Sustainable development is now the focus of researchers and organizations worldwide. Several concepts, such as reverse logistics (RLs) and closed-loop supply chains, have been introduced to encourage sustainability in supply chains. RLs refers to the set of activities needed by consumers to collect the product used for reuse, repair, remanufacturing, recycling, or disposal of the used product. There are various processes involved in RL, and one of them is collection systems. Collection refers to a company obtaining custody of specific items. We review the literature on RLs collection systems. A bibliometric analysis was conducted to provide better insight into the field and establish any trends. Firstly, we present the classification methods used in the field, based on available review papers. Secondly, we evaluate literature from several fields that are related to either the problem setting or the technical features. Different perspectives are presented and classified. This method facilitates the identification of manuscripts related to the reader’s specific interests. Throughout the literature review, trends in measuring the performance of collection systems are identified, and directions for future research are identified and presented.

The advancements in human lifestyle result in growth in daily demands of products, and accordingly, an increased rate of manufacturing. However, the resources on the planet Earth are limited, thus depleting day-by-day. More goods also contribute to more end-of-life (EOL) dumping or even before EOL in some cases. Therefore, an interest in remanufacturing has appeared, and it offers a solution that can solve or perhaps mitigate the risks of consuming more resources and increasing waste. Remanufacturing is a procedure of bringing used products to “like-new” functional status with a matching warranty. However, due to its relative novelty in terms of research field and industry, remanufacturing is poorly understood. People often mix it with other terms such as recycling, reconditioning, or repair. Therefore, in this research, the focus is on the remanufacturing systems’ definition, relevance, main phases, case studies, and solution methods proposed by various researchers. The word ‘remanufacturing’ is clearly described in this paper by differentiating it from alternative green manufacturing initiatives. Both qualitative and quantitative analysis of literature are performed. The quantitative analysis is conducted using a bibliometric method. For quantitative analysis, a systematic approach is utilized for research papers’ selection. The qualitative analysis has been carried out by discussing different aspects of remanufacturing and how the researchers are working on its different domains and phases. The review showed that researchers focused on some phases more as compared with others. Moreover, it is also revealed from the literature that the common solutions methods applied in this domain are optimization techniques. Future research directions are also identified and presented.

Machine failures cause adverse impact on operational efficiency of any manufacturing concern. Identification of such critical failures and examining their associations with other process parameters pose a challenge in a traditional manufacturing environment. This research study focuses on the analysis of critical failures and their associated interaction effects which are affecting the production activities. To improve the fault detection process more accurately and efficiently, a conceptual model towards a smart factory data analytics using cyber physical systems (CPS) and Industrial Internet of Things (IIoTs) is proposed. The research methodology is based on a fact‐driven statistical approach. Unlike other published work, this study has investigated the statistical relationships among different critical failures (factors) and their associated causes (cause of failures) which occurred due to material deficiency, production organization, and planning. A real business case is presented and the results which cause significant failure are illustrated. In addition, the proposed smart factory model will enable any manufacturing concern to predict critical failures in a production process and provide a real‐time process monitoring. The proposed model will enable creating an intelligent predictive failure control system which can be integrated with production devices to create an ambient intelligence environment and thus will provide a solution for a smart manufacturing process of the future.

Advances in additive manufacturing (AM) processes have increased the number of relevant applications in various industries. To keep up with this development, the process stability of AM processes should be monitored, which is conducted through the assessment of the outputs or product characteristics. However, the use of univariate control charts to monitor an AM process might lead to misleading results, as most additively manufactured products have more than one correlated quality characteristic (QC). This paper proposes a framework for monitoring the multivariate quality characteristics of AM processes, and the proposed framework was applied to monitor a fused filament fabrication (FFF) process. In particular, specimens were designed and produced using the FFF process, and their QCs were identified. Then, critical quality characteristic data were collected using a precise measurement system. Furthermore, we propose a transformation algorithm to ensure the normality of the collected data. After examining the correlations between the investigated quality characteristics, a multivariate exponential weighted moving average (MEWMA) control chart was used to monitor the stability of the process. Furthermore, the MEWMA parameters were optimized using a novel heuristic technique. The results indicate that the majority of the collected data are not normally distributed. Consequently, the efficacy of the proposed transformation technique is demonstrated. In addition, our findings illustrate the correlations between the QCs. It is worth noting that the MEWMA optimization results confirm that the considered AM process (i.e., FFF) is relatively stable.

Process capability indices (PCIs) have always been used to improve the quality of products and services. Traditional PCIs are based on the assumption that the data obtained from the quality characteristic (QC) under consideration are normally distributed. However, most data on manufacturing processes violate this assumption. Furthermore, the products and services of the manufacturing industry usually have more than one QC; these QCs are functionally correlated and, thus, should be evaluated together to evaluate the overall quality of a product. This study investigates and extends the existing multivariate non-normal PCIs. First, a multivariate non-normal PCI model from the literature is modeled and validated. An algorithm to generate non-normal multivariate data with the desired correlations is also modeled. Then, this model is extended using two different approaches that depend on the well-known Box–Cox and Johnson transformations. The skewness reduction is further improved by applying heuristics algorithms. These two approaches outperform the investigated model from the literature because they can provide more precise results regardless of the skewness type. The comparison is made based on the generated data and a case study from the literature.

Recently, several heuristics have been interested in scheduling problems, especially those that are difficult to solve via traditional methods, and these are called NP-hard problems. As a result, many methods have been proposed to solve the difficult scheduling problems; among those, effective methods are the tabu search algorithm (TS), which is characterized by its high ability to adapt to problems of the large size scale and ease of implementation and gives solution closest to the optimum, but even though those difficult problems are common in many industries, there are only a few numbers of previous studies interested in the scheduling of jobs on unrelated parallel machines. In this paper, a developed TS algorithm based on lower bound (LB) and exact algorithm (EA) solutions is proposed with the objective of minimizing the total completion time (makespan) of jobs on nonidentical parallel machines. The given solution via EA was suggested to enhance and assess the solution obtained from TS. Moreover, the LB algorithm was developed to evaluate the quality of the solution that is supposed to be obtained by the developed TS algorithm and, in addition, to reduce the period for searching for the optimal solution. Two numerical examples from previous studies from the literature have been solved using the developed TS algorithm. Findings show that the developed TS algorithm proved its superiority and speed in giving it the best solution compared to those solutions previously obtained from the literature.