The main focus of this study is to conduct a systematic literature review to integrate lean, agile, resilient, green and sustainable (LARGS) paradigms in the supply chain (SC) domain. To achieve this aim, several research questions were designed: First, how to locate LARGS research in context of SC domain? For this, it is important to understand which types of research articles should be selected for the study? Further, where such studies were conducted (geographical location)? Second, what is the focus of research in LARGS paradigm in SCs? For this, it is important to study, which types of industries or sectors have been targeted in literature? In addition, which tools and techniques have been used mostly? Third, what are the current trends in the relationships of LARGS paradigms, among themselves, and with SC performance measures? Fourth, what are the emerging issues, unexplored areas in this field, based on these what could be future research avenues in this subject domain have been proposed? A total of 160 relevant articles published during 1999–2019 were used for analysis. Based on analysis, findings are summarised, and main research issues and possible future research directions in LARGS paradigms in SCs are highlighted.
In this paper, we address a scheduling problem for minimizing total weighted flowtime, observed in automobile gear manufacturing. Specifically, the bottleneck operation of the pre-heat treatment stage of gear manufacturing process has been dealt with in scheduling. Many real-life scenarios like unequal release times, sequence dependent setup times, and machine eligibility restrictions have been considered. A mathematical model taking into account dynamic starting conditions has been proposed. The problem is derived to be NP-hard. To approach the problem, a few heuristic algorithms have been proposed. Based on planned computational experiments, the performance of the proposed heuristic algorithms is evaluated: (a) in comparison with optimal solution for small-size problem instances and (b) in comparison with the estimated optimal solution for large-size problem instances. Extensive computational analyses reveal that the proposed heuristic algorithms are capable of consistently yielding nearstatistically estimated optimal solutions in a reasonable computational time.
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