Supply Chain Optimization in Pulp Distribution Using a Rolling Horizon Solution Approach

It is our great pleasure to welcome you to the 2014 GECCO Workshop on Problem Understanding and Real World Optimisation (PURO). This year's workshop builds upon the success of last year's even which was organized as part of GECCO 2013, held in the VU University in Amsterdam. Last year's event was a joint workshop combining workshops on problem understanding organised by Kent McClymont of the University of Exeter and on real world optimisation organized by Emma Hart from Edinburgh Napier University. Based on the success of last year's joint workshop this year's event merges the two workshops into a single event covering both ranges of topics.

One of the major impediments in deployment of Autonomous Driving Systems (ADS) is their safety and reliability. The primary reason for the complexity of testing ADS is that it operates in an open world characterized by its non-deterministic, high-dimensional and non-stationary nature where the actions of other actors in the environment are uncontrollable from the ADS's perspective. This leads to a state space explosion problem and one way of mitigating this problem is by concretizing the scope for the system under test (SUT) by testing for a set of behavioral competencies which an ADS must demonstrate. A popular approach to testing ADS is scenario-based testing where the ADS is presented with driving scenarios from real world (and synthetically generated) data and expected to meet defined safety criteria while navigating through the scenario. We present SAFR-AV, an end-to-end ADS testing platform to enable scenario-based ADS testing. Our work addresses key real-world challenges of building an efficient large scale data ingestion pipeline and search capability to identify scenarios of interest from real world data, creating digital twins of the real-world scenarios to enable Software-in-the-Loop (SIL) testing in ADS simulators and, identifying key scenario parameter distributions to enable optimization of scenario coverage. These along with other modules of SAFR-AV would allow the platform to provide ADS pre-certifications.

To evaluate the concordance between data derived from randomized controlled trial (RCT) and real-world estimates of HbA1c and weight change after 24 weeks of initiation of a basal-bolus compared with a premixed insulin regimen in people with Type 2 diabetes. Data eight RCTs were pooled after a systematic review of studies examining basal-bolus (n = 1893) or premixed (n = 1517) regimens. Real-world data were extracted from the UK primary care dataset for people on basal-bolus (n = 7483) or premixed insulin regimens (n=10 744). The mean differences between HbA1c and weight from baseline were calculated using t-tests, while analysis of variance was used to compare the two treatment regimens. Linear regression analyses were used to determine the predictors of this change. Both insulin regimens were associated with HbA1c reductions (real-world data -0.28%; RCT data, -1.4%) and weight gain (real-world data, +0.27 kg; RCT data, +2.96 kg) but there were no significant differences between basal-bolus and premixed insulin. Discordances in the pattern of treatment response were observed, however, between real-world and RCT data for both insulin regimens. For any given baseline HbA1c concentration, the change in HbA1c in the RCTs was greater than in real-world conditions and for those with baseline weight above ~60 kg, RCT data showed overall weight gain in contrast to slight weight loss in the real-world population. Lastly, for both randomized controlled trial and real-world populations, while greater baseline weight was associated with reduced response to treatment, the association was much steeper in the RCT than in the real-world population. In addition, greater baseline weight was associated with greater weight reductions in both premixed insulin and basal-bolus insulin regimens, although to a lesser extent with the latter. These results highlight specific discrepancies in the HbA1c reduction and weight change in insulin regimen between real world versus RCT populations; with greater reduction in HbA1c and greater increase in weight observed in the RCT population than in the real-world population. Also, the basal-bolus regimens in both real-world and RCT populations showed greater reduction in HbA1c compared to the premix regimen (though more marked in RCTs), while the premix regimen showed greater increase in weight in real-world, as against basal-bolus in the RCT population.

Identifying relationships between entities in data is a central topic across various industries and businesses, from social networks to supply chain and heavy manufacturing industries. In this paper we present data from a database of machinery represented in terms of machine parts. The machine parts are originally organised in tree structures where the vertices are machine part types, and the edges are “part-of” relations. Hence, each tree represents a type of machinery broken down into its machine part constituent types. The data we present is the union over these trees, making up a directed acyclic graph describing the type hierarchy of the machine parts.The motivation for publishing the dataset is the following real-world industry problem: Each tree represents a mechanical design, and over time some designs have been copy-pasted with minor modifications. The new instances have been given new identifiers with no reference to where from they were copied. In hindsight, it is desirable to recover the copy-paste links to for interchange between essentially identical designs. However, telling which parts are copies of which other parts has turned out to be difficult. In particular, the metadata has a tendency of displaying higher similarities within a composite part than between a part and its copy. Due to non-disclosure, we cannot provide the metadata, but we provide element wise dissimilarities that are generated based on the metadata using classical methods such as Jaccard similarity on description texts, material types etc. The dissimilarities are obtained from a data science project in the company owning the data, trying to tackle the very problem of recovering the copy-paste links.Availability of labeled data on this data set is limited, so based on our in-depth knowledge of the problem domain, we present a data synthesisation method that can generate arbitrarily large problem instances of the copy-paste problem based on the sample data, that provides a realistic representation of the real world problem. The problems are presented as planted partitions of vertices of directed acyclic graphs with vertex dissimilarities, and thus constitutes a typical classification problem along the lines of graph- or network clustering.The type of industry data we present is usually company confidential, bound by intellectual property rights, and generally not available to scientists. We therefore publish this anonymised dataset to offer real world sample data and generated problem instances for researchers that are interested in this type of classification problems, and on which theories and algorithms can be tested.The data and the problem generation methodology are backed by a Python implementation, providing both data access and an API for parameterised problem generation. The data is also available as raw files.

<sec> <title>UNSTRUCTURED</title> The amount of data generated in clinical practice is increasing substantially, which has benefitted the use of real-world data (RWD) for real-world evidence (RWE) in biomedical research. While early RWE efforts focused on structured EHR and claims data, advances in analytical methods, such as artificial intelligence, are expected to further enhance the ability to obtain deeper insights from RWD. Most recently, real-world imaging data (RWiD) has emerged as a novel and valuable resource, enabled by improvements in imaging infrastructure, data standardization, and de-identification technologies. Medical imaging is essential at multiple stages of clinical care, ranging from screening to post treatment assessment and surveillance. Medical imaging has become a key component of patient management, as it augments clinical decision-making across many medical specialties. RWiD is the retrospective collection of routinely gathered clinical imaging data. Using only radiology reports results in limited information compared to datasets that contain the actual images. Radiology reports primarily focus on clinical decision making rather than research purposes. Therefore, the actual images add value to real world datasets. However, using RWiD is challenging due to complex de-identification and harmonization, as well as requirements for file storage, file transfer, and computation. This article describes the background, challenges, and opportunities of real world imaging data with a focus on life sciences and biopharmaceutical applications. </sec>

Dynamic reserve site selection under contagion risk of deforestation

A single source transportation algorithm

A few analytical and heuristic solution methods were developed for some problems in Protean systems. The aim of this paper is to provide guidelines for using computer software developed in an IBM-PC/AT compatible environment to implement these solution methods. The problems and models are briefly stated to facilitate the use of this software.

Use of real-world data (RWD) and real-world evidence (RWE) are increasingly recognized as important for synthesizing relevant information to aid clinical decision-making, and for advancing healthcare. Acceleration in the use of RWD and RWE is expected to continue with newer developments in technology and advancements in computer-aided analytics. This chapter integrates some key sections to provide a state-of-the-art summary and synthesis of the latest thinking on the topic with potential practical implications for clinical research and development: (1) What is RWD in the context of drug development and clinical practice? (2) Why is RWD important? (3) For what purposes can RWD be useful? (4) What study designs and statistical methods will be necessary to ensure high quality RWE? The chapter concludes with application examples from the biopharmaceutical real world, a summary of the main findings, and cautions and recommendations to researchers and practitioners interested in conducting real-world studies to generate and use real-world evidence.

There is great unmet need in discovering novel treatment for Parkinson's disease (PD) and identifying the new agents potentially causing drug-induced parkinsonism. New indications and contraindications of drugs are typically approved following rigorous randomized controlled trial (RCT) evaluation. However, RCTs have their inherent limitations, since they are usually conducted in ideal conditions, with high cost and limited follow-up periods. In the past decade, large cohort studies with long follow-up outcome data was derived from a PD database in a real-world setting. Studies based on real world data (RWD) can help to augment and extrapolate data obtained in RCTs and provide information about the safety and effectiveness of a medication in heterogeneous, large populations. In the present review, we focus on the published real world studies designed to develop new treatment strategies for repurposing drugs and identifying contraindications for PD. We also outline the challenges and limitations in these studies. Subsequently we introduce PaWei app platform, which hopefully can facilitate PD management and address real-world problems associated with PD. Better understanding of RWD collection and analysis is needed if RWD is to achieve its full potential.

A mathematical programming model to select maintenance strategies in railway networks

PCP65 - KEY CONSIDERATIONS FOR THE COLLECTION OF PATIENT REPORTED OUTCOME (PRO) DATA IN REAL WORLD (RW) STUDIES

Treatment Sequencing and Survival in Follicular Lymphoma: A National Population-Based Study

Clustering high dimensional data is an emerging research field.Subspace clusteringorprojected clusteringgroup similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation and comparison between these paradigms on a common basis.Conclusive evaluation and comparison is challenged by three major issues. First, there is no ground truth that describes the "true" clusters in real world data. Second, a large variety of evaluation measures have been used that reflect different aspects of the clustering result. Finally, in typical publications authors have limited their analysis to their favored paradigm only, while paying other paradigms little or no attention.In this paper, we take a systematic approach to evaluate the major paradigms in a common framework. We study representative clustering algorithms to characterize the different aspects of each paradigm and give a detailed comparison of their properties. We provide a benchmark set of results on a large variety of real world and synthetic data sets. Using different evaluation measures, we broaden the scope of the experimental analysis and create a common baseline for future developments and comparable evaluations in the field. For repeatability, all implementations, data sets and evaluation measures are available on our website.

This article investigates how to simultaneously optimize both strategic and tactical decisions in the supply chain network design. For this purpose, a bi-level programming model is developed in which supply chain network design problem is considered as a strategic decision in the upper-level model, while the lower-level model contains the assembly line balancing as a tactical decision. In addition, the problem is extended to include push–pull strategy where decisions such as production amount and inventory level of each component in manufacturers are made. Based on the special structure of the model, a heuristic method is proposed to solve the developed bi-level model. A numerical example is employed to show the performance of proposed method in terms of feasibility and convergence. Finally, computational experiments on several problem instances are presented to demonstrate the applications of developed model and the solution method.