This paper focuses on inverse data envelopment analysis (DEA) under the following question:” If the inputs of DMU_o increase, how much should the outputs of DMU_o increase to decrease the efficiency score of DMU_o ? In these problems, a non-radial enhanced slack-based model is employed in the presence of hybrid interval data, which includes both continuous and integer interval data. Under this scenario, novel necessary and sufficient conditions to estimate outputs are considered based on the Pareto solution of related multi-objective nonlinear programming (MONLP). Furthermore, an example is presented to illustrate the problems associated with hybrid interval data. Finally, the conclusion and future work are presented

Purpose The purpose of this study is to investigate resource-constrained multi-project scheduling problems (RCMPSP) involving uncertainty in the form of time-dependent renewable resource reliability. A key focus is to minimize the makespan (completion time) of projects when resources can become unavailable or fail over time at non-constant rates. Accounting for realistic resource reliability seeks to provide scheduling solutions that better reflect potential delays in practical multi-project environments. Design/methodology/approach A new discrete-time binary integer programming formulation of RCMPSP is expanded to include time-dependent resource reliability and simultaneously evaluate the time-dependent failure rate and constant repair rate of a resource. A new hybrid immune genetic algorithm with local search (HIGALS) is developed to solve this NP-hard problem. HIGALS incorporates a new coding mechanism, initialization method and local search operator. Findings A case study tests the proposed HIGALS approach. The validity of the mathematical model is confirmed by solving small-sized problems with GAMS software. The proposed HIGALS algorithm is validated by solving small-sized problems and comparing its solutions with GAMS. The superiority of HIGALS is demonstrated by comparing its solutions with six basic algorithms on medium- and large-sized problems. Results show that HIGALS outperforms existing algorithms, achieving an average reduction in makespan of over 11.79%, while maintaining the advantages of genetic, immune and local search algorithms and avoiding their disadvantages. Practical implications Considering time-dependent resource reliability can help project managers plan for disruptions and delays in resource-critical projects. HIGALS provides decision support for robust multi-project scheduling. Originality/value This study contributes to the field by investigating RCMPSP with time-dependent renewable resource reliability, which reflects real-world uncertainty more accurately. HIGALS presents a novel approach to balance intensification and diversification for this challenging problem.

Evaluation of sustainable circular suppliers in the project-driven supply chain (PDSC) is critical because project-driven companies are progressively dependent on their suppliers due to the rising trend of subcontracting and focusing on their core. Since construction operations expend 50 % of all extracted materials and generate 33 % of all waste, evaluating suppliers from a sustainable circular view is essential. Also, in many practical situations of multi-criteria decision-making (MCDM) challenges, assessments of suppliers through the sustainable circular criteria may not be fixed in some states. This kind of MCDM problem is recognized as stochastic MCDM (SMCDM). In this paper, a new scenario-based MCDM model is introduced. Also, interval-valued fuzzy soft stochastic sets (IVFSSSs) are presented to cope with SMCDM problems. The beta distribution and its median are used to tackle the uncertainty of SMCDM problems. Moreover, to use the merits of the best worst method (BWM), it extended under IVFSSS to better address the diverse possible states. The BWM method is more powerful than traditional methods, like AHP, and is used to determine criteria weights. Furthermore, the weighted distance-based approximation (WDBA) approach is extended under IVFSSS to rank suppliers. Eventually, two actual cases of the PDSC are solved to illustrate the practicality of the proposed model. Reverse logistics and energy consumption in production and recycling products have been the most important criteria in the first case study, and cost and quality in the second study. Suppliers 7 and 10 in the first case and suppliers 5 and 4 in the second case have been the best, respectively. Finally, activities 8, 10, and 12 had the lowest level of sustainable circularity in the first case and B and B1 in the second case.

