Abstract The Resource-Constrained Project Scheduling Problem (RCPSP) is a general problem in scheduling that has a wide variety of applications in manufacturing, production planning, project management, and various other areas. The RCPSP has been studied since the 1960s and is an NP-hard problem. As being an NP-hard problem, solution methods are primarily heuristics. Over the last two decades, the increasing interest in operations research for metaheuristics has resulted in a general tendency of moving from pure metaheuristic methods for solving the RCPSP to hybrid methods that rely on different metaheuristic strategies. The purpose of this paper is to survey these hybrid approaches. For the primary hybrid metaheuristics that have been proposed to solve the RCPSP over the last two decades, a description of the basic principles of the hybrid metaheuristics is given, followed by a comparison of the results of the different hybrids on the well-known PSPLIB data instances. The distinguishing features of the best hybrids are also discussed.
Abstract The involvement of corporate social responsibility (CSR) in sustainable development (SD) is becoming a popular topic on research and business domain. However, the co-themed research is still rather new and hasn’t been fully studied. An in-depth bibliometric analysis using the ‘CiteSpace’ software is applied to analyze and visualize the knowledge map of the CSR research related to SD. Main findings show that the CSR involvement in SD is a lasting but recent prosperous research topic. The top 3 influential journals in this area are Corporate Social Responsibility and Environment Management; Sustainability; and Journal of Cleaner Production. Porter ME, Carroll AB, etc., are the most impactful authors. The co-author network is fragmented, while cross-national co-operations occur in groups. 11 clusters are identified to be highly concerned, among which, “stakeholder” and “NGO” are long lasting till now. 13 burst terms has changed over 15 years (2005–2019) indicated the research frontiers evolution in this field, with the earliest “sustainability” to “strategy”, “performance” and then “stakeholder”, “developing country”, “disclosure” and “supply chain management”, etc., and “climate change” being the newest but strongest. Four stages of the evolution can be identified: initial phase (1997–2004), debating phase (2005–2009), rapid developing phase (2010–2013), and research specialization phase (2014–2019). Finally, contributions, limitations and further research directions are discussed.
Most cropland in the United States is characterized by large monocultures, whose productivity is maintained through a strong reliance on costly tillage, external fertilizers, and pesticides (Schipanski et al., 2016). Despite this, farmers have developed a regenerative model of farm production that promotes soil health and biodiversity, while producing nutrient-dense farm products profitably. Little work has focused on the relative costs and benefits of novel regenerative farming operations, which necessitates studying in situ, farmer-defined best management practices. Here, we evaluate the relative effects of regenerative and conventional corn production systems on pest management services, soil conservation, and farmer profitability and productivity throughout the Northern Plains of the United States. Regenerative farming systems provided greater ecosystem services and profitability for farmers than an input-intensive model of corn production. Pests were 10-fold more abundant in insecticide-treated corn fields than on insecticide-free regenerative farms, indicating that farmers who proactively design pest-resilient food systems outperform farmers that react to pests chemically. Regenerative fields had 29% lower grain production but 78% higher profits over traditional corn production systems. Profit was positively correlated with the particulate organic matter of the soil, not yield. These results provide the basis for dialogue on ecologically based farming systems that could be used to simultaneously produce food while conserving our natural resource base: two factors that are pitted against one another in simplified food production systems. To attain this requires a systems-level shift on the farm; simply applying individual regenerative practices within the current production model will not likely produce the documented results.
Pablo Pérez-Gosende, Josefa Mula, Manuel Díaz-Madroñero
ABSTRACT Facility layout planning (FLP) involves a set of design problems related to the arrangement of the elements that shape industrial production systems in a physical space. The fact that they are considered one of the most important design decisions as part of business operation strategies, and their proven repercussion on production systems’ operation costs, efficiency and productivity, mean that this theme has been widely addressed in science. In this context, the present article offers a scientific literature review about FLP from the operations management perspective. The 232 reviewed articles were classified as a large taxonomy based on type of problem, approach and planning stage and characteristics of production facilities by configuring the material handling system and methods to generate and assess layout alternatives. We stress that the generation of layout alternatives was done mainly using mathematical optimisation models, specifically discrete quadratic programming models for similar sized departments, or continuous linear and non-linear mixed integer programming models for different sized departments. Other approaches followed to generate layout alternatives were expert’s knowledge and specialised software packages. Generally speaking, the most frequent solution algorithms were metaheuristics.
Davidov Ivana, Stevančević Ognjen, Božić Aleksandar
et al.
The primary objective of this study was to assess the potential economic losses incurred by pig farms as a result of respiratory diseases. These diseases, often arising from the combined effects of viral and bacterial pathogens, can impose significant financial burdens and compromise pig health. The adverse effects of respiratory diseases extend beyond animal health, directly diminishing farm profitability. The research was conducted on five commercial farrow-to-finish pig farms located in the South Bačka District, with sow herd sizes ranging from 300 to 1,150. A comprehensive questionnaire was administered to collect data on farm management practices, health protocols, and production performance. Respiratory diseases in pigs represent a major source of economic losses, undermining both animal welfare and the financial viability of farms. Implementing effective management and preventive measures is essential to mitigating these losses and sustaining the health and productivity of pig operations.
