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PurposeThe vehicle routing problem (VRP) has been widely investigated during last decades to reduce logistics costs and improve service level. In addition, many researchers have realized the importance of green logistic system design in decreasing environmental pollution and achieving sustainable development.Design/methodology/approachIn this paper, a bi-objective mathematical model is developed for the capacitated electric VRP with time windows and partial recharge. The first objective deals with minimizing the route to reduce the costs related to vehicles, while the second objective minimizes the delay of arrival vehicles to depots based on the soft time window. A hybrid metaheuristic algorithm including non-dominated sorting genetic algorithm (NSGA-II) and teaching-learning-based optimization (TLBO), called NSGA-II-TLBO, is proposed for solving this problem. The Taguchi method is used to adjust the parameters of algorithms. Several numerical instances in different sizes are solved and the performance of the proposed algorithm is compared to NSGA-II and multi-objective simulated annealing (MOSA) as two well-known algorithms based on the five indexes including time, mean ideal distance (MID), diversity, spacing and the Rate of Achievement to two objectives Simultaneously (RAS).FindingsThe results demonstrate that the hybrid algorithm outperforms terms of spacing and RAS indexes with p-value Originality/valueIn this research, an environment-friendly transportation system is addressed by presenting a bi-objective mathematical model for the routing problem of an electric capacitated vehicle considering the time windows with the possibility of recharging.

Purpose Recognizing the factors affecting employees’ job motivation is one of the necessities that can improve people’s performance and increase their effectiveness. This study aims to determine the factors affecting job motivation and to examine effective strategies to increase motivation through identifying internal and external factors. Design/methodology/approach In this descriptive study, the statistical population was the employees of the largest petrochemical company in Iran. The questionnaire was randomly distributed to the organization’s employees and managers based on Herzberg’s motivation-hygiene theory. To analyze the obtained data, first, the best and the worst factors were identified using SPSS software and then were ranked using best–worst method (BWM). Findings The results demonstrated that the highest rank among the motivational factors of employees is related to working environment conditions and the lowest rank is related to career advancement and development indicator. In the second stage, the best strategies for motivational factors were determined using the fuzzy goal programming method. The findings showed that 12 out of the 17 proposed solutions have the highest motivation among employees, the implementation of which can increase employee productivity in the petrochemical company under study. Originality/value Further to the best of the authors’ knowledge, job motivation factors in the petrochemical industry have never been examined and ranked by using the BWM method so far. Also, the goal programming approach has never been applied to determine strategies for increasing job motivation and ultimately productivity.

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.

The construction supply chain network has been facing challenges in relation to reducing cost and delivery time, increasing the quality of the built assets, and reducing environmental pollution. These issues have caused contractors and project managers in this industry to note the concept of green construction supply chain management (GCSCM). This study examined the most important challenges and barriers to the implementation of GSCM in the construction industry. In this paper, the components and sub-components of GCSCM were identified using the literature review and opinions of the experts according to the supply chain management. The opinions of construction experts and project managers were collected through focus group meetings. The components were categorized into five main and supporting groups, with “Green Design”, “Green Procurement”, and “Green Production” as the main components and “Green Management” and “Green Information” as the supporting components. Subsequently, the sub-components, in regard to each component, were distinguished. Finally, the fuzzy best–worst method (BWM) was utilized to determine the importance weights of the identified components and sub-components through the opinions of five experts with practical relevant experience. The findings of the fuzzy BWM method show that “Green Design” is the most important component, followed by “Green Management” and “Green Implementation”. Additionally, “Lack of designers, contractors and planners” was ranked the first among the identified sub-components. This paper can assist construction managers, contractors, and policymakers with finding and overcoming the barriers and obstacles of implementing GCSCM.