Investing in renewable energy has been a vital aspect of different governments’ attempts to handle unwanted negative social and environmental impacts of fossil-based energy production. Biomass energy production is considered one of the proper renewable alternatives to replace fossil fuels. Therefore, the optimal design of biofuel energy production networks is an essential part of strategic decision-making to enhance social, environmental, and economic aspects of attempts to produce clean energy. This paper investigates various aspects impacting biorefinery supply chain network design. This includes the possible disruptions in feedstock production as well as, market competition and interactions that can affect feedstock prices. As a result, a mathematical model is developed that minimizes the costs of network design under uncertainty in feedstock demands. The model computes the required demand of feedstock as well as other key parameters that are important in this agent-based simulation process. This is done by developing an agent-based learning approach based on the Roth-Erev method, which considers agents involved in feedstock pricing and flows between the main elements of its supply chain. Then, the final price of feedstock and the tradeoffs among the agents are applied as inputs in the mathematical model to relocate each facility in its supply chain. The data is computed and updated in the mathematical model by using the agent-based approach until the locations of the facilities in two consecutive runs stay intact. The applicability and validity of the model are investigated through a case study presentation. Moreover, the existing literature is utilized to validate each part of the proposed approach

Risks are uncertain events that can affect criteria such as project cost, quality, and completion time and ultimately lead to project failure. That is why the project risk management process, which is one of the most fundamental parts of project management, must be done properly. In this paper, a new multi-objective fuzzy model is proposed to respond appropriately to the primary and secondary project risks. The contributions, such as risk interdependency, considering the project with multi-mode activities, resource considerations, as well as attention to interval-valued fuzzy uncertainties are regarded simultaneously for the first time. The purpose of the proposed multi-objective mathematical model is to minimize cost, quality reduction, and project completion time. Then, a new two-stage uncertain solution approach is introduced in this paper. In this solution approach, using an equivalent method in the first step and then using an extended multi-choice goal programming method in the second step, for both lower and upper limits of the membership functions for interval-valued fuzzy numbers, the computational processes are performed. To show the application and effectiveness of the proposed model, a case study adapted from the literature is given. Finally, sensitivity analysis is conducted to analyze the behavior of the developed mixed-integer programming model. The analysis of obtained results indicates that the utilization of the proposed model leads to better and more appropriate solutions.

Setting the optimal vehicle routes and the optimal level of inventory to avoid shortage, in addition to reducing costs of transportation, are two of the main objectives in developing the distribution systems of perishable goods. Despite its importance, an area that still requires more investigation is the simultaneous consideration of hub location selection and routing and warehousing operations. In other words, addressing decisions related to the locations of the warehouses, vehicle routing, and warehouse management at the same time could improve the outcome. Therefore, this paper presents a new non-linear mixed-integer model for warehouse location selection, in addition to an integrated multi-objective mixed-integer model for routing and warehousing operations. Moreover, features of real operations are considered, such as dynamic conditions, order picking, and delivery, customer prioritization, time windows for customers, and different perishable goods. Minimizing the operations' costs and time under uncertain conditions is achieved by proposing a possibilistic-robust optimization algorithm. The optimization approach regards the weight of each objective using the opinions of experts by using a novel hesitant fuzzy approach. Finally, a case study is presented, and the results are compared with the actual data along with validating the model by using several numerical examples. The results have shown that the method can perfectly form the supply chain of perishable goods. Furthermore, the optimization method can provide the experts with more flexibility in finding a compromise solution.

