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This article proposes a risk assessment method based on interval intuitionistic integrated cloud Petri net (IIICPN). The cloud model is widely used in data mining and knowledge discovery, especially in risk assessment problems with linguistic variables. However, the cloud models proposed in the literature do not express interval-valued intuitionistic linguistic satisfactorily, and the reasoning methods based on the cloud models cannot perform risk assessment well. The work in this article includes the definition of IIIC and IIICPN, the method of converting the interval-valued intuitionistic uncertain linguistic numbers into IIIC, and the reasoning method of IIICPN. As proofs, a subway fire accident model is adopted to confirm the feasibility of the proposed method, and comparison experiments between the IIICPN with general fuzzy Petri net and the trapezium cloud model are conducted to verify the superiority of the proposed model.

Application of construction and demolition (C&D) wastes were considered as sustainable development goals (SDGs) for maintaining raw materials. Also, lightweight concretes such as aerated autoclaved concrete (AAC) were used for partitioning spaces in the building industry. Moreover, the waste products of the mentioned materials were increased due to the rise of old construction demolitions. This study contributes a calcinated aerated autoclaved concrete (CAAC) which is efficient, powerful, highly rapid, non-expensive and novel adsorbent for the removal of cationic dyes including malachite green (MG), methyl violet (MV) and methylene blue (MB) form water samples. The impacts of different variables for the proposed system including initial pH value, stirring rate, dye concentration and contact time are explored to optimize the selected analyses. Most notably, this study analyzes the experimental isotherm data by using two-parameter isotherms such as Dubinin–Radushkevich, Temkin, Langmuir and Freundlich equations and three-parameter isotherms including Koble–Corrigan, Toth, Redlich–Peterson and Sips models. The maximum adsorption capacities for MG, MB and MV are 370.4, 256.4 and 277.8 mg g−1, respectively. In addition, five kinetic models, Elovich, intraparticle diffusion, main equations of pseudo-first- and second-order, and Boyd mathematical models are employed to follow the kinetic parameters and adsorption process of each dye. The Boyd equations indicate that with regard to all three dyes and at all concentrations, the film diffusion is dominant over intraparticle diffusions. Almost none of the geometric plots of adsorption and desorption curves intersected, indicating the adsorption process is optimally performed.

This paper proposes a multi-objective robust-stochastic humanitarian logistics model to assist disaster management officials in making optimal pre- and post-disaster decisions. This model identifies the location of temporary facilities, determines the amount of commodity to be pre-positioned, and provides a detailed schedule for the distribution of commodities and the dispatch of vehicles. Uncertainties in demand, node reachability by a particular mode of transportation, and condition of pre-positioned supplies after a disaster are considered. Another supposition of this paper is the equity in the distribution of commodities. This paper contributes to the existing literature by adding vehicle flow and multi-periodicity into a robust-stochastic optimisation model. A real-life case study of a flood in Bangladesh shows the applicability of our model. Finally, the findings show that the proposed model can aid decision-makers in allocating resources optimally.

Warehouses constitute a key component of supply chain networks. An improvement to the operational efficiency and the productivity of warehouses is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers preemptively estimate the overall warehouse performance (OWP), which requires an accurate prediction of a warehouse’s key performance indicators (KPIs). This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with a low forecasting error in order to precisely measure OWP. Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies 13 KPIs. The traditional grey method (GM)—the GM (1, 1) model—is established to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this paper introduces a novel particle swarm optimization (PSO)-based grey model—PSOGM (1, 1)—to predict the warehouse’s KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model—GAGM (1, 1)—the discrete grey model—DGM (1, 1)—to assess the performance of the proposed model in terms of the mean absolute percentage error and other assessment metrics. The proposed model outperforms the existing grey models in projecting OWP through the forecasting of KPIs over a 5-month period. To find out the optimal parameters of the PSO and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make quick OWP estimations in advance to take control measures regarding warehouse productivity and efficiency.

One of the major challenges of the supply chain managers is to select the best suppliers among all possible ones for their business. Although the research on the supplier selection with regards to green, sustainability or resiliency criteria has been contributed by many papers, simultaneous consideration of these criteria in a fuzzy environment is rarely studied. Hence, this study proposes a fuzzy decision framework to investigate the sustainable-resilient supplier selection problem for a real case study of palm oil industry in Malaysia. Firstly, the resilient-based sustainable criteria are localized for the suppliers’ performance evaluation in palm oil industry of Malaysia. Accordingly, 30 criteria in three different aspects (i.e. general, sustainable and resilient) are determined by statistical tests. Moreover, a hyper-hybrid model with the use of FDEMATEL (fuzzy decision-making trial and evaluation laboratory), FBWM (fuzzy best worst method), FANP (fuzzy analytical network process) and FIS (fuzzy inference system), simultaneously is developed to employ their merits in an efficient way. In this framework, regarding the outset, the relationships among the criteria/sub-criteria are obtained by FDEMATEL method. Then, initial weights of the criteria/sub-criteria are measured by FBWM method. Next, the final weights of criteria/sub-criteria considering the interrelationships are calculated by FANP. Finally, the performance of the suppliers is evaluated by FIS method. To show the applicability of this hybrid decision-making framework, an industrial case of palm oil in Malaysia is presented. The findings indicate the high performance of the proposed framework in this concept and identify the most important criteria including the cost in general aspects, resource consumption as the most crucial sustainable criterion and agility as the most important resilient criterion. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-12491-y.

There is a great deal of interest in addressing humanitarian logistics due to the need for emergency services in the case of disaster. Controlling both operational and disruption uncertainties in t...

