motivated by this observation, we propose extensions of some classical continuous-time inventory models with no setup costs and with a bound on the number of setups. We used standard tools from Continuous Optimization to compute the optimal macroscopic indicators.Finally, planning the production is a short-term decision consisting in deciding which items must be produced by the assembly line during the current period. This problem belongs to the well-studied class of Lot-Sizing Problems. As for mid-term decisions, these problems often rely on a trade-off between holding and setup costs. Basing our model on industrial considerations, we keep the same point of view (no setup cost and a bound on the number of setups) and propose a new model. Although these are short-term decisions, production decisions must take future demand into account, which remains uncertain. We solve our production planning problem using standard tools from Operations Research and Stochastic Optimization, test the efficiency on real datasets, and compare it to heuristics used by Argon Consulting's clients

Grid connected inverter requires accurate and appropriate sizing which depends on the temperature, inverter operating efficiency, performance ratio, annual system yield and solar radiation characteristics. The aim of this study was to design and size for optimum sizing factor for grid connected inverter. The main component to be considered in any photovoltaic grid connected system is the inverter since the output depends on the inverter sizing ratio, therefore optimal sizing factor was designed by considering factors that affects inverter sizing such as temperature, irradiance and the location. Large and small systems of 50 kW and 5 kW respectively were considered to determine grid connected inverter sizing factors for different climates in Kenya, Sweden, and India using PVsyst simulation. Two different inverter brands of SMA and ABB with 20 kW and 25 kW rating for large system and 4.6 kW, 4 kW inverters for small system. PVsyst simulation result showed that different locations with different orientation angles, the optimum sizing varies hence affects the annual performance of the system. Photovoltaic system inverters are sized based on the rated power of the installed system and this can be achieved when the inverter size is either almost matching or not. In this case the study presents the optimal sizing factor for grid connected inverter for Mandera in Kenya, Norrköping in Sweden and Kerala in India. The determination was done through the use of designing, assessing and analyzing of the relationship between the sizing factor with performance ratio, operational efficiency and annual hourly energy yield. The unique weather profile in Kerala and in Mandera favors the adoption of solar energy technology in the location. Solar radiation for one year was used as a baseline input and the result reveals that Mandera receives yearly radiation of 2.1 MWh/m² while Kerala and Norrköping receives 2 MWh/m² and 1.1 MWh/m² respectively. Design simulation using PVsyst tool made it possible for the determination of the optimal sizing factor for the grid connected system. Considerations such as the losses and the variations within the specific location was done and a graph showing the relationship between the sizing factor in relation to the operational inverter efficiency as well as energy yield and performance ratio was later on compared to see the behavior of the sizing factor. The study concludes that operational efficiency, performance ratio and energy yield affects the array optimum sizing ratio. For the three locations, inverters (SMA and ABB) shows different variations because optimal sizing ratio depends on the location and irradiation. The results reveal that Mandera has an optimal grid connected inverter sizing of the range from 1.1 to 1.4 while in Kerala it has from 1.2 to 1.4 and Norrköping has the range from 1.1 to 1.3. Optimal sizing of grid connected inverters depends on the energy yield and the location therefore the inverter mismatch voltage and its rating values have to be considered while determining the optimal sizing factor. The 25 kW inverters in all the locations had better efficiency and sizing factor and this proves that sizing the photovoltaic inverter will give better performance and efficiency.

The continuous scaling of interconnect wires in deep submicron (DSM)circuits result in increased interconnect delay, power and crosstalk noise. In this dissertation, we address the problem of multi-metric optimization at post layout level in the design of deep submicron designs and develop a game theoretic framework for its solution. Traditional approaches in the literature can only perform single metric optimization and cannot handle multiple metrics. However, in interconnect optimization, the simultaneous optimization of multiple parameters such as delay, crosstalk noise and power is necessary and critical. Thus, the work described in this dissertation research addressing multi-metric optimization is an important contribution.Specifically, we address the problems of simultaneous optimization of interconnect delay and crosstalk noise during (i) wire sizing (ii) gate sizing (iii) integrated gate and wire sizing, and (iv) gate sizing considering process variations. Game the ory provides a natural framework for handling conflicting situations and allows optimization of multiple parameters. This property is exploited in modeling the simultaneous optimization of various design parameters such as interconnect delay, crosstalk noise and power, which are conflicting in nature. The problem of multi-metric optimization is formulated as a normal form game model and solved using Nash equilibrium theory. In wire sizing formulations, the net segments within a channel are modeled as the players and the range of possible wire sizes forms the set of strategies. The payoff function is modeled as (i) the geometric mean of interconnect delay andcrosstalk noise and (ii) the weighted-sum of interconnect delay, power and crosstalk noise, in order to study the impact of different costfunctions with two and three metrics respectively. In gate sizing formulations, the range of possible gate sizes is modeled as the set of strategies and the payoff function is modeled as the geome tric mean of interconnect delay and crosstalk noise. The gates are modeled as the players while performing gate sizing, whereas, the interconnect delay and crosstalk noise are modeled as players for integrated wire and gate sizing framework as well as for statistical gate sizing under the impact of process variations.The various algorithms proposed in this dissertation (i) perform multi-metric optimization (ii) achieve significantly better optimization and run times than other methods such as simulated annealing, genetic search, and Lagrangian relaxation (iii) have linear time and space complexities, and hence can be applied to very large SOC designs, and (iv) do not require rerouting or incur any area overhead. Thecomputational complexity analysis of the proposed algorithms as well as their software implementations are described, and experimental results are provided that establish the efficacy of the proposed algorithms.

