Journal of Applied Research on Industrial Engineering
Volume:8 Issue: 1, Winter 2021

The use of an Electric Vehicle (EV), particularly in different operations of goods distribution is a solution for salvaging the crowded cities of the world from air and noise pollutions as well as Green House Gas (GHG) emission. This paper presents a Multi-Depot Electric Vehicle Routing Problem (MD-EVRP) with recharging stations by considering the expected penalty of fuzzy time windows in pickup/delivery. Since the MD-EVRP with Fuzzy Time Windows and Pickup/Delivery (MD-EVRP-FTW-PD) constraints is an NP-hard problem, three meta-heuristics (i.e., Simulated Annealing (SA), Variable Neighborhood Search (VNS) and a hybrid of SA and VNS (VNS-SA)) are used to solve such a hard problem. The parameters of these algorithms are measured by the Taguchi experimental design method. The proposed hybrid VNS-SA algorithm is more efficient in comparison with other algorithms.

The recognition of pathways and identification of cars was seen with a prospective camera, which recognizes trajectories and predicts control points. The aim is to propose the location of the path. In this paper, lane detection algorithm Steering Assistance System (SAS) is introduced. Guiding helps to learn driving and anticipates the control points and defines the direction that makes it easy to learn in a potential way and a lane keeping assistance system which warns the driver on unintended lane departures. Path keeping is an important element for self-driving cars. This article describes the beginning to end adapting the approach to holding the car in the right direction.

The evolution of the means aimed at improving the availability of strategic equipment requires a credible level of maintenance with the development of original monitoring techniques such as vibration diagnostics. The vibration monitoring of strategic equipment through a vibration diagnosis requires mainly the identification of vibrational images of the different types of damage. However, one of the important vibration problems is essentially due to the phenomenon of imbalance. This phenomenon corresponds to an imbalance of the rotor due to the offset between the axis of inertia and the axis of rotation, which causes significant and cyclical vibrations. The aim of this study is to analyze the vibratory behavior of a screw compressor to improve its reliability and consequently its availability. To identify the imbalance fault, two sets of vibration measurements on April and January were fundamentally examined at the compressor level. Fourier transform based on spectral analysis was used to create a vibration detection approach with vibration signals. The comparison of the results obtained with that of the simulation resulting from the model of dynamic behavior of the compressor is conclusive.

Scheduling is an important decision-making process that aims to allocate limited resources to the jobs in a production process. Among scheduling problems, Hybrid Flow Shop (HFS) scheduling has good adaptability with most real world applications including innumerable cases of uncertainty of parameters that would influence jobs processing when the schedule is executed. Thus a suitable scheduling model should take parameters uncertainty into account. The present study develops a multi-objective Robust Mixed-Integer Linear Programming (RMILP) model to accommodate the problem with the real-world conditions in which due date and processing time are assumed uncertain. The developed model is able to assign a set of jobs to available machines in order to obtain the best trade-off between two objectives including total tardiness and makespan under uncertain parameters. Fuzzy Goal Programming (FGP) is applied to solve this multi objective problem. Finally, to study and validate the efficiency of the developed RMILP model, some instances of different size are generated and solved using CPLEX solver of GAMS software under different uncertainty levels. Experimental results show that the developed model can find a solution to show the least modifications against uncertainty in processing time and due date in an HFS problem.

The significance of energy in human lives cannot be overemphasized. It is crucial to all facets of economic growth, progress, and development as well as poverty eradication and security.  Household energy is generally required for a variety of purposes. It is required for lighting and heating. This study evaluated domestic energy utilization in major Nigeria cities using Warri, Benin, Port Harcourt, and Calabar as a case study. A total of four locations with the highest population were targeted at each selected city. Responses were randomly selected from energy utilization in the selected region. A total of 1439 questionnaires were administered all through the selected cities in this study. Data obtained were evaluated using descriptive statistic. The result obtained revealed that kerosene has the highest percentage of utilization (88%), LNG (68.9%), electricity (67.7%), PMS (67.1), diesel (5.6%), charcoal (16.4%), wood (28.2%), and solar (3%) within these cities. Therefore, kerosene is the most preferred energy and this can be as a result of cost when compared to LNG, electricity, PMS, diesel, charcoal, solar, and wood fuel.

Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years, the application of meta-heuristic algorithms for such problems increased dramatically and it is widely used in various fields. These algorithms, in contrast to exact optimization methods, find the solutions which are very close to the global optimum solution as possible, in such a way that this solution satisfies the threshold constraint with an acceptable level. Most of the meta-heuristic algorithms are inspired by natural phenomena. In this research, a comprehensive review on meta-heuristic algorithms is presented to introduce a large number of them (i.e. about 110 algorithms). Moreover, this research provides a brief explanation along with the source of their inspiration for each algorithm. Also, these algorithms are categorized based on the type of algorithms (e.g. swarm-based, evolutionary, physics-based, and human-based), nature-inspired vs non-nature-inspired based, population-based vs single-solution based. Finally, we present a novel classification of meta-heuristic algorithms based on the country of origin.

In this paper, a one-dimensional fractional advection-diffusion equation is considered. First, we propose a numerical approximation of the Riemann-Liouville fractional derivative which is fourth-order accurate, then a numerical method for the fractional advection-diffusion equation using a high order finite difference scheme is presented. It is proved that the scheme is convergent. The stability analysis of numerical solutions is also discussed. The method is applied in several examples and the accuracy of the method is tested in terms of error norm. Furthermore, the numerical results have been compared with some other methods.