Nature has been considered as an inspiration of several recent meta-heuristic algorithms. This paper firstly studies and mimics the behavior of Scottish red deer in order to develop a new nature-inspired algorithm. The main inspiration of this meta-heuristic algorithm is to originate from an unusual mating behavior of Scottish red deer in a breading season. Similar to other population-based meta-heuristics, the red deer algorithm (RDA) starts with an initial population called red deers (RDs). They are divided into two types: hinds and male RDs. Besides, a harem is a group of female RDs. The general steps of this evolutionary algorithm are considered by the competition of male RDs to get the harem with more hinds via roaring and fighting behaviors. By solving 12 benchmark functions and important engineering as well as multi-objective optimization problems, the superiority of the proposed RDA shows in comparison with other well-known and recent meta-heuristics.

The pollution and emission caused by the manufacturing, operation, and scrapping of marine ships have considerable impact on the environment. Therefore, it is essential to develop an effective and efficient approach toward evaluating the environmental impact of ships. This study uses the green degree of ships concept to evaluate different alternatives. Because environmental evaluation involves several criteria, the problem can be formulated as a traditional multi-criteria decision-making (MCDM) model. In this regard, the main innovation of this study is the development of an improved MCDM model using a novel hybrid method, namely the group fuzzy entropy and cloud technique for order of preference by similarity to ideal solution theory. The developed model calculates the criteria weight and rank based on the selected criteria and alternatives. Finally, a criteria system is developed to evaluate several classical alternatives, and various sensitivity analyses were carried out to validate the robustness of the proposed methodology. The results revealed that the model and criteria system can provide several quantitative and qualitative indicators for governmental policy-makers and agencies, environmental consultants, and environmental service organizations.

The recent advances in sustainable supply chain management are integrated with Industry 4.0 concepts. This study develops a new integrated model to consider the sustainability and Industry 4.0 criteria for the supplier selection management. The proposed approach consists of the fuzzy best worst method (FBWM) and the two-stage fuzzy inference system (FIS) to assess the selection of suppliers. Firstly, this study determines a comprehensive list of Industry 4.0 and sustainability criteria along with their definitions. Then, the importance weight of each criterion is computed by the FBWM. Subsequently, a two-stage FIS is devoted to nominate the suppliers' performance with regard to the sustainability and Industry 4.0 criteria. To show the applicability of our integrated model, a case study for a textile company in Iran is provided. Finally, some sensitivity analyses are done to assess the efficiency of the proposed integrated approach. One finding is to establish a decision-making framework to evaluate suppliers separately, rather than relatively in a fuzzy environment using Industry 4.0 and sustainability criteria.

This work proposes a capacitated fuzzy disassembly scheduling model with cycle time and environmental cost, which has broad applications in remanufacturing and many other production systems. Disassembly scheduling is not always given accurately as a time quota in a production system, particularly in the obsolete products remanufacturing process. It is meaningful to study a novel model and algorithm based on uncertainty processing time to solve uncertainty disassembly scheduling problems. Therefore, a mixed-integer mathematical programming model is proposed to minimize the cycle time and environmental cost, whilst a metaheuristic approach based on a fruit fly optimization algorithm is developed to find a fuzzy disassembly scheduling scheme. To estimate the effectiveness of the proposed method, the proposed algorithm is tested with different size cases of products disassembly scheduling. Furthermore, experiments are conducted to compare with other multi-objective optimization algorithms. The computational results demonstrate the proposed algorithm outperforms other algorithms on computational efficiency and applicability performance. Finally, a case study is described to illustrate the proposed method. The main finding of this current work is to provide a new idea to solve the problem of disassembly scheduling in an uncertain environmental practically and efficiently.

Nowadays, energy crisis is considered an essential active issue for future urbanization in megacities. While the\ud rate of population growth increases, the volume of municipal solid waste production increases significantly. This\ud highlights the need of Sustainable Development Goals (SDGs) for both developed and developing countries. This\ud paper constructs a novel smart framework for supplying biogas energy. Our study is applicable for fields of waste\ud management and energy supply in green buildings. The proposed framework integrates the Response Surface\ud Methodology (RSM), Artificial Intelligence (AI), and Petri net modeling. In this regard, the AI techniques\ud including the Random Tree (RT), Random Forest (RF), Artificial Neural Network (ANN) and, Adaptive-Networkbased\ud Fuzzy Inference System (ANFIS) are employed. In addition, for creating the optimum condition, a dynamic\ud control system using the Petri Net modeling is applied. Among all machine learning methods, ANFIS with 0.99\ud correlation coefficient had the best accuracy for Accumulated Biogas Production (ABP) based on effective factors.\ud Finally, the main findings of this paper are to introduce a novel framework for addressing different scientific\ud issues such as supplying the clean energy in green buildings, the development of a smart and sustainable biogas\ud production control system, integration of solid waste management with the SDGs in green buildings.

Nowadays, an efficient and robust plan for maintenance activities can reduce the total cost significantly in the equipment-driven industry. Maintenance activities are directly associated with the impact on the plant output, production quality, production cost, safety, and the environmental performance. To address this challenge more broadly, this paper presents an optimization model for the problem of flexible flowshop scheduling in a series-parallel waste-to-energy (WTE) system. To this end, a preventive maintenance (PM) policy is proposed to find an optimal sequence for processing tasks and minimize the delays. To deal with the uncertainty of the flexible flowshop scheduling of waste-to-energy in practice, the work processing time is modeled to be uncertain in this study. Therefore, a robust optimization model is applied to address the proposed problem. Due to the computational complexity of this model, a novel scenario-based genetic algorithm is proposed to solve it. The applicability of this research is shown by a real-life case study for a WTE system in Iran. The proposed algorithm is compared against an exact optimization method and a canonical genetic algorithm. The findings confirm a competitive performance of the proposed method in terms of time savings that will ultimately save the cost of the proposed PM policy.

