A synergetic combination of small and large neighborhood schemes in developing an effective procedure for solving the job shop scheduling problem

Abstract

This paper presents an effective procedure for solving the job shop problem. Synergistically combining small and large neighborhood schemes, the procedure consists of four components, namely (i) a construction method for generating semi-active schedules by a forward-backward mechanism, (ii) a local search for manipulating a small neighborhood structure guided by a tabu list, (iii) a feedback-based mechanism for perturbing the solutions generated, and (iv) a very large-neighborhood local search guided by a forward-backward shifting bottleneck method. The combination of shifting bottleneck mechanism and tabu list is used as a means of the manipulation of neighborhood structures, and the perturbation mechanism employed diversifies the search. A feedback mechanism, called repeat-check, detects consequent repeats and ignites a perturbation when the total number of consecutive repeats for two identical makespan values reaches a given threshold. The results of extensive computational experiments on the benchmark instances indicate that the combination of these four components is synergetic, in the sense that they collectively make the procedure fast and robust.

Impact of Problems Associated with Scheduling and Capacity Planning of a Production Process – An Overview

“…In the data energetic world, collection and generation of data are important, data can be extracted from the machine sensors in form of quality data, product data infrastructure data, process data, plant data, logistics data,and data from partners [11]. New methods for processing, management, and storing of such data are required also to use and gain the actual benefits from the data, new visualization techniques, algorithms, products, and models are required [12]. Companies are facing a shortage of skilled staff due to various factors.…”

Complexity of line-seru conversion for different scheduling rules and two improved exact algorithms for the multi-objective optimization

“…However, the different scheduling rules produce different performances or complexity of a system (Chuen and Robert 1993 ; Grabot and Geneste 1994 ; Holthaus and Rajendran 1997 ; Amirghasemi and Zamani 2014 ; Nurre and Sharkey 2014 ; Xu et al 2015 ; Zeng et al 2015 ). The comparative analysis of scheduling rules in some specific industrial environments can be found in Rajendran and Holthaus ( 1999 ), Kizil et al ( 2006 ), Chiang and Fu ( 2007 ), Akturk ( 2011 ), and Li et al ( 2015 ).…”

Hybrid Genetic Algorithm with Multiparents Crossover for Job Shop Scheduling Problems

“…ORB problem contains ten 10 × 10 instances. To illustrate the effectiveness, the proposed multiparent crossover is compared with the other algorithms such as synergistically combining small and large neighborhood schemes (SLENP) [31], parallel genetic algorithms (PGA) [13] with two-parent crossover, and greedy randomized adaptive search procedure (GRASP) [32]. The relative errors (REs) are calculated based on deviation between BKS and the best solution found (Best).…”