b. vahdani

Providing appropriate home health care is one of the increasing concerns in the health care organizations. Home Health Care provides various services for disabled or elderly individuals at their homes. Also, deal with the current critical situation of the coronavirus disease (COVID-19) due to the limited capacity of hospitals and the feeling of insecurity in crowded places, home health care is more recommended. This paper addresses a Home Health Care Routing and Scheduling Problem (HHCRSP) with two modes of transportations including public and private modes. Also, multi-depot version of the problem is studied to enhance the service delivery in scattered points. In this study, a mathematical model is presented based on a Mixed Integer Linear P rogramming (MILP) whose objective function is minimization of the sum of the travel distance and overtime costs. Furthermore, three meta-heuristic algorithms including Invasive Weed Optimization (IWO), Grasshopper Optimization Algorithm (GOA) and Simulated Annealing (SA) are presented for solving the large-sized problems. Since the performance of meta-heuristic algorithms depends on setting the parameters, Taguchi method is used to statistically set parameters of the developed algorithms. The computational results have shown that the proposed IWO has worked better than the other two proposed algorithms statistically.

The Vehicle Routing Problem (VRP) is one of well-established and important topic in supply chain and logistic networks. The location-routing decision-making has a significant role to optimize the traveling cost per distance, total traveling time and the number of vehicles in the supply chain networks. One of recent advances in these systems refers to container terminals in the global trade centers. The new contributions of this model are to consider the routes of two types of ships (Tramp and Liner) with different properties and the time windows in order to minimize costs. The proposed is solved by an exact method (B&B) as well as because of inherent complexity of problem proposed in the real-world cases, the Genetic (GA) algorithm has been used to find an optimal/global solution in a reasonable time. Finally, an in-depth analysis and discussion is provided to conclude the main findings and practical implications of the results.

Mourning and grief are common experiences that can be stressful events with adverse physical, social, and psychological consequences. This study aimed to design and develop Inter-Organizational Psychosocial Support for the family and survivors of the deceased Covid-19

This qualitative study was conducted in 2021. After a review of literature, several focus group discussions and rounds of Delphi were carried out with 35 experts, who were selected purposively. The interventions were presented to the target group as a pilot for six months.

Inter-organizational Interventions are presented in 5 main stages. Initial social worker assessment, mental assessment by mental health experts, complete a checklist and send it to the expert in charge of social work at the university, providing social and economic interventions by social workers in cooperation with support organizations and finally follow-up, termination, and documentation.

The results of this study can be used as inter-organizational interventions to facilitate the realization of social equality and justice especially for vulnerable people.

The last decade has seen an important role of container terminals in the global trade centers. By another point of view, the high cost of quay cranes on the other hand is a motivation for a set of real-world problems including of Quay Crane Assignment Problem (QCAP) and the Quay Crane Scheduling Problem (QCSP) in the hotspot of research. The main innovation of this proposal is to integrate both QCAP and QCSP to improve Quay Crane (QC) performance by an optimization goal, i.e., QCASP. A real case study in Iran has been applied to validate the proposed problem which has been formulated by a mixed integer linear programming (MILP). Due to inherent complexity of problem proposed in the real-world cases, the Teaching-Learning-Based-Optimization (TLBO) algorithm has been used to find an optimal/global solution in a reasonable time. The applied TLBO has been tuned by Taguchi method and validated in small instances in comparison with an exact method. The computational results show that our proposed TLBO algorithm can solve QCASP, especially in large size instances, successfully. Finally, a set of managerial implications has been recommended to consider the benefits of proposed methodology and algorithm regarding the real case study presented

Resource constrained project scheduling problem with multiple routes for flexible project activities (RCPSP-MR) is a generalization of the RCPSP, in which for the implementation of each flexible activity in main structure of the project, several exclusive sub-networks are considered. Each sub-network is regarded as a route for the flexible activity. The routes are considered for each flexible activity that are varied in terms of: 1) Number of activities required to execute; 2) Precedence relationship between activates; 3) Allocation of different renewable and nonrenewable resources to each activity; and 4) Effectiveness on the duration and cost of project completion. In this paper, a new mathematical formulation of RCPSP-MR is firstly presented. Then, two solving approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize costs of project completion. To evaluate the effectiveness of these proposed approaches, 50 problems (in very small, small, medium, and large-sized test problems) are designed and then are solved; Finally, comparisons are provided. Computational results show that the proposed GA generates high-quality solutions in a timely fashion.

In recent years, the implementation of safety management has been increased in construction projects by institutions, and many companies have recognized environmental and social effects of injuries at project work systems. In this regard, a novel decision model is presented based on a new version of complex proportional assessment method with last aggregation under a hesitant fuzzy environment. The decision makers (DMs) assign their opinions by hesitant linguistic variables that are converted to the hesitant fuzzy elements. Also, the DMs’ judgments are aggregated in last step of decision making to decrease information loss. Since weights of the DMs or professional safety experts and evaluation criteria are not equal in practice, a new version of hesitant fuzzy compromise solution method is proposed to compute these weights. In addition, the criteria weights are determined based on proposed hesitant fuzzy entropy method. A real case study in developing countries about the safety of construction projects is considered to indicate the suitability and applicability of the proposed new hesitant fuzzy decision model with last aggregation approach. In addition, an illustrative example is prepared to show that the proposed approach is suitable and reliable in larger size safety problems