The environmental consequences and the epidemiologic results of noise pollution have chronic effects and lead to widespread complications in the long run. Thus it might receive less attention by pollution monitoring and control systems when compared with other environmental pollutants. The city of Mashhad is Iran’s second largest metropolitan city and it is one of the biggest world religious cities in the world. Each year it becomes one of the main destinations for travel for tourists and pilgrims in Iran and abroad in the holidays of the beginning of the year and the summer. In this research study the distribution of sound pollutants in the streets and sidewalks leading to the Holy shrine of Imam Reza is investigated. Field measurements were carried out by noise level meters and on longitudinal and transverse points of the sidewalk of the streets leading to the Holy Shrine. All of the measurements and recordings were done during the peak of morning crowd (AM 10-12) and evening crowd (PM 4-6) and on both of the sidewalks of each street. The study showed that the pollution in all of the studied streets in the evening time span (PM 4-6) has the maximum level of noise. Among all of the studied streets, Tabarsi street has the most amount of noise pollution with a mean of 78 dB(A) for the mean intensity for each point and Imam Reza street has the minimum amount of pollution with a mean of 72.75 dB(A). The investigations showed that, from the temporal perspective, the noise pollution peaks in the evening, when weather conditions are most favorable for visiting the area. From the spatial perspective, the most intensive noise pollution was observed in the areas with residential and accommodation land use, which have the highest number of arterial routes towards the shrine.

The growing environmental concerns, excessive utilization of natural resources, and high energy consumption have put a severe pressure on the construction industry to adopt new methods in response to these challenges. As a remedy, prefabricated construction methods are broadly utilized to enhance the productivity of the construction activities. However, challenges associated with on-time production of precast components have not adequately been addressed. Thus, this study proposes a new insight to the theory of scheduling dealing with sequence-dependent due dates, in which total weighted earliness and tardiness are minimized. To tackle uncertainties and complexities associated with the stochastic nature of production problems, three integrated simulation-optimization algorithms, which employ simulation methods inside a metaheuristic framework, are proposed. Three metaheuristic algorithms, including genetic algorithm (GA), differential evolution (DE), and imperialist competitive algorithm (ICA), are employed to minimize objective function. A series of computational tests are conducted to investigate the performance of these approaches. Results indicate that integrated DE-simulation approach can provide better results in comparison with other approaches.

Nowadays, there is a great deal of attention about the care of aging population. This motivates the home healthcare services to provide nursing and general hygiene cares at patient home for elderly...

Recent years have seen a great deal of attention in energy conservation for production and manufacturing activities, particularly for energy-intensive industries. One of the useful strategies in reducing unnecessary energy consumption is to schedule these activities by considering both energy-driven and time-oriented criteria. This scheduling model can make an interaction between the energy consumption and the production cost to realize an efficient and sustainable production process. In this regard, the customers' expectation for due date is another important factor for decision-makers to control the delay in delivery. Making these decisions is extremely difficult due to uncertain circumstances to extract the accurate information of facilities and jobs in advance. Aforementioned issues in the context of urgent need for energy-conservation as well as the advent of globalized and multi-factory manufacture motivate our attempts to address a stochastic multi-objective distributed permutation flow shop scheduling problem by considering total tardiness constraint via minimizing the makespan and the total energy consumption. Due to the uncertainty of the proposed problem, a chance-constrain approach is used to describe decision-makers’ awareness for the total tardiness, and accordingly, a chance-constrained programming model is utilized to formulate this problem. As a complicated optimization problem, a new multi-objective brain storm optimization algorithm incorporating stochastic simulation approach is specifically designed to better solve problem. A comparative study based on a set of benchmark test problems as well as two classical and popular algorithms is provided. The experimental results demonstrate that the proposed algorithm shows a very competitive performance in dealing with the investigated problem.

Nowadays, the aging population and the little availability of informal care are two of the several factors leading to an increased need for assisted living support. Hence, home healthcare (HHC) operations including a set of nurses and patients have been developed recently by both academia and health practitioners to consider elderlies’ preferences willing to receive their cares at their homes instead of hospitals or retirement homes. Commonly, different services, e.g., nursing, physiotherapy, housekeeping and cleaning, for an HHC system are performed by nurses at patients’ homes after scheduling and routing the nurses by decision makers. Due to the difficulty of the problem, recent studies show a great deal of interest in applying various metaheuristics and heuristics to solve this problem. To alleviate the drawbacks of previous works and make HHC more practical, this paper develops not only a new mathematical formulation considering new suppositions in this research area but also a lower bound based on Lagrangian relaxation theory has been employed. As such, three new heuristics and a hybrid constructive metaheuristic are utilized in this study to solve the proposed model. Finally, the performance of the proposed algorithms is validated by the developed lower bound and also analyzed by different criteria and also the efficiency of developed formulation is probed through some sensitivity analyses.

One of the significant challenges in urbanization is the air pollution. This highlights the need of the green city concept with reconsideration of houses, factories and traffics in a green viewpoint. The literature review confirms that this reconsideration for green space, has a positive effect on the air quality of large cities and to remove the air pollution. The purpose of this study is to evaluate the annual vegetation changes in the green space of Mashhad, Iran as a very populated city in the middle east to study the air pollution. To investigate the relationship between the air pollution and vegetation, the Landsat 8 satellite images for summers of 2013-2019 were used to extract changes in vegetation by calculating the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Optimized Soil Adjusted Vegetation Index (OSAVI). The main contribution in comparison with the relevant studies is to study the relationship between clean, healthy and unhealthy days with the green space area for the first time in Mashhad, Iran. The results show that the implementation of green city concept in Mashhad, Iran has been increased by 64%, 81% and 53% by NDVI, EVI and OSAVI, respectively during the study period. The vegetation area of this city is positively correlated to clean and healthy days and has a negative correlation to unhealthy days, in which the greatest values for NDVI, EVI and OSAVI are 0.33, 0.52 and -0.53, respectively.

