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Showing papers on "Single-machine scheduling published in 2020"


Journal ArticleDOI
TL;DR: A hybrid multiobjective optimization algorithm is developed that maintains two populations executing the global search in the whole solution space and the local search in promising regions, respectively that has a great advantage in dealing with the investigated problem.
Abstract: Hybrid flow shop scheduling problems have gained an increasing attention in recent years because of its wide applications in real-world production systems. Most of the prior studies assume that the processing time of jobs is deterministic and constant. In practice, jobs’ processing time is usually difficult to be exactly known in advance and can be influenced by many factors, e.g., machines’ abrasion and jobs’ feature, thereby leading to their uncertain and variable processing time. In this paper, a dual-objective stochastic hybrid flow shop deteriorating scheduling problem is presented with the goal to minimize makespan and total tardiness. In the formulated problem, the normal processing time of jobs follows a known stochastic distribution, and their actual processing time is a linear function of their start time. In order to solve it effectively, this paper develops a hybrid multiobjective optimization algorithm that maintains two populations executing the global search in the whole solution space and the local search in promising regions, respectively. An information sharing mechanism and resource allocating method are designed to enhance its exploration and exploitation ability. The simulation experiments are carried out on a set of instances, and several classical algorithms are chosen as its peers for comparison. The results demonstrate that the proposed algorithm has a great advantage in dealing with the investigated problem.

108 citations


Journal ArticleDOI
TL;DR: A two-stage decomposition method is proposed such that an industrial size problem can be solved and can get an optimal solution of the concerned problem with one-week-scale batches and jobs in short time, thereby proving the readiness to put it in industrial use.
Abstract: Production scheduling is a crucial task in modern steel plants. The scheduling of a wire rod and bar rolling process is challenging in many steel plants, which has a direct impact on their production efficiency and profit. This article studies a new single-machine scheduling problem with sequence-dependent setup time, release time, and due time constraints originated from a wire rod and bar rolling process in steel plants. In this problem, jobs have been assigned to batches in advance. The objective is to schedule the batches and jobs on continuous time to minimize the number of late jobs. A mixed-integer program is created as a baseline model. A baseline method is used to solve this NP-hard problem by solving the baseline model. We further design a two-stage decomposition method after analyzing the characteristics of this problem. Both actual and simulated instances with varying sizes are solved by using the proposed methods. The results demonstrate that the baseline method can only solve some small-scale cases, while the decomposition method can solve all small-scale cases and some medium-scale cases. Finally, we reveal the impacts of different instances on the performance of the proposed decomposition method. Note to Practitioners —This article deals with a new single-machine scheduling problem arising from an industrial wire rod and bar rolling process. A baseline method is given to tackle this problem by solving an established mixed-integer program. Afterward, a two-stage decomposition method is proposed such that an industrial size problem can be solved. Computational results of both actual and simulated cases show that it is more efficient than the baseline method in solving the scheduling problem. It can get an optimal solution of the concerned problem with one-week-scale batches and jobs in short time, thereby proving the readiness to put it in industrial use.

84 citations


Journal ArticleDOI
TL;DR: A bi-objective mixed-integer non-linear programming model is designed, and a hybrid multi-objectives genetic algorithm (HMOGA) is proposed to handle medium- and large-sized problems and yields better outcomes than the NSGA-2 and faster than Baron solver.

36 citations


Journal ArticleDOI
TL;DR: A pseudo-polynomial dynamic programming algorithm and a $$(1+\epsilon )$$ ( 1 + ϵ ) -approximate Pareto-optimal frontier are designed to solve the competitive multi-agent scheduling problem on a single machine.
Abstract: We consider the competitive multi-agent scheduling problem on a single machine, where each agent’s cost function is to minimize its total weighted late work. The aim is to find the Pareto-optimal frontier, i.e., the set of all Pareto-optimal points. When the number of agents is arbitrary, the decision problem is shown to be unary $$\mathcal {NP}$$ -complete even if all jobs have the unit weights. When the number of agents is two, the decision problems are shown to be binary $$\mathcal {NP}$$ -complete for the case in which all jobs have the common due date and the case in which all jobs have the unit processing times. When the number of agents is a fixed constant, a pseudo-polynomial dynamic programming algorithm and a $$(1+\epsilon )$$ -approximate Pareto-optimal frontier are designed to solve it.