This research seeks to examine and distinguish the factors influencing empowerment and exertion of knowledge management along with the effect of knowledge management on organizational performance. In addition, this paper investigates human capital as mediator between the relationship of knowledge management and organizational performance. The conceptual model was extended based on the literature review. The primary data were gathered using a questionnaire comprising 48 questions distributed to the personnel of all branches of one of the largest private banks in Iran. Three hundred sixty-one questionnaires were randomly distributed among the personnel, and 334 questionnaires were answered. The gathered data were examined through SPSS and SMART PLS. The results demonstrated the positive impacts of variables including structure, organizational technology, strategy, culture, and trust on organizational knowledge management under study. Moreover, knowledge management directly affects the performance of the organization and through human resource management as mediator. This study motivates the managers and personnel to exploit the accessible organizational assets for enhancing knowledge management practices as well as human resources as the most exquisite organizational capitals.

PurposeThis study aims to develop a model for project portfolio selection considering organizational goals such as budgets, sustainability cash flow and reinvestment strategy under an uncertain environment.Design/methodology/approachA multi-objective mathematical programming model is proposed for project selection, which takes the social, environmental and financial aspects into account as the objectives of the project portfolio selection problem. The project evaluation and selection process in one of the large capitals in the Middle East with numerous urban construction projects was considered as a real case study, in which the subjects of environmental and social sustainability are of great importance. Then, the most significant criteria for project evaluation and selection based on sustainability were identified and ranked using the fuzzy best-worst method (BWM).FindingsThe criterion of “defining clear and real objectives” was ranked first, “project investment return period” was ranked second, “minimum changes in the predicted range” was ranked third, and the other ten sustainability indicators were ranked as well. Next, the presented mathematical programming model was solved using the augmented e-constraint method. The sensitivity analysis indicated that increasing the amount of investments in projects would increase their net present value. Also, increased investment had no effect on sustainability, while decreased investment caused sustainability to not being optimal.Originality/valueThis study focuses on the impact of the amount of investments on projects, and the associated costs of sustainable projects. Further to the authors' knowledge, there has been no relevant study taking uncertainty into account. Also, very few studies proposed a mathematical programming model for the project portfolio selection problem. Moreover, this research uses the brainstorming and Delphi method to identify the sustainability indicators influencing the organization and screens the evaluation indicators. Furthermore, the weights of the evaluation indicators are determined using the fuzzy BWM based on the consistency of opinions.

Nowadays organizations outsource transportation of goods or services to reduce cost which leads to a particular type of problem called open location-routing. Also, each logistic organization possesses a limited number of specific vehicles that may not be enough in certain circumstances. This issue indicates the importance of simultaneously considering both open and closed routs. On the other hand, the growing concerns about the detrimental environmental impacts of human activities reveal the necessity of paying attention to environmental issues in logistics. In this study, a bi-objective mathematical programming model is proposed for two-echelon close and open location-routing problem (2E-COLRP) including two echelons of factories, depots and customers to minimize costs and CO

In this study, we evaluate several nongradient (evolutionary) search strategies for minimizing mathematical function expressions. We developed and tested the genetic algorithms, particle swarm optimization, and differential evolution in order to assess their general efficacy in optimization of mathematical equations. A comparison is then made between the results and the efficiency, which is determined by the number of iterations, the observed accuracy, and the overall run time. Additionally, the optimization employs 12 functions from Easom, Holder table, Michalewicz, Ackley, Rastrigin, Rosen, Rosen Brock, Shubert, Sphere, Schaffer, Himmelblau’s, and Spring Force Vanderplaats. Furthermore, the crossover rate, mutation rate, and scaling factor are evaluated to determine the effectiveness of the following algorithms. According to the results of the comparison of optimization algorithms, the DE algorithm has the lowest time complexity of the others. Furthermore, GA demonstrated the greatest degree of temporal complexity. As a result, using the PSO method produces different results when repeating the same algorithm with low reliability in terms of locating the optimal location.