Product disassembly and recycling are important issues in green design. Disassembly sequence planning (DSP) is an important problem in the product disassembly process. The core idea is to generate the best or approximately optimal disassembly sequence to reduce disassembly costs and time. According to the characteristics of the DSP problem, a new algorithm to solve the DSP problem is proposed. Firstly, a disassembly hybrid graph is introduced, and a disassembly constraint matrix is established. Secondly, the disassembling time, replacement frequency of disassembly tool and replacement frequency of disassembly direction are taken as evaluation criteria to establish the product fitness function. Then, an improved social engineering optimizer (SEO) method is proposed. In order to enable the algorithm to solve the problem of disassembly sequence planning, a swap operator and swap sequence are introduced, and steps of the social engineering optimizer are redefined. Finally, taking a worm reducer as an example, the proposed algorithm is used to generate the disassembly sequence, and the influence of the parameters on the optimization results is analyzed. Compared with several heuristic intelligent optimization methods, the effectiveness of the proposed method is verified.

Blockchain is a distributed ledger technology that has attracted both practitioners and academics attention in recent years. Several conceptual and few empirical studies have been published focusing on addressing current issues and recommending the future research directions of supply chain management. To identify how blockchain can contribute to supply chain management, this paper conducts a systematic review through bibliometric and network analysis. We determined the key authors, significant studies, and the collaboration patterns that were not considered by the previous publications on this angel of supply chain management. Using citation and co-citation analysis, key supply chain areas that blockchain could contribute are pinpointed as supply chain management, finance, logistics, and security. Furthermore, it revealed that Internet of Things (IoT) and smart contracts are the leading emerging technologies in this field. The results of highly cited and co-cited articles demonstrate that blockchain could enhance transparency, traceability, efficiency, and information security in supply chain management. The analysis also revealed that empirical research is scarce in this field. Therefore, implementing blockchain in the real-world supply chain is a considerable future research opportunity.

A bi-objective non-linear optimization model with the goal of maximizing the profit of inventory and minimizing the carbon emissions of transportation, simultaneously, is developed. Another contribution of this work is to propose three capable metaheuristics to solve it optimality in large-scale samples. In this regard, the Non-dominated Sorting Genetic Algorithm (NSGA-II) as a well-known method as well as Multi-Objective of Keshtel Algorithm (MOKA) and Multi-Objective of Red Deer Algorithm (MORDA) are firstly applied in this research area. The results of metaheuristics are checked by the e-constraint method in a set of small-scale samples as compared with the results of literature. Finally, the outputs confirm that the allowed shortage situation along with the lack of cost reduction shows a greater amount of shipping and orders. As such, the performance of MORDA is approved in comparison with MOKA and NSGA-II through different criteria.

Nowadays, there is a great deal of interest in employing home health care (HHC) services for elderly people in developed countries. An HHC system aims to consider a number of caregivers started from a pharmacy, serving a set of patients at their home with different home care services including housekeeping, nursing, and physiotherapist. Next, they analyze the biological samples from patients to update their health records in a laboratory. The logistics activities of this system need efficient optimization models and algorithms to find a valid plan for the caregivers addressing the HHC routing and scheduling problem (HHCRSP). To meet the demand of patients in a timely fashion, the caregiver has to deal with some stochastic parameters to carry out the valid plan to visit the patients. To achieve sustainability for HHC organizations, the gas dimensions of these logistic activities should be investigated. Although there are many papers in this research area from recent years, there is no study to consider the gas emissions as well as the stochastic travel and service times by using a robust optimization model. A bi-objective robust optimization model is developed for the first time and solved by a set of efficient metaheuristic algorithms. Having already been applied to similar problems, the Keshtel algorithm (KA) has been never employed in this research area. Therefore, another contribution of this study is to apply the KA and to compare it with well-known algorithms from the literature. Finally, an extensive comparison by different multi-objective optimization criteria confirms the performance of the proposed KA as well as the efficiency of the developed robust optimization approach.

Home Health Care (HHC) is generally defined and considered as a set of nurses serving the patients at home by providing the required drugs and other HHC services (e.g. nursing, housekeeping and cleaning etc.). By increasing the life expectancy in the developed countries, the number of hospitals and requirement homes along with medical staffs have not been grown at the same rate. Accordingly, the demand of HHC services is growing, dramatically. According to the literature and the increasing of HHC usage in real world, less attention to environmental pollution or green emission of such systems has been reported. This motivates our attempt to contribute the issue of green HHC among the first studies by developing a bi-objective optimization model. To alleviate the drawbacks of existing solution methodologies in the literature, this work not only uses a number of modification and hybrid versions of recent and old metaheuristics but also develops four fast heuristics. An extensive comparative study confirms the superiority of fourth heuristic along with modification and hybridizing algorithms proposed when solving the large-scale problems as well as the performance of developed model has been evaluated through a set of sensitivity analyses.

Nowadays, operation managers usually need efficient supply chain networks including important design factors such as economic and social considerations. The recent decade has seen a rapid development of controlling the uncertainty in supply chain configurations along with proposing novel solution approaches. By investigating the related studies, this paper shows that most of the current studies consider the economic aspects and just a few works present the two-stage stochastic programming as well as social considerations to design a closed-loop supply chain network. This motivated our attempts to consider economic and social aspects simultaneously by using the mentioned suppositions among the first studies. Another main contribution of this paper is the hybridization and tuning of a number of recent algorithms to address the problem. The results show that the proposed hybrid metaheuristic algorithms outperform the best existing techniques on the majority of case studies.

