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A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries
Nowadays, the intensification of a competitive environment in markets in conjunction with sustainability issues has forced organizations to concentrate on designing sustainable closed-loop supply chains. In this study, a sustainable closed-loop supply chain network is configured under uncertain conditions based on fuzzy theory. The proposed network is a multi-product multi-period problem which is formulated by a bi-objective mixed-integer linear programming model with fuzzy demand and return rate. The objectives are to maximize the supply chain profit and customer satisfaction at the same time. Moreover, the carbon footprint is included in the first objective function in terms of cost (tax) to affect the total profit and treat the environmental aspect. Fuzzy linear programming and Lp-metric method are then applied to deal with the uncertainty and bi-objectiveness of the model, respectively. In order to validate the methodology, a case study problem in the dairy industry is investigated where the proposed Lp-metric is also compared to goal attainment method. The obtained results demonstrate the superiority of Lp-metric against goal attainment method as well as the applicability and efficiency of the proposed methodology to treat a real case study problem. Furthermore, from the management perspective, outsourcing the production during high-demand periods is highly recommended as an efficient solution.
Journal Article
Digital input requirements for global carbon emission reduction
To answer the question of whether the growth of digital inputs can be beneficial for carbon neutrality, we thoroughly explore the impacts of digital inputs on carbon emission reduction in this work. We propose a combined framework of panel regression model and multi-objective optimization model to identify the key digital sectors and obtain their optimal total outputs. First, the results show that digital inputs continue to increase in most countries (regions) from 2000 to 2021, especially in the USA, EU countries and China. Digital equipment inputs in China are the most significant, while digital service inputs in the USA and EU countries are relatively important. Second, the regression results show that digital service inputs have significantly negative influence on carbon emissions, which means that the growth of digital service inputs will decrease carbon emissions. This result indicates that the key point of industrial digitalization for carbon emission reduction may be increasing the digital service inputs. Third, the optimization results show that the digital-input-oriented optimization model, which encourages an increase in digital service inputs, could achieve greater targets of economic growth and carbon emission reduction. The total outputs of Telecommunication Services and Computer Services should increase globally by 10.24% and 8.89%, respectively.
Journal Article
Robust optimization model of anti-epidemic supply chain under technological innovation: learning from COVID-19
The anti-epidemic supply chain plays an important role in the prevention and control of the COVID-19 pandemic. Prior research has focused on studying the facility location, inventory management, and route optimization of the supply chain by using certain parameters and models. Nevertheless, uncertainty, as a vital influence factor, greatly affects the supply chain. As such, the uncertainty that comes with technological innovation has a heightened influence on the supply chain. Few studies have explicitly investigated the influence of technological innovation on the anti-epidemic supply chain under the COVID-19 pandemic. Hence, the current research aims to investigate the influences of the uncertainty caused by technological innovation on the supply chain from demand and supply, shortage penalty, and budget. This paper presents a three-level model of the anti-epidemic supply chain under technological innovation and employs an interval data robust optimization to tackle the uncertainties of the model. The findings are obtained as follows. Firstly, the shortage penalty will increase the costs of the objective function but effectively improve demand satisfaction. Secondly, if the shortage penalty is sufficiently large, the minimum demand satisfaction rate can ensure a fair distribution of materials among the affected areas. Thirdly, technological innovation can reduce costs. The technological innovation related to the transportation costs of the anti-epidemic material distribution center has a greater influence on the optimal value. Meanwhile, the technological innovation related to the transportation costs of the supplier has the least influence. Fourthly, both supply and demand uncertainty can influence costs, but demand uncertainty has a greater influence. Fifthly, the multi-scenario budgeting approach can decrease the calculation complexity. These findings provide theoretical support for anti-epidemic dispatchers to adjust the conservativeness of uncertain parameters under the influence of technological innovation.
Journal Article
Metaheuristics for maritime operations
'Metaheuristic Algorithms in Maritime Operations' focuses on the seaside and port side problems regarding the maritime transportation. The book reviews and introduces the most important problems regarding the shipping network design, long-term and short-term scheduling and planning problems in both bulk and container shipping as well as liquid maritime transportation. Application of meta heuristic algorithm is important for these problems, as most of them are hard and time-consuming to be solved optimally.
Book
How megacities can achieve carbon peak through structural adjustments: an input–output perspective
There is still a huge gap between the emissions pathways of megacities and the pathways to meeting the targets set by the Paris agreement. Compared with technological emission reductions, structural emission reduction can provide cities with more stable and sustainable carbon-peaking solutions. This study constructs a scenario-based input–output optimization model, adopting a novel carbon emission accounting method for purchased electricity that considers shared responsibility, and systematically evaluates the decarbonization paths of megacities and their impacts on economic growth, energy consumption, and carbon emissions. The results show that (a) through industry substitution and manufacturing restructuring, Shenzhen is projected to peak at 57.68 MtCO2 emissions in 2026, with a 10.57% energy and a 19.55% carbon reduction by 2030. (b) Shenzhen can achieve its carbon emission peak target through the energy transition while accepting a loss of 0.97%–3.23% of GDP, requiring the maximum economic concession of 16.45% from the transportation sector (S10) in the early stage of transformation, while 12.24% from the extractive industry (S2) in the later stage. (c) The comprehensive structure adjustment proved to be more effective than other mitigation approaches, capable of achieving high-quality economic growth of 6.4% during the study period while reaching a peak target of 53.55 million tons of CO2 by 2026. (d) The emission reduction effect of the power sector was the most significant among all the scenarios, with emission reduction rates between 6.26% and 35.63%, and the cumulative emission reduction potential reached 38.1–110.6 MtCO2. The priority for emission reduction in the power sector is the coal phase-out plan, which is essential for achieving these significant reductions. This study provides an important reference for megacities facing similar challenges, especially those in developing countries, to achieve a stable and sustainable carbon peak pathway through structural adjustment.
Journal Article
Robust optimization of spline models and complex regulatory networks : theory, methods and applications
This book introduces methods of robust optimization in multivariate adaptive regression splines (MARS) and Conic MARS in order to handle uncertainty and non-linearity. The proposed techniques are implemented and explained in two-model regulatory systems that can be found in the financial sector and in the contexts of banking, environmental protection, system biology and medicine. The book provides necessary background information on multi-model regulatory networks, optimization and regression. It presents the theory of and approaches to robust (conic) multivariate adaptive regression splines - R(C)MARS - and robust (conic) generalized partial linear models - R(C)GPLM - under polyhedral uncertainty. Further, it introduces spline regression models for multi-model regulatory networks and interprets (C)MARS results based on different datasets for the implementation. It explains robust optimization in these models in terms of both the theory and methodology. In this context it studies R(C)MARS results with different uncertainty scenarios for a numerical example. Lastly, the book demonstrates the implementation of the method in a number of applications from the financial, energy, and environmental sectors, and provides an outlook on future research.
Book
Optimizing emission reduction task sharing: technology and performance perspectives
One effective way to achieve emission reduction targets is to allocate overall emission reduction tasks among regions. However, existing AEP optimization models do not consider technology heterogeneity between regions. This study addresses this problem, by first incorporating a meta-frontier technique into the data envelope analysis model (DEA) to measure the level of energy conservation and emission reduction (ECER) technology of different regions in China. Then, the study proposes an optimization model for emission reduction task sharing, by integrating DEA and ECER technology. Compared with previous models, the optimization model proposed in this study considers both technology and efficiency factors. The proposed model was applied to an empirical analysis of 176 cities in China from 2012 to 2016. The empirical results show that the average comprehensive efficiency of all the sample cities is very low. This indicates there is great potential for improving the environmental performance of Chinese cities. The environmental performance results of the sample cities further verify the Kuznets hypothesis: environmental performance and economic development level follow a U-shaped curve. ECER technology levels in China's third- and fourth-tier cities have not significantly changed in recent years. There is an increased reduction in sulfur dioxide (SO2) emissions in Chinese cities, but dust emission reduction is unstable, especially in the third-tier cities. Based on these results, this article also proposes a series of policy recommendations for cities to improve ECER performance.
Journal Article