Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

Sustainable Inventory Management Model for High-Volume Material with Limited Storage Space under Stochastic Demand and Supply

Inventory management and control has become an important management function, which is vital in ensuring the efficiency and profitability of a company’s operations. Hence, several research studies attempted to develop models to be used to minimise the quantities of excess inventory, in order to reduce their associated costs without compromising both operational efficiency and customers’ needs. The Economic Order Quantity (EOQ) model is one of the most used of these models; however, this model has a number of limiting assumptions, which led to the development of a number of extensions for this model to increase its applicability to the modern-day business environment. Therefore, in this research study, a sustainable inventory management model is developed based on the EOQ concept to optimise the ordering and storage of large-volume inventory, which deteriorates over time, with limited storage space, such as steel, under stochastic demand, supply and backorders. Two control systems were developed and tested in this research study in order to select the most robust system: an open-loop system, based on direct control through which five different time series for each stochastic variable were generated, before an attempt to optimise the average profit was conducted; and a closed-loop system, which uses a neural network, depicting the different business and economic conditions associated with the steel manufacturing industry, to generate the optimal control parameters for each week across the entire planning horizon. A sensitivity analysis proved that the closed-loop neural network control system was more accurate in depicting real-life business conditions, and more robust in optimising the inventory management process for a large-volume, deteriorating item. Moreover, due to its advantages over other techniques, a meta-heuristic Particle Swarm Optimisation (PSO) algorithm was used to solve this model. This model is implemented throughout the research in the case of a steel manufacturing factory under different operational and extreme economic scenarios. As a result of the case study, the developed model proved its robustness and accuracy in managing the inventory of such a unique industry

Responsible Inventory Models for Operation and Logistics Management

The industrialization and the subsequent economic development occurred in the last century have led industrialized societies to pursue increasingly higher economic and financial goals, laying temporarily aside the safeguard of the environment and the defense of human health. However, over the last decade, modern societies have begun to reconsider the importance of social and environmental issues nearby the economic and financial goals. In the real industrial environment as well as in today research activities, new concepts have been introduced, such as sustainable development (SD), green supply chain and ergonomics of the workplace. The notion of “triple bottom line” (3BL) accounting has become increasingly important in industrial management over the last few years (Norman and MacDonald, 2004). The main idea behind the 3BL paradigm is that companies’ ultimate success should not be measured only by the traditional financial results, but also by their ethical and environmental performances. Social and environmental responsibility is essential because a healthy society cannot be achieved and maintained if the population is in poor health. The increasing interest in sustainable development spurs companies and researchers to treat operations management and logistics decisions as a whole by integrating economic, environmental, and social goals (Bouchery et al., 2012). Because of the wideness of the field under consideration, this Ph.D. thesis focuses on a restricted selection of topics, that is Inventory Management and in particular the Lot Sizing problem. The lot sizing problem is undoubtedly one of the most traditional operations management interests, so much so that the first research about lot sizing has been faced more than one century ago (Harris, 1913). The main objectives of this thesis are listed below: 1) The study and the detailed analysis of the existing literature concerning Inventory Management and Lot Sizing, supporting the management of production and logistics activities. In particular, this thesis aims to highlight the different factors and decision-making approaches behind the existing models in the literature. Moreover, it develops a conceptual framework identifying the associated sub-problems, the decision variables and the sources of sustainable achievement in the logistics decisions. The last part of the literature analysis outlines the requirements for future researches. 2) The development of new computational models supporting the Inventory Management and Sustainable Lot Sizing. As a result, an integrated methodological procedure has been developed by making a complete mathematical modeling of the Sustainable Lot Sizing problem. Such a method has been properly validated with data derived from real cases. 3) Understanding and applying the multi-objective optimization techniques, in order to analyze the economic, environmental and social impacts derived from choices concerning the supply, transport and management of incoming materials to a production system. 4) The analysis of the feasibility and convenience of governmental systems of incentives to promote the reduction of emissions owing to the procurement and storage of purchasing materials. A new method based on the multi-objective theory is presented by applying the models developed and by conducting a sensitivity analysis. This method is able to quantify the effectiveness of carbon reduction incentives on varying the input parameters of the problem. 5) Extending the method developed in the first part of the research for the “Single-buyer” case in a "multi-buyer" optics, by introducing the possibility of Horizontal Cooperation. A kind of cooperation among companies in different stages of the purchasing and transportation of raw materials and components on a global scale is the Haulage Sharing approach which is here taken into consideration in depth. This research was supported by a fruitful collaboration with Prof. Robert W. Grubbström (University of Linkoping, Sweden) and its aim has been from the beginning to make a breakthrough both in the theoretical basis concerning sustainable Lot Sizing, and in the subsequent practical application in today industrial contexts