This paper introduces a novel two-phase framework for designing a proactive–reactive scheduling model in the multi-mode resource-constrained project scheduling problem under disruptions. The proactive phase involves constructing a resilient baseline scheduling model using a mixed-integer linear programming model. This phase contributes to a multi-objective model that minimizes the project completion time and total project cost while maximizing resilience criteria. In this context, resilience refers to allocating float time to project activities to protect their start and finish times against future disruptions as much as possible. The reactive phase involves a bi-objective mathematical model that mitigates the impact of disruptions through preempt-repeat, preempt-resume, and activity-crashing strategies. Real-world projects involve many uncertain parameters that can negatively affect the optimization of rescheduling problems if overlooked. Therefore, for the first time, a scenario-based robust optimization approach is proposed to cope with the uncertainty of the reactive phase. Additionally, a novel hybrid multi-objective method based on goal programming is introduced to solve the proposed multi-objective model. Finally, to demonstrate the capability of the proposed approach, an oil and gas project in Iran is regarded as a real case study. The results indicate that the negative impact of disruptions on the makespan and total cost of the project can be largely mitigated by considering resilience criteria in the proactive phase and preempt-repeat, preempt-resume, and activity-crashing strategies in the reactive phase.

Energy demands worldwide have been rising for a while and will continue in recent years. Most of today's energy conditions are met by non-renewable energy sources, which contaminate the conditions and consume pretty rapidly. Renewable energy alternatives, i.e., biomass, can be regarded as inexpensive, reliable, secure, and sustainable energy for ever-increasing people. To choose the best renewable energy alternative to meet the rising energy needs, various elements, such as economic, social, and environmental, must be considered by decision-makers. Thus, this paper examines a new weighting method to compute the criteria weights and experts weights with a new integrated dynamic interval-valued hesitant fuzzy set (DIVHFS). The introduced decision-maker weighting method is based on the direct and indirect decision matrixes. Afterward, the criteria weights are computed using a new maximizing deviation method and the proposed entropy approach under DIVHFS conditions. Afterward, a new soft computing ranking method is proposed based on the positive and negative ideal solution values under DIVHFS to rank the main alternatives that are related to oilseeds products. A sensitivity analysis is discussed on experts’ weights and criteria weights. In this respect, the amount of experts’ weights changes to measure its impacts on the criteria weights. Furthermore, the dependency of the criteria weights on final ranking results is obtained by changing the weights among each other. A comparative analysis is introduced to compare the proposed model with two existing ranking methods in the current literature. The results indicate that jatropha is the optimum oilseed to select in the presented case study.

The selection of appropriate solution strategies to deal with the disruption in project management has become an essential topic. The evaluation of the ranking of solution strategies for dealing with disruption as a complex multi-criteria decision-making (MCDM) problem includes several alternatives with conflicting criteria in the implementation phase of the project. This paper introduces a new MCDM method by a novel integration of grey relational analysis (GRA) and measurement alternatives and ranking according to the compromise solution (MARCOS) approaches under interval-valued fuzzy sets (IVFSs). The contribution of this paper is not only to extend the MARCOS method with both GRA techniques and IVFSs but also to propose a method for weighting the criteria that considers both the subjective and the objective weight based on entropy in an integrated manner. In this proposed method, the subjective weights assigned by decision makers (DM) and the objective weights are based on interval Shannon's entropy theory. Subsequently, the proposed method is evaluated through an empirical example, which demonstrates its applicability and validity in prioritising solution strategies for managing project disruption. The paper also provides managerial insights.

The organ transplantation network has an important role in saving people with disabilities. The proposed network in this paper considers various components, including donor hospitals, isolation hospitals, additional isolation hospitals, and transplant centers. The donor hospitals transfer the brain-dead individuals to isolation hospitals or additional isolation hospitals. These hospitals start to remove the organs from bodies and, after testing, send them to transplant centers. However, this process can lead to queuing issues, thereby increasing wait times for recipients. Once healthy organs are transferred to transplant centers, the transplant operation can commence. The organ's characteristics are pivotal in ensuring successful matching with the recipient. Coordination within the network is facilitated through the organ procurement unit. For this purpose, a novel mixed-integer non-linear problem (MINLP) model is presented with three objective functions: minimizing network costs, reducing network times, and minimizing outdated units. A new integrated robust possibilistic solution approach is suggested to deal with uncertain parameters and to manage the multi-objective nature of the problem. To analyze the performance of the presented model, a real case study in Iran is applied with four scenarios. The main results indicate that these scenarios have varying impacts on the primary decision-making process. Consequently, the utilization of air mode vehicles is prioritized over ground vehicles in the second and third scenarios. The expiration time of the shelf life of organs and the duration of ischemia are crucial factors that significantly impact the transfer of organs among different echelons within the transplantation network. Also, the effectiveness of the proposed solution method is examined through two compromise solution approaches from the related literature and compared against a basic model to assess the efficiency. Finally, sensitivity analyses are conducted to assess the performance of the proposed model and solution approach in relation to the introduced network.