The performance of supply chain systems directly depends on the design of distribution networks. On the other hand, uncertainty is one of important and challenging subjects in distribution networks. Another real supposition is that the decisions in a distribution network are usually adopted in a hierarchical manner. By adopting these topics and contrary to previous works, this study firstly develops a two-stage stochastic bi-level decision-making model to simulate the behavior of a distribution network, more efficiently. Generally, the proposed problem is based on the static Stackelberg game between the Distribution Centers (DCs) in the upper-level and Customer Zones (CZs) in the lower-level of the model. Due to the uncertainty of the proposed problem, a financial risk model has been considered as well. The literature reveals that the most of different exact methods are not recommended to solve the bi-level models for large-scale problems. Another contribution of this study is to propose a set of quick heuristics along with two new hybrid metaheuristics based on the benefits of recent and traditional algorithms to solve the developed model in large-scale networks. In order to check the quality of algorithms’ results, an explicit enumeration algorithm structured by e -constraint method is considered in small sizes. A comparative study reveals that although the proposed heuristics reach the optimal solution in less time, the solution quality of hybrid metaheuristics is strongly better than others. Finally, the efficiency of the proposed model is validated by a set of sensitivity analyses through a real industrial example.

Growing environmental concerns and social legislations enforce decision makers to design their supply chains considering environmental and social impacts as well as economical objectives. Degradation difficulties and recovering profits lead to recycle scraped tires regarding the sustainability factors. This paper firstly develops a multi-objective mixed integer linear programming model for designing of sustainable tire closed-loop supply chain network. The proposed model aims to optimize total cost, environmental impacts of establishment of facilities, processing of tires and transportation between each level as well as social impacts including job opportunities and work's damages. To alleviate the drawbacks of existing metaheuristic algorithms when solving the large-scale networks, four new hybrid metaheuristic algorithms based on the advantages of recent and old ones are developed. To evaluate the quality of the proposed hybrid algorithms, extensive computational experiments, comparison, and sensitivity analyses are conducted with different criteria. Results reveal that hybrid algorithms are effective approaches to solve the underlying problem in large-scale networks.

The Closed-loop Supply Chain (CLSC) is one way to collect and recycle scraped tires. CLSC consists of a set of suppliers, manufacturers, distributors and customers in a forward system. Collectors and recyclers are formed a reverse one as well. The decisions in such systems are adopted in a hierarchy. Regarding this issue, this study develops a tri-level programming model to design the location-allocation of the tire CLSC for the first time. The proposed model is formulated on the static Stackelberg game between manufacturers, distributors and collectors in the framework of CLSC. The performance of the current exact solutions for this problem suffers from degraded performance when solving large-scale problems. To alleviate this drawback, this study proposes a number of new hybrid optimizers by considering the benefits of recent metaheuristics. An extensive comparative study confirms the efficiency of developed model and the performance of the hybrid optimizers proposed when solving the large-scale problems.

Nowadays, there is a great deal of attention for regulations of carbon emissions to enforce the decision-makers of production and distribution networks to redesign their systems satisfactorily. The literature has seen a rapid interest in developing novel metaheuristics to solve this problem as a complicated optimization problem. Such difficulties motivate us to address a production-distribution network design problem considering carbon emissions policies among the first studies in this area by a novel hybrid whale optimization algorithm. Accordingly, a mixed integer non-linear programming model has been developed. To tackle the proposed problem, a new hybrid metaheuristic based on whale optimization algorithm and simulated annealing as a successful optimizer is employed to solve the proposed problem. The calibration of the algorithms has been designed by Taguchi method, comprehensively. Finally, an extensive analysis has been evaluated through a comparative study along with some assessment metrics of Pareto solutions.

In today's world, environmental impacts usually motivate the governments and international comities to control special properties of the designing supply chain networks at the early steps. Closed-loop Supply Chain (CLSC) is one way to consider the recycling and the remanufacturing of used products to control environmental considerations more efficiently. Besides, uncertainty management is one of significant tools for controlling and predicting the CLSC’s behavior for managers. The literature shows that using the stochastic models to design a CLSC is still scare and needed. Hence, this paper is among the first studies to develop a two-stage stochastic multi-objective model for a CLSC by considering the environmental aspects and downside risk, simultaneously. To solve the model, a number of memetic metaheuristics has been considered. Furthermore, e-constraint method is used to validate the metaheuristic results in small sizes. The parameters of the proposed algorithms are tuned by Response Surface Method (RSM) via an MODM approach. A comparative study confirms the efficiency and effectiveness of Virus Colony Search (VCS) as a recent nature-inspired algorithm in comparison with other well-known and recent metaheuristics. Results show the importance of controlling the uncertainty to improve the environmental and economical aspects of CLSC through an industrial example.

Although several meta-heuristics have been developed in the last two decades, most of them are population-based, undergo many steps along with several parameters that make them hard to understand and code. In addition, there are same procedures in recent metaheuristics which make them similar. So, the researchers usually are confused to select a metaheuristic and cannot find any superiority or at least in any algorithms. Because of this, the researchers still use the old algorithms instead of the recent ones. Contrary to previous work, this paper aims to develop a simple, intelligent and new single-solution algorithm that has just four main steps and three simple parameters to tune. Social Engineering Optimizer (SEO) starts with two initial solutions divided into attacker and defender. The attacker obtains the rules of Social Engineering techniques to reach its desired goals. By these simple features, the algorithm does precisely both intensification and diversification phases. The basis of the algorithm depends on how an attacker attacks to a defender by four different associated techniques. Finally, the proposed SEO is applied to solve a set of benchmark functions, important engineering and multi-objective optimization problems. The result shows its superiority in comparison with other well-known and recent meta-heuristics. A new single-solution meta-heuristic approach inspired by Social Engineering is developed.The proposed easy and intelligent method has four main steps and just three simple parameters to tune.The SEO is tested on the different benchmark functions, engineering and also multi-objective optimization problems.The algorithm gives the opportunity to a user to select the suitable technique among four ones.The results confirm the performance and effectiveness of SEO in practice.

