This paper presents a literature review for inventory control of deteriorating items since 2018. A classification including 18 classes and 33 subclasses is offered to categorize inventory control models, constraints, and solution methods used in previous studies. Providing standard classes in this field, such as demand, deterioration, shortages, number of warehouses, and time value of money alongside new classes, for example, the type of model costs and supply chain, inventory constraints, number of supply chain levels, time horizon, lead time, considering multi-item models, preservation technology, financial conditions, non-instantaneous deteriorating items, environmental issues, and solution methods made this classification more comprehensive. A brief history and explanation are given to understand each class better, and related articles are grouped in these classes. The research gaps and a crucial aspect that paves the way for future research are presented in each category. A broad view of the future of this topic is provided, and exciting opportunities are highlighted for researchers to contribute to this field and inspire them to explore these potential areas of research. The potential for future research in this subject is vast and promising; this article offers numerous opportunities for researchers to make significant contributions. The results show that the best ways to extend this topic are using variable deterioration rates, costs, and demand functions, considering realistic assumptions, including allowable shortages with partial backlogging, two warehouses, inflation and discounts, preservation technology, uncertain lead time, and environmental issues. Developing cyclic (if possible), multi-item, and production models with financial conditions and various inventory constraints is an excellent way to develop existing models. Finally, solving the proposed models using exact methods to find the global answer is a great effort to contribute to this field.

This study examines the sustainability of Agricultural Trade Supply Chains (ATSC) from the perspective of agricultural sustainable development, with a focus on five key aspects: production, processing and storage, transportation and logistics, trade, and consumer and market. Based on an analysis of 756 academic papers published between 2013 and 2022, scientific metric techniques were used to identify changes in the field's sustainability. The study highlights opportunities for the development of a sustainable agricultural product trade supply chain, including the use of blockchain technology to improve transparency and traceability, increasing consumer demand for sustainable and eco-friendly products, corporate zero-deforestation commitments, bioenergy market expansion, and the role of farm advisors in promoting sustainable production methods. This paper contributes to a structured understanding of the status and future directions of ATSC sustainability, emphasizing the importance of joint efforts across all links of the supply chain to meet the sustainable needs of consumers and promote environmental protection and social-friendly markets.

Among the different tasks in production logistics, job scheduling is one of the most important at the operational decision-making level to enable organizations to achieve competiveness. Scheduling consists in the allocation of limited resources to activities over time in order to achieve one or more optimization objectives. Flow-shop (FS) scheduling problems encompass the sequencing processes in environments in which the activities or operations are performed in a serial flow. This type of configuration includes assembly lines and the chemical, electronic, food, and metallurgical industries, among others. Scheduling has been mostly investigated for the deterministic cases, in which all parameters are known in advance and do not vary over time. Nevertheless, in real-world situations, events are frequently subject to uncertainties that can affect the decision-making process. Thus, it is important to study scheduling and sequencing activities under uncertainties since they can cause infeasibilities and disturbances. The purpose of this paper is to provide a general overview of the FS scheduling problem under uncertainties and its role in production logistics and to draw up opportunities for further research. To this end, 100 papers about FS and flexible flow-shop scheduling problems published from 2001 to October 2016 were analyzed and classified. Trends in the reviewed literature are presented and finally some research opportunities in the field are proposed.

Six Sigma is recognized as an essential tool for continuous improvement of quality. A large num-ber of publications by various authors reflect the interest in this technique. Reviews of literature on Six Sigma have been done in the past by a few authors. However, considering the contributions in the recent times, a more comprehensive review is attempted here. The authors have examined vari-ous papers and have proposed a different scheme of classification. In addition, certain gaps that would provide hints for further research in Six Sigma have been identified. As a results the rela-tionship between Six Sigma, Design for Six Sigma (DFSS), and how these two concepts support the quality system for organizational learning and innovation performance have been discussed that would help researchers, academicians and practitioners to take a closer look at the growth, devel-opment and applicability of Six Sigma in Design.

Disruptive innovation has attracted much interest, discussion and argument in academia and industry since it was defined. Based on a review of the related research publications, this paper aims to clarify the concept of disruptive innovation on controversies, summarize ex-ante studies on project management for disruptive product innovation, and propose potential research areas of the disruptive product innovation. The ex-ante studies are classified into three aspects: timing and conditions of disruptive innovation, factors for the implementation of disruptive innovation, forecasting and implementation of disruptive innovation. After a review of the existing methods of disruptive innovation, the potential research of disruptive innovation is presented.