A comparative life cycle assessment review of conventional pulverized coal-fired electricity generation and underground coal gasification (UCG) linked with an integrated gasification combined cycle (IGCC)

In a global climate where sustainable development is being prioritized for the benefit of current and future generations, it is necessary to make informed decisions about the type of technologies being deployed. Because coal plays such a significant role in the generation of electricity, understanding the impact it has on the environment is an important part of understanding energy and environmental issues in general. To this end, a Life Cycle Assessment (LCA) was performed on two methods of electricity generation which employ coal as a primary source of energy. This assessment focused on the impacts from the power plants such as the emissions, resource consumption, and energy use of all processes required for the power plant to operate, including any necessary waste disposal and material recycling. Two technologies were selected. A PCC plant which represents the average emissions and efficiencies of currently operating coal-fired power plants in the world, and an IGCC plant which uses combustible gas derived from an UCG plant, hence a UCG-IGCC plant. The results of the LCA suggest that UCG-IGCC technology is more sustainable than the conventional PCC technology. The results yielded significant reductions in environmental stressors related to air, water, resource consumption and waste used in the impact assessment. Ultimately, LCA can be seen to be an ideal integrated environmental management tool to facilitate decision making on competing technologies to be deployed by assessing them throughout their entire life cycles

Analyzing the Impacts of Policy Supports and Incentive Programs on Resource Management

Feedstock-based renewable fuels, and ecosystem restoration practices such as afforestation are long-term solutions to mitigating greenhouse gas (GHG) emissions. This dissertation aligns with assessing the effects of policy supports and voluntary incentive programs on renewable fuel production and forest-based carbon sequestration.Higher investment risks and novelty of the feedstock-based conversion technologies hinder large-scale deployment of renewable fuels at present. In the first essay, a two-stage stochastic model is employed to evaluate the impact of federal subsidies in designing a switchgrass-based bioethanol supply chain in west Tennessee wherein decisions driven by minimized expected and Conditional Value-at-Risk of system cost reflected the risk-neutral and risk-averse perspective of the biofuel sector, respectively. Major contribution of this study is the impact assessment of Biomass Crop Assistance Program (BCAP) on investment decisions (including land allocation) of a risk-sensitive biofuel industry under feedstock supply uncertainty.In the second essay, impacts of renewable jet fuel (RJF) production from switchgrass on farmland allocation, processing facility configuration, and GHG emissions are estimated in response to fulfilling the RJF demand at the Memphis International Airport in Tennessee. Importantly, a potential carbon market is used to explore the impact of hypothetical carbon credits on the GHG emissions reduction and net supply-chain welfare while addressing the economic motives of the supply-chain participants. Considering the attention paid by the Unites States aviation sector with respect to GHG emissions, this study highlights the importance of Renewable Identification Number (RIN) credits and tradable carbon credits in achieving the desired economic viability and emission abatement goals through a Stackelberg interaction between the feedstock suppliers and the feedstock processor.In the third essay, discriminatory-price auction and agent-based model are used to examine the cost-efficiency of cost-ranked and cost-benefit-ranked auction-based payment designs for forest-based carbon sequestration with varying degree of correlation between opportunity costs of afforestation and carbon sequestration capacities, when bidders learn in multi-round procurement auctions. Simulation outcomes are expected to guide decision makers in choosing an optimal payment design that ensures efficiency gains for auction-based payments compared to fixed-rate payments, and more importantly ensures minimal loss in cost-efficiency in a dynamic setting

Sustainable Assessment in Supply Chain and Infrastructure Management

In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management