Dr Chengbo Wang

In recent years, the value chain facilitates the transition of competition among enterprises from a single link to a comprehensive one, thereby driving intelligent upgrades within manufacturing enterprises. The intelligent upgrading also imposes new constraints on the traditional assembly line production mode. Inspired by the real production practices of company L, a global intelligent terminal manufacturing enterprise headquartered in China, this study addresses the parallel heterogeneous assembly line scheduling problem with fixture constraints (HALSFC). To tackle this challenging problem, we propose a mixed integer linear programming (MILP) model that aims to maximize the number of completed work orders within a specified time. To our best knowledge, this study is among the first attempts to address the HALSFC problem with setups and related work orders. Due to the NP-hardness of the problem, we propose an improved adaptive large neighborhood search algorithm (IALNS) for solving HALSFC. We evaluate both model functionalities and algorithm effectiveness using instances generated based on the real production data of company L. Extensive experimental results demonstrate the effectiveness and efficiency of IALNS compared to MILP, Tabu search algorithm (TS) and genetic algorithm (GA), especially for medium- and large-scale instances. Additionally, the sensitivity analysis of the quality inspection time, the related work orders proportion and the minimum cooling time of fixtures is also conducted.

Purpose – This paper reports findings of up-to-date insights to fill the knowledge gap of lack of theoretical and practical understandings of how knowledge is utilized in medium-sized enterprises (MEs) for ensuring their performance excellence, healthy survival and growth, particularly using the contextual background of quality improvement as the standing point to concretize the research content and research participants’ mind-set for data collection. 

Design/methodology/approach – The empirical data were attained by conducting firstly an multiple-case study and thereafter a structured interview. Insights were obtained through analysing the collected data as well as triangulating the findings with the contention from the extant literature where available.

Findings – A set of approaches for effective quality improvement knowledge (QIK) utilization in MEs have been identified and attested as well as prioritised for a clear guidance on their application by practical businesses.

Originality/value – As a pioneering study on the particularly focused issue, namely a current knowledge gap – QIK utilization in MEs, theoretically the research contributes to the enrichment of the current KM and QI literature with a primary focus on knowledge utilization in MEs. Practically its findings provide insightful guidance to practice on the approaches of QIK utilization. 

This paper discusses the transmission mechanism and effects that trade liberalization influences energy intensity through energy‐saving technological change (ESTC). We construct panel data from manufacturing industries in China over the period of 1994–2017, thereby conducting empirical tests through mediation models and discontinuity regression models. The results find that trade liberalization significantly promotes the lowering of energy intensity through ESTC. Heterogeneity test results show that the trade liberalization effect is higher in industries with low barriers to entry and industries with high pollution. Fuzzy regression discontinuity (FRD) results show that energy intensity decreasing as China's accession to the World Trade Organization (WTO).

As a fundamental element of knowledge management (KM), knowledge identification is a crucial issue in contemporary business organisations. As evidenced by research, medium sized enterprises (MEs) contribute constructively and significantly to economic development, society stabilisation and employment increase. Their healthy survival and growth are of critical importance to a nation. Among the approaches ensuring the successful development of MEs, quality improvement (QI) is a crucial one. However, what is and how to identify the knowledge most relevant to the MEs’ QI, the drives and sources for identifying the QI knowledge (QIK) as well as the underpinning rationales, are currently lacking of sufficient exploration. A research focusing on these issues has been strongly emphasised by literature and attested by this research itself of its meaningfulness. Through analysing empirical data collected and attested by a combination of firstly semi-structured interview, focus group following a case study strategy and then a structured interview, this exploratory research has obtained and prioritised the up-to-date answers to these questions, leading to the enrichment of the theoretical understanding of KM approaches in operations with a consideration of
quality management. Real world MEs can rely on these findings as a guidance to obtain, select and apply appropriate QIK for their operations performance improvement. The findings can also be referential for knowledge identification and application in view of QI in other type business organisations. 

In recent years, the automotive industry has faced an unprecedented crisis. In particular, the zero-inventory approach, which has been widely pursued by many automobile companies, seems to be impractical in some real production contexts since it requires an inventory of all parts but in low amounts. In this paper, we investigate a new logistics method which collects automobile parts by integrating the progress-lane (P-LANE) into the corresponding vehicle routing problem. We propose a mixed integer programming formulation for this new model, which can simultaneously determines the trip routes to collect automobile parts, as well as the P-LANE that each collected part should be assigned to, so as to minimize the total costs of the production and inbound logistics. The comparison with the zero-inventory model shows that the use of the P-LANE within the milk-run system could significantly decrease the total costs and also improve the transportation efficiency. To be specific, for small and large size instances, the total costs of the zero-inventory model are about 10% and 30% higher than the ones with P-LANE, respectively, which suggests that the periodic part collection model with P-LANE could be more appropriate for automobile manufacturing.