Abstract
Purpose The purpose of this paper is to identify patterns of project risk management (PRM) practices' adoption, and provides empirical evidence concerning the importance (and key attributes) of organizational PRM maturity to the use of risk-related practices and project performance. Design/methodology/approach The research involved two phases: interviews with five project managers, and a worldwide survey of project managers that resulted in the analysis of 865 valid questionnaire responses. Cluster analysis was used to classify PRM practices' use, factor analysis to detect the structure of the relationship between the variables measuring PRM practices' use and a multiple regression analysis (with canonical correlation) to further reveal the different degrees to which PRM practices and organizational maturity are associated. Findings The identified patterns of risk practices' adoption indicate that different contexts of organization PRM maturity and project complexity influence practices selection. The PRM practices related with targets (e.g. time-phased budget plan) are the most used, and those related to tools and techniques (e.g. S-curve) are the least used. Additionally, the obtained results confirm that organizational PRM maturity influences risk practices' usage, moderated by project complexity, and organizational PRM maturity influences project performance. Originality/value Empirical methods were used to investigate the relationship between organizational PRM maturity and a large set of PRM practices with project complexity as a moderator. Gaps in the use of PRM practices (i.e. areas where more PRM knowledge and training are needed) were identified. Finally, this work identifies the attributes of organizational maturity with implications in practices' usage and project performance.

Abstract
This study presents an assessment of the main research problems addressed in the literature on New Product Development (NPD) and its methodologies, for the pharmaceutical industry. The work is particularly focused on the establishment of an evolutionary perspective of the relevant modelling approaches, and on identifying the main current research challenges, considering the fast-changing business context of the industry. Main findings suggest a generalized misalignment of recent studies with today's technological and market trends, highlighting the need for new modelling strategies.

Abstract

Abstract
This work proposes a mathematical programming model for jointly scheduling of production and transport in flexible manufacturing systems considering alternative job routing. Although production scheduling and transport scheduling have been vastly researched, most of the works address them independently. In addition, the few that consider their simultaneous scheduling assume job routes as an input, i.e., the machine -operation allocation is previously determined. However, in flexible manufacturing systems, this is an important source of flexibility that should not be ignored. The results show the model efficiency in solving small -sized instances.

Abstract
This work proposes a biased random key genetic algorithm (BRKGA) for the integrated scheduling of manufacturing, transport, and storage/retrieval operations in flexible manufacturing systems (FMSs). Only recently, research on this problem has been reported; however, no heuristic approaches have yet been reported. The computational results show the BRKGA to be capable of finding good quality solutions quickly.

Abstract
This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions.