The effect of customer satisfaction on parcel delivery operations using autonomous vehicles: An agent-based simulation study

The quality of Third-Party Logistics (3PL) services represented by delivery time decides the outcome of customer satisfaction. The result of this satisfaction judges the type of Word of Mouth (WoM) that, if positive, plays a vital role in attracting non-customers who are willing in 3PL services to join as customers. In this paper, we investigate the effect of an essential factor represented by Word of Mouth on the number of customers in 3PL companies. Therefore, an agent-based model for parcel delivery is developed to investigate the impact of social factors such as WoM and other operational factors, including vehicle number and speed, on customer number and satisfaction, average service time, and vehicle utilization. As a methodology, state charts of Vehicle, Customer, Hub agents are developed to mimic the messaging protocols between these agents under the WoM concept. A case study based in 3PL in Jordan is used as a test bench of the developed model. A sensitivity analysis study is conducted to test the developed model's performance, including different levels of influential model parameters such as targeting non-customers parameters by Loyal/Unhappy customers. Key results reveal that the best scenario is achieved when the WoM value equals 10, the vehicle number equals 30, and the vehicle speed equals 60 km/h. These model parameters result in higher customer numbers of 873, vehicle utilization equals 63%, and customer satisfaction equals 99%. Video of our proposed model showing it in action can be found at: https://www.youtube.com/watch?v=3rR4l130-QU

An Improved Fuzzy Knowledge-Based Model For Long Stay Container Yards

This paper considers the problem of allocating newly arrived containers to stacks of existing containers in a yard when the departure date/time for containers is unknown. Many factors and constraints need to be considered when modelling this storage allocation problem. These constraints include the size, type and weight of the containers. The factors are the number of containers in a stack and the duration of stay of the topmost container in the stack. This paper aims to develop an improved Fuzzy Knowledge-Based ‘FKB’ model for best allocation practice of long-stay containers in a yard. In this model, the duration of stay factor does not need to be considered in the allocation decision if the duration of stay for the topmost containers in a stack is similar; hence, a new ‘ON/OFF’ strategy is proposed within the Fuzzy Knowledge-Based model to activate/deactivate this factor in the stacking algorithm whenever is required. Discrete Event Simulation and Fuzzy Knowledge-Based techniques are used to develop the proposed model. The model’s behaviour is tested using three real-life scenarios, including allocating containers in busy, moderately busy and quiet yards. The total number of re-handlings, the number of re-handlings per stack, and the number of re-handlings for containers were considered KPIs in each scenario

Application of Intelligent Computational Techniques in Power Plants:A Review

Growing worldwide demand for energy leads to increasing the levels of challenge in power plants management. These challenges include but are not limited to complex equipment maintenance, power estimation under uncertainty, and energy optimisation. Therefore, efficient power plant management is required to increase the power plant’s operational efficiency. Conventional optimisation tools in power plants are not reliable as it is challenging to monitor, model and analyse individual and combined components within power systems in a plant. However, intelligent computational tools such as artificial neural networks (ANN), nature-inspired computations and meta-heuristics are becoming more reliable, offering a better understanding of the behaviour of the power systems, which eventually leads to better energy efficiency. This paper aims to provide an overview of the development and application of intelligent computational tools such as ANN in managing power plants. Also, to present several applications of intelligent computational tools in power plants operations management. The literature review technique is used to demonstrate intelligent computational tools in various power plants applications. The reviewed literature shows that ANN has the greatest potential to be the most reliable power plant management tool

Industrial occupational risks: application study in renewable energy companies

An organization’s work environment is considered an essential factor in maximizing its value; therefore, a professional work environment ensures higher worker safety and fewer professional accidents by offering offices with suitable environment designs that contribute to maintaining the health and welfare of employees. Therefore, a new conceptual framework is introduced to identify the causes of sudden job accidents and their effect on employees’ safety. This study develops an occupational safety model based on ISO 45001:2018 standards for optimizing the industrial professional work environment that seeks to adopt the Occupational Safety, Health, and Environment standards (OSHE) to reduce work accident risks like industrial companies of renewable energy and sustainability. This model identifies fundamental factors that have a risk level on workers’ lives, which might expose staff lives to death, injury, and disability. These factors include the industrial professional environment, work accidents, current OSHE procedures, and the effectiveness of current health insurance. Based on quantitative analysis methods to evaluate risk-based work accidents, this study proved a significant relationship between the characteristics of the industrial occupational environment in the selected factories and the increase in sudden work accidents. The main suggestion is that industrial company needs to adopt the OSHE ISO45001:2018 standards

Parametric investigation of combustion process optimization for Gas Turbines at SJ Putrajaya

Gas Turbine (GT) power plants have been represented as essential assets of energy units because of their numerous advantages compared to conventional coal power plants. However, their low thermal efficiency may make the continuous baseload operations a lossmaking alternative and threaten to continue. This fact is raising the importance of performing thermodynamic investigation according to the current operations’ conditions. This paper aims to conduct a thermodynamic investigation for two Siemens V94.2 gas turbine (GT) units based on current operations’ conditions. The reason for selecting these units is because they are operating at a much lower thermal efficiency than the designed thermal efficiency, and due to the age factor, the GTs are not suitable for major retrofitting due to poor return on investments. A numerical model is designed to simulate the overall thermodynamic process in the gas turbine using MATLAB SIMULINK.The obtained numerical results are validated by comparing them with the operational data collected from the stations. The thermal efficiency is increased by 30%, with a maximum output power equal to 140MW. The power output had decreased by 0.2% when the ambient temperature was increased by about 6.0 oC. A graphical optimization, where various conditions are plotted as graphs, is also carried out to achieve the maximum thermal efficiency and power output. Finally, a number of recommendations are made to address decreased thermal efficiency and output power

Recovery of Green Hospital Systems Based on ISO 14001: 2015 Standards

This study targets six North Asia and African hospitals that provide clinical treatment, diagnostic, and consultation services. The study revealed a lack in the selected hospitals' infrastructure to support implementation successfully Environmental Management Systems (EMS). A factor analysis was performed based on five factors; EMS efficiency, the hospital’s culture, support from top management, legislation, and time and budget controls. Out of 23 sub-factors tested, it has been found that 7 of them can control the differentiation of the factors. The results highlight human errors in chemical and medical materials that lead to environmental pollution. It was followed by a sub-factor that indicated that controls of hospital activities and correction of its performance depend on the feedback received from the environmental information system. The study's main conclusion is that recovering green hospital systems depends on successfully carrying out the ISO 14001: 2015 standard