Marco Antonio Cruz-Chavez

Simulated annealing with restart to job shop scheduling problem using upper bounds

An algorithm of simulated annealing for the job shop scheduling problem is presented. The proposed algorithm restarts with a new value every time the previous algorithm finishes. To begin the process of annealing, the starting point is a randomly generated schedule with the condition that the initial value of the makespan of the schedule does not surpass a previously established upper bound. The experimental results show the importance of using upper bounds in simulated annealing in order to more quickly approach good solutions.

Neighborhood Hybrid Structure for Discrete Optimization Problems

In this paper a comparative analysis of a neighborhood structures group are presented, including a hybrid structure, which arises of a combination of this set of structures. The efficiency and effectiveness of each structure was tested using the Classical Symmetric Travelling Salesman Problem. This study identifies the neighborhood structure that allows performing a better exploration and exploitation of the space solutions to discrete optimization problems. A neighborhood hybrid structure proposed has better performance comparing with other techniques, this is experimentally proved, in addition a competitive efficiency is shown.

Genetic-Annealing Algorithm in Grid Environment for Scheduling Problems

This paper presents a parallel hybrid evolutionary algorithm executed in a grid environment. The algorithm executes local searches using simulated annealing within a Genetic Algorithm to solve the job shop scheduling problem. Experimental results of the algorithm obtained in the “Tarantula MiniGrid” are shown. Tarantula was implemented by linking two clusters from different geographic locations in Mexico (Morelos-Veracruz). The technique used to link the two clusters and configure the Tarantula MiniGrid is described. The effects of latency in communication between the two clusters are discussed. It is shown that the evolutionary algorithm presented is more efficient working in Grid environments because it can carry out major exploration and exploitation of the solution space.

A local search algorithm for a SAT representation of scheduling problems

This paper presents the application of a local search algorithm for a logical representation of the Job Shop Scheduling Problem (JSSP). This logical representation represents the JSSP transformed as a satisfiability problem (SAT). The proposed algorithm uses a local search in a wide neighborhood. This algorithm, called Walk Wide Search - SAT, is a variant of the WalkSAT algorithm. This search is possible because the included tabu list prevents an excessive number of repetitions of movements during the search process. This paper describes the algorithm and compares results of Walk Wide Search - SAT to WalkSAT.

Search Algorithm for the Constraint Satisfaction Problem of VRPTW

This paper presents an algorithm called CSP-IRPTW for the vehicles routing problem with time windows (VRPTW), which applies the PCP method (precedence constraint posting) used for models of scheduling as a CSP (constraint satisfaction problem). PCP involves the calculation of the shortest path in partial and global form, between pairs of nodes and among all the nodes respectively, in the graph that represents the VRPTW model. In order to apply PCP to VRPTW, the problem is treated as a CSP. The results show that the proposed search algorithm is efficient in the search for the global optimum for some problems.

Memetic Algorithm Based on a Constraint Satisfaction Technique for VRPTW

In this paper a Memetic Algorithm (MA) is proposed for solving the Vehicles Routing Problem with Time Windows (VRPTW) multi-objective, using a constraint satisfaction heuristic that allows pruning of the search space to direct a search towards good solutions. An evolutionary heuristic is applied in order to establish the crossover and mutation between sub-routes. The results of MA demonstrate that the use of Constraints Satisfaction Technique permits MA to work more efficiently in the VRPTW.

Feasible Initial Population with Genetic Diversity for a Population-Based Algorithm Applied to the Vehicle Routing Problem with Time Windows

A stochastic algorithm for obtaining feasible initial populations to the Vehicle Routing Problem with Time Windows is presented. The theoretical formulation for the Vehicle Routing Problem with Time Windows is explained. The proposed method is primarily divided into a clustering algorithm and a two-phase algorithm. The first step is the application of a modified -means clustering algorithm which is proposed in this paper. The two-phase algorithm evaluates a partial solution to transform it into a feasible individual. The two-phase algorithm consists of a hybridization of four kinds of insertions which interact randomly to obtain feasible individuals. It has been proven that different kinds of insertions impact the diversity among individuals in initial populations, which is crucial for population-based algorithm behavior. A modification to the Hamming distance method is applied to the populations generated for the Vehicle Routing Problem with Time Windows to evaluate their diversity. Experimental tests were performed based on the Solomon benchmarking. Experimental results show that the proposed method facilitates generation of highly diverse populations, which vary according to the type and distribution of the instances.

Simulated Annealing Algorithm for the Weighted Unrelated Parallel Machines Problem

In this paper, a solution is presented to the unrelated parallel machines problem that minimizes the total weighted completion time. Simulated annealing is applied to the problem, which is modeled as a Weighted Bipartite Matching Problem. Experimental results with benchmarks are presented, evaluating the efficiency and efficacy of the algorithm. It is then compared with an exact algorithm that solves the pondered model of Integer Linear Programming. The results demonstrate that Simulated Annealing Algorithm has high performance because for all the evaluated instances, it finds the optimum global solution.

Experimental Analysis of a Neighborhood Generation Mechanism Applied to Scheduling Problem

This paper presents a neighborhood generation mechanism for the job shop scheduling problems (JSSP). In order to obtain a feasible neighbor with the generation mechanism, it is only necessary to generate a permutation of an adjacent pair of operations in a scheduling of the JSSP. If there is no slack-time between the adjacent pair of operations that is permuted, then it is proven, through experimentation that the new neighbor (schedule) generated is feasible

Empirical Transformation of Job Shop Scheduling Problem to the Hydraulic Networks Problem in a Water Distribution System

In this paper an analogy of the Job Shop Scheduling Problem to the Hydraulic Networks Problem is presented by mapping this model of scheduling, using as a base the disjunctive graph model. The mapping carried out allows visualization of the Hydraulic Networks problem as an NP-complete model with constraints defined in the Job Shop Scheduling Problem. The mapping presented indicates that the Hydraulic Networks Problem is a difficult problem to solve by using an approach with the constraints of an NP-complete problem.