Integrating Genetic Algorithm, Tabu Search and Simulated Annealing For Job Shop Scheduling Proble

Abstract

Job Shop Scheduling Problem (JSSP) is an optimization problem in which ideal jobs are assigned to resources at particular times. In recent years many attempts have been made at the solution of JSSP using a various range of tools and techniques such as Branch and Bound and Heuristics algorithms. This paper proposed a new algorithm based on Genetic Algorithm (GA), Tabu Search (TS) and Simulated Annealing (SA) algorithms to solve JSSP. The proposed algorithm is mainly based on the genetic algorithm. The reproduction phase of the genetic algorithm uses the tabu search to generate new population. Simulated annealing algorithm is used to speed up the genetic algorithm to get the solution by applying the simulated annealing test for all the population members. The proposed algorithm used many small but important features such as chromosome representation, effective genetic operators, and restricted neighbourhood strategies. The above features are used in the hybrid algorithm to solve several bench mark problems. Keywords

A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study

“…The work reported here is one such attempt using clever ways of combining the search techniques GA and TS along with intelligent subtechniques. There have been a small number of systems that use the combination of GA and TS in providing a solution to JSSP (Meeran and Morshed 2007;Tamilselvan and Balasubramanie 2009;González et al 2009;Chiu et al 2007;Zhang et al 2010). Tamilselvan and Balasubramanie (2009)) have used GA as the base search mechanism and TS to improve their search.…”

“…There have been a small number of systems that use the combination of GA and TS in providing a solution to JSSP (Meeran and Morshed 2007;Tamilselvan and Balasubramanie 2009;González et al 2009;Chiu et al 2007;Zhang et al 2010). Tamilselvan and Balasubramanie (2009)) have used GA as the base search mechanism and TS to improve their search. They have demonstrated the effectiveness of the combination of GA and TS, which is called GTA against standalone GA and TS using a limited number of example problems that they have devised.…”

Multi-Robot Coalitions Formation with Deadlines: Complexity Analysis and Solutions

“…On account of [17], the coalition formation problem can be defined in terms of the Job Shop Scheduling (JSS) problem as follows: given a set of m jobs J = { J 1 , J 2 , …, J m } and a set of n machines, each job J i has m i associated operations O i = { O i 1 , O i 2 , …, O im i } each one of them with an execution time for each machine T ij . The system’s goal is to decide (schedule) in which order the tasks must be carried out on the machines to minimize the execution time.…”

A color petri net based scheduling model for remanufacturing system with stochastic process routing

“…In this paper, SA is used to determine the optimal process routings for components. Starting from an initial solution g 0 , SA uses certain mechanism [14,15] to generate a neighborhood solution g'. To accept g' over g 0 , g' either is a better neighborhood solution or has a certain probability to escape from a local minimum although g' is worse than g 0 .…”

“…The standard SA neighborhood structure that features various types of moves [15] is adopted in the paper to generate a neighborhood solution from the current one. The process randomly selects an element in the matrix R, and uses one type of moves (i.e., ς-opt, ς∈{1, 2, …, H-h}) to exchange r ldh with r ld [h+ς] for a new neighborhood solution.…”