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Towards an efficient approach for identification and selection of stakeholder engagement strategies: a case study
(Technical University of Liberec, 2021-01-01)
The goals and objectives of a project as well as the needs, requirements and expectations of the project stakeholders may contradict or non-fulfillment of them may have different detrimental and negative consequences for the project. Therefore, project stakeholders should be effectively managed, but it is not possible to satisfy all project stakeholders and meet all of their expectations and requirements. As a result, project team must strike a balance between the project goals and objectives and the needs, requirements and expectations of the project stakeholders in order to complete the project successfully. Despite highlighting the significant importance of project stakeholder management, there exists a notable gap in exerting an effective decision support system to adopt stakeholder engagement strategies particularly in oil and gas construction projects. This study proposes a comprehensive framework for the identification, prioritization and selection of the stakeholder engagement strategies in one of the large size oil and gas construction projects in Iran. In this paper, a hybrid method which is the combination of the SWOT (strengths, weakness, opportunities and threat) analysis and fuzzy Delphi method is first exploited for identifying the appropriate stakeholder engagement strategies. Subsequently, fuzzy SWARA (Step-wise Weight Assessment Ratio Analysis) is employed to weight the crucial criteria, and finally, fuzzy WASPAS (Weighted Aggregated Sum Product Assessment) is utilized to prioritize the identified stakeholder engagement strategies. This research contributes to the body of knowledge on project stakeholder management by presenting a novel framework for identifying, ranking and selecting the suitable strategies for effective stakeholder engagement considering one of the largest oil and gas construction projects in the country. The value of this study is in applicability of the proposed methodology for project managers and practitioners in other oil and gas construction projects.
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Identification and selection of stakeholder engagement strategies: case study of an Iranian oil and gas construction project
(Taylor & Francis, 2021-03-19)
Despite emphasizing the great importance of stakeholder management, there is a gap in applying a decision support system to adopt sustainable strategies for stakeholder engagement and conflict resolution especially in oil and gas projects. This paper presents a new framework for adopting the strategies in one of the largest oil and gas projects in Iran. In this proposed framework, firstly, the hybrid SWOT analysis and fuzzy Delphi method are used to identify and rank the sustainable stakeholder engagement strategies for managing conflicts. Then, a mathematical programming model was proposed to select the optimal strategies to maximize stakeholder engagement. The findings indicate that the results of the hybrid SWOT analysis and fuzzy Delphi method and the mathematical programming model are the same and the high ranked strategies obtained by the hybrid SWOT analysis and fuzzy Delphi method are selected in the proposed mathematical programming model as well. Also, each selected strategy can cover more than one conflict and improve stakeholder engagement.
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Application of three metaheuristic algorithms to time-cost-quality trade-off project scheduling problem for construction projects considering time value of money
(MDPI, 2021-12-01)
Time, cost, and quality have been known as the project iron triangles and substantial factors in construction projects. Several studies have been conducted on time-cost-quality trade-off problems so far, however, none of them has considered the time value of money. In this paper, a multi-objective mathematical programming model is developed for time-cost-quality trade-off scheduling problems in construction projects considering the time value of money, since the time value of money, which is decreased during a long period of time, is a very important matter. Three objective functions of time, cost, and quality are taken into consideration. The cost objective function includes holding cost and negative cash flows. In this model, the net present value (NPV) of negative cash flow is calculated considering the costs of non-renewable (consumable) and renewable resources in each time period of executing activities, which can be mentioned as the other contribution of this study. Then, three metaheuristic algorithms including multi-objective grey wolf optimizer (MOGWO), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective particle swarm optimization (MOPSO) are applied, and their performance is evaluated using six metrics introduced in the literature. Finally, a bridge construction project is considered as a real case study. The findings show that considering the time value of money can prevent cost overrun in projects. Additionally, the results indicate that the MOGWO algorithm outperforms the NSGA-II and MOPSO algorithms.
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The sustainable two-echelon location-routing problem considering combined open and closed routes under uncertainty
(Research Square, 2022-03-14)
Location-routing is an extremely important problem in supply chain management. In the location-routing problem, decisions are made about the location of facilities such as distribution centers as well as the set of vehicle routes. Today, organizations seek to reduce the transportation cost by outsourcing which leads to a specific type of transportation problems called open routing. On the other hand, the growing concerns of environmental impacts have led to paying more attention to environmental issues and reducing the environmental impacts of logistics activities. To this end, in this paper, both open and closed routes are simultaneously addressed by developing a multi-objective mixed integer linear programming model that included three economic, environmental, and social responsibility aspects. The three objective functions of the proposed model encompass the minimization of total costs and greenhouse gas emissions, and the maximization of employment rate and economic development. Also, in this study, a different type of routing is considered in each echelon. A small-sized problem instance is solved using the Augmented Epsilon Constraint (AEC) method with the CPLEX Optimizer Solver for the validation of the proposed model. Due to the NP-Hardness of the problem, two efficient metaheuristic algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Stochastic Fractal Search (MOSFS) are exploited to solve the medium and large size problems. The performance of the algorithms is compared in terms of time, MID, diversity, spacing, SNS, and RAS indexes. The results show that the MOSFS algorithm outperforms the NSGA-II based on several indexes.
