Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm

A multi-objective evolutionary optimisation model for heterogeneous vehicles routing and relief items scheduling in humanitarian crises

In a disaster scenario, relief items distribution is required as early as possible for the disaster victims to reduce the associated risks. For the distribution tasks, an effective and efficient relief items distribution model is essential to generate relief items distribution schedules to minimise the impact of disaster to the disaster victims. However, developing efficient distribution schedules is challenging as the relief items distribution problem has multiple objectives to look after where the objectives are mostly contradictorily creating a barrier to simultaneous optimisation of each objective. Also, the relief items distribution model has added complexity with the consideration of multiple supply points having heterogeneous and limited vehicles with varying capacity, cost and time. In this paper, multi-objective evolutionary optimisation with the greedy heuristic search has been applied for the generation of relief items distribution schedules under heterogeneous vehicles condition at supply points. The evolutionary algorithm generates the disaster region distribution sequence by applying a global greedy heuristic search along with a local search that finds the efficient assignment of heterogeneous vehicles for the distribution. This multi-objective evolutionary approach provides Pareto optimal solutions that decision-makers can apply to generate effective distribution schedules to optimise the distribution time and vehicles’ operational cost. In addition, this optimisation process also incorporated the minimisation of unmet relief items demand at the disaster regions. The optimised distribution schedules with the proposed approach are compared with the single-objective optimisation, weighted single-objective optimisation and greedy multi-objective optimisation approaches. The comparative results showed that the proposed multi-objective evolutionary approach is an efficient alternative for finding the distribution schedules with optimisation of distribution time and operational cost for the relief items distribution with heterogeneous vehicles in humanitarian crisis

A multi-objective evolutionary optimisation model for heterogeneous vehicles routing and relief items scheduling in humanitarian crises

In a disaster scenario, relief items distribution is required as early as possible for the disaster victims to reduce the associated risks. For the distribution tasks, an effective and efficient relief items distribution model to generated relief items distribution schedules is essential to minimise the impact of disaster to the disaster victims. However, developing efficient distribution schedules is challenging as the relief items distribution problem has multiple objectives to look after where the objectives are mostly contradictorily creating a barrier to simultaneous optimisation of each objective. Also, the relief items distribution model has added complexity with the consideration of multiple supply points having heterogeneous and limited vehicles with varying capacity, cost and time. In this paper, multi-objective evolutionary optimisation with the greedy heuristic search has been applied for the generation of relief items distribution schedules under heterogeneous vehicles condition at supply points. The evolutionary algorithm generates the disaster region distribution sequence by applying a global greedy heuristic search along with a local search that finds the efficient assignment of heterogeneous vehicles for the distribution. This multi-objective evolutionary approach provides Pareto optimal solutions that decision-makers can apply to generate effective distribution schedules that optimise the distribution time and vehicles’ operational cost. In addition, this optimisation also incorporated the minimisation of unmet relief items demand at the disaster regions. The optimised distribution schedules with the proposed approach are compared with the single-objective optimisation, weighted single-objective optimisation and greedy multi-objective optimisation approaches. The comparative results showed that the proposed multi-objective evolutionary approach is an efficient alternative for finding the distribution schedules with optimisation of distribution time and operational cost for the relief items distribution with heterogeneous vehicles

Assessment and implementation of evolutionary algorithms for optimal management rules design in water resources systems

