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

A MULTI-COMMODITY NETWORK FLOW APPROACH FOR SEQUENCING REFINED PRODUCTS IN PIPELINE SYSTEMS

In the oil industry, there is a special class of pipelines used for the transportation of refined products. The problem of sequencing the inputs to be pumped through this type of pipeline seeks to generate the optimal sequence of batches of products and their destination as well as the amount of product to be pumped such that the total operational cost of the system, or another operational objective, is optimized while satisfying the product demands according to the requirements set by the customers. This dissertation introduces a new modeling approach and proposes a solution methodology for this problem capable of dealing with the topology of all the scenarios reported in the literature so far. The system representation is based on a 1-0 multi commodity network flow formulation that models the dynamics of the system, including aspects such as conservation of product flow constraints at the depots, travel time of products from the refinery to their depot destination and what happens upstream and downstream the line whenever a product is being received at a given depot while another one is being injected into the line at the refinery. It is assumed that the products are already available at the refinery and their demand at each depot is deterministic and known beforehand. The model provides the sequence, the amounts, the destination and the trazability of the shipped batches of different products from their sources to their destinations during the entire horizon planning period while seeking the optimization of pumping and inventory holding costs satisfying the time window constraints. A survey for the available literature is presented. Given the problem structure, a decomposition based solution procedure is explored with the intention of exploiting the network structure using the network simplex method. A branch and bound algorithm that exploits the dynamics of the system assigning priorities for branching to a selected set of variables is proposed and its computational results for the solution, obtained via GAMS/CPLEX, of the formulation for random instances of the problem of different sizes are presented. Future research directions on this field are proposed

Supply chain management for the process industry

This thesis investigates some important problems in the supply chain management (SCM) for the process industry to fill the gap in the literature work, covering production planning and scheduling, production, distribution planning under uncertainty, multiobjective supply chain optimisation and water resources management in the water supply chain planning. To solve these problems, models and solution approaches are developed using mathematical programming, especially mixed-integer linear programming (MILP), techniques. First, the medium-term planning of continuous multiproduct plants with sequence-dependent changeovers is addressed. An MILP model is developed using Travelling Salesman Problem (TSP) classic formulation. A rolling horizon approach is also proposed for large instances. Compared with several literature models, the proposed models and approaches show significant computational advantage. Then, the short-term scheduling of batch multiproduct plants is considered. TSP-based formulation is adapted to model the sequence-dependent changeovers between product groups. An edible-oil deodoriser case study is investigated. Later, the proposed TSP-based formulation is incorporated into the supply chain planning with sequence-dependent changeovers and demand elasticity of price. Model predictive control (MPC) is applied to the production, distribution and inventory planning of supply chains under demand uncertainty. A multiobjective optimisation problem for the production, distribution and capacity planning of a global supply chain of agrochemicals is also addressed, considering cost, responsiveness and customer service level as objectives simultaneously. Both ε- constraint method and lexicographic minimax method are used to find the Pareto-optimal solutions Finally, the integrated water resources management in the water supply chain management is addressed, considering desalinated water, wastewater and reclaimed water, simultaneously. The optimal production, distribution and storage systems are determined by the proposed MILP model. Real cases of two Greek islands are studied

SOLUTION TO A PIPELINE SCHEDULING PROBLEM BY USING A MIXED INTEGER LINEAR PROGRAMMING MODEL

Pipelines are efficient ways of conveying huge amounts of refined petroluem products to distant points. Different products are pumped successively, in the pipelines without a need of a separator between them. Pipelines should be chosen very carefully based on the pumping sequences, volumes to be conveyed, covering the constraints involved by cutting operational costs and focusing on market demands. The real life problem considered in this study consists of a unidirectional pipe distribution system used for pumping petroleum products between the sources and distribution centers.  . Problem was stated as a Mixed Integer Linear Programming (MILP) model and solved by using GAMS software thorough actual data. As a result of the study, an optimal pumping schedule for pipeline operations at a certain period of time was achieved