Cette thèse a pour objectif d'étudier l'utilisation de la programmation par contraintes pour développer un solveur de planification de production. Nous nous concentrons sur des problèmes de dimensionnement de lots de production (lot-sizing) qui sont des problèmes majeurs et difficiles de la planification de la production et profitons d'une des principales forces de la programmation par contraintes, à savoir les contraintes globales. Nous définissons une contrainte globale LotSizing qui s'appuie sur un problème générique de lot-sizing mono-produit à un seul niveau, qui tient compte des capacités de production et de stockage, des coûts unitaires de production et de stockage et des coûts fixes. Cette contrainte globale est un outil de modélisation intuitif pour les problèmes complexes de lot-sizing car elle permet de modéliser chaque nœud des réseaux de distribution. Nous utilisons des techniques de programmation dynamique classiques du lot-sizing pour développer des algorithmes de filtrage pour la contrainte globale. Nous modélisons également des problèmes multi-produits.Enfin, nous introduisons un nouvel algorithme de filtrage générique s'appuyant sur la programmation linéaire. Nous montrons que la cohérence d'arc pour les contraintes considérées peut être obtenue avec la résolution d'un seul programme linéaire lorsque la contrainte a une formulation idéale et nous généralisons le résultat pour faire du filtrage partiel lorsqu'aucune restriction n'est faite sur ces contraintes. Cette technique peut être pertinente lors de la résolution de sous-problèmes de flot ou de séquence sous-jacents au lot-sizing. / In this thesis we investigate the potential use of constraint programming to develop a production planning solver. We focus on lot-sizing problems that are crucial and challenging problems of the tactical level of production planning and use one of the main strengths of constraint programming, namely global constraints. The goal of this work is to set the grounds of a constraint programming framework for solving complex lot-sizing problems. We define a LotSizing global constraint based on a generic single-item, single-level lot-sizing problem that considers production and inventory capacities, unitary production and inventory costs and setup costs. This global constraint is an intuitive modeling tool for complex lot-sizing problems as it can model the nodes of lot-sizing networks. We use classical dynamic programming techniques of the lot-sizing field to develop powerful filtering algorithms for the global constraint. Furthermore we model multi-item problems that are natural extensions of the core problem.Finally we introduce a new generic filtering algorithm based on linear programming. We show that arc consistency can be achieved with only one call to a linear programming solver when the global constraint has an ideal formulation and adapt the result to provide partial filtering when no restriction is made on the constraints. This technique can be useful to tackle polynomial lot-sizing underlying flow and sequence sub-problems.

