Abstract
This paper presents a new formulation for a time window assignment vehicle routing problem where time windows are defined for multiple product segments. This two-stage stochastic optimization problem is solved by means of a fix-and-optimize based matheuristic. The first stage assigns product dependent time windows while the second stage defines delivery schedules. Our approach outperforms a general-purpose solver and achieves an average cost decrease of 5.3% over expected value problem approaches. Furthermore, a sensitivity analysis on three operational models shows that it is possible to obtain significant savings compared to the solutions provided by a large European food retailer.

Abstract

Abstract
This paper investigates the performance of the largest Brazilian hydropower plants. This study covers 78% of the total installed capacity from hydros in the country, and considers indicators reflecting operational and maintenance costs as well as quality of service. The assessment was conducted using a new approach for the construction of composite indicators, based on a directional distance function model. First, we assessed the hydropower plants allowing for complete flexibility in the definition of weights, enabling the identification of underperforming plants, and quantification of their potential for improvement. Next, we assessed the plants considering different perspectives regarding the importance attributed to each indicator. This allowed reflecting different points of view, focusing primarily on operation and maintenance costs or quality issues. The results identify the hydropower plants that can be considered benchmarks in different scenarios, and allow testing the robustness of plants' classification as benchmarks in the unrestricted model.

Abstract
The implementation of efficient strategic decisions such as process design and capacity investment under uncertainty, during the product development process, is critical for the pharmaceutical industry. However, to tackle these problems the widely used multi-stage/scenario-based optimization formulations are still ineffective, especially for the first-stage (here-and-now) solutions where uncertainty has not yet been revealed. This study extends the authors' previous work addressing the stochastic product-launch planning problem, by developing a new Multi-Objective Integer Programming model, embedded in a unified decision-making framework, to obtain the final design strategy that "maximizes" productivity while considering the decision-maker preferences. An approximation of the efficient Pareto-front is determined, and a subsequent Pareto solutions analysis is made to guide the decision process. The developed approach clearly identifies the process designs and production capacities that "maximize" productivity as well as the most promising solutions region for investment. Moreover, a good balance between investment and capacity allocation was achieved.

Abstract
In a paper written by by Vanhoucke et al. (2016), an overview of artificial and empirical project databases has been given for integrated project management and control. These databases are collections of the most wellknown and widespread data instances available in literature for the construction of a baseline schedule, the analysis of schedule risk or the use for project control. The current paper serves as a follow-up study to further elaborate on the use of these data instances, and to give researchers an incentive to use these datasets for their research on the development and validation of new algorithms for project scheduling. Therefore, unlike the general focus of the previous paper on baseline scheduling, schedule risk analysis and project control, the focus on the current paper is restricted to resource-constrained project scheduling. The intention of this follow-up overview is fourfold. First and foremost, a procedure is proposed to facilitate the reporting of best known solutions for the well-known single- and multi-mode resource-constrained project scheduling problem to minimize the project makespan. Secondly, the paper reports our best known solutions we obtained so far, and reflects on the network and resource parameters that increase the project complexity. In doing so, areas to focus on for future research are detected, and an attempt to define hard problem instances is given. Thirdly, a new dataset is presented for the resource-constrained project scheduling problem that is much more diverse in both the network topology and resource scarceness and will enable the future researcher to develop algorithms to solve a wider range of project problems. Finally, the paper also adds some links to tutorials and other relevant information to stimulate researchers to download the data and update best known solutions once available.

Abstract
Cutting raw-material into smaller parts is a fundamental phase of many production processes. These operations originate raw-material waste that can be minimised. These problems have a strong economic and ecological impact and their proper solving is essential to many sectors of the economy, such as the textile, footwear, automotive and shipbuilding industries, to mention only a few. Two-dimensional (2D) nesting problems, in particular, deal with the cutting of irregularly shaped pieces from a set of larger containers, so that either the waste is minimised or the value of the pieces actually cut from the containers is maximised. Despite the real-world practical relevance of these problems, very few approaches have been proposed capable of dealing with concrete characteristics that arise in practice. In this paper, we propose a new general heuristic (H4NP) for all 2D nesting problems with limited-size containers: the Placement problem, the Knapsack problem, the Cutting Stock problem, and the Bin Packing problem. Extensive computational experiments were run on a total of 1100 instances. H4NP obtained equal or better solutions for 73% of the instances for which there were previous results against which to compare, and new benchmarks are proposed.