Integrated batch process development based on mixed-logic dynamic optimization

Specialty chemicals industry relies on batch manufacturing, since it requires the frequent adaptation of production systems to market fluctuations. To be first in the market, batch industry requires decision-support systems for the rapid development and implementation of chemical processes. Moreover, the processes should be competitive to ensure their long-term viability. General-purpose and flexible plants and the consideration of physicochemical insights to define an efficient operation are also cornerstones for the success of specialty chemical industries. Precisely, this thesis tackles the systematic development of batch processes that are efficient, economically competitive, and environmentally friendly, to assist their agile introduction into production systems in grassroots and retrofit scenarios. Synthesis of conceptual processing schemes and plant allocation subproblems are solved simultaneously, taking into account the plant design. With this purpose, an optimization-based approach is proposed, where all structural alternatives are represented in a State-Equipment Network (SEN) superstructure, following formulated into a Mixed-Logic Dynamic Optimization (MLDO) problem which is later solved to minimize an objective function. Essentially, the strength of the proposed methodology lies in the modeling strategy which combines the different kinds of decisions of the integrated problem in a unique optimization model. Accordingly, it considers: (i) synthesis and allocation alternatives combination, (ii) dynamic process performance models and dynamic control variable profiles, (iii) discrete events associated to transitions of batch phases and operations, (iv) quantitative and qualitative information, (v) material transference synchronization to ensure batch integrity between unit procedures, and (vi) batch and semicontinuous processing elements. Different strategies can be used to solve the resulting MLDO problem. A deterministic direct-simultaneous approach is first proposed. The mixed-logic problem is reformulated into a mixed-integer one, which is fully-discretized to provide a Mixed-Integer Non-Linear Programming (MINLP) that is optimized using conventional solvers. Then, a Differential Genetic Algorithm (DGA) and a hybrid approach are presented. The purpose of these evolutionary strategies is to pose solution alternatives that keep solution goodness while seek for the improvement of computational efficiency to handle industrial-size problems. The optimization-based approach is applied in retrofit scenarios to solve the simultaneous process synthesis and plant allocation, taking into account the physical restrictions of existing plant elements. The production of specialty chemicals based on a competitive reactions system in an existing reactor network is first defined through process development and improvement according to different economic scenarios, decision criteria, and plant modifications. Additionally, a photo-Fenton process is optimized to eliminate an emergent wastewater pollutant in a given pilot plant, pursuing the minimization of processing time and cost. Batch process development in grassroots scenarios is also proven to be a problem of utmost importance to deal with uncertainty in future markets. Seeking for plant flexibility in several demand scenarios, the expected profit is maximized through a two-stage stochastic formulation that includes simultaneous plant design, process synthesis, and plant allocation decisions. A heuristic solution algorithm is used to handle the problem complexity. A grassroots plant design is defined to implement the previous competitive reaction system, where decisions like the feed-forward trajectories or operating modes allow the adaptation of master recipes to different demands. Finally, an acrylic fiber production example is presented to illustrate process development decisions like the selection of tasks, technological alternatives, chemicals, and solvent reuse.La indústria de productes químics especials es basa en la fabricació discontinua, ja que permet adaptar de forma freqüent els sistemes de producció en funció de les fluctuacions de mercat. Per ser líder al sector, són necessàries eines de suport a la decisió que ajudin a l’àgil desenvolupament i implementació de nous processos. A més, aquests han de ser competitius per garantir la seva viabilitat a llarg termini. Altres peces clau per una operació eficient són l’ús de plantes flexibles així com l’estudi dels fenòmens fisicoquímics. Aquesta tesis aborda justament el desenvolupament sistemàtic de processos químics discontinus que siguin eficients, econòmicament competitius i ecològics, per contribuir a la seva ràpida introducció en els sistemes de producció, tant en escenaris de plantes existents com des de les bases. En concret, es planteja la resolució simultània de la síntesi conceptual d’esquemes de procés i l’assignació d’equips, tenint en compte el disseny de la planta. Amb aquest objectiu, es proposa una metodologia de solució basada en optimització, on les alternatives estructurals es representen en una Xarxa d’Estats i Equips (SEN per les sigles en anglès) que es formula mitjançant un problema d’Optimització Dinàmica Mixta-Lògica (MLDO per les sigles en anglès) que es resol minimitzant una funció objectiu. La solidesa de la metodologia proposada rau en la estratègia de modelat del problema MLDO, que integra els diferents tipus de decisions en un sol model d’optimització. En concret, es consideren: (i) la combinació d’alternatives de síntesi i assignació d’equips, (ii) models de procés i trajectòries de control dinàmics, (iii) esdeveniments discrets associats al canvi de fase i operació, (iv) informació quantitativa i qualitativa, (v) sincronització de transferències de material en tasques consecutives, i (vi) elements de processat discontinus i semi-continus. Existeixen diverses estratègies per resoldre el problema MLDO resultant. En aquesta tesi es proposa en primer lloc un mètode determinístic directe-simultani, on