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Purpose -The purpose of this paper is to examine how the rapid pace of technological change, attrition rate, global complexities and the increasing amount of data and information available have complicated the task of managing knowledge for third-party logistics (3PL) service providers. Based on literature, there is a need for research into the development of a generic taxonomy components framework (GTCF) for the implementation of knowledge management (KM) solution for 3PL service providers. Design/methodology/approach -A four-stage model has been devised for the development of a GTCF to implement KM solution for 3PL service providers. The authors proposed modified Q-sort method and also used Delphi analysis in the four-stage model. The KM components were identified through literature study and discussion with subject experts. The hierarchical structure of the taxonomy was derived and refined through a survey among 3PL experts by employing Q-sort method. Findings -This paper makes several important contributions toward the objective of better understanding the role of 3PL operations in knowledge creation. The feedback from the respondents shows that the GTCF is of potential employment by 3PL service providers irrespective of the nature of the primary service they offer.Research limitations/implications -The GTCF has been devised based on survey responses gathered from 3PL experts in India. The findings of this study have implications for understanding the key KM components required for 3PL service provider relationship and also the weightage for KM components. Practical implications -The aim of this research is for the development of a GTCF which can be taken as the base for implementation of KM solutions for 3PL service providers. Originality/value -The contribution of this study lies in extending the body of knowledge of KM for 3PL service providers. It tests a proposed framework which has only limited empirical validation, and provides a broader understanding of KM components required for 3PL service provider.

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Evolutionary algorithms are stochastic search methods that mimic the principles of natural biological evolution to produce better and better approximations to a solution and have been used widely for optimization problems. A general problem of continuous-time aggregate production planning for a given total number of changes in production rate over the total planning horizon is considered. It is very important to identify and solve the problem of continuous-time production planning horizon with varying production rates over the interval of the planning period horizon. Some of the researchers have proposed global search methods for the continuous-time aggregate productionplanning problem. So far, less work is reported to solve the problem of continuous-time production planning using local search methods like genetic algorithms (GA) and simulated annealing (SA). So in this work, we propose a modified single objective evolutionary program approach, namely GA, SA, and hybrid genetic algorithms-simulated annealing (GA-SA) for continuoustime production plan problems. The results are compared with each other and it was found that the hybrid algorithm performs better.

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