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A green project scheduling with material ordering problem considering cost constraints and quality of materials
(Emerald Publishing Limited, 2024-06-17)
Purpose Integrating project scheduling and material ordering problems is vital in realistically estimating project cost and duration. Also, the quality level of materials is important as one of the key project success factors. Design/methodology/approach In this paper, a three-objective mathematical model is presented for green project scheduling with materials ordering problems considering rental resources. The first objective is to minimize the total cost of the project site and logistics. The second objective is to minimize the environmental impacts of producing materials and the third objective is to maximize the total quality of materials. Since costs trigger several challenges in projects, cost constraints are considered in this model for the first time and also the cost of delay in supplying of materials by the suppliers has been deducted from the project costs. Subsequently, the model was implemented in a real case and solved by the Lagrangian Relaxation algorithm as an exact method on GAMS software for model validation. Findings Based on sensitivity analysis of some parameters, the findings indicate that the cost constraint and lead time have considerable effects on the project duration. Also, integrating project scheduling and material ordering improves the robustness of the project schedule. Originality/value The primary contributions of the present research can be stated as follows: considering the cost constraints in the project scheduling with material ordering problem, incorporating the rental resources and taking the quality levels of materials as well as the environmental impacts into account.
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A sustainable hub location-allocation model considering the inspection of defective wagons in the rail freight network
(Inderscience Publishers, 2024-06-15)
The increasing demand for rail transport necessitates an effective transportation network design for the shipment of goods with minimum cost and time. Since the breakdown of wagons is a main delay factor in rail transportation, the maintenance and repairs of defective wagons becomes prominent. In this study, main stations are considered as hubs, and hubs are places where defective wagons are collected. For this purpose, a robust multi-objective mathematical model is proposed to minimise transportation costs considering customer demand. Also, the model seeks to minimise the total transportation time and emissions. The AEC method is exploited to solve and validate the proposed model. Moreover, the sensitivity analysis is performed to demonstrate the effect of changing the main parameters on the outcomes. The results show that repairs and maintenance can affect the capacity. Also, the findings demonstrate the applicability and validity of the proposed model in the railway sector. [Submitted: 29 May 2023; Accepted: 16 January 2024]
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A Two-Echelon Location Routing Problem Considering Sustainability and Hybrid Open and Closed Routes Under Uncertainty
(Elsevier Ltd, 2023-03-01)
Location-routing is an extremely important problem in supply chain management. In the location-routing problem, decisions are made about the location of facilities such as distribution centers as well as the set of vehicle routes. Today, organizations seek to reduce the transportation cost by outsourcing leading to a particular kind of transportation problems known as open routing. However, the increasing attention to environment have led to paying more attention to environmental issues and reducing the environmental impacts of logistics activities. To this end, in this paper, both open and closed routes were simultaneously addressed by developing a multi-objective mixed integer linear programming model that included three economic, environmental, and social responsibility aspects. The three objective functions of the proposed model encompass the minimization of total costs and greenhouse gas emissions, and the maximization of employment rate and economic development. Also, in this study, a different type of routing was considered in each echelon. A small-sized problem instance was solved using the Augmented Epsilon Constraint (AEC) method with the CPLEX Optimizer Solver for the validation of the proposed model. Moreover, the sensitivity analysis was performed to investigate the effect of changing main parameters on the values of the objective function. Due to the NP-Hardness of the problem, two efficient metaheuristic algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Stochastic Fractal Search (MOSFS) were exploited to solve the medium and large size problems. The performance of the algorithms was compared on the basis of six different well-known indexes of Time, MID, RAS, Diversity, Spacing, and SNS. According to the obtained results, the performance of the MOSFS algorithm was %20, %9, %11.22, %10.03, and %19.06 higher than the performance of the NSGA-II on the basis of SNS, RAS, MID, Diversity, and Time indexes, respectively. On the other hand, the NSGA-II performance was %6.3 higher than the MOSFS performance in terms of Spacing index.
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A sustainable hub location-allocation model considering the inspection of defective wagons in the rail freight network
(Inderscience Publishers, 2025-01-01)
The increasing demand for rail transport necessitates an effective transportation network design for the shipment of goods with minimum cost and time. Since the breakdown of wagons is a main delay factor in rail transportation, the maintenance and repairs of defective wagons becomes prominent. In this study, main stations are considered as hubs, and hubs are places where defective wagons are collected. For this purpose, a robust multi-objective mathematical model is proposed to minimise transportation costs considering customer demand. Also, the model seeks to minimise the total transportation time and emissions. The AEC method is exploited to solve and validate the proposed model. Moreover, the sensitivity analysis is performed to demonstrate the effect of changing the main parameters on the outcomes. The results show that repairs and maintenance can affect the capacity. Also, the findings demonstrate the applicability and validity of the proposed model in the railway sector. [Submitted: 29 May 2023; Accepted: 16 January 2024]
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A Bi-Objective Mathematical Programming Model for a Maximal Covering Hub Location Problem Under Uncertainty
(SAGE Publishing, 2025-01-01)
Properly locating these facilities is a substantial factor in the success of the logistics systems. In this paper, a bi-objective mathematical model for a maximal covering hub location problem is presented to minimize time and environmental risks. The Goal Attainment method was employed to solve the small-sized problems for model validation. Since the problem is NP-Hard, the Multi-Objective Imperialist Competitive Algorithm (MOICA) meta-heuristic algorithm was exploited for solving the medium and large-sized problems. The performance of MOICA was compared with the performance of the Goal Attainment method and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to validate the proposed model and solution approach. This paper can direct the logistics companies to reduce the cost, time, and environmental effects of their transportation networks. In addition, this research can optimize energy consumption in the transportation sector for the continuation of low-cost services and reduce fuel consumption, which leads to reducing environmental pollution.
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