PurposeThis paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.Design/methodology/approachA new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.FindingsAccording to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.Originality/valueAn integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores' desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

One of the commitments held out by the project managers is to make sure that the project will end on time, within the stated funding, and with the highest standard. Environmental pollution is an impact of implementing construction projects. Therefore, environmental impact has recently been taken into account to judge project success. In this paper, a new framework is offered that concentrates on project scheduling from the standpoint of duration, expenditure, and environmental trade-off by viewing quality loss expense. To be more precise, the methodology has four steps. First grey critical path (GCP) analysis is carried out. In the second step, activities' environmental impacts on each execution mode under Fermatean fuzzy sets (FFSs) uncertainty are calculated utilizing a multi-criteria decision-making (MCDM) approach. Third, a new mathematical model under grey uncertainty is developed with crashing and overlapping strategies to minimize project duration, expense, quality loss expense, and environmental influences. At last, the fourth step is presented to categorize the activities into three groups based on their criticality level. A case study in the construction field is implemented to validate the methodology. Also, through sensitivity analysis, the methodology is confirmed.

One of the most important areas of infrastructure projects that can contribute to the goals of sustainable development is the energy sector. Forming the right set of energy projects can lead to developing a sustainable environment. Another important aspect is the resilience of energy projects. This paper introduces a novel approach that simultaneously considers resilience and sustainability in forming energy project portfolios. In the first part, the relative importance of each project is computed under a triangular neutrosophic (TN) environment. This approach keeps the vagueness of data while evaluating projects versus resiliency and sustainability criteria. In the second part, an interval mathematical model is presented to maximize the total value of the selected portfolio while addressing bi-level budgeting and skill utilization. In this study, uncertainty is expressed in a hybrid way which enhances the flexibility of computations in addition to providing experts with more freedom in expressing their opinions. To present the application of this method, data from an existing case study has been used, and the process has been implemented step by step. This application demonstrates the importance of addressing resilience and sustainability in energy projects. Moreover, it shows the benefits of applying a hybrid solution approach in presenting the vagueness of energy projects in addition to assigning the budget in a bi-level process.

Contractor selection is a crucial aspect of construction projects, with a significant impact on project success. However, traditional methods may not effectively handle the complexities and uncertainties involved in decision-making. To address this, advanced techniques like Multi-Criteria Decision-Making (MCDM) have been developed. In this study, we propose a new approach based on two uncertain methods, Interval-Valued Fuzzy Step-Wise Weight Assessment Ratio Analysis (IVF-SWARA) and Interval-Valued Fuzzy Combined Compromise Solution (IVF-CoCoSo), for contractor selection in construction projects. These methods use interval-valued fuzzy numbers (IVFNs) to handle decision-making under uncertainty and imprecision. By leveraging the benefits of IVFNs, the proposed methods enhance accuracy and flexibility, enabling more informed and reliable decisions. An application example illustrates the effectiveness of the methods, and sensitivity analysis examines how varying criteria weights affect contractor rankings. The study concludes that the IVF-SWARA and IVF-CoCoSo methods assist decision-makers in selecting suitable contractors and achieving project success. These methods provide a robust framework to navigate complexities and uncertainties, leading to improved decision-making in contractor selection for construction projects.