28 citations


Proceedings ArticleDOI
06 Jul 2020
TL;DR: This paper provides competitive algorithms for the online setting with speed augmentation, and gives a lower bound proving thatSpeed augmentation is in fact necessary, and improves and generalizes the results of Jia et al. on completion times by giving an O(1)-competitive algorithm for arbitrary sizes and release times.
Abstract: New optical technologies offer the ability to recon-figure network topologies dynamically, rather than setting them once and for all. This is true in both optical wide area networks (optical WANs) and in datacenters, despite the many differences between these two settings. Because of these new technologies, there has been a surge of both practical and theoretical research on algorithms to take advantage of them. In particular, Jia et al. [INFOCOM '17] designed online scheduling algorithms for dynamically reconfigurable topologies for both the makespan and sum of completion times objectives. In this paper, we work in the same setting but study an objective that is more meaningful in an online setting: the sum of flow times. The flow time of a job is the total amount of time that it spends in the system, which may be considerably smaller than its completion time if it is released late. We provide competitive algorithms for the online setting with speed augmentation, and also give a lower bound proving that speed augmentation is in fact necessary. As a side effect of our techniques, we also improve and generalize the results of Jia et al. on completion times by giving an O(1)-competitive algorithm for arbitrary sizes and release times even when nodes have different degree bounds, and moreover allow for the weighted sum of completion times (or flow times).

21 citations


Journal ArticleDOI
TL;DR: The computational results demonstrate the efficiency of the approach for solving ALP, which can simultaneously improve punctual performance, enhance runway utilization, reduce air traffic controller workload, and maintain equity among airlines.

21 citations


Journal ArticleDOI
TL;DR: This paper presents pseudo-polynomial-time algorithms for solving all the problems studied in this paper and reveals the tradeoff between the scheduling cost of the accepted jobs and the total rejected jobs.

17 citations


Journal ArticleDOI
TL;DR: An approach to developing a fast fully polynomial-time approximation scheme (FPTAS) for the single machine scheduling problem to minimize the total weighted earliness and tardiness about a nonrestrictive common due date.
Abstract: We address the single machine scheduling problem to minimize the total weighted earliness and tardiness about a nonrestrictive common due date. This is a basic problem with applications to the just-in-time manufacturing. The problem is linked to a Boolean programming problem with a quadratic objective function, known as the half-product. An approach to developing a fast fully polynomial-time approximation scheme (FPTAS) for the problem is identified and implemented. The running time matches the best known running time for an FPTAS for minimizing a half-product with no additive constant.

17 citations


Journal ArticleDOI
TL;DR: This paper deals with the enhancement of a scheduling problem for additive manufacturing just present in literature and the presentation of a new meta-heursitic (adapted to the new requirements of the additive manufacturing technology) based on the tabu-search algorithms.
Abstract: Article history: Received October 8 2019 Received in Revised Format December 28 2019 Accepted December 31 2019 Available online January 2 2020 The Additive Manufacturing (AM) scheduling problem is becoming a very felt issue not only by the scholars but also by the practitioners who are looking to this new technology as a new integrated part of their traditional production systems. They need new scheduling models to adapt the traditional scheduling rules to the changed ones of the additive manufacturing. This paper deals with the enhancement of a scheduling problem for additive manufacturing just present in literature and the presentation of a new meta-heursitic (adapted to the new requirements of the additive manufacturing technology) based on the tabu-search algorithms. © 2020 by the authors; licensee Growing Science, Canada

16 citations


Journal ArticleDOI
TL;DR: A single-machine problem with generalized due dates, where the objective is maximizing the number of jobs completed exactly on time, is studied and it is proved that the problem is NP-hard in the strong sense.
Abstract: In scheduling problems with generalized due dates (gdd), the due dates are specified according to their position in the sequence, and the j-th due date is assigned to the job in the j-th position. We study a single-machine problem with generalized due dates, where the objective is maximizing the number of jobs completed exactly on time. We prove that the problem is NP-hard in the strong sense. To our knowledge, this is the only example of a scheduling problem where the job-specific version has a polynomial-time solution, and the gdd version is strongly NP-hard. A branch-and-bound (B&B) algorithm, an integer programming (IP)-based procedure, and an efficient heuristic are introduced and tested. Both the B&B algorithm and the IP-based solution procedure can solve most medium-sized problems in a reasonable computational effort. The heuristic serves as the initial step of the B&B algorithm and in itself obtains the optimum in most cases. We also study two special cases: max-on-time for a given job sequence and max-on-time with unit-execution-time jobs. For both cases, polynomial-time dynamic programming algorithms are introduced, and large-sized problems are easily solved.