Nowadays, several methods in production management mainly focus on the different partners of supply chain management. In real world, the capacity of planes is limited. In addition, the recent decade has seen the rapid development of controlling the uncertainty in the production scheduling configurations along with proposing novel solution approaches. This paper proposes a new mathematical model via strong recent meta-heuristics planning. This study firstly develops and coordinates the integrated air transportation and production scheduling problem with time windows and due date time in Fuzzy environment to minimize the total cost. Since the problem is NP-hard, we use four meta-heuristics along with some new procedures and operators to solve the problem. The algorithms are divided into two groups: traditional and recent ones. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as traditional algorithms, also Keshtel Algorithm (KA) and Virus Colony Search (VCS) as the recent ones are utilized in this study. In addition, by using Taguchi experimental design, the algorithm parameters are tuned. Besides, to study the behavior of the algorithms, different problem sizes are generated and the results are compared and discussed.

Supply chain network design (SCND) is one of the important, primary and strategic decisions affecting competitive advantages and all other decisions in supply chain management. Although most of papers in SCND focus only on the economic performance, this study considers simultaneously economic, social and environmental aspects. In this study, a new mixed integer nonlinear programming model is developed to formulate a multi-objective sustainable closed-loop supply chain network design problem by considering discount supposition in the transportation costs for the first time. In order to address the problem, not only traditional and recent metaheuristics are utilized, but also the algorithms are hybridized according to their strengths especially in intensification and diversification. To evaluate the efficiency and effectiveness of these algorithms, they are compared with each other by four assessment metrics for Pareto optimal analyses. Although the results indicate the performance of three proposed new hybridization algorithms, KAGA achieves better solutions compared with the others, but it needs more time. At the end, we introduced a real industrial example in glass industry to verify the proposed model and the algorithms.

The design of supply chain network usually directly influences the performance of location-allocation of facilities, especially for the main parties. This paper firstly addresses the tri-level location-allocation design problem which considers the forward and reverse network, simultaneously. The proposed problem is formulated on the static Stackelberg game between the Distribution Centers (DCs), Customer Zones (CZs) and Recover Centers (RCs) in the framework. The literature reports that most of previous works have utilized the various exact approaches which are not efficient and are so complex. In this study, three old and successful methods consist of Variable Neighborhood Search (VNS), Tabu Search (TS) and Particle Swarm Optimization (PSO), as well as two recent nature-inspired algorithms; Keshtel Algorithm (KA) and Water Wave Optimization (WWO) are utilized. Besides, according the nature of the problem, this study proposes a simple nested approach named as tri-level metaheuristic for the first time in order to solve the large scale problems. The performances of the algorithms are probed by using Taguchi experimental method to set the proper values for the parameters. Eventually, the efficiency of the algorithms is compared by different criteria and validated through a real case study. The obtained results show that tri-level metaheuristics are effective approaches to solve the underlying tri-level models in large scale network.

Based on a triple bottom line concept, sustainable development is characterized by the simultaneous pursuit of economic, environmental and social goals. The implementation of this concept in production scheduling can result in the resolution of a sustainable Distributed Permutation Flow Shop Scheduling Problem (DPFSP). The present study conceptually shifts an energy-efficient DPFSP to a sustainable DPFSP, simultaneously contributing to economic, environmental and social improvements. The study aims not only to minimize the total energy consumption related to production, but also, to maximize, for the first time, the social factors linked to job opportunities and lost working days. Different production centers and technologies are considered as new suppositions to establish a sustainable DPFSP. In this regard, a novel multi-objective mixed integer linear model is developed. To manage the high complexity of the proposed model, a novel multi-objective learning-based heuristic is established, as an extension of the Social Engineering Optimizer (SEO). The applicability of the proposed model is determined in the context of the wood industry in Canada. Several simulated tests are considered to verify the model. The proposed heuristic is compared with one of the other well-known, recent and state-of-the art methods. In order to guarantee a fair comparison, the Taguchi method is used to tune the parameters of the algorithms. Finally, sensitivity analyses are done to assess the efficiency of the proposed model.

Nowadays, releasing the Emerging Pollutants (EPs) in the nature is one of the main reasons for many health and environmental disasters. Amoxicillin as an antibiotic is one of the EPs and categorized as the Endocrine Disrupting Compounds (EDCs) in hazardous materials. Accumulation of amoxicillin in the soil bulk increases the cancer risk, drug resistances and other epidemiological diseases. Hence, the soil bioremediation of antibiotics can be a solution for this problem which is more environmental-friendly system. This study technically creates a bio-engine setup in soil bulk for remediation of amoxicillin based on Aspergillus Flavus (AF) activities and Removal Percentage (RP) of amoxicillin with Aflatoxin B1 Generation (AG) controls. The main novelty is to propose a hybrid computational intelligence approach to do optimization for mechanical and biological aspects and to predict the behavior of bio-engine's effective mechanical and biological features in an intelligent way. The optimization model is formulated by the Central Composite Design (CCD) which is set by the Response Surface Methodology (RSM). The prediction model is formulated by the Random Forest (RF), Adaptive Neuro Fuzzy Inference System (ANFIS) and Random Tree (RT) algorithms. According to the experimental practices from real soil samples in different times and places, concentration of amoxicillin and Aflatoxin B1 are set equal to 25 mg/L (ppm) and 15 μg/L (ppb). Likewise, the outcomes of experiments in CCD-RSM computations are evaluated by curve fitting comparisons between linear, 2FI, quadratic and cubic polynomial equations with considering to regression coefficient and predicted regression coefficient values, ANOVA and optimization by sequential differentiation. Based on the results of CCD-RSM, the RP performance in the optimum conditions is measured around 86% and in 25 days after runtime, the RP and AG are balanced in the safe mode. The proposed hybrid model achieves the 0.99 accuracy. The applicability of the research is done using real field evaluations from drug industrial park in Mashhad city in Iran. Finally, a broad analysis is done and managerial insights are concluded. The main findings of the present research are: (I) with application of bioremediation from fungus activities, amoxicillin amounts can be control in soil resources with minimum AG, (II) ANFIS model has the best accuracy for smart monitoring of amoxicillin bioremediation in soil environments and (III) based on the statistical assessments Aeration Intensity and AF/Biological Waste ratio are most effective on the amoxicillin removal percentage.

