Showing papers on "Single-machine scheduling published in 2019"
TL;DR: It is proved that both single-machine scheduling problems with deteriorating effects and a machine maintenance are polynomial time solvable and the corresponding algorithms are provided.
Abstract: In this paper, the single-machine scheduling problems with deteriorating effects and a machine maintenance are studied. In this circumstance, the deterioration rates of the jobs during the machinin...
39 citations
TL;DR: The general version of the complexity analysis of several energy-oriented single-machine scheduling problems addressed in the literature with Time-Of-Use (TOU) energy prices and different processing times is investigated in two versions: with and without the fixed sequence for the jobs.
27 citations
TL;DR: The concept of price of fairness in resource allocation is investigated and applied to two-agent single-machine scheduling problems, in which two agents, each having a set of jobs, compete for use of a single machine to execute their jobs.
25 citations
TL;DR: Experimental results show that the robust sequences perform better than nominal sequences, especially when the due dates are relatively loose, and this is the first time in the relevant literature that a DRO approach is adopted to minimize the total tardiness criterion for machine scheduling.
25 citations
TL;DR: It is shown that the single machine scheduling problem with rejection is W[1]-hard for the first parameter, while it is fixed-parameterized tractable for all other parameters.
23 citations
TL;DR: In this study, machine conditions are evaluated by machine reliability and the relationship between reliability and processing energy consumption was developed and modified Emmons rules were proposed and embedded into an ant colony algorithm to solve real-world problems from a rotor production workshop.
22 citations
TL;DR: In this article, the authors consider the problem of scheduling a set of jobs with deadlines to minimize the total weighted late work on a single machine, where the late work of a job is the amount of processing of the job that is scheduled after its due date and before its deadline.
Abstract: We consider scheduling a set of jobs with deadlines to minimize the total weighted late work on a single machine, where the late work of a job is the amount of processing of the job that is scheduled after its due date and before its deadline. This is the first study on scheduling with the late work criterion under the deadline restriction. In this paper, we show that (i) the problem is unary NP‐hard even if all the jobs have a unit weight, (ii) the problem is binary NP‐hard and admits a pseudo‐polynomial‐time algorithm and a fully polynomial‐time approximation scheme if all the jobs have a common due date, and (iii) some special cases of the problem are polynomially solvable.
22 citations
TL;DR: This work proposes a branch-and-bound algorithm and proves its efficiency by comparing it to an e-constrained approach on benchmark instances based on those proposed in the literature on similar problems.
22 citations
TL;DR: This paper illustrates that operations management, which is typically observed as a significant technique that allows manufacturing to operate in challenging data enabled environment in an industrial internet of things ecosystem, can also carefully optimize a production process to improve energy cost in order to manage environmental challenges.
Abstract: Energy-aware scheduling in a manufacturing environment with real-time energy pricing is a challenging problem. The purpose of this paper is to study a nonpreemptive scheduling problem on a single-machine to minimize the total tardiness and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multi-objective mathematical programming model. To achieve these objectives, we develop several new holistic genetic algorithms. The proposed model is solved via several methods including weighted sum method and multiobjective genetic algorithms based on dominance rank (GA-1), weighted sum aggregation (GA-2), dominance ranking procedure and crowding distance comparison (GA-3), and heuristic approach (GA-H). This paper illustrates that operations management, which is typically observed as a significant technique that allows manufacturing to operate in challenging data enabled environment in an industrial internet of things ecosystem, can also carefully optimize a production process to improve energy cost in order to manage environmental challenges. Thus, the findings provide information on when to start each job. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we illustrate how the results of the multiobjective model could be utilized in decision making using the technique for order preference by similarity to ideal solution method.
22 citations
TL;DR: In this study, a two-stage stochastic-programming method is utilised for the optimal solution of the single machine scheduling problem and a Genetic Algorithm approach is proposed to solve the large-size problems approximately.
Abstract: In this study, we consider stochastic single machine scheduling problem. We assume that setup times are both sequence dependent and uncertain while processing times and due dates are deterministic....
18 citations
TL;DR: In this article, a problem arising in a manufacturing environment concerning the joint scheduling of multiple jobs and a maintenance activity on a single machine is addressed, where the maintenance activity must be processed within a given time window and its non-deterministic duration takes values in a given interval.
TL;DR: In this article, six different mixed integer programming (MIP) formulations are proposed and analyzed, based on the knowledge of four different paradigms for single machine scheduling problems (SMSP) with sequence dependent setup times and release dates.
Abstract: The scheduling of jobs over a single machine with sequence dependent setups is a classical problem setting that appears in many practical applications in production planning and logistics. In this work, we analyze six mixed-integer formulation paradigms for this classical context considering release dates and two objective functions: the total weighted completion time and the total weighted tardiness. For each paradigm, we present and discuss a MIP formulation, introducing in some cases new constraints to improve performance. A dominance hierarchy in terms of strength of their linear relaxations bounds is developed. We report extensive computational experiments on a variety of instances to capture several aspects of practical situations, allowing a comparison regarding size, linear relaxation and overall performance. Based on the results, discussions and recommendations are made for the considered problems.
