A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly

How can smart technologies contribute to sustainable product lifecycle management?

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordThis Virtual Special Issue (VSI) was proposed on par with the fascinating and exponentially growing development of smart enabling technologies, such as Internet of Things (IoT), Cyber-Physical System (CPS), Cloud Computing (CC), Artificial Intelligence (AI), Big Data Analytics (BDA), Digital Twin (DT), etc, which have greatly advanced the development of sustainable smart manufacturing throughout the lifecycle. The VSI addressed issues that were not properly or even incorrectly addressed in the existing literature. The authors of this VSI sought to introduce new knowledge and debates to lead the research directions to new paths. The editorial team invited well-established researchers in this area and received about 40 highly qualified submissions, out of which 12 were accepted after standard peer-review procedure of the Journal of Cleaner Production, which covered the three main themes defined in the “Call-for-Papers”. The contributing authors were from Brazil, China, Finland, Pakistan, Sweden, USA (in alphabetical order). The coordinators of this VSI are confident that the contents of this VSI will advance the science of digitalisation and will help society to make real progress towards sustainable societies

Trends and topics in IJPR from 1961 to 2017:a statistical history

This paper studies the history of the International Journal of Production Research (IJPR) by analysing the topics that have received the most attention in each of the journal’s publication years. Text mining exposed for scrutiny the most frequently mentioned and cited terms contained in the titles, abstracts and keywords of IJPR papers. Analyses suggest that the triad of scheduling/optimisation/simulation and supply-chain-related topics have been IJPR’s mainstays, but valuable opportunities remain for relevant topics that have not yet been concurrently and frequently studied. Results also show that terms related to sustainability and risk management topics have gained recent relevance. In addition, IJPR appears to complement its modelling technique focus with empirical methodological approaches to provide a well-balanced perspective, since the ‘case study’ term is common. Finally, a linear relationship is found between the number of papers that have covered certain topics and the number of citations those topics have received, highlighting which topics had fewer or more citations than expected, given the number of papers that covered those topics. IJPR stands as one of the most prestigious and established journals in its field and the results from this study indicate the evolving interests of the field for over half a century