L'incertitude a été toujours présente dans les problèmes d'optimisation. Dans ce travail, nous nous intéressons aux problèmes d'optimisation multi-niveaux où l'incertitude apparaît très naturellement. Les problèmes d'optimisation multi-niveaux avec incertitude ont suscité un intérêt à la fois théorique et pratique. L'optimisation robuste fait partie des méthodes les plus étudiées pour traiter ces problèmes. En optimisation robuste, nous cherchons une solution qui optimise la fonction objective pour le pire scénario appartenant à un ensemble d'incertitude donné. Les problèmes d'optimisation robuste multi-niveaux sont difficiles à résoudre, même de façon heuristique. Dans cette thèse, nous abordons les problèmes d'optimisation robuste à travers le prisme des méthodes de décomposition. Ces méthodes décomposent le problème en un problème maître (MP) et plusieurs problèmes satellites de séparation (AP). Dans ce contexte, les solutions et les relaxations heuristiques ont une importance particulière. Même pour les problèmes d'optimisation combinatoires, les relaxations sont importantes pour analyser l'écart de l'optimalité des solutions heuristiques. Un autre aspect important est l'utilisation des heuristiques comme integrés dans une méthode exacte. Les principales contributions de ce travail sont les suivantes. Premièrement, nous proposons une nouvelle relaxation pour les problèmes multi-niveaux basée sur l’approche dite d’information parfaite dans le domaine de l’optimisation stochastique. L'idée principale derrière cette méthode est d'éliminer les contraintes de non anticipativité du modèle pour obtenir un problème plus simple. Nous pouvons ensuite fournir des algorithmes combinatoires ad-hoc et des formulations de programmation mixte en nombres entiers compactes pour ce problème. Deuxièmement, nous proposons de nouveaux algorithmes de programmation dynamique pour résoudre les problèmes satellites apparaissant dans une classe spécifique de problèmes robustes pour un ensemble d'incertitude de type budget. Ce type d'incertitude est basé sur le nombre maximum d'écarts autorisés et leur taille. Ces algorithmes peuvent être appliqués à des problèmes de lot-sizing et à des problèmes de tournées de véhicules. Enfin, nous proposons un modèle robuste pour un problème lié à l’installation équitable de capteurs. Ce modèle fait le lien entre l'optimisation robuste et l'optimisation stochastique avec contraintes probabilistes ambigües. / Uncertainty has always been present in optimization problems, and it arises even more severely in multistage optimization problems. Multistage optimization problems underuncertainty have attracted interest from both the theoretical and the practical level.Robust optimization stands among the most established methodologies for dealing with such problems. In robust optimization, we look for a solution that optimizes the objective function for the worst possible scenario, in a given uncertainty set. Robust multi-stage optimization problems are hard to solve even heuristically. In this thesis, we address robust optimization problems through the lens of decompositions methods. These methods are based on the decomposition of the robust problem into a master problem (MP) and several adversarial separation problems (APs). The master problem contains the original robust constraints, however, written only for finite numbers of scenarios. Additional scenarios are generated on the y by solving the APs. In this context, heuristic solutions and relaxations have a particular importance. Similarly to combinatorial optimization problems, relaxations are important to analyze the optimality gap of heuristic solutions. Heuristic solutions represent a substantial gain from the computational viewpoint, especially when used to solve the separation problem. Because the adversarial problems must be solved several times, good heuristic solution may avoid the exact solution of the APs. The main contributions of this work are three-fold. First, we propose a new relaxation for multi-stage problems based on the approach named perfect information in the field of stochastic optimization. The main idea behind this method is to remove nonanticipativity constraints from the model to obtain a simpler problem for which we can provide ad-hoc combinatorial algorithms and compact mixed integer programming formulations. Second, we propose new dynamic programming algorithms to solve the APs for robust problems involving budgeted uncertainty, which are based on the maximum number of deviations allowed and on the size of the deviations. These algorithms can be applied to lot-sizing problems and vehicle routing problems among others. Finally, we study the robust equitable sensor location problem. We make the connection between the robust optimization and the stochastic programming with ambiguous probabilistic constraints. We propose linear models for several variants of the problem together withnumerical results.

En période de crise financière, les entreprises ont besoin de trésorerie pour réagir efficacement aux aléas et assurer leur solvabilité. Cette thèse se situe à l’interface entre l’opérationnel et la finance pour développer des modèles de planification tactique gérant simultanément les flux physiques et financiers dans la supply chain. Le coût de financement des opérations basé sur le besoin en fond de roulement (BFR) est intégré comme un nouvel aspect financier jamais considéré dans la littérature de lot-sizing. Nous débutons par une extension du modèle EOQ considérant les coûts de financement du BFR. L’objectif est la maximisation du profit. Une quantité de production optimale est obtenue analytiquement ainsi que l’analyse de la sensibilité du modèle. De plus, les comparaisons avec le modèle EOQ et un modèle qui considère le coût du capital sont étudiées. Ensuite, un modèle basé sur un lot-sizing dynamique est établi. La propriété ZIO est démontrée et permet l’utilisation d’un algorithme en temps polynomial. Enfin un scénario multi-niveau à capacité infini est étudié avec une approche séquentielle puis centralisée. La propriété ZIO est prouvée dans ces deux cas. Des algorithmes de programmation dynamique sont utilisés pour obtenir une solution optimale. Cette thèse peut être considérée comme un premier, mais significatif, travail combinant la planification de production et la gestion du besoin en fond de roulement dans des modèles de planification tactique. Nous montrons que les aspects financiers ont un impact significatif sur les plans de production. Les cas étudiés dans cette thèse peuvent être considérés comme des sous-problèmes dans l’étude de scénario plus réalistes. / In financial crisis, companies always need free cash flow to efficiently react to any uncertainties to ensure solvency. Thus, this thesis serves as an interface between operations and finance to develop tactical production planning models for joint management of physical and financial flows in the supply chain. In these models, the financing cost of operation-based working capital requirement (WCR) is integrated as a new financial aspect never before considered in the lot-sizing literature. We first focus on extending the classic EOQ model by considering the financing cost of WCR with a profit maximization objective. The optimal analytic production quantity formula is derived as well as sensitivity analysis of this model. Moreover, a comparison with the EOQ model and with the formula which considers the cost of capital are discussed. Secondly, a dynamic lot-sizing-based, discounted cash flow model is established based on Uncapacitated lot-sizing model. The zero-inventory ordering property is proven valid for this case and a polynomial-time algorithm can thus be established. Thirdly, multi-level and infinite capacity scenario is investigated with both sequential and centralized approaches. The ZIO property is demonstrated valid in both cases. Dynamic-programming based algorithms are constructed in order to obtain an optimal solution. This thesis should be considered as a first, but significant setup of combining production planning and working capital management. It is shown the significant financial consequences of lot-sizing decision on production planning. The cases investigated in this thesis may be tackled as subproblems in the study of more realistic scenarios.