The article considers the issues of automation of the process of data aggregation of the production and logistics system of an enterprise of rocket and space industry for simulation modeling of production processes of manufacturing wax models for casting. Potential problems of data collection from various information systems and data compatibility are analyzed. The analysis of the data required for simulation modeling is carried out. Based on the analysis, the information systems of the production and logistics system that act as a data source for each parameter are determined and the main groups of data are identified, such as: duration of technological operations, warehouse parameters and production plan for the release of castings. An experiment is carried out in the AnyLogic simulation system using the generated data based on the aggregation of data of the production and logistics system. The use of the aggregation approach reduces the time for collecting and combining data from various sources due to the automation of this process. It is proposed to use the developed integration platform, which allows implementing the processes of collecting, storing and processing data of production and logistics systems for their subsequent analysis. The key advantage of the developed integration platform is its scalability, which allows the system to be adapted to expand the supported information management systems of production and logistics systems.
[Significance] To provide a reference for advancing high-quality agricultural production driven by data, this paper focuses on the issues of inconsistent acquisition standards, incomplete data collection, and ambiguous governance mechanisms in China's agricultural production data, examines existing governance models for agricultural production big data, and clarifies the technical pathways for realizing the value of data elements through the integrated and innovative application of key big data governance technologies and tools in practical scenarios. [Progress] From the perspective of agricultural production big data governance, this paper explores 17 types of big data governance technologies and tools across six core processes: Data acquisition and processing, data storage and exchange, data management, data analysis, large models, and data security guarantee. It conducts in-depth research on the application methods of big data governance technologies in agricultural production, revealing that: Remote sensing, unmanned aerial vehicle(UAV), Internet of Things (IoT), and terminal data acquisition and processing systems are already reatively mature; data storage and exchange system are developing rapidly, data management technologies remain in the initial stage; data analysis technologies have been widely applied; large model technology systems have taken initially shape; and data security assurance systems are gradually being into parctice. The above technologies are effectively applied in scenarios through tools and middleware such as data matching, computing power matching, network adaptation, model matching, scenario matching, and business configuration. This paper also analyzes the data governance throughout the entire agricultural production chain, including pre-production, in-production, and post-production, stages, as well as service cases involving different types of agricultural parks, research institutes and universities, production entities, and farmers. It demonstrates that sound data governance can provide sufficient planning and input analysis prior to production, helping planting entities in making rational plans. In production, it can provide data-driven guidance for key scenarios such as agricultural machinery operations and agricultural technical services, thereby fully supporting decision-making in the production process; and based on massive data, it can achieve reliable results in yield assessment and production benefit evaluation. Additionally, the paper introduces governance experience from national-level industrial parks, provincial-level agricultural science and technology parks, and some single-product entities, and investigates domestic and international technologies, practices, and tools related to agricultural production big data governance, indicating that there is a need to break through the business chains and service model of agricultural production across regions, themes, and scenarios. [Conclusions and Prospects] This paper presents insights into the future development directions of agricultural production big data governance, encompassing the promotion of standard formulation and implementation for agricultural production big data governance, the establishment of a universal resource pool for such governance, the expansion of diversified application scenarios, adaptation to the new paradigm of large-model- and massive-data-driven agricultural production big data governance, and the enhancement of security and privacy protection for agricultural production big data.
We postulate that the study of diversity, equity, and inclusion can deepen and add relevance to research in operations management (OM). Specifically, the role of gender is little studied in the existing OM literature—to the detriment of the field. This article considers OM issues by employing the theories, data, and topics from the field of Women's Studies. Our findings indicate that incorporating viewpoints from Women's Studies can change what is considered research, improve the accuracy of OM research, extend existing studies in the field through new parameters, and open new areas of inquiry.
This article describes the rapid upward march to research excellence of the Operations Management Department in the Naveen Jindal School of Management from 1996 to 2023. It reflects along the way on the internal strategy, as well as the institutional and external contributing factors that led to its phenomenal growth.
This study discusses how the tremendous volume of available data collected by firms has been transforming the service industry. The focus is primarily on services in the following sectors: finance/banking, transportation and hospitality, and online platforms (e.g., subscription services, online advertising, and online dating). We report anecdotal evidence borrowed from various collaborations and discussions with executives and data analysts who work in management consulting or finance, or for technology/startup companies. Our main goals are (i) to present an overview of how big data is shaping the service industry, (ii) to describe several mechanisms used in the service industry that leverage the potential information hidden in big data, and (iii) to point out some of the pitfalls and risks incurred. On one hand, collecting and storing large amounts of data on customers and on past transactions can help firms improve the quality of their services. For example, firms can now customize their services to unprecedented levels of granularity, which enables the firms to offer targeted personalized offers (sometimes, even in real‐time). On the other hand, collecting this data may allow some firms to utilize the data against their customers by charging them higher prices. Furthermore, data‐driven algorithms may often be biased toward illicit discrimination. The availability of data on sensitive personal information may also attract hackers and gives rise to important cybersecurity concerns (e.g., information leakage, fraud, and identity theft).
