A NeuroGenetic Approach for Multiprocessor Scheduling

This chapter presents a NeuroGenetic approach for solving a family of multiprocessor scheduling problems. We address primarily the Job-Shop scheduling problem, one of the hardest of the various scheduling problems. We propose a new approach, the NeuroGenetic approach, which is a hybrid metaheuristic that combines augmented-neural-networks (AugNN) and genetic algorithms-based search methods. The AugNN approach is a nondeterministic iterative local-search method which combines the benefits of a heuristic search and iterative neural-network search. Genetic algorithms based search is particularly good at global search. An interleaved approach between AugNN and GA combines the advantages of local search and global search, thus providing improved solutions compared to AugNN or GA search alone. We discuss the encoding and decoding schemes for switching between GA and AugNN approaches to allow interleaving. The purpose of this study is to empirically test the extent of improvement obtained by using the interleaved hybrid approach instead of applied using a single approach on the job-shop scheduling problem. We also describe the AugNN formulation and a Genetic Algorithm approach for the JobShop problem. We present the results of AugNN, GA and the NeuroGentic approach on some benchmark job-shop scheduling problems

A Random Keys Genetic Algorithm for Job Shop Scheduling

https://deepblue.lib.umich.edu/bitstream/2027.42/154143/1/39015099114574.pd

Times assincronos para o Job shop scheduling problem : heuristicas de melhoria

Orientadores: Pedro Sergio de Souza, Marcus Vinicius Poggi de AragãoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Este trabalho aborda o problema de seqüenciamento de tarefas conhecido como Job Shop Scheduling Problem (JSP). O objetivo aqui é mostrar a adequação de uma técnica conhecida como Times Assíncronos (A-Teams), para resolver este problema de otimização combinatória, que é bastante freqüente em ambientes industriais. Esta abordagem tem sido aplicada com sucesso na resolução de outros problemas, como o Traveling Salesman Problem, o Flow-Shop Problem e até mesmo o próprio Job Shop Problem sob uma abordagem de heurísticas de construção. Esta técnica está baseada na cooperação de algoritmos heurísticos no sentido de obter soluções, possivelmente, melhores que aquelas obtidas quando os mesmos algoritmos são executados isoladamente. Neste trabalho, o enfoque é dado a heurísticas de melhoria. Outros tipos de algorit­mos foram desenvolvidos para compor os A-Teams. Estes A-Teams desenvolvidos foram acoplados a um outro já existente, baseado em heurísticas de construção. Algumas instâncias de JSP foram testadas e os resultados obtidos atestam a adequação desta técnica para a resolução deste problema.Abstract: This work treats the sequencing of tasks problem known as Job Shop Scheduling Problem. The goal here is to show the adequability of a technique known as Assynchronous Teams (A-Teams) to solve this optimization problem which is used in industrial environments. This approach has been applied successfully in the solving of other problems such as the Traveling Salesman Problem, Flow Shop Problem and the Job Shop Problem itself using construction heuristics algorithms. This technique is based on the cooperation of some heuristics algorithms in order to obtain solutions, possibly better then ones obtained when same algorithms are working alone. In this work, the focus is on the development of improvement heuristics algorithms. Another type of algorithms were also developed to form the A-Teams. These A-Teams developed were joined to another one, based in construction heuristics. Some instances of the JSP were tested and the results obtained show the adequability oí this technique to solve this problem.MestradoMestre em Ciência da Computaçã

Tabu search for ship routing and scheduling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 20/12/2006.This thesis examines exact and heuristic approaches to solve the Ship Routing and Scheduling Problem (SRSP). The method was developed to address the problem of loading cargos for many customers using heterogeneous vessels. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and vessel capacities. The objective is to minimise the overall operation cost, where all customers are satisfied. Two types of routing and scheduling are considered, one called single-cargo problem, where only one cargo can be loaded into a ship, and the second type called multi-cargo problem, where multiple products can be carried on a ship to be delivered to different customers. The exact approach comprises two stages. In the first stage, a number of candidate feasible schedules is generated for each ship in the fleet. The second stage is to model the problem as a set partitioning problem (SPP) where the columns are the candidate feasible schedules obtained in the first stage. The heuristic approach uses Tabu Search (TS). Most of the TS operations, such as insert and swap moves, tenure, tabu list, intensification, and diversification are used. The results of a computational investigation are presented. Solution quality and execution time are explored with respect to problem size and parameters controlling the tabu search such as tenure and neighbourhood size. The results showed that the average of the solution gap between TS solution and SPP solution is up to 28% (for small problems) and up to 18% for large problems. However, obtaining an optimal solution requires a large amount of computer time to produce the solution compared to obtaining approximate solutions using the TS approach. The use of Tabu Search for SRSP is novel and the results indicate that it is viable approach for large problems