H. L. Ong

Solving the feeder bus network design problem by genetic algorithms and ant colony optimization

This paper proposes the design and analysis of two metaheuristics, genetic algorithms and ant colony optimization, for solving the feeder bus network design problem. A study of how these proposed heuristics perform is carried out on several randomly generated test problems to evaluate their computational efficiency and the quality of solutions obtained by them. The results are also compared to those published in the literature. Computational experiments have shown that both heuristics are comparable to the state-of-the-art algorithms such as simulated annealing and tabu search.

Metaheuristics for minimizing the makespan of the dynamic shop scheduling problem

For the shop scheduling problems such as flow-shop, job-shop, open-shop, mixed-shop, and group-shop, most research focuses on optimizing the makespan under static conditions and does not take into consideration dynamic disturbances such as machine breakdown and new job arrivals. We regard the shop scheduling problem under static conditions as the static shop scheduling problem, while the shop scheduling problem with dynamic disturbances as the dynamic shop scheduling problem. In this paper, we analyze the characteristics of the dynamic shop scheduling problem when machine breakdown and new job arrivals occur, and present a framework to model the dynamic shop scheduling problem as a static group-shop-type scheduling problem. Using the proposed framework, we apply a metaheuristic proposed for solving the static shop scheduling problem to a number of dynamic shop scheduling benchmark problems. The results show that the metaheuristic methodology which has been successfully applied to the static shop scheduling problems can also be applied to solve the dynamic shop scheduling problem efficiently.

A fast tabu search algorithm for the group shop scheduling problem

Three types of shop scheduling problems, the flow shop, the job shop and the open shop scheduling problems, have been widely studied in the literature. However, very few articles address the group shop scheduling problem introduced in 1997, which is a general formulation that covers the three above mentioned shop scheduling problems and the mixed shop scheduling problem. In this paper, we apply tabu search to the group shop scheduling problem and evaluate the performance of the algorithm on a set of benchmark problems. The computational results show that our tabu search algorithm is typically more efficient and faster than the other methods proposed in the literature. Furthermore, the proposed tabu search method has found some new best solutions of the benchmark instances.

METAHEURISTICS FOR THE MIXED SHOP SCHEDULING PROBLEM

In this paper, three metaheuristics are proposed for solving a class of job shop, open shop, and mixed shop scheduling problems. We evaluate the performance of the proposed algorithms by means of a set of Lawrence’s benchmark instances for the job shop problem, a set of randomly generated instances for the open shop problem, and a combined job shop and open shop test data for the mixed shop problem. The computational results show that the proposed algorithms perform extremely well on all these three types of shop scheduling problems. The results also reveal that the mixed shop problem is relatively easier to solve than the job shop problem due to the fact that the scheduling procedure becomes more flexible by the inclusion of more open shop jobs in the mixed shop.

APPLYING METAHEURISTICS TO FEEDER BUS NETWORK DESIGN PROBLEM

This paper proposes the design and analysis of two metaheuristics, simulated annealing (SA) and tabu search (TS), for solving the feeder bus network design problem. The results are compared to those published in the literature. A comparative study is also carried out on several test problems generated at random to evaluate the performance of these heuristics in terms of their computational efficiency and solution quality. Computational experiments have shown that TS is a more effective metaheuristic in solving the problem than SA.

AN ASSIGNMENT-BASED LOCAL SEARCH METHOD FOR SOLVING VEHICLE ROUTING PROBLEMS

In this paper, we propose an assignment-based local search method for solving vehicle routing problems. This method is a multi-route improvement algorithm that can operate on several routes at a time. To evaluate the performance of the proposed method, extensive computational experiments on the proposed method applied to a set of benchmark problems are carried out. The results show that the proposed method, when coupled with metaheuristics such as simulated annealing, is comparable with other efficient heuristic methods proposed in the literature.

A modified tabu search algorithm for cost-based job shop problem

In this paper, a cost-based job shop problem (JIT-JSP) is proposed to model the multi-order processing procedure in a just-in-time (JIT) environment. The objective of JIT-JSP is to minimize three costs: work-in-process holding cost of half-finished orders, inventory holding cost of finished orders and backorder cost of unfulfilled orders. A modified tabu search (MTS) method is developed to improve the schedule quality by searching the neighbourhood of a feasible schedule iteratively. The MTS method is comprised of three components that help to ensure a more effective searching procedure: neighbourhood structure, memory structure and filter structure. Computational results show that the MTS method significantly improves the initial schedule generated by an arbitrarily selected dispatching rule.

Two composite methods for soft drink distribution problem

In this paper, we propose two methods for solving a real-life soft drink distribution problem. The objective of this problem is to minimize the total number of vehicles used. The proposed methods are composite procedures involving the solution of bin packing problems, and the application of an assignment-based procedure or a generalized crossing procedure, respectively for improvement. We apply the proposed composite procedures to a number of real-life problem instances. The computational results show that our methods are able to obtain better solutions than the methods proposed in the literature.

A generalized crossing local search method for solving vehicle routing problems

In this paper, we propose a generalized crossing local search method for solving vehicle routing problems. This method is a generalization of the string crossing method described in the literature. To evaluate the performance of the proposed method, extensive computational experiments on the proposed method applied to a set of benchmark problems are carried out. The results show that the proposed method, when coupled with metaheuristics such as simulated annealing, is comparable with other efficient heuristic methods proposed in the literature.

Finding the exact volume of a polyhedron

This paper addresses the design and development of a computer program for finding the exact volume of a multi-dimensional polyhedron that is enclosed by a set of linear inequalities. The program is designed to calculate the volume of a polyhedron of any dimensions defined by a set of linear inequalities. The speed of the program depends on the number of inequalities and the number of variables. The program has been tested against several two- and three-dimensional polygons in which the volume can be calculated by formulae. The results of the tests show that the accuracy of the program is at least up to 10-6 and it can calculate the volume of a three-dimensional polygon defined by a few hundred inequalities in just a few minutes. However, as the number of variables increases, the computation time increases exponentially.The program can be used in some science and engineering application such as finding the probability of an event, the volume of a crystal in a wafer fabrication industry, and other applications in the manufacturing industry.