In today’s world, reverse logistics (RLs) activities have become increasingly crucial for companies looking for improved customer service, cost reduction, and sustainability perspectives. Limited resources, and insufficient technology and knowledge, have led manufacturing firms to cooperate with professional RL providers. However, evaluating the right third-party reverse logistics providers (3PRLPs) is a complex, uncertain multi-criteria decision-making (MCDM) problem, which is affected by many conflicting qualifications, the complexity of the human mind, and imprecise and uncertain information. This paper aims to introduce a novel framework that integrates fuzzy best worst method (BWM) and a new last aggregation fuzzy compromise solution for providing systematic decision support for organizations to select the most preferred partner. The reasons behind choosing both methods in an integrated way are that; the fuzzy BWM requires a smaller number of comparisons but can provide more reliable weights due to consistent criteria comparisons. Furthermore, the approaches based on compromise solutions are powerful decision-making tools because a compromise solution is a more feasible solution closest to the ideal that can be efficient in selecting the best alternative in the presence of conflicting decision criteria. Therefore, the fuzzy BWM approach is utilized to investigate the performance criteria of the 3PRLPs from economic, environmental, and social aspects of sustainability as well as risk factor. Expert weights and the preferences of sustainable 3PRLPs are then calculated simultaneously using a new last aggregation fuzzy compromise solution. Compared with the available literature, the proposed framework considers all possible sustainable criteria along with risk factor, which offers greater flexibility for experts to articulate their evaluations. In addition, the last aggregation fuzzy method eliminates distortion and loss of information and allows decision-makers to control the outcome’s precision. Moreover, the applicability of the proposed method is numerically demonstrated by developing a decision-support tool for the food industry. Sensitivity analysis and comparative analysis further illustrate the flexibility and practicability of proposed framework through which the decision-makers can make more accurate judgments regarding the evaluation of 3PRLPs.

This study introduces a new multi-criteria group decision-making model in organ trans-plant transportation networks under uncertain situations. A new combined weighting approach is presented to obtain expert weights with various kinds of opinions by integrating similarity measure and subjective judgments of experts. Also, the CRITIC approach is given to obtain transportation criteria weights. Finally, a novel integrated ranking approach is proposed to calculate the rank of each alternative based on ideal point solution and relative preference relation (RPR) methods. This study regards an interval-valued intuitionistic fuzzy set to cope with the vagueness of uncertain conditions in a real case study.

This study introduces a new three-stage optimization approach with a circular economy perspective for sustainable-resilient supply chain network design for perishable products. The proposed model specifies the number of facilities and the number of products flowing within the supply chain in case of disruption. The three contributions of the mathematical model are considering product lifetime, monitoring financial resources, and selecting technology levels. It incorporates suppliers’ green image and circular economy rating in the supply chain network structure. Epistemic uncertainty is considered in the optimization model to deal with the unknown capacity, cost, and demand. The study includes three main stages. In the first stage, to determine suppliers' green image and circular economy rating, a new interval-valued fuzzy (IVF)-compromise decision-making method is presented based on possibilistic mean and standard deviation evaluations. In the second stage a new multi-objective mathematical model is proposed to reduce total costs, reduce environmental impacts, and increase social sustainability. To deal with uncertainties in the mathematical model, a new IVF-robust solution approach is introduced. In the third stage, the solution is incorporated with the AUGMECON2 method to produce separate Pareto-optimal solutions, offering a hybrid of trade-offs between cost, emissions, and social responsibility. A case study in the food, dairy, and drink industry is presented to show how the suggested approach might be applied. Finally, several sensitivity analyses and comprehensive comparisons of the proposed approach with the literature were performed.