The ageing population in most parts of the world becomes a grand challenge for healthcare decision-makers. The care of elderly persons and general hygienic care at patients’ homes are two main reasons to motivate an optimization problem, namely, home healthcare (HHC). A robust plan for caregivers to have sustainable HHC operations management is to consider working-time balancing of caregivers, care continuity and uncertainties, e.g., the uncertainty of patients’ availability in addition to service and travel times as well as the regulations of companies to meet the standards of high-quality home care services. Based on these motivations and challenges to this field, this study firstly established a multi-objective robust optimization of the HHC which is multi-depot, multi-period and multi-service. The demand of each patient in each period may be different due to promptness of services. Each caregiver plays one of the roles of nurses, doctors, physiotherapists and nutritionists. The types of services are directly related to these roles. The objectives were optimizing the total cost of logistic activities as well as the total unemployment time of caregivers and care continuity. As a complicated optimization problem, this study innovated efficient heuristics and an enhanced nature-inspired metaheuristic. Finally, an extensive comparison with regards to the criteria of the multi-objective algorithms’ assessment was conducted. Some sensitivity analyses were conducted to conclude some practical insights.

Energy supply of megacities is considered as an active research topic in the new aspects of urban management, especially in developing countries like Iran. With an introduction to the sustainable development goals, the smart city concept presents a novel idea for providing energy in a city with the use of Artificial Intelligence (AI), renewable energy, such as Photovoltaic (PV) technologies, and Transformational Participation (TP) based on motivational programs for citizens. This study aims to evaluate the electrical energy consumption in Mashhad, Iran, based on machine learning tools and present the dynamic strategies for promoting citizens’ willingness for renewable energy generation based on the experts’ knowledge. The main novelty of this research is simultaneous application of Artificial Neural Network (ANN) and statistical analysis for creating a Decision Support System (DSS). Then, the solar energy potential is appraised by the PV system simulation tool during one year in our case study in Mashhad, Iran. Furthermore, a Classical Delphi (CD) method is applied for motivational strategies and further TP implementation. In particular, the motivational strategies are suggested by 45 experts and then are prioritized in sequential expert meetings. The outcomes of this research indicate that the ANN model can successfully forecast the electrical energy consumption in summer and winter periods with a 99% accuracy. Then, based on the solar energy computations in the PV system, the peak of electrical energy consumption can be controlled in the hottest and coldest months. Last but not least, the superposition of experts’ and citizens’ opinions reveal A4 (sharing benefits of optimized costs with the citizens by solar energy generation), B2 (reducing the electrical energy cost for solar energy generation, especially in peak times) and C1 (creating the energy coin in the city with credits instead of spending money in urban activities fits to solar energy generation) as the main motivational strategies for solar energy generation in short, middle and long-term planning horizons.

There is so much interest in online purchasing within supply chain networks nowadays. After expanding the internet access and services, customers’ behavior has changed. Today, a customer’s shopping manner usually begins with the internet search. With this approach, we face some new trends in this field, such as online-to-offline (O2O) commerce that aims to balance online and offline sales. Regarding the supply chain management, the O2O commerce can help the managers to conduct both online and offline businesses. The tire industry is one of the applications of the O2O approach, which also directly affects the supply chain network design (SCND). Therefore, this work for the first time proposes a dual-channel, multi-product, multi-period, multi-echelon closed-loop SCND under uncertainty for the tire industry. To tackle the uncertain parameters of the problem (e.g., prices and demand), a fuzzy approach, so-called the Jimenez’s method, is applied. Another main innovation of this work is two new hybrid meta-heuristic algorithms with new procedures. Two recent nature-inspired algorithms (i.e., red deer algorithm (RDA) and whale optimization algorithm (WOA)) are hybridized with the genetic algorithm (GA) and simulated annealing (SA) to strengthen the diversification and intensification phases, respectively. The numerical experiments demonstrate that the hybrid version of WOA and SA returns high-quality solutions and requires an acceptable amount of computational time. The conducted sensitivity analyses underline the importance of tire remanufacturing. Furthermore, setting the appropriate prices in different channels for the available tire types is critical for sustainable tire supply chain management.