Conflict in human relations is unavoidable; therefore, it can occur in construction projects that are full of many human relationships. These conflicts can lead to claims if interlocutors do not agree. The main result of the claims is the delay and overrun of costs in construction projects. Additionally, poor management of claims affects the success of construction projects and their budget and schedule. Moreover, controlling claims ensures the successful completion of construction projects and minimizes delays and disputes. This study sought to improve the project performance by ranking the causes of claims and examining their impacts on Key Performance Indicators (KPI) using the combined AHP-TOPSIS (Analytic Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution) method. Given that construction projects are constantly evolving and becoming more complex and have many linear and nonlinear relationships and structures, these projects can be considered as a kind of system dynamics. The system dynamics have been used for nearly four decades to analyze and improve the performance of construction projects. This study attempted to provide a model with a system dynamics approach to predict the performance of construction projects based on the reasons for the claim. In this model, the most important performance indicators, interactions between them, and the relationship of these indicators with the reasons for claiming were simulated. The results showed that delays are the most important reasons for making claims, and they have a great impact on the KPIs such as scheduling, sustainability, customer satisfaction, quality, and team satisfaction, respectively. In the modeling and simulation discussion in this study, the proposed model simulated the dependencies between the KPIs of construction projects and their changes over time and provides a better understanding of how performance indicators interact with each other. Additionally, it can be used as a basis for simulating different management policies to find the best solution to prevent and correct the negative effects of poor performance.

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.

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.

Sustainable development of any country to some extent depends on successful accomplishment of construction projects, particularly infrastructures. Contractors have a key role in the success of these projects. Hence, the selection of a competent contractor as a complicated and hard decision process has a vital importance in the destiny of any construction project. Contractor selection is in essence a multicriteria decision-making that ought to encompass so many aspects of the project and the client’s requirements on one hand and the capabilities and past records of the contractors on the other hand. Failure in selecting a competent contractor may cause time and cost overruns, quality shortcomings, increasing in claims, disputes and change orders, and even failure of the project. In spite of deficiencies of selecting a contractor by the rule of “the lowest bid price”, it still prevails in many countries including Iran. In this paper, a new contractor selection model based on the best-worst method (BWM) and well-known Fuzzy-VIKOR techniques is proposed as a solution to overcome the deficiencies of the traditional “lowest bid price” rule. An illustrative example of a water channel construction project verified the applicability of the proposed model in practice.

Facility layout problem is one of the most important problems in a huge range of industries and services organizations. Simultaneous study of some qualitative and quantitative parameters like closeness relationship between facilities, physical constraints such as input/output points and how to arrange facilities can play a key role to determine the facility layout. Considering these parameters can lead to reduce production costs, increase production capacity, and remove additional displacements. A two-stage approach is proposed to achieve these goals. In the first stage, a goal programming model is proposed to determine weights of the attributes based on the experts' opinions with different preference representation structures. Afterwards, the closeness ratings of the facilities are calculated using weights of attributes. In the second stage, an efficient layout is designed by determining facilities placement sequence, location of the next facility adjacent to the previous one, location of input/output points, and rectilinear feasible shortest path between facilities. Two meta-heuristic algorithms including particle swarm optimization and genetic algorithm are designed due to computational complexity. The objective function is to minimize the sum of products distance between facilities and closeness rating and also to minimize the dead space. A case study of an Auto Body Parts company is demonstrated to verify the efficiency of the proposed two-stage approach. Furthermore, in order to assess the performance of the proposed approach, a comparison is drawn between the proposed approach and five existing approaches in the literature to solve different problems by using the two meta-heuristic algorithms.

Forecasting spare parts requirements is a challenging problem, because the normally intermittent demand has a complex nature in patterns and associated uncertainties, and classical forecasting approaches are incapable of modeling these complexities. The present study introduces a hybrid model that can impressively overcome the limitations of classical models while simultaneously using their unique advantages in dealing with the complexities in intermittent demand. The strategy of the proposed hybrid model is to use the three individual autoregressive moving average (ARMA), single exponential smoothing (SES), and multilayer perceptron (MLP) models simultaneously. Each of them has the potential of modeling a different structure and patterns of behavior among the data. The accuracy in forecasting ability is also increased by the suitable examination of these in the intermittent data. Croston’s method is the backbone of the suggested model. The proposed hybrid model is based on CV2 and ADI criteria, which improve its efficacy in examining inappropriate structures by reducing the cost of inappropriate modeling while increasing the prediction model accuracy. Using these results prevents the hybrid model from being confused or weakened in the modeling of all groups and reduces the risk of choosing the disproportionate model. The accuracy of prediction models was evaluated and compared using mean absolute percentage error (MAPE) by implementing an example, and promising results were achieved.

