Marcone Jamilson Freitas Souza

A hybrid heuristic algorithm for the open-pit-mining operational planning problem

This paper deals with the Open-Pit-Mining Operational Planning problem with dynamic truck allocation. The objective is to optimize mineral extraction in the mines by minimizing the number of mining trucks used to meet production goals and quality requirements. According to the literature, this problem is NP-hard, so a heuristic strategy is justified. We present a hybrid algorithm that combines characteristics of two metaheuristics: Greedy Randomized Adaptive Search Procedures and General Variable Neighborhood Search. The proposed algorithm was tested using a set of real-data problems and the results were validated by running the CPLEX optimizer with the same data. This solver used a mixed integer programming model also developed in this work. The computational experiments show that the proposed algorithm is very competitive, finding near optimal solutions (with a gap of less than 1%) in most instances, demanding short computing times.

A Tabu search heuristic with efficient diversification strategies for the class/teacher timetabling problem

The Class/Teacher Timetabling Problem (CTTP) deals with the weekly scheduling of encounters between teachers and classes of an educational institution. Since CTTP is a NP-hard problem for nearly all of its variants, the use of heuristic methods for its resolution is justified. This paper presents an efficient Tabu Search (TS) heuristic with two different memory based diversification strategies for CTTP. Results obtained through an application of the method to a set of real world problems show that it produces better solutions than a previously proposed TS found in the literature and faster times are observed in the production of good quality solutions.

A GRASP-tabu search algorithm for solving school timetabling problems

This paper proposes a hybrid approach to solve school timetabling problems. This approach is a GRASP that uses a partially greedy procedure to construct an initial solution and attempts to improve the constructed solution using Tabu Search. When an infeasible solution without overlapping classes is generated, a procedure called Intraclasses-Interclasses is activated, trying to retrieve feasibility. If successful, it is reactivated, in an attempt to improve the timetables compactness as well as other requirements. Computational results show that the Intraclasses-Interclasses procedure speeds up the process of obtaining better quality solutions.

An ILS-based algorithm to solve a large-scale real heterogeneous fleet VRP with multi-trips and docking constraints

Distribution planning is crucial for most companies since goods are rarely produced and consumed at the same place. Distribution costs, in addition, can be an important component of the final cost of the products. In this paper, we study a VRP variant inspired on a real case of a large distribution company. In particular, we consider a VRP with a heterogeneous fleet of vehicles that are allowed to perform multiple trips. The problem also includes docking constraints in which some vehicles are unable to serve some particular customers, and a realistic objective function with vehicles’ fixed and distance-based costs and a cost per customer visited. We design a trajectory search heuristic called GILS-VND that combines Iterated Local Search (ILS), Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Descent (VND) procedures. This method obtains competitive solutions and improves the company solutions leading to significant savings in transportation costs.

GOAL solver: a hybrid local search based solver for high school timetabling

This work presents a local search approach to the High School Timetabling Problem. The addressed timetabling model is the one stated in the Third International Timetabling Competition (ITC 2011), which considered many instances from educational institutions around the world and attracted seventeen competitors. Our team, named GOAL (Group of Optimization and Algorithms), developed a solver built upon the Kingston High School Timetabling Engine. Several neighborhood structures were developed and used in a hybrid metaheuristic based on Simulated Annealing and Iterated Local Search. The developed algorithm was the winner of the competition and produced the best known solutions for almost all instances.

A SA-VNS approach for the High School Timetabling Problem

The High School Timetabling Problem consists in assigning timeslots and resources to events, satisfying constraints which heavily depend on the specific institution. This work deals with the problem of the ongoing III International Timetabling Competition (ITC), which includes a diverse set of instances from many educational institutions around the world. We proposed an approach based on Simulated Annealing and Variable Neighborhood Search metaheuristics. One important structural feature of our approach is the use of the Kingston’s High School Timetabling Engine (KHE) to generate initial solutions combined with the multi-neighborhood search. Such approach led us to the finals of the ongoing competition.

Multi-objective metaheuristic algorithms for the resource-constrained project scheduling problem with precedence relations

This study addresses the resource-constrained project scheduling problem with precedence relations, and aims at minimizing two criteria: the makespan and the total weighted start time of the activities. To solve the problem, five multi-objective metaheuristic algorithms are analyzed, based on Multi-objective GRASP (MOG), Multi-objective Variable Neighborhood Search (MOVNS) and Pareto Iterated Local Search (PILS) methods. The proposed algorithms use strategies based on the concept of Pareto Dominance to search for solutions and determine the set of non-dominated solutions. The solutions obtained by the algorithms, from a set of instances adapted from the literature, are compared using four multi-objective performance measures: distance metrics, hypervolume indicator, epsilon metric and error ratio. The computational tests have indicated an algorithm based on MOVNS as the most efficient one, compared to the distance metrics; also, a combined feature of MOG and MOVNS appears to be superior compared to the hypervolume and epsilon metrics and one based on PILS compared to the error ratio. Statistical experiments have shown a significant difference between some proposed algorithms compared to the distance metrics, epsilon metric and error ratio. However, significant difference between the proposed algorithms with respect to hypervolume indicator was not observed.

A hybrid heuristic, based on Iterated Local Search and GENIUS, for the Vehicle Routing Problem with Simultaneous Pickup and Delivery

This work deals with the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). The VRPSPD is a common problem in the area of reverse logistics, which aims to plan the transportation of products to customers, as well as the return of leavings or products used by them for recycling or to special depots. The VRPSPD is NP-hard, since it can be reduced to the classical Vehicle Routing Problem (VRP) when no client needs the pickup service. To solve it, we propose a hybrid heuristic algorithm, called GENILS, based on Iterated Local Search (ILS), Variable Neighbourhood Descent and GENIUS. The proposed algorithm was tested on three well-known sets of instances found in literature and it was competitive with the best existing approaches. Among the 72 test-problems of these sets, the GENILS was capable to improve the result of nine instances and to equal another 49.

An adaptive genetic algorithm to the single machine scheduling problem with earliness and tardiness penalties

This paper deals with the Single Machine Scheduling Problem with Earliness and Tardiness Penalties, considering distinct due windows and sequence-dependent setup time. Due to its complexity, an adaptive genetic algorithm is proposed for solving it. Five search operators are used to explore the solution space and the choice probability for each operator depends on the success in a previous search. The initial population is generated by the combination between construct methods based on greedy, random and GRASP techniques. For each job sequence generated, a polynomial time algorithm are used for determining the processing initial optimal date to each job. During the evolutive process, a group with the best five individuals generated by each crossover operator is built. Then, periodically, a Path Relinking module is applied taking as base individual the best one so far generated by the algorithm and as guide individual each one of the five best individuals generated by each crossover operator. Three variations of this algorithm were submitted to computational experiments. The results shows the effectiveness of the proposal.

An Efficient Tabu Search Heuristic for the School Timetabling Problem

The School Timetabling Problem (STP) regards the weekly scheduling of encounters between teachers and classes. Since this scheduling must satisfy organizational, pedagogical and personal costs, this problem is recognized as a very difficult combinatorial optimization problem. This work presents a new Tabu Search (TS) heuristic for STP. Two different memory-based diversification strategies are presented. Computational experiments with real world instances, in comparison with a previously proposed TS found in literature, show that the proposed method produces better solutions for all instances, as well as observed increased speed in the production of good quality solutions.