Bibliometric Study of Metaheuristics Application for Solving Inventory Routing Problem

Abstract

Purpose: The objective of this study is to perform a review and thematic analysis focusing on the application of metaheuristics for addressing the inventory routing problem. Employing bibliometric methods, this research endeavors to conduct a comprehensive assessment of the current scholarly literature, aiming to enhance and expand upon prior research findings. Additionally, this study will review metaheuristics algorithms utilized in previous studies to provide a comprehensive understanding of their application in this context.   Design/methodology/approach: A bibliometric analysis was performed on IRP research, involving 346 publications retrieved from the Scopus database spanning the period 1978 to 2023. Biblioshiny and VOSviewer were employed for data analysis to identify significant patterns and trends in the application of metaheuristics for addressing the inventory routing problem.    Results and discussions: This study seeks to analyze co-citation, keyword co-occurrence, thematic mapping, and thematic evolution to gain insights into the field. The findings indicate that the top 10 authors with high citation indexes of the authors, and top 10 nations with articles addressing IRP. The thematic mapping indicates four key areas of research within applying heuristics for solving inventory and transportation problem. The motor themes focus on inventory routing problem, such as stochastic demand, column generation, scheduling and time window are shaping the future of manufacturing. The study reveals that the Genetic Algorithm emerges as the predominant choice.   Research Implications: Through this research, it provides researchers and businesses seeking solutions to real-world IRR problems with a comprehensive analysis from multiple perspectives, thereby enabling focused in-depth research and effective utilization of research findings.   Originality/Value: This study contributes to the literature by reviewing thematic analysis of metaheuristic applications for the IRP problem. The value of this research is evidenced by researchers and professionals, as it helps identify the gaps in the literature and provide insightful information about the level of research within the field at the time, predict future research directions, and propose innovative avenues for advancing the field.