The aim of this work is to research the issue of optimal sizing and location of photovoltaic distributed generation (PVDG) units on radial distribution feeders, and develop new procedures by which the optimal location may be determined. The procedures consider the concept that the PVDG production varies independently from changes in feeder load demand. Based on that, the developed procedures deal with two performance curves; the feeder daily load curve driven by the consumer load demand, and the PVDG daily production curve driven by the solar irradiance. Due to the mismatch in the profile of these two curves the PVDG unit might end up producing only part of its capacity at the time the feeder meets its peak load demand. An actual example of that is the summer peak load demand in Abu Dhabi city that occurs at 5:30 pm, which is 5 hours after the time the PV array yields its peak. Consequently, solving the optimization problem for maximum line power loss reduction (∆PPL) is deemed inappropriate for the connection of PVDG units. Accordingly, the procedures have been designed to solve for maximum line energy loss reduction (∆EL). A suitable concept has been developed to rate the ∆EL at one time interval over the day, namely feasible optimization interval (FOI). The concept has been put into effect by rating the ∆EL in terms of line power loss reduction at the FOI (ΔPLFOI). This application is deemed very helpful in running the calculations with no need to repeat the energy-based calculations on hourly basis intervals or even shorter. The procedures developed as part of this work have been applied on actual feeders at the 11kV level of Abu Dhabi distribution network. Two main scenarios have been considered relating to the avoidance and allowance of reverse power flow (RPF). In this course, several applications employing both single and multiple PVDG units have been solved and validated. The optimization procedures are solved iteratively. Hence, effective sub-procedures to help determine the appropriate number of feasible iterative steps have been developed and incorporated successfully. Additionally, the optimization procedures have been designed to deal with a 3-phase feeder under an unbalanced load condition. The line impedances along the feeder are modeled in terms of a phase impedance matrix. At the same time, the modeling of feeder load curves along with the power flow calculations and the resulting losses in the lines are carried out by phase. The resulting benefits from each application have been evaluated and compared in terms of line power loss reduction at the FOI (∆PLFOI) along with voltage and current flow profile.

High variety and low volume manufacturing environment always been a challenge for organisations to maintain their overall performance especially because of the high level of variability induced by ever changing customer demand, high product variety, cycle times, routings and machine failures. All these factors consequences poor flow and degrade the overall organisational performance. For most of the organisations, therefore, process improvement has evidently become the core component for long term survival. The aim of this research here is to develop a methodology for automating operations in process improvement as a part of lean creative problem solving process. To achieve the stated aim, research here has investigated the job sequence and buffer management problem in high variety/low volume manufacturing environment, where lead time and total inventory holding cost are used as operational performance measures. The research here has introduced a novel approach through integration of genetic algorithms based multi-objective combinatorial optimisation and discrete event simulation modelling tool to investigate the effect of variability in high variety/low volume manufacturing by considering the effect of improvement of selected performance measures on each other. Also, proposed methodology works in an iterative manner and allows incorporating changes in different levels of variability. The proposed framework improves over exiting buffer management methodologies, for instance, overcoming the failure modes of drum-buffer-rope system and bringing in the aspect of automation. Also, integration of multi-objective combinatorial optimisation with discrete event simulation allows problem solvers and decision makers to select the solution according to the trade-off between selected performance measures.