16 citations


Proceedings ArticleDOI
19 Jul 2020
TL;DR: A novel general variable neighborhood search through Q-learning (GVNS-QL) algorithm is proposed to solve the no-idle flowshop scheduling problem with the makespan objective and outperforms the traditional iterated greedy (IG) algorithm.
Abstract: In this study, a novel general variable neighborhood search through Q-learning (GVNS-QL) algorithm is proposed to solve the no-idle flowshop scheduling problem with the makespan objective. In the outer loop of the GVNS-QL, insertion, and exchange operators are used to shaking the permutation. On the other hand, in the inner loop of variable neighborhood descent procedure, variable iterated greedy and variable block insertion heuristic algorithms are employed with two effective insertion local search procedures. The proposed GVNS-QL defines the parameters of the algorithm using a Q-learning mechanism. The developed GVNS-QL algorithm is compared with the traditional iterated greedy (IG) algorithm using the well-known benchmark set. The comprehensive computational experiments show that the GVNS-QL outperforms the traditional IG algorithm. The results of the IG and GVNS-QL algorithms are also compared with the current best-known solutions reported in the literature. The computational results show that the proposed GVNS-QL algorithm improves the current best-known solutions for 104 out of 250 instances.

Journal ArticleDOI
TL;DR: From extensive computational experiments, it is found that the GC algorithm outperforms all other alternatives and is shown to be strongly NP-hard even when the capacity of the inventory is infinite.

Journal ArticleDOI
TL;DR: This paper establishes the complexity status of the single-machine scheduling problem by proving its NP-hardness and developing, a dynamic programming algorithm that solves the problem in pseudo-polynomial time.

Journal ArticleDOI
TL;DR: This paper studies the integrated optimization of production planning and preventative maintenance scheduling on a single machine to improve the total flow time by allowing repacking jobs in bins from the bin packing problem.

Journal ArticleDOI
TL;DR: This work studies a class of multi-scenario single-machine scheduling problems where the scheduling criterion can be any one of the following three: the total weighted completion time, the weighted number of tardy jobs, and the weightedNumber of jobs completed exactly at their due-date.
Abstract: We study a class of multi-scenario single-machine scheduling problems. In this class of problems, we are given a set of scenarios with each one having a different realization of job characteristics. We consider these multi-scenario problems where the scheduling criterion can be any one of the following three: The total weighted completion time, the weighted number of tardy jobs, and the weighted number of jobs completed exactly at their due-date. As all the resulting problems are NP-hard, our analysis focuses on whether any one of the problems becomes tractable when some specific natural parameters are of limited size. The analysis includes the following parameters: The number of jobs with scenario-dependent processing times, the number of jobs with scenario-dependent weights, and the number of different due-dates.

Proceedings ArticleDOI
26 Sep 2020
TL;DR: Four of the most important RL techniques, namely Q-learning, Sarsa, Watkins’s Q($\lambda$), and SarsA, are applied to the online single-machine scheduling problem to provide insights on how such techniques perform.
Abstract: Online scheduling has been an attractive field of research for over three decades. Some recent developments suggest that Reinforcement Learning (RL) techniques have the potential to deal with online scheduling issues effectively. Driven by an industrial application, in this paper we apply four of the most important RL techniques, namely Q-learning, Sarsa, Watkins’s Q($\lambda$), and Sarsa($\lambda$), to the online single-machine scheduling problem. Our main goal is to provide insights on how such techniques perform. The numerical results show that Watkins’s Q($\lambda$) performs best in minimizing the total tardiness of the scheduling process.

Journal ArticleDOI
16 Jan 2020
TL;DR: This paper designs a combinatorial algorithm based on the primal-dual framework to deal with the single machine scheduling problem with release dates and nonmonotone submodular rejection penalty, and studies its property under two cases.
Abstract: In this paper, we consider the single machine scheduling problem with release dates and nonmonotone submodular rejection penalty. We are given a single machine and multiple jobs with probably different release dates and processing times. For each job, it is either accepted and processed on the machine or rejected. The objective is to minimize the sum of the makespan of the accepted jobs and the rejection penalty of the rejected jobs which is determined by a nonmonotone submodular function. We design a combinatorial algorithm based on the primal-dual framework to deal with the problem, and study its property under two cases. For the general case where the release dates can be different, the proposed algorithm have an approximation ratio of 2. When all the jobs release at the same time, the proposed algorithm becomes a polynomial-time exact algorithm.