Reviewing and integrating decisions in the supply chain and project management is crucial. This problem is known as a project-driven supply chain problem. By the growing dependency of project-driven companies on suppliers since they prefer to concentrate on their core and subcontracting processes, the project-based organization's dependence on suppliers has increased. A project-driven supply chain (PDSC) focuses on integrating project management activities into the supply chain. In this paper, a new model is presented for the integration of supply chain and project management decisions. Firstly, a new method is proposed to determine the resilience score of suppliers. This new model consists of three parts: the weighting of criteria, the weighting of experts, and the ranking of suppliers. To give weight to the experts, a new version of the combinative distance-based assessment (CODAS) method is presented based on the average and positive ideal concepts. Also, the importance of the criteria is specified by an expanded best-worst method (BWM). Computing the resilience score of suppliers is done through a developed CODAS method that is extended by the average and positive concepts. Furthermore, in order to consider the uncertainty of project-driven supply chain problems, interval type-2 fuzzy sets (IT2FSs) are used. Secondly, a new subtraction operation is defined to avoid producing negative in recursive critical path method (CPM) calculations in the IT2F environment. Also, the criticality score of the project's activities is defined. According to the criticality and resilience indexes of activities, suggestions are made to project management to prevent delays. Finally, a case study of hospital construction is solved to illustrate the strengths of the proposed model.

One of the most remarkable subjects in multi-criteria group decision-making (MCGDM) is determining the weight and importance of criteria. The weighting methods based on inputs are categorized in the manifold group. This paper presents a novel method for weighting the criteria in a network structure. This approach, namely MOWSCER, is used when the relationships among the criteria are modeled by a cause-and-effect directed graph. The directed graph demonstrates the cause and effect relationship among criteria. In the presented method, the criteria are divided into three groups. The basic idea of the criteria segmentation is derived from European Foundation for Quality Management (EFQM). These groups include (1) effect criteria, (2) cause criteria, and (3) connector criteria. Then, the connector criteria are allocated fewer weights than the other two types. In other words, the introduced method follows two purposes of decreasing criteria number and appropriate allocating of weights among remaining criteria. Accordingly, first, the connector criteria are detected, so they are assigned less weight, and remained criteria are allocated a proper weight according to their importance. Furthermore, a new weighting method for determining the weights of decision makers (DMs) in group decision-making problems is presented to achieve a comprehensive manner. In the end, to prove the practicality of the proposed method, the weights of criteria and DMs are computed in a case study and two illustrative examples. Besides, to confirm the accuracy of that, it is compared with the DEMATEL method.

PurposeMost projects are facing delays, and accelerating the pace of project progress is a necessity. Project managers are responsible for completing the project on time with minimum cost and with maximum quality. This study provides a trade-off between time, cost, and quality objectives to optimize project scheduling.Design/methodology/approachThe current paper presents a new resource-constrained multi-mode time–cost–quality trade-off project scheduling model with lags under finish-to-start relations. To be more realistic, crashing and overlapping techniques are utilized. To handle uncertainty, which is a source of project complexity, interval-valued fuzzy sets are adopted on several parameters. In addition, a new hybrid solution approach is developed to cope with interval-valued fuzzy mathematical model that is based on different alpha-levels and compensatory methods. To find the compatible solution among conflicting objectives, an arithmetical average method is provided as a compensatory approach.FindingsThe interval-valued fuzzy sets approach proposed in this paper is denoted to be scalable, efficient, generalizable and practical in project environments. The results demonstrated that the crashing and overlapping techniques improve time–cost–quality trade-off project scheduling model. Also, interval-valued fuzzy sets can properly manage expressions of the uncertainty of projects which are realistic and practical. The proposed mathematical model is validated by solving a medium-sized dataset an adopted case study. In addition, with a sensitivity analysis approach, the solutions are compared and the model performance is confirmed.Originality/valueThis paper introduces a new continuous-based, resource-constrained, and multi-mode model with crashing and overlapping techniques simultaneously. In addition, a new hybrid compensatory solution approach is extended based on different alpha-levels to handle interval-valued fuzzy multi-objective mathematical model of project scheduling with influential uncertain parameters.