el model mixt-lògic es transforma en un mixt-enter. Aquest es discretitza al seu torn de forma completa per obtenir un problema de Programació No-Lineal Mixta-Entera (MINLP per les sigles en anglès) el qual es pot resoldre utilitzant algoritmes d’optimització convencionals. A més, es presenten un Algoritme Genètic Diferencial (DGA per les sigles en anglès) i un mètode híbrid. Totes dues estratègies esdevenen alternatives de cerca amb l’objectiu de mantenir la bondat de la solució i millorar l’eficàcia de computació per tractar problemes de dimensió industrial. La metodologia de solució proposada s’aplica al desenvolupament de processos discontinus en escenaris de plantes existents, tenint en compte les restriccions físiques dels equips. Un primer exemple aborda la manufactura de productes químics basada en un sistema de reaccions competitives. Concretament, es desenvolupa i millora el procés de producció implementat en una xarxa de reactors considerant diferents escenaris econòmics, criteris de decisió, i modificacions de planta. En un segon exemple, s’optimitza el procés foto-Fenton per ser executat en una planta pilot per eliminar contaminants emergents. Buscant integrar el desenvolupament de procés i el disseny de plantes flexibles en escenaris de base, es presenta una formulació estocàstica en dues etapes per a optimitzar el benefici esperat d’acord a diversos escenaris de demanda. Per gestionar la complexitat d’aquest problema es proposa la utilització d’una heurística. Com a exemple, es planteja el disseny d’una planta de base on implementar l’anterior sistema de reaccions competitives. Decisions com les trajectòries dinàmiques de control o la configuració d’equips permeten adaptar la recepta màster en funció de la demanda. Un darrer exemple defineix el procés de producció de fibra acrílica, il·lustrant decisions com la selecció de tasques, tecnologia, reactius o reutilització de dissolvents.La industria productos químicos especiales se basa en la fabricación discontinua, la cual permite la adaptación frecuente de los sistemas de producción en función de las fluctuaciones de mercado. Para ser líder en el sector, son necesarias herramientas de soporte a la decisión que contribuyan al ágil desarrollo e implementación de nuevos procesos. Además, éstos deben ser competitivos para garantizar su viabilidad a largo plazo. Otras piezas clave para una operación eficiente son la utilización de plantas flexibles y el estudio de los fenómenos fisicoquímicos. Esta tesis aborda justamente el desarrollo sistemático de procesos químicos discontinuos que sean eficientes, económicamente competitivos y ecológicos, para contribuir a su rápida introducción en los sistemas de producción, ya sea en escenarios de plantas existentes o desde las bases. En particular, se plantea la resoluciónsimultánea de la síntesis conceptual de esquemas de proceso y la asignación de equipos, teniendo en cuenta además el diseño de planta.Con este fin, se propone una metodología de solución basada en optimización, donde todas las alternativas estructurales se representan en una Red de Estados y Equipos (SENpor sus siglas en inglés) que se formula mediante un problema de Optimización Dinámica Mixta-Lógica (MLDO por sus siglas en inglés) que se resuelve minimizando una función objetivo. La solidez de la metodología propuesta reside en la estrategia de modelado delproblema MLDO, que integra los diferentes tipos de decisiones en un solo modelo de optimización. En concreto, se consideran: (i) la combinación de alternativas de síntesis y asignación de equipos, (ii) modelos de proceso y trayectorias de control dinámicos, (iii)eventos discretos asociados al cambio de fase y operación, (iv) información cuantitativa y cualitativa, (v) sincronización de la transferencia de material en tareas consecutivas, y(vi) elementos de procesado discontinuos y semicontinuos.Existen diversas estrategias para resolver el problema MLDO resultante. En esta tesis se propone en primer lugar un método determinístico directo-simultáneo, donde el problema mixto-lógico se reformula en un mixto-entero. A su vez, éste se discretiza de formacompleta para obtener un problema de Programación No-Lineal Mixta-Entera (MINLP por sus siglas en inglés) el cual se puede resolver mediante algoritmos de optimización convencionales. Además, se presentan un Algoritmo Genético Diferencial (DGA por sussiglas en inglés) y un método híbrido. Ambas estrategias se plantean como alternativas de búsqueda con objeto de mantener la bondad de la solución y mejorar la eficacia de computación para tratar problemas de dimensión industrial.La metodología de solución propuesta se aplica al desarrollo de procesos discontinuos en escenarios con plantas existentes, teniendo en cuenta las restricciones físicas de los equipos. Un primer ejemplo aborda la fabricación de productos químicos basada en un sistema de reacciones competitivas. En concreto, se desarrolla y mejora el proceso de producción a implementar en una red de reactores considerando diferentes escenarios económicos, criterios de decisión, y modificaciones de planta. En un segundo ejemplo,se optimiza el proceso foto-Fenton a ser ejecutado en una planta piloto para eliminar contaminantes emergentes.Persiguiendo la integración del desarrollo de proceso con el diseño de plantas flexi-bles en escenarios base, se presenta asimismo una formulación estocástica en dos etapas para optimizar el beneficio esperado de acuerdo a varios escenarios de demanda. Paramanejar la complejidad de dicho problema se propone la utilización de una heurística.Como ejemplo, se plantea el diseño de una planta de base para implementar el anterior sistema de reacciones competitivas, donde decisiones como las trayectorias dinámicas de control o la configuración de equipos permiten adaptar la receta máster en función de lademandas. Por último, se presenta un ejemplo donde se define el proceso de producción de fibra acrílica, ilustrando decisiones como la selección de tareas, alternativas tecnológicas, reactivos químicos o la reutilización de disolventes.Postprint (published version