TL;DR: This paper proposes an online approach that reconsiders the actual schedule every time a new event arrives, and compares the results of this approach to several job insertion methods broadly used in practice, and to a Perfect Information Model.
TL;DR: Some polynomially solvable special cases are discussed and it is shown that under very strong assumptions, such as the processing time, the resource consumption and the weight is the same for each job; minimizing the total weighted completion time is still NP-hard.
Abstract: In this paper, we describe new complexity results and approximation algorithms for single-machine scheduling problems with non-renewable resource constraints and the total weighted completion time objective. This problem is hardly studied in the literature. Beyond some complexity results, only a fully polynomial-time approximation scheme (FPTAS) is known for a special case. In this paper, we discuss some polynomially solvable special cases and also show that under very strong assumptions, such as the processing time, the resource consumption and the weight is the same for each job; minimizing the total weighted completion time is still NP-hard. In addition, we also propose a 2-approximation algorithm for this variant and a polynomial-time approximation scheme (PTAS) for the case when the processing time equals the weight for each job, while the resource consumptions are arbitrary.
TL;DR: In this article, a mixed-integer linear programming model (MILP) and a relaxation method of MILP model were proposed to solve the order acceptance and scheduling (OAS) problems.
Abstract: Order acceptance and scheduling (OAS) problems are realistic for enterprises. They have to select the appropriate orders according to their capacity limitations and profit consideration, and then complete these orders by their due dates or no later than their deadlines. OAS problems have attracted significant attention in supply chain management. However, there is an issue that has not been studied well. To our best knowledge, no prior research examines the carbon emission cost and the time-of-use electricity cost in the OAS problems. The carbon emission during the on-peak hours is lower than the one in mid-peak and off-peak hours. However, the electricity cost during the on-peak hours is higher than the one during mid-peak and off-peak hours when time-of-use electricity (TOU) tariff is used. There is a trade-off between sustainable scheduling and the electricity cost. To calculate the objective value, a carbon tax and carbon dioxide emission factor are included when we evaluate the carbon emission cost. The objective function is to maximize the total revenue of the accepted orders and then subtract the carbon emission cost and the electricity cost under different time intervals on a single machine with sequence-dependent setup times and release date. This research proposes a mixed-integer linear programming model (MILP) and a relaxation method of MILP model to solve this problem. It is of importance because the OAS problems are practical in industry. This paper could attract the attention of academic researchers as well as the practitioners.
09 May 2019
TL;DR: The experimental results demonstrate that the proposed two-stage mixed integer program can be solved optimally by CPLEX for small- scale cases and the proposed algorithm can effectively solve the second stage for large-scale cases.
Abstract: This paper studies a new single machine scheduling problem with sequence-dependent setup time, release time, due time and group technology assumption originated from a wire rod and bar rolling process in steel plants. The objective is to find an optimal batch sequence and job sequences of all batches to minimize the number of late jobs. A two-stage mixed integer program is created to describe and solve this problem. The first stage can be solved in a short time by CPLEX while the second one is time-consuming when dealing with large-scale cases. Thus, an iterated greedy algorithm able to solve the second stage fast is developed. The experimental results demonstrate that the proposed two-stage mixed integer program can be solved optimally by CPLEX for small-scale cases and the proposed algorithm can effectively solve the second stage for large-scale cases.
TL;DR: In this article, the branch and bound (BAB) method is used as the main method for solving the problem, where four upper bounds and one lower bound are proposed and a number of dominance rules are considered to reduce the number of branches in the search tree.
Abstract: In this paper, one of the Machine Scheduling Problems is studied, which is the problem of scheduling a number of products (n-jobs) on one (single) machine with the multi-criteria objective function. These functions are (completion time, the tardiness, the earliness, and the late work) which formulated as . The branch and bound (BAB) method are used as the main method for solving the problem, where four upper bounds and one lower bound are proposed and a number of dominance rules are considered to reduce the number of branches in the search tree. The genetic algorithm (GA) and the particle swarm optimization (PSO) are used to obtain two of the upper bounds. The computational results are calculated by coding (programing) the algorithms using (MATLAP) and the final results up to (18) product (jobs) in a reasonable time are introduced by tables and added at the end of the research.
TL;DR: An approximation algorithm, a novel mixed-integer linear programming block formulation, and an efficient exact branch-and-price decomposition for two criticality levels are proposed to solve an NP -hard single machine scheduling problem with makespan minimization, where the non-preemptive tasks can have multiple processing times.
TL;DR: It is shown that the scheduling problem can be solved in polynomial time when slacks are bounded, and extensions to more couriers and restaurants are discussed.
TL;DR: This work model the resulting decision problem as a special single machine scheduling problem and proposes different mixed integer programs to solve it and assess their performance through a computational study on a set of test instances derived by the real-world application.
TL;DR: In this paper, a class of single machine scheduling problems is considered, where job processing times and due dates can be specified in the form of discrete scenario set and a probability distribution in the scenario set is known.