Scheduling plays an important role in service and manufacturing environments for the delivery of reliable products on time. The scheduling literature reveals that the vast majority of the investigated scheduling problems are for the deterministic case where all parameters of jobs are known in advance and are fixed. However, in some real-world environments, the assumption of fixed parameters of jobs is not valid since job parameters are uncertain. An uncertain parameter can be modelled as having a probability distribution, or it can be modelled as a fuzzy number, or it can be modelled as a random variable within some interval with lower and upper bounds, distribution free. If the uncertain parameter, e.g., processing time, is modelled as a random variable within some lower or upper bounds, it is called interval or bounded processing time. The objective of this paper is to survey the investigated scheduling problems with interval or bounded processing/setup times. The scheduling literature is reviewed, the addressed problems are analyzed, and classified based on shop environments (single machine, parallel machine, flowshop, job shop), performance measures, the approach taken in the papers to solve the considered problem, and interval/bounded processing times or setup times. Some future research opportunities with interval/bounded processing/setup times are presented.

Nowadays the use of fossil fuels as a non-renewable energy source has become a major challenge because of the pollution and the environmental impact. Substitution of biomass as an energy source and its supply chain design is the main question. Because of difficulties such as supply chain complexity, uncertainty in variables and selecting the site of bio-refineries many studies have been conducted in this regard. Studies in the field of biomass and biofuel production are described and classified. Also the strategic decisions such as choosing the sites, selecting energy conversation technology, ensuring economic, environmental, technical, and social sustainability and tactical decisions including allocating resources to productive plants, selecting transport modes, and types of warehouses are addressed in the reviewed papers. We have reviewed 140 papers in the interval between 1997 and 2016 and classified them based on the objective functions. The articles are classified based on their being single or multi objectivity, linearity or nonlinearity. Finally, a classification based on the regions in which the studies have been done.

Due to the increasing technological advances in all fields, a considerable amount of data has been collected to be processed for different purposes. Data mining is the process of determining and an-alyzing hidden information from different perspectives to obtain useful knowledge. Data mining can have many various applications, one of them is in medical diagnosis. Today, many diseases are regarded as dangerous and deadly. Heart disease, breast cancer, and diabetes are among the most dangerous ones. This paper investigates 168 articles associated with the implementation of data mining for diagnosing such diseases. The study concentrates on 85 selected papers which have received more attention between 1997 and 2018. All algorithms, data mining models, and evaluation methods are thoroughly reviewed with special consideration. The study attempts to determine the most efficient data mining methods used for medical diagnosing purposes. Also, one of the other significant results of this study is the detection of research gaps in the application of data mining in health care.

The project scheduling problem is both practically and theoretically of paramount importance. From the practical perspective, improvement of project scheduling as a critical part of project management process can lead to successful project completion and significantly decrease of the relevant costs. From the theoretical perspective, project scheduling is regarded as one of the in-teresting optimization issues, which has attracted the attention of many researchers in the area of operations research. Therefore, the project scheduling issue has been significantly evaluated over time and has been developed from various aspects. In this research, the topics related to Re-source-Constrained Project Scheduling Problem (RCPSP) are reviewed, recent developments in this field are evaluated, and the results are presented for future studies. In this regard, first, the standard problem of RCPSP is expressed and related developments are presented from four as-pects of resources, characteristics of activities, type of objective functions, and availability level of information. Following that, details about 216 articles conducted on RCPSP during 1980-2017 are expressed. At the end, in line with the statistics obtained from the evaluation of previ-ous articles, suggestions are made for the future studies in order to help the development of new issues in this area.

Meta-heuristic approaches are general algorithmic framework, often nature-inspired and designed to solve NP-complete optimization problems in cellular manufacturing systems and has been a growing research area for the past two decades. This paper discusses various meta-heuristic techniques such as evolutionary approach, Ant colony optimization, simulated annealing, Tabu search and other recent approaches, and their applications to the vicinity of group technology/cell formation (GT/CF) problem in cellular manufacturing. The nobility of this paper is to incorporate various prevailing issues, open problems of meta-heuristic approaches, its usage, comparison, hybridization and its scope of future research in the aforesaid area.