Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method

Insight into the Sustainable Integration of Bio- and Petroleum Refineries for the Production of Fuels and Chemicals.

A petroleum refinery heavily depends on crude oil as its main feedstock to produce liquid fuels and chemicals. In the long term, this unyielding dependency is threatened by the depletion of the crude oil reserve. However, in the short term, its price highly fluctuates due to various factors, such as regional and global security instability causing additional complexity on refinery production planning. The petroleum refining industries are also drawing criticism and pressure due to their direct and indirect impacts on the environment. The exhaust gas emission of automobiles apart from the industrial and power plant emission has been viewed as the cause of global warming. In this sense, there is a need for a feasible, sustainable, and environmentally friendly generation process of fuels and chemicals. The attention turns to the utilization of biomass as a potential feedstock to produce substitutes for petroleum-derived fuels and building blocks for biochemicals. Biomass is abundant and currently is still low in utilization. The biorefinery, a facility to convert biomass into biofuels and biochemicals, is still lacking in competitiveness to a petroleum refinery. An attractive solution that addresses both is by the integration of bio- and petroleum refineries. In this context, the right decision making in the process selection and technologies can lower the investment and operational costs and assure optimum yield. Process optimization based on mathematical programming has been extensively used to conduct techno-economic and sustainability analysis for bio-, petroleum, and the integration of both refineries. This paper provides insights into the context of crude oil and biomass as potential refinery feedstocks. The current optimization status of either bio- or petroleum refineries and their integration is reviewed with the focus on the methods to solve the multi-objective optimization problems. Internal and external uncertain parameters are important aspects in process optimization. The nature of these uncertain parameters and their representation methods in process optimization are also discussed

Global supply chains of high value low volume products

Imperial Users onl

Design of biomass value chains that are synergistic with the food-energy-water nexus: strategies and opportunities

Humanity’s future sustainable supply of energy, fuels and materials is aiming towards renewable sources such as biomass. Several studies on biomass value chains (BVCs) have demonstrated the feasibility of biomass in replacing fossil fuels. However, many of the activities along the chain can disrupt the food–energy–water (FEW) nexus given that these resource systems have been ever more interlinked due to increased global population and urbanisation. Essentially, the design of BVCs has to integrate the systems-thinking approach of the FEW nexus; such that, existing concerns on food, water and energy security, as well as the interactions of the BVCs with the nexus, can be incorporated in future policies. To date, there has been little to no literature that captures the synergistic opportunities between BVCs and the FEW nexus. This paper presents the first survey of process systems engineering approaches for the design of BVCs, focusing on whether and how these approaches considered synergies with the FEW nexus. Among the surveyed mathematical models, the approaches include multi-stage supply chain, temporal and spatial integration, multi-objective optimisation and uncertainty-based risk management. Although the majority of current studies are more focused on the economic impacts of BVCs, the mathematical tools can be remarkably useful in addressing critical sustainability issues in BVCs. Thus, future research directions must capture the details of food–energy–water interactions with the BVCs, together with the development of more insightful multi-scale, multi-stage, multi-objective and uncertainty-based approaches

Agribusiness supply chain risk management: A review of quantitative decision models

Supply chain risk management is a large and growing field of research. However, within this field, mathematical models for agricultural products have received relatively little attention. This is somewhat surprising as risk management is even more important for agricultural supply chains due to challenges associated with seasonality, supply spikes, long supply lead-times, and perishability. This paper carries out a thorough review of the relatively limited literature on quantitative risk management models for agricultural supply chains. Specifically, we identify robustness and resilience as two key techniques for managing risk. Since these terms are not used consistently in the literature, we propose clear definitions and metrics for these terms; we then use these definitions to classify the agricultural supply chain risk management literature. Implications are given for both practice and future research on agricultural supply chain risk management