Tesis por compendioWater is an essential resource from an environmental, biological, economic or social point of view. In basin management, the irregular distribution in time and in space of this resource is well known. This issue is worsened by extreme climate conditions, generating drought periods or flood events. For both situations, optimal management is necessary. In one case, different water uses should be supplied efficiently using the available surface and groundwater resources. In another case, the most important goal is to avoid damages in flood areas, including the loss of human lives, but also to optimize the revenue of energy production in hydropower plants, or in other uses. The approach presented in this thesis proposes to obtain optimal management rules in water resource systems. With this aim, evolutionary algorithms were combined with simulation models. The first ones, as optimization tools, are responsible for guiding the process iterations. In each iteration, a new management rule is defined in the simulation model, which is computed to comprehend the situation of the system after applying this new management. For testing the proposed methodology, four evolutionary algorithms were assessed combining them with two simulation models. The methodology was implemented in four real case studies. This thesis is presented as a compendium of five manuscripts: three scientific papers published in journals (which are indexed in the Journal Citation Report), another under review, and the last manuscript from Conference Proceedings. In the first manuscript, the Pikaia optimization algorithm was combined with the network flow SIMGES simulation model for obtaining four different types of optimal management rules in the Júcar River Basin. In addition, the parameters of the Pikaia algorithm were also analyzed to identify the best combination of them to use in the optimization process. In the second scientific paper, the multi-objective NSGA-II algorithm was assessed to obtain a parametric management rule in the Mijares River basin. In this case, the same simulation model was linked with the evolutionary algorithm. In the Conference manuscript, an in-depth analysis of the Tirso-Flumendosa-Campidano (TFM) system using different scenarios and comparing three water simulation models for water resources management was developed. The third published manuscript presented the assessment and comparison of two evolutionary algorithms for obtaining optimal rules in the TFM system using SIMGES model. The algorithms assessed were the SCE-UA and the Scatter Search. In this research paper, the parameters of both algorithms were also analyzed as it was done with the Pikaia algorithm. The management rules in the three first manuscripts were focused to avoid or minimize deficits in urban and agrarian demands and, in some case studies, also to minimize the water pumped. Finally, in the last document, two of the algorithms used in previous manuscripts were assessed, the mono-objective SCE-UA and the multi-objective NSGA-II. For this research, the algorithms were combined with RS MINERVE software to manage flood events in Visp River basin minimizing damages in risk areas and losses in hydropower plants. Results reached in the five manuscripts demonstrate the validity of the approach. In all the case studies and with the different evolutionary algorithms assessed, the obtained management rules achieved a better system management than the base scenario of each case. These results usually mean a decrease of the economic costs in the management of water resources. However, comparing the four algorithms assessed, SCE-UA algorithm proved to be the most efficient due to the different stop/convergence criteria and its formulation. Nevertheless, NSGA-II is the most recommended due to its multi-objective search focus on the enhancement of different objectives with the same importance where the decision makers can make the best decision for the management of the system.El agua es un recurso esencial desde el punto de vista ambiental, biológico, económico o social. En la gestión de cuencas, es bien conocido que la distribución del recurso en el tiempo y el espacio es irregular. Este problema se agrava debido a condiciones climáticas extremas, generando períodos de sequía o inundaciones. Para ambas situaciones, una gestión óptima es necesaria. En un caso, el suministro de agua a los diferentes usos del sistema debe realizarte eficientemente empleando los recursos disponibles, tanto superficiales como subterráneos. En el otro caso, el objetivo más importante es evitar daños en las zonas de inundación, incluyendo la pérdida de vidas humanas, pero al mismo tiempo, optimizar los beneficios de centrales hidroeléctricas, o de otros usos. El enfoque presentado en esta tesis propone la obtención de reglas de gestión óptimas en sistemas reales de recursos hídricos. Con este objetivo, se combinaron algoritmos evolutivos con modelos de simulación. Los primeros, como herramientas de optimización, encargados de guiar las iteraciones del proceso. En cada iteración se define una nueva regla de gestión en el modelo de simulación, que se evalúa para conocer la situación del sistema después de aplicar esta nueva gestión. Para probar la metodología propuesta, se evaluaron cuatro algoritmos evolutivos combinándolos con dos modelos de simulación. La metodología se implementó en cuatro casos de estudio reales. Esta tesis se presenta como un compendio de cinco publicaciones: tres de ellas en revistas indexadas en el Journal Citation Report, otra en revisión y la última como publicación de un congreso. En el primer manuscrito, el algoritmo de optimización Pikaia se combinó con el modelo de simulación SIMGES para obtener reglas de gestión óptimas en la cuenca del río Júcar. Además, se analizaron los parámetros del algoritmo para identificar la mejor combinación de los mismos en el proceso de optimización. El segundo artículo evaluó el algoritmo multi-objetivo NSGA-II para obtener una regla de gestión paramétrica en la cuenca del río Mijares. En el trabajo presentado en el congreso se desarrolló un análisis en profundidad del sistema Tirso-Flumendosa-Campidano utilizando diferentes escenarios y comparando tres modelos de simulación para la gestión de los recursos hídricos. En el tercer manuscrito publicado se evaluó y comparó dos algoritmos evolutivos (SCE-UA y Scatter Search) para obtener reglas de gestión óptimas en el sistema Tirso-Flumendosa-Campidano. En dicha investigación también se analizaron los parámetros de ambos algoritmos. Las reglas de gestión de estas cuatro publicaciones se enfocaron en evitar o minimizar los déficits de las demandas urbanas y agrarias y, en ciertos casos, también en minimizar el caudal bombeado, utilizando para ello el modelo de simulación SIMGES. Finalmente, en la última publicación se evaluó el algoritmo mono-objetivo SCE-UA y el multi-objetivo NSGA-II. Para esta investigación, los algoritmos se combinaron con el software RS MINERVE para gestionar los eventos de inundación en la cuenca del río Visp minimizando los daños en las zonas de riesgo y las pérdidas en las centrales hidroeléctricas. Los resultados obtenidos en las cinco publicaciones demuestran la validez del enfoque. En todos los casos de estudio y, con los diferentes algoritmos evolutivos evaluados, las reglas de gestión obtenidas lograron una mejor gestión del sistema que el escenario base de cada caso. Estos resultados suelen representar una disminución de los costes económicos en la gestión de los recursos hídricos. Comparando los cuatro algoritmos, el SCE-UA demostró ser el más eficiente debido a los diferentes criterios de convergencia. No obstante, el NSGA-II es el más recomendado debido a su búsqueda multi-objetivo enfocada en la mejora, con la misma importancia, de diferentes objetivos, donde los tomadores de decisiones pueden selL'aigua és un recurs essencial des del punt de vista ambiental, biològic, econòmic o social. En la gestió de conques, és ben conegut que la distribució del recurs en el temps i l'espai és irregular. Este problema s'agreuja a causa de condicions climàtiques extremes, generant períodes de sequera o inundacions. Per a ambdúes situacions, una gestió òptima és necessària. En un cas, el subministrament d'aigua als diferents usos del sistema ha de realitzar-se eficientment utilitzant els recursos disponibles, tant superficials com subterranis. En l'altre cas, l'objectiu més important és evitar danys en les zones d'inundació, incloent la pèrdua de vides humanes, però al mateix temps, optimitzar els beneficis de centrals hidroelèctriques, o d'altres usos. La proposta d'esta tesi és l'obtenció de regles de gestió òptimes en sistemes reals de recursos hídrics. Amb este objectiu, es van combinar algoritmes evolutius amb models de simulació. Els primers, com a ferramentes d'optimització, encarregats de guiar les iteracions del procés. En cada iteració es definix una nova regla de gestió en el model de simulació, que s'avalua per a conéixer la situació del sistema després d'aplicar esta nova gestió. Per a provar la metodologia proposada, es van avaluar quatre algoritmes evolutius combinant-los amb dos models de simulació. La metodologia es va implementar en quatre casos d'estudi reals. Esta tesi es presenta com un compendi de cinc publicacions: tres d'elles en revistes indexades en el Journal Citation Report, una altra en revisió i l'última com a publicació d'un congrés. En el primer manuscrit, l'algoritme d'optimització Pikaia es va combinar amb el model de simulació SIMGES per a obtindre regles de gestió òptimes en la conca del riu Xúquer. A més, es van analitzar els paràmetres de l'algoritme per a identificar la millor combinació dels mateixos en el procés d'optimització. El segon article va avaluar l'algoritme multi-objectiu NSGA-II per a obtindre una regla de gestió paramètrica en la conca del riu Millars. En el treball presentat en el congrés es va desenvolupar una anàlisi en profunditat del sistema Tirso-Flumendosa-Campidano utilitzant diferents escenaris i comparant tres models de simulació per a la gestió dels recursos hídrics. En el tercer manuscrit publicat es va avaluar i va comparar dos algoritmes evolutius (SCE-UA i Scatter Search) per a obtindre regles de gestió òptimes en el sistema Tirso-Flumendosa-Campidano. En dita investigació també es van analitzar els paràmetres d'ambdós algoritmes. Les regles de gestió d'estes quatre publicacions es van enfocar a evitar o minimitzar els dèficits de les demandes urbanes i agràries i, en certs casos, també a minimitzar el cabal bombejat, utilitzant per a això el model de simulació SIMGES. Finalment, en l'última publicació es va avaluar l'algoritme mono-objectiu SCE-UA i el multi-objetiu NSGA-II. Per a esta investigació, els algoritmes es van combinar amb el programa RS MINERVE per a gestionar els esdeveniments d'inundació en la conca del riu Visp minimitzant els danys en les zones de risc i les pèrdues en les centrals hidroelèctriques. Els resultats obtinguts en les cinc publicacions demostren la validesa de la metodología. En tots els casos d'estudi i, amb els diferents algoritmes evolutius avaluats, les regles de gestió obtingudes van aconseguir una millor gestió del sistema que l'escenari base de cada cas. Estos resultats solen representar una disminució dels costos econòmics en la gestió dels recursos hídrics. Comparant els quatre algoritmes, el SCE-UA va demostrar ser el més eficient a causa dels diferents criteris de convergència. No obstant això, el NSGA-II és el més recomanat a causa de la seua cerca multi-objectiu enfocada en la millora, amb la mateixa importància, de diferents objectius, on els decisors poden seleccionar la millor opció per a la gestió del sistema.Lerma Elvira, N. (2017). Assessment and implementation of evolutionary algorithms for optimal management rules design in water resources systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90547TESISCompendi