We describe the process used to identify the ten most influential papers published in the Production and Operations Management ( POM) journal in its first thirty years. We also briefly discuss and highlight the selected papers.
Production costs are always the top concern of company managers in improving production and business efficiency. The cost of energy is one of the major costs that manufacturing companies must pay. This research paper proposes a Hybrid Six Sigma method based on fuzzy Multi-Attribute Decision Making (MADM), Industry 4.0, and digital numerical control (DNC). A fuzzy MADM method to select problems to improve and build an Industry 4.0 system with Internet of Things (IoT) devices, calling for automatic machining programs using Radio Frequency Identification (RFID) systems and management. Manage production equipment maintenance system using a digital numerical control (DNC) system. Measuring industry 4.0 system user satisfaction in manufacturing using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results of research on applying industry 4.0 techniques to the induction heat treatment process eliminate the dependence on worker skills and simplify the operation of the induction heat treatment process. Improve employee satisfaction with process operating conditions. Reduce the cost of electrical energy arising due to the coil maintenance system by applying the Industry 4.0 system. The result after the improvement is that the defect rate decreased from 47.2% to 4.9%. In terms of money, the reduction in losses due to defects is reduced from 6,593 USD per year to 549 USD per year. This research paper builds a sample continuous improvement model to apply to other production processes at other manufacturing companies in terms of applying industry 4.0 systems with IoT devices such as RFID and barcode readers in operations. automatically call the machining program of the machining machine and build an autonomous and preventive maintenance system using the industry 4.0 system to make improvements in process automation, smart data management, and analytics, using Internet of Things (IoT) to connect devices in the production process create a flexible production process.
Purpose: This study aims to investigate a supply chain problem of operating room consumable items that are not reused after consumption. In this supply chain, maximizing the satisfaction of the surgeons and minimizing the total costs are considered. Also, due to the importance of choosing suppliers from the surgeons' point of view, it is possible to prioritize suppliers based on criteria such as quality and cost. Furthermore, to get closer to real-world situations, uncertain demands of patients due to their physical conditions and various diseases, the capacities of the pharmacy, operating rooms, and the sterile core used for sterilizing the non-sterile items have been considered. The scope of this research includes different operating rooms, and the initially required number of consumable items according to the opinion of the surgeon. If an emergency occurs during the operation (such as sudden bleeding, item failure, or operating room personnel error) and the patient needs more items, the nurse goes to the hospital pharmacy to get the necessary items and brings them to the operating room, during the operation.
Design/methodology/approach: In this research, due to the uncertain demand for consumable items in the operating room, three pessimistic, probable, and optimistic scenarios have been used; and due to the discreteness and uncertainty of the data distribution, Mulvey's robust method has been applied. The problem has been solved in two phases. In the first phase, the additive ratio assessment (ARAS) multi-criteria decision-making method has been used to prioritize suppliers, and in the second phase, according to the size of the problem, the epsilon-constraint method, for the small-sized problem, and Non-dominated Sorting Genetic Algorithms (NSGA-II) and Multi-Objective Harmony Search (MOHS) for large-sized problems have been used to minimize the total costs of the supply chain, and maximize surgeons’ satisfaction. In addition, to set the parameters of both meta-heuristic algorithms, the Taguchi method, which is one of the most well-known parameter-setting methods, has been used.
Findings: To compare exact and metaheuristic algorithms, 10 examples were designed randomly. The comparisons showed that the results of the epsilon-constraint method were better than the meta-heuristic algorithms but it could only solve small-sized problems, and it required more time as a sensitive influencing factor in operating room planning. Also, to analyze the NSGA-II and MOHS algorithms, the obtained results were examined from the perspective of solution time, Number of Pareto Solutions (NPS), Mean Ideal Distance (MID), Diversification Metric (DM), and Spacing Measure (SM) indicators. They were also compared with each other using statistical hypothesis tests. The results showed that such algorithms had a significant difference from the point of view of the NPS and DM indicators at the significance level of 0.05, but they did not differ much in terms of the other two indicators. However, in terms of solution time, the MOHS was more suitable than the NSGA-II algorithm.
Research limitations/implications: One of the limitations of this research is the collection of real-world data, especially in estimating the demand for each item according to different conditions.
Practical implications: Comparing the NSGA-II and MOHS algorithms using different indicators, especially solution time which is significant for operating room planning, MOHS algorithms were better than the NSGA-II.
Social implications: Using the proposed algorithms, hospital managers can reduce total costs, guarantee the quality of consumable operating room items, and increase the satisfaction of the surgeon, who is in charge of providing better services to the patients.
Originality/value: In this paper, two meta-heuristic algorithms were proposed for non-deterministic supply chain planning for consumable operating room items, considering surgeon satisfaction and cost, and their efficiencies were compared with each other. The two-mentioned algorithms have not been used in previous studies. Both academic researchers and hospital managers can benefit from applying the findings of this study.
Management. Industrial management, Production management. Operations management
This publication discusses the types of hurricane damage that can be incurred in blueberry production operations, the impacts of these damages, preparations to make before the storm arrives, and suggestions on best management practices in the aftermath of a storm.