Shrimp closed-loop supply chain network design

Recent developments in food industries have attracted both academic and industrial practitioners. Shrimp as a well-known, rich, and sought-after seafood, is generally obtained from either marine environments or aquaculture. Central prominence of Shrimp Supply Chain (SSC) is brought about by numerous factors such as high demand, market price, and diverse fisheries or aquaculture locations. In this respect, this paper considers SSC as a set of distribution centers, wholesalers, shrimp processing factories, markets, shrimp waste powder factory, and shrimp waste powder market. Subsequently, a mathematical model is proposed for the SSC, whose aim is to minimize the total cost through the supply chain. The SSC model is NP-hard and is not able to solve large-size problems. Therefore, three well-known metaheuristics accompanied by two hybrid ones are exerted. Moreover, a real-world application with 15 test problems are established to validate the model. Finally, the results confirm that the SSC model and the solution methods are effective and useful to achieve cost savings

Extending the solid step fixed-charge transportation problem to consider two-stage networks and multi-item shipments

This paper develops a new mathematical model for a capacitated solid step fixed-charge transportation problem. The problem is formulated as a two-stage transportation network and considers the option of shipping multiple items from the plants to the distribution centers (DC) and afterwards from DCs to customers. In order to tackle such an NP-hard problem, we propose two meta-heuristic algorithms; namely, Simulated Annealing (SA) and Imperialist Competitive Algorithm (ICA). Contrary to the previous studies, new neighborhood strategies maintaining the feasibility of the problem are developed. Additionally, the Taguchi method is used to tune the parameters of the algorithms. In order to validate and evaluate the performances of the model and algorithms, the results of the proposed SA and ICA are compared. The computational results show that the proposed algorithms provide relatively good solutions in a reasonable amount of time. Furthermore, the related comparison reveals that the ICA generates superior solutions compared to the ones obtained by the SA algorithm

Heuristic approaches to address vehicle routing problem in the Iot-based waste management system

Nowadays, population growth and urban development lead to having an efficient waste management system (WMS) based on recent advances and trends. Alongside all functions and procedures in these systems, the waste collection plays a significant role. This study proposes a two-echelon WMS to minimize operational costs and environmental impact by utilizing the industry 4.0 concept. Both models utilize modern traceability Internet of Thing-based devices to compare real-time information of waste level in bins and separation centers with the threshold waste level (TWL) parameter. The first model optimizes the operational cost and Co2 emission of collecting waste from bins to the separation center by considering the time windows. A capacitated vehicle routing problem is designed as a later model-based to minimize the cost of waste transferring to recycling centers. In addition, to find the optimal solution, recent meta-heuristic algorithms are employed, and several novel heuristics based on the problem's specifications are developed. Furthermore, the developed heuristics methods are utilized to generate the initial feasible solutions in meta-heuristics and compared with random ones. The performance of the proposed algorithms is probed, and Best Worst Method (BWM) is applied to rank the algorithms based on relative percentage deviation, relative deviation index and hitting time

Analyzing Green Construction Development Barriers by a Hybrid Decision-Making Method Based on DEMATEL and the ANP

There is a great deal of interest in analyzing construction development barriers to identify and rank them based on sustainability criteria and have less environmental pollution. Due to the importance of construction projects in developing countries such as Iran, this study implements a green construction development paradigm to identify and rank barriers for a case study in Tehran, Iran. The main novelty of this paper is the development of a new decision-making method using the DEMATEL and Delphi techniques and the ANP. In this regard, first of all, data collection is performed through a literature review and survey studies using questionnaires, interviews, and observations. The applied method for experts’ agreement was integrated through brainstorming and the classical Delphi method. By analyzing different economic, environmental, cultural, and social criteria using a hybrid decision-making framework, the results show that the main economic barrier with a weight of 0.2607 is ranked first, while the main feature of economic assessment is connected to the risk of investment. The cultural and social barriers, with a weight of 0.2258, ranked second, and the managerial barrier, with a weight of 0.2052, ranked third. In the social and managerial aspects, the main barriers were related to looking at green construction as luxurious and the uncertainty of green construction performance due to the climate and texture of the local area, respectively. According to the findings and results, the proposed barriers and sub-barriers in this study can be used to develop and create planning at the strategic level for the development of green construction for our case study in Tehran, Iran. With a concentration on the outcomes of the present research, the sustainable green building framework can be implemented by the application of a prioritized knowledge management concept

Analyzing Green Construction Development Barriers by a Hybrid Decision-Making Method Based on DEMATEL and the ANP

