A two-stage no-wait job shop scheduling problem by using a neuro-evolutionary variable neighborhood search

Abstract

A two-stage order scheduling problem with an additional no-wait condition is studied in this article. All orders must be processed from start to completion without any waiting between operations and any interruption within operations. Examples of such a problem occur in steel production and pharmaceutical, chemical, and construction industries. A new optimization method which is based on a combination of an enhanced variable neighborhood search (VNS) and an artificial neural network (ANN) is devised in this paper. The aim is to simultaneously employ the key characteristics of two approaches to achieve superior solutions in solving the addressed optimization problem. In proposed algorithm, the VNS performs a global search whereas the ANN plays the role of intensive initial solution. The individual obtained by feed forward back-propagation ANN is improved within enhanced VNS iterations. The two techniques complement each other when ANN feeds its best solution to the VNS algorithm. Furthermore, to establish the best sequence of order pairs, a team process algorithm is also embedded into the VNS to enhance the search diversification as well as its intensification. The performance of proposed neuro-evolutionary algorithm is investigated via computational experiments, and the results prove the appropriate performance of suggested VNS-ANN algorithm.

Optimizing the Job Shop Scheduling Problem with a no Wait Constraint by Using the Jaya Algorithm Approach

“…Metaheuristics have been widely used by researchers as approaches to solve the NWJSP. Mokhtari (2014) proposed a new optimization method that is based on a combination of an enhanced variable neighbourhood search and an artificial neural network. Aitzai (2016) proposed an exact method based on the branch-and-bound algorithm by defining a new technique of branching and a swarm optimization (PSO) algorithm with an efficient approach to move a particle to the new position.…”

Integrated Scheduling Algorithm of Two Workshops Based on Process End Time Driven and Processing Area Priority

“…For decades, a large number of job shop scheduling problem optimization algorithms have been proposed. For the two-stage nowait job shop scheduling problem, Hadi Mokhtari proposed a new optimization method by combining an enhanced variable neighborhood search and an artificial neural network [2]. For the flexible job shop scheduling problem, Zhao proposed a hybrid algorithm mixed with bi-level neighborhood search and genetic algorithms to optimize the maximum completion time [3].…”