The present paper aims to propose a fuzzy multi-objective model to allocate order to supplier in uncertainty conditions and for multi-period, multi-source, and multi-product problems at two levels with wastages considerations. The cost including the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects, partial coverage of suppliers in respect of distance and finally, suppliers weights which make the products orders more realistic are considered as the measures to evaluate the suppliers in the proposed model. Supplier's weights in the fifth objective function are obtained using fuzzy TOPSIS technique. Coverage and wastes parameters in this model are considered as random triangular fuzzy number. Multi-objective imperial competitive optimization (MOICA) algorithm has been used to solve the model,. To demonstrate applicability of MOICA, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms and results are analyzed using quantitative criteria and performing parametric.&nbsp

A chemically modified carbon paste electrode (MCPE) containing ferrocene (FC) and carbon nanotubes (CNT) was constructed. The electrochemical behavior and stability of the MCPE were investigated by cyclic voltammetry. The electrocatalytic activity of the MCPE was investigated and it showed good characteristics for the oxidation of carbidopa (CD) in phosphate buffer solution (PBS). A linear concentration range of 5 to 600 ?M CD, with a detection limit of 3.6?0.17 ?M CD, was obtained. The diffusion coefficient of CD and the transfer coefficient (?) were also determined. The MCPE showed good reproducibility, remarkable long-term stability and especially good surface renewability by simple mechanical polishing. The results showed that this electrode could be used as an electrochemical sensor for the determination of CD in real samples, such as urine samples.

this paper proposes a new emotional stress recognition system using multi-modal bio-signals. Since electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research, it is used as the main signal. In order to choose the proper EEG channels we used the cognitive model of the brain under emotional stress. We designed an efficient acquisition protocol to acquire the EEG and psychophysiological signals under pictures induction environment (calm-neutral and negative-excited) for participants. Qualitative and quantitative evaluation of psychophysiological signals have been tried to select suitable segments of EEG signal for improving efficiency and performance of emotional stress recognition system. After pre-processing the signals, both Linear and nonlinear features were employed to extract the EEG parameters. Wavelet coefficients and chaotic invariants like fractal dimension by Higuchi's algorithm and correlation dimension were used to extract the characteristics of the EEG signal which showed that the classification accuracy in two emotional states was 82.7% using the Elman classifier. This is a great improvement in results compared with other similar published work.

Introduction Blood pressure (BP) is one of the important vital signs that need to be monitored for personal healthcare. Arterial blood pressure is estimated from pulse transit time (PTT). This study uses two pulse sensors to get PTT. The aim of this study was to construct an optical system and to monitor blood pressure continuously and without cuff in people with different ages. Materials and Methods To measure blood pressure changes, two infrared optical transmitters were used at the distances of 5 mm to the receivers. Output of the optical receivers was inserted in analog circuits. PTT was defined as the time between the two peaks of pulse signals by the software. Signals were measured continuously through a serial network communication. An external personal computer monitored measured waveforms in real time. BP was related to PTT and the relationship coefficients were calculated at different physical activities. After determining the linear correlation coefficients for each individual, blood pressure was measured by the cuff and the PTT method and the results were compared. Results PTT computed between the two peaks of wave pulses was strongly correlated with systolic blood pressure (R=0.88±0.034) and the diastolic blood pressure (R=0.82±0.058). Systolic blood pressure (SBP) was measured more accurately than the diastolic blood pressure (DBP). The results of SBP showed that the maximum difference and the error percentages between the cuff method and the present method were 7.98±2.88 and 6.33±2.51%, respectively. Moreover, the maximum difference and the percentage errors between the cuff method and the present method of DBP were 10.13±3.82 and 10.97±3.89%, respectively. Conclusion Monitoring blood pressure with the designed system can be recommended as a useful method to indicate cardiovascular diseases and used for personal healthcare purposes.