There is a great deal of interest in analyzing construction development barriers to identify and rank them based on sustainability criteria and have less environmental pollution. Due to the importance of construction projects in developing countries such as Iran, this study implements a green construction development paradigm to identify and rank barriers for a case study in Tehran, Iran. The main novelty of this paper is the development of a new decision-making method using the DEMATEL and Delphi techniques and the ANP. In this regard, first of all, data collection is performed through a literature review and survey studies using questionnaires, interviews, and observations. The applied method for experts’ agreement was integrated through brainstorming and the classical Delphi method. By analyzing different economic, environmental, cultural, and social criteria using a hybrid decision-making framework, the results show that the main economic barrier with a weight of 0.2607 is ranked first, while the main feature of economic assessment is connected to the risk of investment. The cultural and social barriers, with a weight of 0.2258, ranked second, and the managerial barrier, with a weight of 0.2052, ranked third. In the social and managerial aspects, the main barriers were related to looking at green construction as luxurious and the uncertainty of green construction performance due to the climate and texture of the local area, respectively. According to the findings and results, the proposed barriers and sub-barriers in this study can be used to develop and create planning at the strategic level for the development of green construction for our case study in Tehran, Iran. With a concentration on the outcomes of the present research, the sustainable green building framework can be implemented by the application of a prioritized knowledge management concept

Metaheuristic optimizers to solve multi-echelon sustainable fresh seafood supply chain network design problem: A case of shrimp products

This is the final version. Available from Elsevier via the DOI in this record. Seafood products are sought-after among communities all over the globe and are the main sources of essential nutrition for humans. Recently, the seafood supply chain networks have encountered obstacles that new sustainability regulations and the pandemic have brought forward. In this study, a novel supply chain network is developed for fresh seafood, considering sustainability aspects, to ideally balance the financial aspect of the network while enhancing the recycling of waste products. Moreover, four metaheuristics are employed to conquer the computational complexity of exact solution methods. To evaluate the performance of the algorithms in addressing the complexity of the proposed seafood supply chain model, some numerical examples in three different scales are designed. The obtained results from metaheuristic optimizers are assessed based on five effective measures. To facilitate the statistical analysis process, each measure is normalized using the relative deviation index indicator. According to the results obtained from the implementation of metaheuristic algorithms, it can be concluded that the multi-objective grey wolf and multi-objective golden eagle optimizers outperform the other two solution methods in terms of quality of solutions. Therefore, they can be applied efficiently in solving real-world seafood supply chain network problems

A Sustainable Decision Support System for Drinking Water Systems: Resiliency Improvement against Cyanide Contamination

Maintaining drinking water quality is considered important in building sustainable cities and societies. On the other hand, water insecurity is an obstacle to achieving sustainable development goals based on the issues of threatening human health and well-being and global peace. One of the dangers threatening water sources is cyanide contamination due to industrial wastewater leakage or sabotage. The present study investigates and provides potential strategies to remove cyanide contamination by chlorination. In this regard, the main novelty is to propose a sustainable decision support system for the dirking water system in a case study in Iran. First, three scenarios have been defined with low ([CN−] = 2.5 mg L−1), medium ([CN−] = 5 mg L−1), and high ([CN−] = 7.5 mg L−1) levels of contamination. Then, the optimal chlorine dosage has been suggested as 2.9 mg L−1, 4.7 mg L−1, and 6.1 mg L−1, respectively, for these three scenarios. In the next step, the residual cyanide was modelled with mathematical approaches, which revealed that the Gaussian distribution has the best performance accordingly. The main methodology was developing a hybrid approach based on the Gaussian model and the genetic algorithm. The outcomes of statistical evaluations illustrated that both injected chlorine and initial cyanide load have the greatest effects on residual cyanide ions. Finally, the proposed hybrid algorithm is characterized by the multilayer perceptron algorithm, which can forecast residual cyanide anion with a regression coefficient greater than 0.99 as a soft sensor. The output can demonstrate a strong positive relationship between residual cyanide- (RCN−) and injected chlorine. The main finding is that the proposed sustainable decision support system with our hybrid algorithm improves the resiliency levels of the considered drinking water system against cyanide treatments

Bio-recovery of municipal plastic waste management based on an integrated decision-making framework

Recent years have seen rapid development in industrialization and urbanization with huge growth in the population throughout the world. In this regard, an efficient and robust framework for the concept of a green city and sustainable development goals to manage municipal plastic wastes is still needed. This study models a bio-recovery of municipal different plastic wastes management based on a new integrated Multi-Criterion Decision-Making (MCDM) approach through a case study in Mashahd, Iran. The proposed integrated MCDM framework includes the Shannon Entropy (SE), Ordered Weighted Aggregation (OWA), Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and, ELimination Et Choice Translating REality (ELECTRE) systems in an intelligent way. Through decision-making computations, all criteria are approved after extraction from the literature review by experts with more than 60% agreement percentage. Different scenarios of economic, energy, and environmental crises are created. One finding of this paper is to create a new entrance in economic competition with plastic biodegradation to present a novel, environmental-friendly product with high-quality and low-cost advantages. Another finding determines that with an application of plastic wastes bio-recovery, citizens' satisfaction from urban management system will be increased from 49% to 64%. Whereas, based on the outcomes of this investigation, the rate of municipal waste industries development, smart city goals’ meeting, and rate of hazardous material emission from municipal solid wastes are increased to 58%, 25%, and 70%, respectively. The declared numerical outcomes illustrate the effectiveness of plastic waste bio-recovery on the smart city approach