This paper describes an efficient and low cost material for treatment of Cu2+, Pb2+ and malachite green from aquatic environments. Aerated Autoclave Concrete (AAC) was used as a low cost bed and modified by chitosan to synthesize of AAC CH composite. Then, it was analyzed by the Energy Dispersive X-ray spectroscopy (EDX), X-Ray Fluorescence (XRF), Field Emission Scanning Electron Microscopy (FE-SEM) and Fourier Transform Infrared spectrophotometry (FT-IR) analysis. The key factors influencing on the removal percentage among each run including, sample's pH, amounts of adsorbent and contact time were optimized by the Central Composite Design in Response Surface Methodology (CCD-RSM). Based on the obtained results, the optimum conditions for the removal of both Cu2+ and Pb2+ are pH 5.8, amounts of adsorbent: 14 mg and 58 min contact time. Also, for MG, its maximum removal percentage obtained at pH 9.7, 13 mg adsorbent and 55 min removal time. In addition, the variations of adsorptive behaviors were scrutinized by Adaptive Network-based Fuzzy Inference System (ANFIS) based on Sugeno model with focusing on removal percentage predictions. Also, to evaluate the adsorption mechanism, Dubinin–Radushkevich (D-R), Langmuir, Temkin, Freundlich (two parameter equations) and Sips, Khan and Toth isotherms (Three parameter equations) were appraised and the outcomes demonstrate that the adsorptive reaction of Pb2+, Cu2+ and MG governed by the Freundlich isotherm with the maximum adsorption capacities of 78.12, 56.82 and 833.33 mg g−1, correspondingly. Likewise, outputs of the D-R and Temkin isotherms show that the physiosorption process is the main interaction of analytes with AAC CH composite. The consequences of the kinetic modeling illustrated that the adsorptive reaction of Cu2+, Pb2+ and MG followed by the pseudo second order adsorption kinetic. The geometry kinetic computing depicted that the adsorption/desorption rates don't have any interfering during reaction process. The advantages such as high efficiency, low cost, reusability and green aspect, make AAC CH composite as a high performance adsorbent for decontamination of Pb2+, Cu2+ and MG from water resources.

The quick growth of urbanization and population, as well as the transformation of industrial and materials, have pushed the management of municipal solid waste into a crisis especially for developing markets based on the grand challenge of sustainable development. The compounding complexity of the multiple objectives and dynamic problem constraints required to represent coordinated solid waste management (CSWM) problem in practice is a hugely significant issue for vehicle routing problem studies. The purpose is to introduce a new coordinated framework for a practical and efficient vehicle routing problem considering the triple bottom line of sustainability. The CSWM multiple objective functions applied in this study incorporate financial, environmental and social considerations to develop a sustainable vehicle routing problem considering heterogeneous vehicle fleets operating across a multi-echelon logistics network with the optimization goals. An entirely novel development and application of the adaptive memory social engineering optimizer (AMSEO) is introduced and is shown to perform significantly better than the simulated annealing (SA) as well as the social engineering optimizer (SEO) itself. Finally, the potential overall waste disposal cost savings achievable through increased recycling (revealed by framing the logistics problem across several echelons) is of particular significance. The main findings are the practical solutions with the use of sustainability goals for the CSWM and further application and development of the AMSEO in the routing optimization.

The purpose of this paper is to develop a framework to identify, analyze, and to assess supply chain disruption factors and drivers. Based on an empirical analysis, four disruption factor categories including natural, human-made, system accidents, and financials with a total of sixteen disruption drivers are identified and examined in a real-world industrial setting. This research utilizes an integrated approach comprising both the Delphi method and the fuzzy analytic hierarchy process (FAHP). To test this integrated method, one of the well-known examples in industrial contexts of developing countries, the ready-made garment industry in Bangladesh is considered. To evaluate this industrial example, a sensitivity analysis is conducted to ensure the robustness and viability of the framework in practical settings. This study not only expands the literature scope of supply chain disruption risk assessment but through its application in any context or industry will reduce the impact of such disruptions and enhance the overall supply chain resilience. Consequently, these enhanced capabilities arm managers the ability to formulate relevant mitigation strategies that are robust and computationally efficient. These strategies will allow managers to take calculated decisions proactively. Finally, the results reveal that political and regulatory instability, cyclones, labor strikes, flooding, heavy rain, and factory fires are the top six disruption drivers causing disruptions to the ready-made garment industry in Bangladesh.

An efficient product distribution is critical for proper supply chain operations. Many supply chains handle perishable products that decay over time. Due to mismanagement of supply chain operations, a significant portion of perishable products is wasted, resulting in substantial monetary losses. Cross-docking terminals (CDTs) have been widely used in cold supply chains for the product distribution but have not received adequate attention in the scientific literature. To improve the efficiency of perishable product distribution, this study introduces for the first time a novel mixed-integer mathematical formulation for the truck scheduling optimization at a cold-chain CDT. The model explicitly captures the decay of perishable products throughout the service of arriving trucks and accounts for the presence of temperature-controlled storage areas that are specifically designated for perishable products. The objective minimizes the total cost incurred during the truck service. Considering the complexity of the proposed model, a customized Evolutionary Algorithm is developed to solve it. The computational performance of the developed algorithm is assessed throughout the numerical experiments based on a detailed comparative analysis against the other metaheuristics. The developed Evolutionary Algorithm is found to be the most promising metaheuristic, considering both solution quality and CPU time perspectives. Furthermore, the proposed algorithm demonstrates an acceptable stability of the solution quality at termination. A set of additional sensitivity analyses are performed in order to draw some significant managerial implications, which would be of potential interest to the supply chain stakeholders that are involved in the distribution of perishable products in cold supply chains.

This paper deals with a closed-loop supply chain network and proposes new approaches in metaheuristics and exact methods as solution methods. Moreover, the downside risk is incorporated into the objective functions as a risk measure. Hence, the developed two-stage stochastic model aims to minimize the expected total cost and the downside risk, simultaneously. Besides, this study presents the closed-loop network which considers the forward and reverse networks in an integrated manner. In the forward network, this case just considers the forward network, while, the reverse logistic fully focused on the backward network by considering recovering centers (i.e. from recovering centers to remanufacture, recycling and disposal centers). In order to address the problem, ICA, PSO, GA, and also ɛ-constraint method, are utilized. In addition, the parameters of algorithms are tuned by Response Surface Method (RSM) with an MODM approach. To explain the efficiency and effectiveness of methods, four assessment metrics are introduced. At the end, the results show the capability of ICA through the most of the tests problem. According to the risk management, a real data set is used to do some sensitivity analyses on the proposed model.