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

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods

A proposed mathematical model for closed-loop network configuration based on product life cycle

Products may be returned over their life cycle. Industrial experiences show that there are three main return–recovery pairs. Commercial returns are repaired. End-of-use returns often are remanufactured. In addition, end-of-life returns are recycled. However, up to now, no optimization model is proposed for closed-loop configuration based on three return–recovery pairs. The repaired and remanufactured products can be sold in the same or secondary market. In this paper, we design and configure a general closed-loop supply chain network based on product life cycle. The network includes a manufacturer, collection, repair, disassembly, recycling, and disposal sites. The returned products are collected in a collection site. Commercial returns go to a repair site. End-of-use and end-of-life returns are disassembled. Then, end-of-life returns are recycled. The manufacturer uses recycled and end-of-use parts and new parts to manufacture new products. The new parts are purchased from external suppliers. A mixed-integer linear programming model is proposed to configure the network. The objective is to maximize profit by determining quantity of parts and products in the network. We also extend the model for the condition that the remanufactured products are sent to the secondary market. The mathematical models are validated through computational testing and sensitivity analysis

Systems approaches and algorithms for discovery of combinatorial therapies

Effective therapy of complex diseases requires control of highly non-linear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of signaling in cellular networks. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper we describe the main current and proposed approaches to the design of combinatorial therapies, including the empirical methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high-throughput measurements is key to these new approaches to combination therapy and essential for the characterization of control landscapes and implementation of the algorithms. Combinatorial optimization in medical therapy is also compared with the combinatorial optimization of engineering and materials science and similarities and differences are delineated.Comment: 25 page

Design of Closed Loop Supply Chains

Increased concern for the environment has lead to new techniques to design products and supply chains that are both economically and ecologically feasible. This paper deals with the product - and corresponding supply chain design for a refrigerator. Literature study shows that there are many models to support product design and logistics separately, but not in an integrated way. In our research we develop quantitative modelling to support an optimal design structure of a product, i.e. modularity, repairability, recyclability, as well as the optimal locations and goods flows allocation in the logistics system. Environmental impacts are measured by energy and waste. Economic costs are modelled as linear functions of volumes with a fixed set-up component for facilities. We apply this model using real life R&D data of a Japanese consumer electronics company. The model is run for different scenarios using different parameter settings such as centralised versus decentralised logistics, alternative product designs, varying return quality and quantity, and potential environmental legislation based on producer responsibility.supply chain management;reverse logistics;facility location;network design;product design

Moving forward in reverse : a review into strategic decision making in reverse logistics

Reverse Logistics, the process of managing the backward flows of materials from a point of use to a point of recovery or proper disposal, has gained increased industry acceptance as a strategy for both competitive advantage and sustainable development. This has stimulated a growing number of researchers to investigate Strategic management issues relating to the set up and control of effective and efficient Reverse Logistics systems. This paper systematically reviews the most important works in this field, with a focus on papers that concentrate on the strategic decision making process involved in the design and operation of a Reverse Logistics process with remanufacturing. The review found that: the majority of work is primarily focused on OEM specific issues; the sectors receiving the most attention are the ones under the greatest pressure from environmental legislation; and previous research findings from Rubio et al. (2009) and Fleischmann et al. (2000) are reaffirmed that the Reverse Logistics field is growing, but characterised by mainly quantitative, mathematical models. Future research efforts should be focused on the empirical investigation of the Reverse Logistics design process for all types of remanufacturers

Partner selection for reverse logistics centres in green supply chains: a fuzzy artificial immune optimisation approach

The design of reverse logistics networks has now emerged as a major issue for manufacturers, not only in developed countries where legislation and societal pressures are strong, but also in developing countries where the adoption of reverse logistics practices may offer a competitive advantage. This paper presents a new model for partner selection for reverse logistic centres in green supply chains. The model offers three advantages. Firstly, it enables economic, environment, and social factors to be considered simultaneously. Secondly, by integrating fuzzy set theory and artificial immune optimization technology, it enables both quantitative and qualitative criteria to be considered simultaneously throughout the whole decision-making process. Thirdly, it extends the flat criteria structure for partner selection evaluation for reverse logistics centres to the more suitable hierarchy structure. The applicability of the model is demonstrated by means of an empirical application based on data from a Chinese electronic equipment and instruments manufacturing company