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Purpose — This paper aims to present results from a survey that investigates the situation in real world supply chains concerning logistics collaboration. Design/methodology/approach — Based on supply chain management literature, a questionnaire was developed covering important topics from the literature. The questionnaire was sent to the logistics manager at Swedish manufacturing companies and a net response rate of 37.8 percent (177/482) was achieved. Apart from purely descriptive statistics, factor analysis, cluster analysis, analysis of variance (ANOVA), and cross tabulations with X² tests were used for the analysis. Findings — From the results, three major conclusions are drawn. First, there is a clear relationship between the intensity of the collaboration and the positive effects experienced from the collaboration. Second, the results indicate that top management is an important driver for higher intensity collaboration. Third, there are serious differences between supply chain management (SCM) theory and practice, due to the absence of strategic elements in the collaboration and the different ways in which supplier and customer collaborations are managed. Research limitations/implications — Related to SCM theory, the lack of strategic elements in the collaboration might prevent or decrease the expected positive effects of the collaboration. It is therefore important to increase the strategic level component in the collaboration. Top management involvement is likely to be important to achieve this and hence more research on the topic is suggested. Originality/value — The study presents a broad overview concerning logistics collaboration that covers many of the most important supply chain management issues. In addition, their relationship is discussed. [ABSTRACT FROM AUTHOR]

This paper investigates the use of Particle Swarm Optimization (PSO) for cluster analysis. In clustering, primitive exploration with minimum prior knowledge consists of research across a wide variety of communities. The diversity in the field of PSO-based clustering equips us with many tools. This paper provides an integrated framework of the diversified PSO-based clustering. In addition, a novel Particle Swarm Optimization (PSO) algorithm using the concept of aging of particles like bird within a flock is provided. The effectiveness of this concept is demonstrated by cluster analysis. Results show that the model provides enhanced performance and maintains more diversity in the swarm and thereby allows the particles to be robust to trace the changing environment. [ABSTRACT FROM AUTHOR]