On the impact of collaborative strategies for goods delivery in city logistics

Abstract

City logistics refers to the process of total optimisation of the logistics and transport activities in urban areas while considering economic, environmental, social and safety aspects. This paper considers a collaborative scenario for the urban goods transport planning and management as a way to reduce transport costs, congestion and environmental impact of this activity. An approach from the Operations Management field, using mathematical modelling, for tactical and operational decision-making is proposed to discuss and compare both collaborative and non-collaborative scenarios. This approach is validated using real data taken from the city of Bogotá, Colombia. Results put in evidence the quantitative benefits that can be achieved when collaborative logistics operations are implemented, represented in both transportation costs and environmental impacts. Although these results might not be surprising (like in the field of supply chain management), the contribution of this paper is centred in the fact that the impacts of collaboration in city logistics have not yet been quantified until now. This allows an ‘ex ante’ evaluation of the benefits of collaborative goods transport in cities.     

Implementation of customisation strategies in collaborative networks through an innovative Reference Framework

Consumer needs and expectations of specific target groups – such as elderly, obese, disabled or diabetic persons – are arising as challenging opportunities for European companies which are asked to supply innovative customised goods of high quality at affordable price. This is particularly true in the fashion as well as in the orthopaedic sector where there are many different competences to conjugate to offer dedicated products to the mentioned target groups. This paper aims at proposing a reference model to support companies in defining collaborative supply networks for customised production. In particular, this work describes the implementation of the developed model in a real case highlighting the changes implied at network level to address the need for fashionable and healthy products.     

Coping with Diversity in Alliances for Innovation: The Role of Relational Social Capital and Knowledge Codifiability

Alliances are increasingly considered a key element for innovation, especially in knowledge‐intensive firms. While this is true, the mere membership to alliances does not explain innovation performance, and thus the alliance's characteristics that determine high performance must be examined. This research address the question of how the diversity of partners in a certain alliance for innovation affects innovation performance, and how this influence can be moderated by certain characteristics, such as the relational dimension of social capital and the type of knowledge shared among partners. The empirical analysis of a sample of 90 biotech companies shows that there is an inverted U‐shaped relationship between alliance partner diversity and innovation performance and confirms the positive moderating effects of relational social capital and knowledge codifiability. These findings contribute to the current research on alliances for innovation by providing empirical evidence on why some alliances perform better than others. Also, the results suggest that the study of alliance partner diversity, as a determinant of alliance performance, should not be addressed in isolation.     

Some stochastic properties of conditionally dependent frailty models

The frailty approach is commonly used in reliability theory and survival analysis to model the dependence between lifetimes of individuals or components subject to common risk factors; according to this model the frailty (an unobservable random vector that describes environmental conditions) acts simultaneously on the hazard functions of the lifetimes. Some interesting conditions for stochastic comparisons between random vectors defined in accordance with these models have been described in the literature; in particular, comparisons between frailty models have been studied by assuming independence for the baseline survival functions and the corresponding environmental parameters. In this paper, a generalization of these models is developed, which assumes conditional dependence between the components of the random vector, and some conditions for stochastic comparisons are provided. Some examples of frailty models satisfying these conditions are also described.     

Human Capital Disparities and Earnings Inequality in The Portuguese Private Labour Market

Social Indicators Research - This paper examines human capital inequality and how it relates to earnings inequality in Portugal using data from Quadros de Pessoal for the period 1986–2017....     

The Beta-Poisson Distribution in Wadley’s Problem

In dose-response studies, Wadley’s problem occurs when the number of organisms that survive exposure to varying doses of a treatment is observed but the number initially present is unknown. The unknown number of organisms initially treated has traditionally been modelled by a Poisson distribution, resulting in a Poisson distribution for the number of survivors with parameter proportional to the probability of survival. Data in this setting are often overdispersed. This study revisits the beta-Poisson distribution and considers its effectiveness in modelling overdispersed data from a Wadley’s problem setting.     

Introducing the Historical Thinking Practice of Contextualizing to Middle School Students

This article describes the design of a social studies unit about the Johnstown Flood of 1889 with a particular emphasis on how specific unit resources engaged middle school students in learning about the geographical and historical context of Johnstown, Pennsylvania. We also report on how the resources supported the teaching and learning of the historical thinking practice of contextualizing.     

Robustness and Precision: How Data Quality May Influence Key Dependent Variables in Infant Eye‐Tracker Analyses

In recent years, eye‐tracking has become a popular method for drawing conclusions about infant cognition. Relatively little attention has been paid, however, to methodological issues associated with infant eye‐tracking. Here, we consider the possibility that systematic differences in the quality of raw eye‐tracking data obtained from different populations and individuals might create the impression of differences in gaze behavior, without this actually being the case. First, we show that lower quality eye‐tracking data are obtained from populations who are younger and populations who are more fidgety and that data quality declines during the testing session. Second, we assess how these differences in data quality might influence key dependent variables in eye‐tracking analyses. We show that lower precision data can appear to suggest a reduced likelihood to look at the eyes in a face relative to the mouth. We also show that less robust tracking may manifest as slower reaction time latencies (e.g., time to first fixation). Finally, we show that less robust data can manifest as shorter first look/visit duration. We argue that data quality should be reported in all analyses of infant eye‐tracking data and/or that steps should be taken to control for data quality before performing final analyses.