Journal ArticleDOI
TL;DR: A single machine, which must process n jobs in sequence, is considered, which follows the two-stage Delay Time Model, and the objective is to find the optimal inspection policy and the jobs sequence, which minimise the total expected makespan.
Abstract: In this paper, we consider a single machine, which must process n jobs in sequence. The machine's failure process follows the two-stage Delay Time Model, i.e. it starts with an initial defect, and ...

Journal ArticleDOI
TL;DR: In this paper, the authors considered the scenario where a manufacturer receives multiple orders that are characterized by the revenue, processing time, due date, and tardiness penalty per time unit.

Journal ArticleDOI
Fan Yue1, Shiji Song, Peng Jia, Guangping Wu1, Han Zhao 
TL;DR: In this article, a robust optimization model is proposed to minimize the maximum tardiness in the worst case scenario over all jobs, and a heuristic approach is proposed based on a robust dominance rule.

Journal ArticleDOI
TL;DR: Algorithms that solve exactly the robust single machine scheduling problem that minimizes the total tardiness are investigated, and a new classifying parameter is introduced to group instances, also extending existing methods for the deterministic problem case.

Journal ArticleDOI
TL;DR: The aim is to find an appropriate sequencing of production jobs and a PM planning to minimize two objectives simultaneously: total tardiness of jobs and machine unavailability on single machine problem.
Abstract: The joint production scheduling and preventive maintenance problems have recently attracted researchers’ attention given their contribution, both the production and the maintenance functions and their integration, to the firms’ efficiency. In this paper, we deal with production scheduling and preventive maintenance (PM) planning on single machine problem. The aim is to find an appropriate sequencing of production jobs and a PM planning to minimize two objectives simultaneously: total tardiness of jobs and machine unavailability. We propose a bi-objective exact algorithm, that we called BOBB, based on bi-objective branch and bound method to find the efficient set. We introduced several properties and bound sets to enhance the performance of the proposed BOBB algorithm. Furthermore, we propose a hybrid method, that we called GA-BBB, based on genetic algorithm and binary branch and bound algorithm to compute an approximate efficient set to be used as an initial upper bound set in the BOBB algorithm. An experimental study was conducted to show the efficiency of the GA-BBB and the BOBB algorithms.

Journal ArticleDOI
TL;DR: The robust version of single machine scheduling problem with the objective to minimize the weighted number of jobs completed after their due-dates is considered, and an exact solution algorithm based on a specialized branch and bound method is developed.
Abstract: We consider the robust version of single machine scheduling problem with the objective to minimize the weighted number of jobs completed after their due-dates. The jobs have uncertain processing times represented by intervals, and decision-maker must determine their execution sequence that minimizes the maximum regret. We develop an exact solution algorithm based on a specialized branch and bound method, using mixed-integer linear programming formulations for a common due-date and for job-dependent due-dates. Finally, we examine the solution algorithm in a series of computational experiments.

Journal ArticleDOI
01 Jun 2020
TL;DR: The proposed HMA integrates two distinguished features - a novel compound neighborhood structure that extends the basic swap and insertion operators, as well as a divide-and-conquer based local search procedure that divides the original problem into subproblems so as to speed up the exploration of local optima.
Abstract: This paper presents a hybrid memetic algorithm (HMA) for makespan minimization of parallel machine schedules with job deteriorating effects. The proposed HMA integrates two distinguished features - a novel compound neighborhood structure that extends the basic swap and insertion operators, as well as a divide-and-conquer based local search procedure. Unlike traditional local search methods, the divide-and-conquer based local search divides the original problem into subproblems so as to speed up the exploration of local optima. Experimental comparisons with the current state-of-the-art approaches show highly competitive performance of HMA in terms of both solution quality and computational efficiency. In particular, HMA obtains optimal solutions for all of the 900 small benchmark instances with an average computing time of 0.013 seconds and a success rate of 100%. Besides, it improves on the previous best-known results for 86 out of the 900 large benchmark instances, while matching the best-known results for the remaining cases. Additionally, we provide an analysis of the key algorithm features to identify its critical success factors.

