The purpose of the study is to fill a gap in the literature on mathematical production planning (joint balancing and sequencing) in the fashion industry. It considers in…
Abstract
Purpose
The purpose of the study is to fill a gap in the literature on mathematical production planning (joint balancing and sequencing) in the fashion industry. It considers in particular situations of mass customization, made-to-measure or small lot sizes.
Design/methodology/approach
The paper develops a mathematical model based on product options and attributes instead of fixed variants. It proposes an easy-to-use genetic algorithm to solve the resulting optimization problem. Functionality and performance of the algorithm are illustrated via a computational study.
Findings
An easy-to-implement, yet efficient algorithm to solve the multi-objective implementation of a problem structure that becomes increasingly relevant in the fashion industry is proposed. Implementation of the algorithm revealed that the algorithm is ideally suited to generate significant savings and that these savings are impervious to problem and thus company size.
Practical implications
The solutions from the algorithm (Pareto-efficient frontier) offer decision-makers more flexibility in selecting those solutions they deem most fitting for their situation. The computational study illustrates the significant monetary savings possible by implementing the proposed algorithm to practical situations.
Originality/value
In contrast to existing papers, for the first time, to the best of the author’s knowledge, the focus of the joint balancing and sequencing approach has been applied in the fashion instead of the automotive industry. The applicability of the approach to specific fields of the fashion industry is discussed. An option and attributes-based model, rarely used in general assembly line sequencing per se, is used for more flexibility in representing a diverse set of model types.
Details
Keywords
This study aims to not only develop measurements of preferential attachment and homophily mechanisms based on their definitions and network theory but also examine the…
Abstract
Purpose
This study aims to not only develop measurements of preferential attachment and homophily mechanisms based on their definitions and network theory but also examine the associations among these network mechanisms, community commitment, knowledge sharing and community citizenship behavior.
Design/methodology/approach
In total, 250 valid questionnaires are collected to examine the hypothesized associations. These hypotheses are examined by using partial least squares structural equation modeling.
Findings
The findings reveal both mechanisms are beneficial to develop new entrants’ emotional attachment to a virtual community, thereby motivating knowledge sharing and community altruistic behavior. The results contribute some practical and theoretical implications that are very helpful for the conceptualization of network mechanisms, community development, relationship management and incentives for extra-role behavior.
Originality/value
The literature on the link between network selection mechanisms and knowledge sharing remains unknown. This study is the pioneer to disclose this unknown association and examine the impacts of preferential attachment and homophily network mechanisms.