Optimisation du développement de nouveaux produits dans l'industrie pharmaceutique par algorithme génétique multicritère

Le développement de nouveaux produits constitue une priorité stratégique de l'industrie pharmaceutique, en raison de la présence d'incertitudes, de la lourdeur des investissements mis en jeu, de l'interdépendance entre projets, de la disponibilité limitée des ressources, du nombre très élevé de décisions impliquées dû à la longueur des processus (de l'ordre d'une dizaine d'années) et de la nature combinatoire du problème. Formellement, le problème se pose ainsi : sélectionner des projets de Ret D parmi des projets candidats pour satisfaire plusieurs critères (rentabilité économique, temps de mise sur le marché) tout en considérant leur nature incertaine. Plus précisément, les points clés récurrents sont relatifs à la détermination des projets à développer une fois que les molécules cibles sont identifiées, leur ordre de traitement et le niveau de ressources à affecter. Dans ce contexte, une approche basée sur le couplage entre un simulateur à événements discrets stochastique (approche Monte Carlo) pour représenter la dynamique du système et un algorithme d'optimisation multicritère (de type NSGA II) pour choisir les produits est proposée. Un modèle par objets développé précédemment pour la conception et l'ordonnancement d'ateliers discontinus, de réutilisation aisée tant par les aspects de structure que de logique de fonctionnement, a été étendu pour intégrer le cas de la gestion de nouveaux produits. Deux cas d'étude illustrent et valident l'approche. Les résultats de simulation ont mis en évidence l'intérêt de trois critères d'évaluation de performance pour l'aide à la décision : le bénéfice actualisé d'une séquence, le risque associé et le temps de mise sur le marché. Ils ont été utilisés dans la formulation multiobjectif du problème d'optimisation. Dans ce contexte, des algorithmes génétiques sont particulièrement intéressants en raison de leur capacité à conduire directement au front de Pareto et à traiter l'aspect combinatoire. La variante NSGA II a été adaptée au problème pour prendre en compte à la fois le nombre et l'ordre de lancement des produits dans une séquence. A partir d'une analyse bicritère réalisée pour un cas d'étude représentatif sur différentes paires de critères pour l'optimisation bi- et tri-critère, la stratégie d'optimisation s'avère efficace et particulièrement élitiste pour détecter les séquences à considérer par le décideur. Seules quelques séquences sont détectées. Parmi elles, les portefeuilles à nombre élevé de produits provoquent des attentes et des retards au lancement ; ils sont éliminés par la stratégie d'optimistaion bicritère. Les petits portefeuilles qui réduisent les files d'attente et le temps de lancement sont ainsi préférés. Le temps se révèle un critère important à optimiser simultanément, mettant en évidence tout l'intérêt d'une optimisation tricritère. Enfin, l'ordre de lancement des produits est une variable majeure comme pour les problèmes d'ordonnancement d'atelier. ABSTRACT : New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline, namely, the presence of uncertainty, the high level of the involved capital costs, the interdependency between projects, the limited availability of resources, the overwhelming number of decisions due to the length of the time horizon (about 10 years) and the combinatorial nature of a portfolio. Formally, the NPD problem can be stated as follows: select a set of R and D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while copying with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGA II type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. An object-oriented model previously developed for batch plant scheduling and design is then extended to embed the case of new product management, which is particularly adequate for reuse of both structure and logic. Two case studies illustrate and validate the approach. From this simulation study, three performance evaluation criteria must be considered for decision making: the Net Present Value (NPV) of a sequence, its associated risk defined as the number of positive occurrences of NPV among the samples and the time to market. Theyv have been used in the multiobjective optimization formulation of the problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. NSGA II has been adapted to the treated case for taking into account both the number of products in a sequence and the drug release order. From an analysis performed for a representative case study on the different pairs of criteria both for the bi- and tricriteria optimization, the optimization strategy turns out to be efficient and particularly elitist to detect the sequences which can be considered by the decision makers. Only a few sequences are detected. Among theses sequences, large portfolios cause resource queues and delays time to launch and are eliminated by the bicriteria optimization strategy. Small portfolio reduces queuing and time to launch appear as good candidates. The optimization strategy is interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems