Optimization of Multi Objective Job Shop Scheduling Problems Using Firefly Algorithm

Abstract

Scheduling is the allocation of resources over time to carry out a collection of tasks assigned in any field of engineering and non engineering. Majority of JSSP are categorized into non deterministic (NP) hard problem because of its complexity. Scheduling are generally solved by using heuristics to obtain optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using available resources in many cases. Many researchers attempted to solve the problem by applying various optimization techniques. While using traditional methods they observed huge difficulty in solving high complex problems and meta-heuristic algorithms were proved most efficient algorithms to solve various JSSP so far. The objective of this paper i) to make use of a newly developed meta heuristic called Firefly algorithm (FA) because of inspiration on Firefly and its characteristic. ii) To find the combined objective function by determining optimal make span, mean flow time and tardiness of different size problems (using Lawrence 1-40 problems) as a bench marking dataset and to find the actual computational time. Iii) to prove that the proposed FFA algorithm is a good problem solving technique for JSSP with multi criteria.

A Neighborhood Limitation Method for Job-Shop Scheduling Based on Simulated Annealing

“…The term production lead time is used for different purposes. It is usually used in the sense of flow time, which is defined as the time between the release time and the finishing time [16,17], and job-shop scheduling considering average flow time has been discussed by various research groups [18][19][20][21][22]. It is sometimes defined as the time between the release time and the due date, and a release time determination method using backward and forward scheduling based on dispatching rules was proposed [23].…”

A Dynamic Adaptive Firefly Algorithm for Flexible Job Shop Scheduling

“…Ong et al [18] compared the prey-predator algorithm with the Firefly algorithm and conducted a Mann-Whitney test to demonstrate the algorithm's efficiency. Udaiyakumar et al [19] solved the instances of Lawrence benchmark by FA for combined objective optimization. Bottani et al [20] solved Flexible Manufacturing System's (FMS) loading problem by discrete FA with bi-objective.…”