Guadalupe Castilla Valdez

DiPro: an algorithm for the packing in product transportation problems with multiple loading and routing variants

The present paper approaches the loading distribution of trucks for Product Transportation as a rich problem. This is formulated with the classic Bin Packing Problem and five variants associated with a real case of study. A state of the art review reveals that related work deals with three variants at the most. Besides, they do not consider its relation with the vehicle routing problem. For the solution of this new rich problem a heuristic-deterministic algorithm was developed. It works together with a metaheuristic algorithm to assign routes and loads. The results of solving a set of real world instances show an average saving of three vehicles regarding their manual solution; this last needed 180 minutes in order to solve an instance and the actual methodology takes two minutes. On average, the demand was satisfied in 97.45%. As future work the use of a non deterministic algorithm is intended.

Improving the performance of heuristic algorithms based on causal inference

Causal inference can be used to construct models that explain the performance of heuristic algorithms for NP-hard problems. In this paper, we show the application of causal inference to the algorithmic optimization process through an experimental analysis to assess the impact of the parameters that control the behavior of a heuristic algorithm. As a case study we present an analysis of the main parameters of one state of the art procedure for the Bin Packing Problem (BPP). The studies confirm the importance of the application of causal reasoning as a guide for improving the performance of the algorithms.

Application of Genetic Algorithms to Strip Hot Rolling Scheduling

This paper presents an application of a genetic algorithm (GA) to the scheduling of hot rolling mills. The objective function used is based on earlier developments on flow stress modeling of steels. A hybrid two-phase procedure was applied in order to calculate the optimal pass reductions, in terms of minimum total rolling time. In the first phase, a non-linear optimization function was applied to evaluate the computational cost to the problem solution. For the second phase, a GA was applied. A comparison with two-point and simulated binary (SBX) crossover operators was established. The results were validated with data of industrial schedules. A GA with SBX crossover operator is shown to be an efficient method to calculate the multi-pass schedules at reduced processing time.

Solving Bin Packing Problem with a Hybridization of Hard Computing and Soft Computing

This paper presents a new hybrid intelligent system that solves the Bin Packing Problem. The methodology involves the fusion of Soft Computing by means a genetic algorithm and Hard Computing using limits criterion and deterministic strategies. The innovative proposal inverts minimum computational resources expressed in generations with a high level quality solution and shows the algorithm performance with statistical methods. The average theoretical ratio for 1370 standard instances was 1.002 and the best known solution was achieved in 83.72% of the cases. As future work, an exhaustive analysis of characteristics of the hardest instances is proposed; the purpose is to find new hybrid methods.

Experimental Analysis for the Lennard-Jones Problem Solution

In this paper the problem of determining the atomic cluster configurations that minimize the Lennard-Jones potential energy is approached. Traditional studies are oriented to improve the quality of the solution and practically do not present statistical information to support the efficiency of the reported solution methods. Without this type of evidence the effectiveness of these methods might highly be dependent only on the capacity of the available computing resources. In this work it is proposed to incorporate statistical information on the performance of the solution methods. An advantage of this approach is that when the performance tests are standardized and statistically supported, we can take advantage of efficient solution methods that have been tested only in conditions of modest computing resources. An experimental study of the problem is presented in which the generated statistical information is used to identify two potential areas to improve the performance of the evaluated method.

Multiple local searches to balance intensification and diversification in a memetic algorithm for the linear ordering problem

The Linear Ordering problem (LOP) is an NP-hard problem, which has been solved using different metaheuristic approaches. The best solution for this problem is a memetic algorithm, which uses the traditional approach of hybridizing a genetic algorithm with a single local search; on the contrary, in this paper we present a memetic solution hybridized with multiple local searches through all the memetic process. Experimental results show that using the best combination of local searches, instead of a single local search, the performance for XLOLIB instances is improved by 11.46% in terms of quality of the solution. For the UB-I instances, the proposed algorithm obtained a 0.12% average deviation from the best known solutions, achieving 17 new best known solutions. A Wilcoxon test was performed, ranking the proposed memetic algorithm as the second best solution of the state of the art for LOP. The results show that the multiple local searches approach can be more effective to get a better control in balancing intensification/diversification than the single local search approach.

Improved Tabu Solution for the Robust Capacitated International Sourcing Problem (RoCIS)

In this paper the robust capacitated international sourcing problem (RoCIS) is approached. It consists of selecting a subset of suppliers with finite capacity, from an available set of potential suppliers internationally located. This problem was introduced by Gonzalez-Velarde and Laguna in [1], where they propose a deterministic solution based on tabu search memory strategies. The process consists of three stages: build an initial solution, create a neighborhood of promising solutions and perform a local search in the neighborhood. In this work we propose improving the construction of the initial solution, the construction of the neighborhood and the local search. Experimental evidence shows that the improved solution outperforms the best solutions reported for six of the considered instances, increases by 13.6% the number of best solutions found and reduces by 34% the deviation of the best solution found, respect to the best algorithm solution reported.

Using Consecutive Swaps to Explore the Insertion Neighborhood in Tabu Search Solution of the Linear Ordering Problem

In this paper the linear Ordering Problem (LOP) is approached. This problem consists in to find an ordering of rows and columns of a matrix weights, such that the sum of all the values above the main diagonal is minimized. We propose in this ongoing research, increases the efficiency of exploration method in the insertion neighborhood in the state of the art Tabu search solution. The approach is evaluated on the broad set of standard instances that include the most difficult XLOLIB instances, from which the optima values are unknown. The results for instances which optimum values are known (OI), show that the proposed method has obtained reductions in execution time ranging between 21% and 97%, while, for the most difficult instances included in the set with unknown optima (BI), the reduction reaching 98%.Wilcoxon test is used to prove that the proposed method ITS, obtains similar % average error for OI instances than the reference method RTS, and a significance reduction in the average time. Now we are working in developing additional diversification strategies that take advantage of the savings in time to explore new regions of the search space.