Abstract

Abstract
Systematic ways to perform ex-ante analysis of urban freight good practices are still missing, deeming transferability efforts prone to failure. We critically analyze state-of-the-art freight modeling methodologies to optimize the configuration of loading/unloading bays, and the associated enforcement measures, quantifying congestion reductions. Existing models can poorly handle some crucial elements for this analysis. An alternative modeling framework is proposed, integrating simulation models and optimization strategies that take into account double-parking derived vehicle obstruction. The framework should lead to deeper insights, even in a low-data availability perspective, between what is regarded as good practices and a quantification of their potential; thus becoming a useful tool in the design and analysis of policies. (C) 2013 The Authors. Published by Elsevier Ltd.

Abstract
In this paper, we presented a probabilistic framework to predict Bus Bunching (BB) occurrences in real-time. It uses both historical and real-time data to approximate the headway distributions on the further stops of a given route by employing both offline and online supervised learning techniques. Such approximations are incrementally calculated by reusing the latest prediction residuals to update the further ones. These update rules extend the Perceptron's delta rule by assuming an adaptive beta value based on the current context. These distributions are then used to compute the likelihood of forming a bus platoon on a further stop - which may trigger an threshold-based BB alarm. This framework was evaluated using real-world data about the trips of 3 bus lines throughout an year running on the city of Porto, Portugal. The results are promising.

Abstract
The purpose of this paper is to characterize the competitive positioning of the construction industry companies and evaluate their financial performance. The methodology proposed involves three major stages., The first stage concerns the identification of the competitive positioning of companies within the construction sector. This is achieved using a hierarchical clustering algorithm suitable for large datasets and mixed type variables. The second stage is the analysis of performance of the different clusters. This is done using the Data Envelopment Analysis technique. To characterize in detail the main performance features of each cluster, a decision tree is used to extract the main rules concerning the performance spread within each cluster and the gap between the cluster best practices and the national benchmarks. The third stage concerns the analysis of the strengths, weaknesses and areas of potential improvement for contractors in each competitive positioning. This required the analysis of benchmark companies of each cluster. The methodology proposed was applied for the analysis of performance of all contractors that operate in the Portuguese construction industry.

Abstract
From long to short term planning, decision processes inherent to operating theater organization are often subject of empiricism, leading to far from optimal results. Waiting lists for surgery have always been a societal problem, which governments have been fighting with different management and operational stimulus plans. The current hospital information systems available in Portuguese public hospitals, lack a decision support system component that could help achieve better planning solutions. Thus, an intelligent decision support system has been developed, allowing the centralization and standardization of planning processes, improving the efficiency of the operating theater and tackling the waiting lists for surgery fragile situation. The intelligence of the system derives from data mining and optimization techniques, which enhance surgery duration predictions and operating rooms surgery schedules. Experimental results show significant gains, reducing overtime, undertime, and better resource utilization. Note to Practitioners-The Operating Theater (OT) is often considered hospitals' biggest budget consumer and revenue center in a hospital. This paper was motivated by a project that aims to reduce expenses and surgery waiting lists in Portuguese public hospitals, by developing an Intelligent Decision Support System (DSS) to support surgery scheduling. Prior to this research, decision makers (Surgeons, Department managers, Operating theatre managers) used their experience to make allocation, scheduling and estimation decisions. Since many of these decisions are made without analyzing past results, mistakes occur frequently, affecting the OT performance. With the help of business intelligence, data mining and optimization algorithms, surgeons' estimations can be more precise and the operating room schedule can be optimized. Preliminary experiments on the usage of DSS reveal a remarkable increase of the efficiency of the whole OT. In future research, we will extend the DSS and the techniques used to address the tactical master surgery scheduling problem, which aims to perform a better allocation of the different specialties to the operating rooms along the week. In addition, upstream and downstream resources shall be considered in the optimization module, as well as a simulation component to better evaluate generated solutions.

Abstract
Commonly in container terminals, the containers are stored in yards on top of each other using yard cranes. The split-platform storage/retrieval system (SP-AS/RS) has been invented to store containers more efficiently and to access them more quickly. The integrated scheduling of quay cranes, automated guided vehicles and handling platforms in SP-AS/RS has been formulated and solved using the simulated annealing algorithm in previous literatures. This paper presents a genetic algorithm (GA) to solve this problem more accurately and precisely. The GA includes a new operator to make a random string of tasks observing the precedence relations between the tasks. For evaluating the performance of the GA, 10 small size test cases were solved by using the proposed GA and the results were compared to those from the literature. Results show that the proposed GA is able to find fairly near optimal solutions similar to the existing simulated annealing algorithm. Moreover, it is shown that the proposed GA outperforms the existing algorithm when the number of tasks in the scheduling horizon increases (e.g. 30 to 100).