The container loading problem, a real hard problem, is usually difficult to obtain even a suboptimal solution because of not only multiple complicated restrictions but also of multiple objectives. In this paper, a heuristic algorithm is proposed for solving the container loading problem in the real-world. The algorithm is based on Drum-Buffer-Rope presented in the Theory of Constraints and the multi-agent cooperative negotiation strategy. A particular attention is focused on improving the constrained agent by striving for the trade-off of restrictions and cooperative negotiations, so that the final solution can arrive its biggest profit. Since many real-world problems are restricted by many complicated restriction that are difficult to be satisfied simultaneously, a method used by human experts called restriction relaxation is embedded in the proposed algorithm, which makes the algorithm have a high degree of flexibility. In this paper, the proposed algorithm is also compared with other two classical optimization algorithms based on Local Search and Tabu Search.
This paper aims to propose an efficient procedure to solve container loading problem considering load stability and weight constrains. As for the freight transportation, these constraints are important from the viewpoint of avoiding freight damage and delivering the freight safely. Hence, it is required that load stability and load bearing strength of freight are taken into account in the practical loading work. In this paper this problem is solved by a relatively simple procedure using a greedy loading algorithm and neighborhood search. The effectiveness of the proposed procedure is shown by comparing the results obtained with the approaches presented in literature by using benchmark problems in the OR-library.
A family of automatic container loading problems is studied and algorithms are proposed. The algorithms are constructed with metaheuristics and include flat and/or vertical loading schemes, loading efficiency, stability of loaded objects, and computational requirement. Handling groups of objects in a metaheuristic scheme is moreover considered. Numerical examples are given.
In this paper, we consider a traffic flow model where the information about the actual travel time for each alternative route is not available when each driver performs a route selection. For such a traffic flow model, we examine the effectiveness of two routing methods for minimizing the average travel time over all vehicles running in the model. One method is to minimize the average travel time globally. In this algorithm, a central manager determines the routes of all vehicles. Since the number of combinations of possible routes of drivers exponentially increases as the number of drivers, we need an efficient combinatorial optimization technique. In this paper, we employ a genetic algorithm to search for a near-optimal route choice of each driver. The other method is to minimize the average travel time locally by each driver with no central manager. In this method, each driver selects the route with the shortest estimated travel time among alternative routes. We employ a neural network to estimate the travel time for each route. Through computational experiments, we compare the two methods with each other and demonstrate the characteristic features of each method.
A method of association rule mining is proposed using Genetic Network Programming (GNP) to improve the performance of rule extraction. Association rule mining is the discovery of association relationships or correlations among a set of attributes in a database. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. GNP examines the attribute values of database tuples using judgement nodes and calculates the measurements of association rules using processing nodes. In addition, the proposed method measures the significance of associations via the chi-squared test for correlation used in classical statistics using GNP's feature. Extracted association rules are stored in a pool all together through the generations in order to find new important rules. Therefore, the proposed method is fundamentally different from the previous methods in its evolutionary way. In this paper, the algorithm capable of finding the important association rules is described and some experimental results are shown.
A tree structure for visual keys, named visual key tree, is proposed for a useful querying method for users and an efficient retrieval in image retrieval system. The relevance feedback technique is modified to apply the visual key tree to the image retrieval system. Experimental results for 1,000 images included in COREL database show that the constructed visual key tree has a suitable structure for the useful querying, and it is confirmed that the modified relevance feedback behaves well with a recall-precision curve.
This paper proposes the pedestrian navigation method reflecting individual preference for route selection, and evaluates the validity of the fuzzy measures and integrals model applied to route selection. The proposed method selects the route with the highest subjective satisfaction degree which are estimated by a Road Satisfaction degree Evaluation Model (RSEM). The RSEM applies fuzzy measures and integrals to calculate the subjective satisfaction degrees of a road. The input to the RSEM is a road attribute expressing subjective impression of a road. The road attributes used for the RSEM are decided according to the individual preference expressed by fuzzy measures. Experimental results and analyses of the RSEM show that the route selected by the proposed method is preferable to other routes and the RSEM is individualized appropriately.