On solving permutation scheduling problems with ant colony optimization

A new approach for solving permutation scheduling problems with ant colony optimization (ACO) is proposed in this paper. The approach assumes that no precedence constraints between the jobs have to be fulfilled. It is tested with an ACO algorithm for the single-machine total weighted deviation problem. In the new approach the ants allocate the places in the schedule not sequentially, as in the standard approach, but in random order. This leads to a better utilization of the pheromone information. It is shown by experiments that adequate combinations between the standard approach which can profit from list scheduling heuristics and the new approach perform particularly well

Ant Colony Optimization for Multilevel Assembly Job Shop Scheduling

Job shop scheduling is one of the most explored areas in the last few decades. Although it is very commonly witnessed in real-life situations, very less investigation has been carried out in scheduling operations of multi-level jobs, which undergo serial, parallel, and assembly operations in an assembly job shop. In this work, some of the dispatch rules, which have best performances in scheduling multilevel jobs in dynamic assembly job shop, are tested in static assembly job shop environment. A new optimization heuristic based on Ant Colony Algorithm is proposed and its performance is compared with the dispatch rules

A single-machine scheduling problem with multiple unavailability constraints: A mathematical model and an enhanced variable neighborhood search approach

AbstractThis research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective

Iterated search methods for earliness and tardiness minimization in hybrid flowshops with due windows

[EN] In practice due dates usually behave more like intervals rather than specific points in time. This paper studies hybrid flowshops where jobs, if completed inside a due window, are considered on time. The objective is therefore the minimization of the weighted earliness and tardiness from the due window. This objective has seldom been studied and there are almost no previous works for hybrid flowshops. We present methods based on the simple concepts of iterated greedy and iterated local search. We introduce some novel operators and characteristics, like an optimal idle time insertion procedure and a two stage local search where, in the second stage, a limited local search on a exact representation is carried out. We also present a comprehensive computational campaign, including the reimplementation and comparison of 9 competing procedures. A thorough evaluation of all methods with more than 3000 instances shows that our presented approaches yield superior results which are also demonstrated to be statistically significant. Experiments also show the contribution of the new operators in the presented methods. (C) 2016 Elsevier Ltd. All rights reserved.The authors would like to thank Professors Lofti Hidri and Mohamed Haouari for sharing with us the source codes and explanations of the lower bounds. Quan-Ke Pan is supported by the National Natural Science Foundation of China (Grant No. 51575212), Program for New Century Excellent Talents in University (Grant No. NCET-13-0106), Science Foundation of Hubei Province in China (Grant No. 2015CFB560), Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20130042110035), Key Laboratory Basic Research Foundation of Education Department of Liaoning Province (LZ2014014), Open Research Fund Program of the State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, China. Ruben Ruiz and Pedro Alfaro-Fernandez are supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) financed by FEDER funds.Pan, Q.; Ruiz García, R.; Alfaro-Fernandez, P. (2017). Iterated search methods for earliness and tardiness minimization in hybrid flowshops with due windows. Computers & Operations Research. 80:50-60. https://doi.org/10.1016/j.cor.2016.11.022S50608

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Multicriteria hybrid flow shop scheduling problem: literature review, analysis, and future research

This research focuses on the Hybrid Flow Shop production scheduling problem, which is one of the most difficult problems to solve. The literature points to several studies that focus the Hybrid Flow Shop scheduling problem with monocriteria functions. Despite of the fact that, many real world problems involve several objective functions, they can often compete and conflict, leading researchers to concentrate direct their efforts on the development of methods that take consider this variant into consideration. The goal of the study is to review and analyze the methods in order to solve the Hybrid Flow Shop production scheduling problem with multicriteria functions in the literature. The analyses were performed using several papers that have been published over the years, also the parallel machines types, the approach used to develop solution methods, the type of method develop, the objective function, the performance criterion adopted, and the additional constraints considered. The results of the reviewing and analysis of 46 papers showed opportunities for future researchon this topic, including the following: (i) use uniform and dedicated parallel machines, (ii) use exact and metaheuristics approaches, (iv) develop lower and uppers bounds, relations of dominance and different search strategiesto improve the computational time of the exact methods,  (v) develop  other types of metaheuristic, (vi) work with anticipatory setups, and (vii) add constraints faced by the production systems itself

A strong preemptive relaxation for weighted tardiness and earliness/tardiness problems on unrelated parallel machines

Research on due date oriented objectives in the parallel machine environment is at best scarce compared to objectives such as minimizing the makespan or the completion time related performance measures. Moreover, almost all existing work in this area is focused on the identical parallel machine environment. In this study, we leverage on our previous work on the single machine total weighted tardiness (TWT) and total weighted earliness/tardiness (TWET) problems and develop a new preemptive relaxation for the TWT and TWET problems on a bank of unrelated parallel machines. The key contribution of this paper is devising a computationally effective Benders decomposition algorithm for solving the preemptive relaxation formulated as a mixed integer linear program. The optimal solution of the preemptive relaxation provides a tight lower bound. Moreover, it offers a near-optimal partition of the jobs to the machines, and then we exploit recent advances in solving the non-preemptive single machine TWT and TWET problems for constructing non-preemptive solutions of high quality to the original problem. We demonstrate the effectiveness of our approach with instances up to 5 machines and 200 jobs

Using Ant Colony Algorithm to solve NP-Complete problems

We consider the problem of scheduling jobs on a single machine about a common due date. Our objective is to determine the common due date and processing sequence of new jobs together with the re-sequencing of old jobs which minimize the sum of jobs’ earliness/tardiness, completion time penalties and due date related penalty. We drive properties that can be used to find the optimal common due date and processing sequence. Since our problem is NP-hard, we propose Ant colony algorithm (ACO) to solve the problem efficiently. Results from computational study reveal that Ant colony algorithm (ACO) ca

An Innovative Maintenance Scheduling Framework for Preventive, Predictive Maintenance Using Ant Colony Optimization

The fourth industrial revelation has brought exponential technologies in the area of digitization, internet of things (IOT), artificial intelligence (AI), and optimization which has helped mining companies to increase the availability and utilization of equipment’s. As mining companies implement predictive maintenance technologies, to improve overall equipment availability, there is more value to be unearthed if predictive maintenance is optimized with the production schedules. Ant colony optimization (ACO) is a metaheuristic that is inspired from the behavior of real ants to solve combinatorial optimization problems. This chapter describes an innovative maintenance scheduling framework in the context of optimizing schedules for preventive maintenance and predictive maintenance, with multiple constraints for optimized dynamic schedule to reduce the maintenance time, and production losses