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The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

The purpose of this research is to study the Multi-Skill Resource-Constrained Multi-Project Scheduling Problem (MSRCMPSP), where (1) durations of activities depend on the familiarity levels of assigned workers, (2) more efficient workers demand higher per-day salaries, (3) projects have different due dates and (4) the budget of each period varies over time. The proposed model is bi-objective, and its objectives are minimization of completion times and costs of all projects, simultaneously.

This paper proposes a two-phase approach based on the Statistical Process Control (SPC) to solve this problem. This approach aims to develop a control chart so as to monitor the performance of an optimizer during the optimization process. In the first phase, a multi-objective statistical model has been used to obtain control limits of this chart. To solve this model, a Multi-Objective Greedy Randomized Adaptive Search Procedure (MOGRASP) has been hired. In the second phase, the MSRCMPSP is solved via a New Version of the Multi-Objective Variable Neighborhood Search Algorithm (NV-MOVNS). In each iteration, the developed control chart monitors the performance of the NV-MOVNS to obtain proper solutions. When the control chart warns about an out-of control state, a new procedure based on the Conway’s Game of Life, which is a cellular automaton, is used to bring the algorithm back to the in-control state.

The proposed two-phase approach has been used in solving several standard test problems available in the literature. The results are compared with the outputs of some other methods to assess the efficiency of this approach. Comparisons imply the high efficiency of the proposed approach in solving test problems with different sizes.

The proposed model and approach have been used to schedule multiple projects of a construction company in Iran. The outputs show that both the model and the NV-MOVNS can be used in real-world multi-project scheduling problems.

Due to the numerous numbers of studies reviewed in this research, the authors discovered that there are few researches on the multi-skill resource-constrained multi-project scheduling problem (MSRCMPSP) with the aforementioned characteristics. Moreover, none of the previous researches proposed an SPC-based solution approach for meta-heuristics in order to solve the MSRCMPSP.

The purpose of this study is to analyze comparatively the properties of open-loop and closed-loop equilibria in a dynamic oligopoly model with price dynamics and reflexive behavior of market agents.

To consider dynamic competitive markets, the authors focus on a differential game theory in oligopolistic structures, using analytical models to illustrate how advertising effort, good differentiation and price stickiness interact simultaneously in the open-loop and the closed-loop Nash equilibria. The comparative assessment of these equilibria obtains some significant results.

An optimization model that enriches the continuous time is presented. Under the open-loop and the closed-loop, Nash equilibrium showed an increase in the total output, advertising in price stickiness and promotional efficiency, while there was a decrease in product differentiation and advertising promotional efficiency. However, the open-loop equilibrium levels are larger than the closed-loop equilibrium. Under the closed-loop information, the long-run equilibrium was faster than the opened-loop in a dynamic oligopoly. The graphical illustration was used to present the behavior of the model parameters.

This study helps managers to choose an appropriate price and advertising adjustment to maximize profit. The obtained results may help firms to make the smart decision and may provide managers the valuable tool for making decisions in the competitive market environments.

This is a first attempt to analyze a dynamic oligopoly in the differentiated market environment. It considers a joint action of the output and advertising in shaping the closed-loop and the open-loop equilibria with N competitors in a dynamic competitive setting.

This study aims to detect the influential factors and their respective variables that affect the effectiveness or demand-driven level of the industrial parks in Iran. A hybrid procedure is sought to be developed, which embraces both qualitative and quantitative methodologies to identify the aforementioned factors and variables.

This study is incorporated with both qualitative and quantitative methodologies. To implement the qualitative approach, the researchers used focus groups and the related literature. The quantitative methodology has been carried out via a reliable questionnaire that obtained the viewpoints of 700 experts. The reliability of the designed questionnaire has been investigated through Cronbach’s alpha coefficient. By conducting several one-sample t-tests, it was confirmed that the identified factors and variables are significantly influential on the effectiveness of Iran’s industrial parks. The Kruskal–Wallis statistical test was used to determine the priorities of the factors. This research also used a multi-criteria decision-making method, namely, the weighted aggregates sum product assessment (WASPAS) to rank 15 industrial parks of Khorasan province in Iran according to the identified factors.