Journal ArticleDOI
TL;DR: This paper tackles the single machine scheduling problem with periodic preventive maintenance in order to minimize the weighted sum of completion times and proposed three properties for this problem which generalize already existing works.
Abstract: This paper tackles the single machine scheduling problem with periodic preventive maintenance in order to minimize the weighted sum of completion times. This criterion is certainly less studied than the makespan but it remains nonetheless interesting on the theoretical and practical levels. Indeed, the weights can quantify the holding cost per unit of time of the products to transform. Thus, this criterion can represent the global holding cost. This problem is proved to be NP-hard and a mixed integer linear programming formulation is proposed to solve small size instances of the problem. To solve large instances, we proposed three properties for this problem which generalize already existing works. These properties have been of great use in designing efficient heuristics capable of solving instances with up to 1000 jobs. To evaluate the performances of the proposed heuristics, lower bounds based on special cases of the problem are provided. Computational experiments show that the average percentage error of the best heuristic is less than 10%.

Journal ArticleDOI
TL;DR: A dynamic differential evolution algorithm that considers the new environment and the previous environment simultaneously simultaneously simultaneously is proposed to solve the single-machine scheduling problem with sequence-dependent setup times under dynamic environment.
Abstract: The paper studies the single-machine scheduling problem with sequence-dependent setup times under dynamic environment. In this problem, jobs arrive over time and all the information on a jo...

Journal ArticleDOI
TL;DR: It can be concluded that the developed ACO is very efficient and effective in solving the problem considered in this paper and outperformed the CPLEX solver with respect to CPU.
Abstract: This paper considers a time window periodic maintenance strategy with different duration windows and job scheduling activities in a single machine environment. The aim is to minimize the number of tardy jobs through the integration of production scheduling and periodic maintenance intervals. A mixed-integer linear programming model (MILP) is proposed to optimize small-sized test instances. Furthermore, an ant colony optimization (ACO) algorithm is developed to solve larger sized test instances. Subsequently, to measure the efficiency of the solutions obtained by ACO, Moore's algorithm is also developed to benchmark with ACO. To test the efficiency and the effectiveness of the ACO algorithm, a set of data for small and large sized problems was generated in which several parameters were adopted and then ten replicates were solved for each combination. The small sized instances were solved by the MILP. Then, the results obtained showed that the proposed ACO was able to obtain the exact solutions within reasonable CPU times, thus, it outperformed the CPLEX solver with respect to CPU. The large sized instances were solved by the Moore's algorithm and compared to ACO. Then, the results obtained showed that the ACO outperforms Moore's algorithm for all the instances tested. It can be concluded that the developed ACOis very efficient and effective in solving the problem considered in this paper.

Journal ArticleDOI
10 Jul 2020
TL;DR: This paper considers the single-machine scheduling problem with given release dates and the objective to minimize the maximum penalty which is NP-hard in the strong sense, and uses the optimal function value of a sub-problem of the dual problem in a branch and bound algorithm for the original single- Machine scheduling problem.
Abstract: In this paper, we consider the single-machine scheduling problem with given release dates and the objective to minimize the maximum penalty which is NP-hard in the strong sense. For this problem, we introduce a dual and an inverse problem and show that both these problems can be solved in polynomial time. Since the dual problem gives a lower bound on the optimal objective function value of the original problem, we use the optimal function value of a sub-problem of the dual problem in a branch and bound algorithm for the original single-machine scheduling problem. We present some initial computational results for instances with up to 20 jobs.

Journal ArticleDOI
TL;DR: This paper studies the scheduling of proportional-linearly deteriorating jobs with positional due indices, release dates, deadlines and precedence relations on a single machine, and presents some new NP-hardness results when processing times of the jobs have no deterioration.
Abstract: In this paper, we study the scheduling of proportional-linearly deteriorating jobs with positional due indices, release dates, deadlines and precedence relations on a single machine. The scheduling criteria studied in this paper include the makespan, maximum lateness, maximum tardiness, maximum flow time, maximum weighted completion time, maximum scheduling cost, total completion time, and the number of tardy jobs. By applying Lawler’s rule and Smith’s rule, polynomially solvable problems are processed by using two unified methods. We also present some new NP-hardness results when processing times of the jobs have no deterioration. Our results generalize a series of known achievements in the literature.

Journal ArticleDOI
TL;DR: Some heuristic algorithms and branch-and-bound algorithm based on the Depth-first Search algorithm are proposed to solve the single machine scheduling problem with release times and a learning effect.