Antoni Wibowo

A framework for landfill site selection using geographic information systems and multi criteria decision making technique

The solid waste disposal is one of the facilities which can cause harm to human health and also contribute to severe environmental pollution if it is not properly managed. Therefore, an effective decision on a landfill site selection in order to identify the most suitable area as a new landfill is very important. Since 25 years ago, the integration of geographic information systems (GIS) and multi criteria decision analysis (MCDA) has drawn significant interest among researchers. This integrated technique is commonly used for land use planning and selecting a new landfill site is one of the plan. This paper proposes a framework of landfill site selection with a consideration of resource requirement. This framework is developed by using the integration of GIS and MCDA to identify an appropriate location for landfill siting. A list of selection criteria obtained from the literature considered in selecting the best landfill site is also presented. The results of this study could later be used to help the waste management team in developing an efficient solid waste management system.

Hybrid kernel principal component regression and penalty strategy of multiple adaptive genetic algorithms for estimating optimum parameters in abrasive waterjet machining

Abstract The quality of machined surface can be improved by choosing the right values of the associated parameters through an optimization process. Recently, researchers have used nonlinear programming models, soft-computing approaches, and hybrid of them to estimate the surface roughness of abrasive waterjet (AWJ) machining. Some researchers have performed a second order polynomial regression (SOPR) as a basis in developing a nonlinear programming in AWJ machining. Nevertheless, the SOPR was developed without considering the existence of multicollinearity which can lead to inappropriate prediction. Besides, it also needs a specific nonlinear regression model in advance. It will be difficult to specify an appropriate nonlinear model of SOPR since it does not usually involve all combination variables in its model. Instead, kernel principal component regression (KPCR) will be employed to overcome the weaknesses of the SOPR. After development and model selection in the KPCR models, the best KPCR is used as an objective function in a nonlinear programming model of AWJ with a certain set of constraints. Single genetic algorithm (GA) and its variants can be conducted to solve the nonlinear programming problem. However, they can yield different decision values due to randomness of their initial population which implies that the best decision values may not converge in a certain optimum solution. Under this circumstance, multiple adaptive probabilities genetic algorithm (MAPGA) combined with a penalty method to solve the optimization problem is proposed. Hybrid of KPCR and MAPGA gives more stable solution compared to hybrid of KPCR with single genetic algorithm and original adaptive probabilities genetic algorithm. Our proposed technique also provides an optimum solution in reasonable time.

Nearest greedy for solving the waste collection vehicle routing problem: A case study

This paper presents a real case study pertaining to an issue related to waste collection in the northern part of Malaysia by using a constructive heuristic algorithm known as the Nearest Greedy (NG) technique. This technique has been widely used to devise initial solutions for issues concerning vehicle routing. Basically, the waste collection cycle involves the following steps: i) each vehicle starts from a depot, ii) visits a number of customers to collect waste, iii) unloads waste at the disposal site, and lastly, iv) returns to the depot. Moreover, the sample data set used in this paper consisted of six areas, where each area involved up to 103 customers. In this paper, the NG technique was employed to construct an initial route for each area. The solution proposed from the technique was compared with the present vehicle routes implemented by a waste collection company within the city. The comparison results portrayed that NG offered better vehicle routes with a 11.07% reduction of the total distance traveled, in comparison to the present vehicle routes.This paper presents a real case study pertaining to an issue related to waste collection in the northern part of Malaysia by using a constructive heuristic algorithm known as the Nearest Greedy (NG) technique. This technique has been widely used to devise initial solutions for issues concerning vehicle routing. Basically, the waste collection cycle involves the following steps: i) each vehicle starts from a depot, ii) visits a number of customers to collect waste, iii) unloads waste at the disposal site, and lastly, iv) returns to the depot. Moreover, the sample data set used in this paper consisted of six areas, where each area involved up to 103 customers. In this paper, the NG technique was employed to construct an initial route for each area. The solution proposed from the technique was compared with the present vehicle routes implemented by a waste collection company within the city. The comparison results portrayed that NG offered better vehicle routes with a 11.07% reduction of the total distance t...

Integrating a Repairing-based Genetic Algorithm-Neighborhood Search Structure in Solving the Course Timetabling Problem

The course timetabling problem is not a trivial task as it is an NP-hard and NP-complete problem and many solutions have been proposed due to its high complexity search landscape. In essence, the nature of the course timetabling problem is to assign a lecturer-course entity to existing teaching venue and timeslot in an academic institution. In this article, the authors propose a Genetic Algorithm-Neighborhood Search (GANS) to construct a feasible timetable for courses offered by a department in the faculty of a local university in Malaysia. The framework of the solution is as follow: The feasible timetable is first constructed by Genetic Algorithm, which includes are pair operator which attempts to repair infeasible timetables. Upon feasibility, the second phase exploits the initial feasible solution using three neighborhood structures to search for an improved solution and global optimum. The experimental results demonstrate the efficiency and effectiveness of the various neighborhood structures in exploiting the feasible solutions to yield the global optimum.

Social learning and principal-agent problems in profit sharing contract

Purpose The purposes of this study are twofold. First, to theoretically examine the profit-sharing (mudarabah) contract that produces an optimal distribution of return in the presence of social learning (shuratic process) within the environment of asymmetric information. Second, to empirically investigate the optimal condition of profit-sharing ratio (PSR) and social learning for profit-sharing (mudarabah) contract in Islamic banking. Design/methodology/approach Data from one of the biggest and earliest Islamic banks in Malaysia were taken as a proxy of an Islamic bank. The data are collected from the period of 2009 to 2013, and these will be used for the simulation process by using the genetic algorithm (GA) technique. Findings The empirical results discovered that Islamic banks had used social learning in their daily activities, especially in the asset side. The results also showed that the trend of social learning has a positive relationship with the trend of Islamic banks’ net profit. Additionally, the results also indicated that the Islamic banks’ net profit has a positive relationship with its PSR from the profit-sharing (mudarabah) financing and securities investment. Originality/value This study is the first of its kind that investigates the implementation of the social learning process in Islamic banking operation. This study also used the latest technique from artificial intelligence system, i.e. a GA, to attain an optimal value for PSR and social learning process.