Comprehensive analyses have been conducted on the identified factors. Results indicate that the infrastructural facilities factor has the highest priority when it comes to affecting the effectiveness of the industrial parks. After that, industrial land and internal factors take the second and the third positions in terms of importance. A total of 15 industrial parks of the aforementioned province have been ranked by the WASPAS. The ranking offered by the WASPAS has been approved by the experts.

Based on the literature investigations, the authors were convinced that there is a scarcity of studies investigating the influential factors that affect the effectiveness or demand-driven level of industrial parks (especially in Iran). Hence, this research has been conducted to propose a procedure equipped with quantitative and qualitative techniques that detect these important factors and their subordinate variables. By means of the developed procedure of this research, it is possible to locate future industrial parks, plan for establishment of future industrial areas and plan for development of current industrial parks.

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously.

The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method.

Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values.

The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects.

Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

This paper aims to investigate the potential relationship between depression, diabetes knowledge and self-care management with quality of life in diabetic patients.

This analytical cross-sectional study was conducted on 309 diabetic patients in Tabriz, Iran in 2015-2017. Quality of life was assessed by a validated questionnaire. The Persian version of the Beck Aeron questionnaire was used to assess the depression status. Knowledge and self-care management was evaluated by the health belief model questionnaire. Simple and multiple regression models were used to determine the relationship between the mentioned factors and the quality of life in diabetic patients.

Thirty-six per cent of the patients suffered from depression. The mean score of the total quality of life was 33.75 ± 8.72. The scores of the three domains of the quality of life were less than the normal range (“complication of diabetes” domain: 9.93 ± 3.16, “diagnosis, therapy and follow-up” domain: 10.91 ± 3.31, “psycho-social effect of diabetes” domain: 12.93 ± 4.41). The score of “complication of diabetes” score in males was significantly higher than females (p = 0.001). There was a significant relationship between depression (p < 0.001), self-care management (p = 0.019) and two parameters of knowledge (diet: p = 0.006 and diabetes: p = 0.004) with quality of life of diabetic patients.

The obtained results presented an unfavorable status of knowledge, physical and mental health in diabetic patients and a strong relationship between health-related factors with quality of life. Therefore, it is suggested to hold community-based health-promoting programs to enhance the overall life satisfaction in people with diabetes.

The purpose of this paper is to fabricate the scaffolds with different pore architectures using additive manufacturing and analyze its mechanical and biological properties for bone tissue engineering applications.

The polylactic acid (PLA)/composite filament were fabricated through single screw extrusion and scaffolds were printed with four different pore architectures, i.e. circle, square, triangle and parallelogram with fused deposition modelling. Afterwards, scaffolds were coated with hydroxyapatite (HA) using dip coating technique. Various physical and thermo-mechanical tests have been conducted to confirm the feasibility. Furthermore, the biological tests were conducted with MG63 fibroblast cell lines to investigate the biocompatibility of the developed scaffolds.

The scaffolds were successfully printed with different pore architectures. The pore size of the scaffolds was found to be nearly 1,500 µm, and porosity varied between 53% and 63%. The fabricated circular pore architecture resulted in highest average compression strength of 13.7 MPa and modulus of 525 MPa. The characterizations showed the fidelity of the work. After seven days of cell culture, it was observed that the developed composites were non-toxic and supported cellular activities. The coating of HA made the scaffolds bioactive, showing higher wettability, degradation and high cellular responses.

The research attempts highlight the development of novel biodegradable and biocompatible polymer (PLA)/bioactive ceramic (Al₂O₃) composite for additive manufacturing with application in the tissue engineering field.