Uncertainty Models in Reverse Supply Chain: A Review

Reverse logistic has become an important topic for the organization due to growing environmental concern, government regulation, economic value, and sustainable competitiveness. Uncertainty is one of the key factors in the reverse supply chain that must be controlled; thus, the company could optimize the reverse supply chain function. This paper discusses progress in reverse logistic research. A total of 72 published articles were selected, analyzed, categorized and the research gaps were found among them. The study began by analyzed previous research articles in reverse logistic. In this stage, we also collected and reviewed journals discussing about the reverse supply chain. Meanwhile, the result of this stage shows that uncertainty factor has not been reviewed in detail. The most common theme as the background research in reverse logistic is environmental and economic aspect. Uncertainty in Close Loop Supply Chain is the most widely used approach, followed by the usage on reverse logistics, reverse supply chain and reverse Model. The most used approach and method on uncertainty are Mixed Integer Linear Programing, mixed integer nonlinear Programing, Robust Fuzzy Stochastic Programming, and Improved kriging-assisted robust optimization method. Customer demand, total cost, product returns are the most widely researched aspects. This paper may be useful for academicians, researchers and practitioners in learning on reverse logistic and reverse supply chain; therefore, close loop supply chain can be guidance for upcoming researches. Research opportunity based on this research combines total cost, quality return product, truck capacity, delivery route, remanufacturing capacity, and facility location got optimum function in uncertainty. The research method and approach for MINLP, IK-MRO and RSFP provide many opportunities for research. For theme and area in reverse logistic, close loop supply chain is the theme that provides the most research opportunities

Decision making under uncertainties for renewable energy and precision agriculture

In this dissertation, mathematical programming models and statistical analysis tools have been formulated and designed to study the strategic and optimal solutions to allocate the resources and manage the risk for the renewable energy and precision agriculture. The dissertation, which consists of four papers, lies at the interface of optimization, simulation, and statistical analysis, with a focus on decision making under uncertainty for biofuel process design, renewable energy supply chain management and precision agriculture. Bio-oil gasification which integrates fast pyrolysis and gasification processes is a relative new conversion technology and this integrated biofuel production pathway has been promoted to take advantage of economies of scale and logistic efficiency. The design of the supply chain networks, especially under uncertainties, is one of the most important decisions faced by the biofuel industry. In the first paper, we proposed a two-stage stochastic programming framework for the biofuel supply chain optimization problem considering uncertainties, including biomass supply availability, technology advancement, and biofuel market price. The results show that the stochastic factors have significant impacts on the decision on fast pyrolysis plant locations, especially when there is insufficient biomass. Also, farmers\u27 participation can have a significant impact on the profitability and robustness of this supply chain design. Another major challenge faced by the cellulosic biofuel industry is that investors are hesitant to take the risk to construct commercial scale production facilities. Techno- economic analysis (TEA) has been widely adopted to overcome this challenge. The optimal facility locations and capacities as well as the logistic flow decisions for biomass supply and biofuel distribution should be incorporated into techno-economic analysis as well. In the second paper, the author aims to provide a new method that integrated the supply chain design into the techno-economic analysis as well by evaluating the economic feasibility of an integrated pathway on biomass pyrolysis and bio-oil gasification. The results indicate that hybrid fast pyrolysis and bio-oil gasification pathway is more suitable for a decentralized supply chain structure while biomass gasification pathway is more suitable for a single centralized facility supply chain structure. Feeding millions of people throughout the world who face hunger every day is a formidable challenge. Precision agriculture has attracted increasing attention in the community of farmland management. Farmland management involves a sequence of planning and decision-making processes, including seed selection and irrigation schedule. In the third paper, a mixed integer programming optimization model is proposed to provide decision support on seed selection and irrigation water allocation for customized precision farmland management. The results show that significant increase of farmers’ annual profit can be achieved by carefully choosing irrigation schedule and type of seed. The proposed model can also serve as a risk analysis tool for farmers facing seasonal irrigation water limits as well as a quantitative tool to explore the impact of precision agriculture. The effect of limited water on corn grain yield is significant and management decisions are essential to optimize farmers’ profits, particularly under stochastic environment. The fourth paper takes uncertainties such as crop price, irrigation water availability and precipitation amount into consideration. A multi-stage stochastic programming is formulated to evaluate the effects of structure of decision making process on farmers’ income. The case study results indicate multi-stage stochastic programming is a promising way for farmland management under uncertainties and can increase farmers’ income significantly. In order to enhance the data utilization and results interpretation, statistical methods such as Monte-Carlo simulation considering parameter interactions, linear regression analysis, and moment matching method for scenario generation are also applied. The overarching goals of this dissertation is to quantify and manage the uncertainties along the modeling process and provide proper mechanisms that lead to optimal decisions. The outcomes of the research have the potential to accelerate the commercialization of second generation of biofuel and lead to sustainable utilization of water resources. The insights derived from the research contributed to the decision making process under uncertainties

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM