Search

This study explores a Robust, Risk-aware, Resilient, and Sustainable Closed-Loop Supply Chain Network Design (3RSCLSCND) to tackle demand fluctuation like COVID-19 pandemic. A two-stage robust stochastic multiobjective programming model serves to express the proposed problems in formulae. The objective functions include minimising costs, CO2 emissions, energy consumption, and maximising employment by applying Conditional Value at Risk (CVaR) to achieve reliability through risk reduction. The Entropic Value at Risk (EVaR) and Minimax method are used to compare with the proposed model. We utilise the Lp-Metric method to solve the multiobjective problem. Since this model is complex, the Lagrange relaxation and Fix-and-Optimise algorithm are applied to find lower and upper bounds in large-scale, respectively. The results confirm the superior power of the model offered in estimating costs, energy consumption, environmental pollution, and employment level. This model and algorithms are applicable for other CLSC problems. [ABSTRACT FROM AUTHOR]

Sustainable development requires scheduling and implementation of projects by considering cost, environment, energy, and quality factors. Using a robust approach, this study investigates the time-cost-quality-energy-environment problem in executing projects and practically indicates its implementation capability in the form of a case study of a bridge construction project in Tehran, Iran. This study aims to take into account the sustainability pillars in scheduling projects and uncertainties in modeling them. To model the study problem, robust nonlinear programming (NLP) involving the objectives of cost, quality, energy, and pollution level is applied with resource-constrained. According to the results, as time diminished, the cost, energy, and pollution initially decreased and then increased, witha reduction in quality. To make the model close to the real world by considering uncertainties, the cost and quality tangibly improved, and pollution and energy consumption declined. We applied the augmented ε ε -constraint method to solve the proposed model. According to the result of the research, with regard to the time-cost, time-quality, time-energy, and time-pollution charts, as uncertainty increases, the cost and quality will improve, and pollution and energy will decrease. The proposed model can be employed for all industrial projects, including roads, construction, and manufacturing. [ABSTRACT FROM AUTHOR]

Nowadays, using Blockchain Technology (BCT) is growing faster in each country. It is essential to apply BCT in Supply Chain Network Design (SCND) and is considered by the designer and manager of SC. This research indicates Viable Supply Chain Network Design (VSCND) by applying BCT. A new form of two-stage robust optimization is suggested. Facility locations and activation BCT for VSCND is the first stage of decisions; finally, we determine flow transshipment between components in the next stage. The GAMS-CPLEX is used for solving the model. The results show that running BCT will decrease 0.99% in costs. There is an economic justification for using BCT when demand is high. A fix-and-optimize and Lagrange relaxation (LR) generate lower and upper bound to estimate large scale in minimum time. The gap between the main model and fix-and-optimize is better than the LR algorithm. Finally, this research suggests equipping VSCND by BCT that becomes more resilient against demand fluctuation, sustainable, and agile. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, using renewable energy (RE) is faster growing by each country. The managerial and designer of supply chain network design (SCND) have to plan to apply RE in pillars of supply chain (SC). This research indicates resilience and sustainable SCND by considering RE (RSSCNDRE) for the first time. A two‐stage new robust stochastic optimization is embedded for RSSCNDRE. The first stage locates facility location and RE and the second stage defines flow quantity between SC components. We solve the model by GAMS‐CPLEX solver to locate components of SC and RE. Effects of changing conservative coefficient and demand are investigated and by increasing 20% for conservative coefficient, the cost function increase by 0.5%. Also, when demand is high, activating RE is economically feasible and we cannot buy and supply energy by the government power network and have to supply energy by RE. After activating RE, by increasing 20% for demand, the cost function increases by 6%. We contribute fix‐and‐optimize strategy to define the upper bound for a large‐scale problem. The proposed upper bound for the main model is less than 10% and appropriate for estimating the cost of large‐scale problems. This research suggested to equip SC by RE that SC becomes resilient against demand fluctuation and sustainable energy resource compatible with sustainable development goal (SGD7). [ABSTRACT FROM AUTHOR]

Summary: One of the key factors in the establishment of power plants is proper location, which entails addressing various criteria. Power plant establishment operations are included in the fundamental designs; in turn, comprehensive studies in this field seem to be a prerequisite. The most important innovation of the present study consists of applying the robust bi‐level programming technique and game theory (Stackelberg competition) for locating renewable energy sites. Robust stochastic method is implemented for the robustification of the model against variations (eg, noise and perturbation) and uncertain conditions. The Karush‐Kuhn‐Tucker (KKT) method is used for solving bi‐level programming. The obtained results reveal that the incorporation of uncertainties can enhance energy generation and supplier's profit. Furthermore, the objective functions of the proposed model are compared with those under conditions without uncertainties. A sensitivity analysis of major parameters is performed to validate the proposed model. By increasing uncertainty, generated energy decrease, and supplier's profit increase. The supplier's profit gradually diminishes by raising the discounting rate. Moreover, as scale of problems increases, the generated energy and supplier's profit are boosted. [ABSTRACT FROM AUTHOR]

In our study, we integrate the data uncertainty of real-world models into our regulatory systems and robustify them. We newly introduce and analyse robust time-discrete target–environment regulatory systems under polyhedral uncertainty through robust optimization. Robust optimization has reached a great importance as a modelling framework for immunizing against parametric uncertainties and the integration of uncertain data is of considerable importance for the model’s reliability of a highly interconnected system. Then, we present a numerical example to demonstrate the efficiency of our new robust regression method for regulatory networks. The results indicate that our approach can successfully approximate the target–environment interaction, based on the expression values of all targets and environmental factors. [ABSTRACT FROM AUTHOR]

• Developing a novel mathematical model for the robust green LAIP Problem. • Analyzing different MSW requirements within the waste management system. • Designing an efficient economical eco-friendly collection and disposal network. • Introducing the concept of RWT considering the budget constraint. The excessive expansion of cities and the consequent increase in urban population, especially in recent years, have led to a significant increase in consumptions and generations of different types of municipal solid waste (MSW). In this research, a robust green location-allocation-inventory problem (LAIP) is investigated to design an efficient MSW management system. Since the exact amount of MSW composition in different regions is not known and is uncertain, robust optimization technique is applied to formulate the problem as a mixed-integer linear programming (MILP) model. The objective is to minimize the total cost including fixed locational costs of collection and processing/disposal facilities, operational costs of facilities, transportation costs, penalty costs of non-collected waste and costs arising from pollution emissions. The validation of the proposed model is performed by different problems based on real-life data in deterministic and uncertain conditions using CPLEX solver of GAMS software. Then, the effects of greenness are evaluated by performing a sensitivity analysis on the parameter of pollution cost. Finally, the concept of robustness worthiness threshold (RWT) under budget constraint is introduced and discussed. [ABSTRACT FROM AUTHOR]