M ultiechelon distribution systems are quite common in supply-chain and logistics. They are used by public administrations in their transportation and traffic planning strategies, as well as by companies, to model own distribution systems. In the literature, most of the studies address issues relating to the movement of flows throughout the system from their origins to their final destinations. Another recent trend is to focus on the management of the vehicle fleets required to provide transportation among different echelons. The aim of this paper is twofold. First, it introduces the family of two-echelon vehicle routing problems (VRPs), a term that broadly covers such settings, where the delivery from one or more depots to customers is managed by routing and consolidating freight through intermediate depots. Second, it considers in detail the basic version of two-echelon VRPs, the two-echelon capacitated VRP, which is an extension of the classical VRP in which the delivery is compulsorily delivered through intermediate depots, named satellites. A mathematical model for two-echelon capacitated VRP, some valid inequalities, and two math-heuristics based on the model are presented. Computational results of up to 50 customers and four satellites show the effectiveness of the methods developed.

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One of the main issues in addressing three-dimensional packing problems is finding an efficient and accurate definition of the points where to place the items inside the bins. The performance of exact and heuristic methods is actually strongly influenced by the choice of a placement rule. We introduce the Extreme Point concept and present a new Extreme Point-based rule for packing items inside a three dimensional container. The Extreme Point rule is independent from the particular packing problem addressed and can handle additional constraints, such as fixing the position of the items. The new Extreme Point rule is also used to derive new constructive heuristics for the Three-Dimensional Bin Packing problem. Extensive computational results show the effectiveness of the new heuristics compared to state-of-the-art results. Moreover, the same heuristics, when applied to the Two-Dimensional Bin Packing problem, outperforms those specifically designed for the problem.

The role of a relatively small cadre of high-tech startup firms in driving innovation and economic growth has been well known and amply celebrated in recent history. At the same time, it is well recognized that, while the overall contribution of startups is crucial, the high-risk and high-reward strategy followed by these startups leads to significant failure rates and a low ratio of successful startups. So, it is curious to notice that literature tends to focus on successful startups and on quantitative studies looking for determinants of success while neglecting the numerous lessons that can be drawn by examining the stories of startups that failed. This paper aims to fill this gap and to contribute to the literature by providing a repeatable and scalable methodology that can be applied to databases of unstructured post-mortem documents deriving startup failure patterns. A further and related contribution is the analysis carried out with this methodology to a large database of 214 startup post-mortem reports. Descriptive statistics show how the lack of a structured Business Development strategy emerges as a key determinant of startup failure in the majority of cases.based on discriminant analysis [5] and multiple discriminant analysis [4], followed by more recent approaches exploiting regression [6][7][8]. Since the 80s, artificial intelligence methods started to be used as well to predict ventures success/failure. Suggested solutions relied on decision trees algorithms [9], artificial neural networks [10], clustering [11] and hybrid genetic algorithms [12]. Approaches based on financial data had the advantage of being potentially applied to a high number of companies, since data could be gathered from their annual reports. Nonetheless, company revenues were frequently consequences of other aspects, such as entrepreneur's ability, company's core competencies, market, etc. In this view, other research works investigated whether such aspects could contribute as well to the success or failure of a venture. For instance, analysis conducted on entrepreneurs examined the influence of their gender and ethnic origin on the likelihood to succeed or fail [13,14]. The work in [15] conducted a logistic regression analysis based on 15 independent variables success versus failure prediction model in Israel, including the management experience, the education, and the age of the owner. The logistic regression analysis was also adopted in [16] to model the relationship between small business mortality rates and the aggregate levels of internal and external risks (e.g., bankruptcy related to interest rates, discontinuance of business or of ownership, etc.). Other researchers focused on entrepreneurial attitudes and linked startup failure to dissonances between corporate goals and the goals of its founders [17]. Others presented failure as the result of entrepreneurs' overconfidence and hubris [18]. In contrast, other researchers argued that, without a reasonable level of positive perception of one's abilities, several successful...

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Abstract. In this paper we address the Two-Echelon Vehicle Routing Problem (2E-VRP ), an extension of the classical Capacitated VRP, where the delivery from a single depot to the customers is managed by routing and consolidating the freight through intermediate depots called satellites. We present a family of Multi-Start heuristics based on separating the depot-to-satellite transfer and the satellite-to-customer delivery by iteratively solving the two resulting routing subproblems, while adjusting the satellite workloads that link them. The common scheme on which all the heuristics are based consists in, after having found an initial solution, applying a local search phase, followed by a diversification; if the new obtained solutions are feasible, then local search is applied again, otherwise a feasibility search procedure is applied, and if it successful, the local search is applied on the newfound solution. Different diversification strategies and feasibility search rules are proposed. We present computational results on a wide set of instances up to 50 customers and 5 satellites and compare them with results from the literature, showing how the new methods outperform previous existent methods, both in efficiency and accuracy.

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