Abstract: In this paper, a class of single machine scheduling problems is considered. It is assumed that job processing times and due dates can be uncertain and they are specified in the form of discrete scenario set. A probability distribution in the scenario set is known. In order to choose a schedule, some risk criteria such as the value at risk and conditional value at risk are used. Various positive and negative complexity results are provided for basic single machine scheduling problems. In this paper, new complexity results are shown and some known complexity results are strengthened.
TL;DR: The proposed SPT-M algorithm can generate an efficient Pareto frontier in remarkably short computing time and could help practitioners to determine the tradeoffs between the jobs of two agents competing for a single resource.
Abstract: This paper studies a single-machine scheduling problem with a two competing agents in which the performance criteria of the first and second agents are to minimize the mean lateness and number of tardy jobs, respectively. Due to the non-deterministic polynomial-time hardness of this problem, we propose an effective and efficient algorithm, denominated as the SPT-M algorithm, to generate the non-dominated solutions of the Pareto set. Computational results conducted on a test problem set reveal that the proposed SPT-M algorithm can generate an efficient Pareto frontier in remarkably short computing time. The contribution of this paper could help practitioners to determine the tradeoffs between the jobs of two agents competing for a single resource.
TL;DR: In this article, a single machine scheduling problem with batch delivery under an uncertain environment is studied and a hybrid algorithm based on uncertain simulation and a g#enetic algorithm (GA) is designed to solve the model.
Abstract: A single machine scheduling problem with batch delivery is studied in this paper. The objective is to minimize the total cost which comprises earliness penalties, tardiness penalties, holding and transportation costs. An integer programming model is proposed and two dominance properties are obtained. However, sometimes due to the lack of historical data, the worker evaluates the processing time of a job according to his past experience. A pessimistic value model of the single machine scheduling problem with batch delivery under an uncertain environment is presented. Since the objective function is non-monotonic with respect to uncertain variables, a hybrid algorithm based on uncertain simulation and a g#enetic algorithm (GA) is designed to solve the model. In addition, two dominance properties under the uncertain environment are also obtained. Finally, computational experiments are presented to illustrate the modeling idea and the effectiveness of the algorithm.
01 Aug 2019
TL;DR: This paper first attempts to study the distributed hybrid flowshop scheduling problem (DHFSP) with total tardiness minimization with a mixed integer linear programming (MILP) model and a novel iterated greedy (IG) algorithm.
Abstract: The trend of globalization has facilitated the development of distributed manufacturing and research on distributed scheduling problem. This paper first attempts to study the distributed hybrid flowshop scheduling problem (DHFSP) with total tardiness minimization. To solve such a complex scheduling problem, a mixed integer linear programming (MILP) model and a novel iterated greedy (IG) algorithm are presented. To minimize total tardiness, an objective-driven decoding method is proposed and a modified Nawaz-Enscore-Ham (NEH) heuristic is presented for initialization. To enhance exploitation, a variable neighborhood search based local intensification is designed, which uses several problem-specific neighborhood structures. Computational results and comparisons demonstrate the effectiveness of the designed decoding method and local intensification. Moreover, it is shown that the proposed IG is effective to solve DHFSP with total tardiness minimization.
TL;DR: An estimator for job completion times is provided and it is proved that DTS using this estimator provides optimum solutions for a number of single machine scheduling problems.
26 Apr 2019
TL;DR: A fast algorithm is developed for finding a job permutation having the largest quasi-perimeter of the optimality set, and the computational results show that they are close to the optimal ones, which can be constructed for the factual durations of all given jobs.
Abstract: We study a single-machine scheduling problem to minimize the total completion time of the given set of jobs, which have to be processed without job preemptions. The lower and upper bounds on the job duration is the only information that is available before scheduling. Exact values of the job durations remain unknown until the completion of the jobs. We use the optimality region for the job permutation as an optimality measure of the optimal schedule. We investigate properties of the optimality region and derive O ( n ) -algorithm for calculating a quasi-perimeter of the optimality set (i.e., the sum of lengths of the optimality segments for n given jobs). We develop a fast algorithm for finding a job permutation having the largest quasi-perimeter of the optimality set. The computational results in constructing such permutations show that they are close to the optimal ones, which can be constructed for the factual durations of all given jobs.
TL;DR: A two-phase heuristic algorithm is proposed where an optimal but non-integer solution is obtained in the first phase by solving a continuous relaxed version of the problem, which serves as a lower bound for the optimal value of the total completion time.
Abstract: This article considers a single machine scheduling problem with batch setups, positional deterioration effects, and multiple optional rate-modifying activities to minimize the total completion time...
TL;DR: In this paper, an improved multi-objective particle swarm optimization algorithm enhanced by a local search strategy (MOPSO-LS) is proposed to solve the JIT single machine scheduling problem which considers the deterioration effect and the energy consumption of job processing operations.
Abstract: In recent years, the concerns on energy efficiency in manufacturing systems have been growing rapidly due to the pursuit of sustainable development. Production scheduling plays a vital role in saving energy and promoting profitability for the manufacturing industry. In this paper, we are concerned with a just-in-time (JIT) single machine scheduling problem which considers the deterioration effect and the energy consumption of job processing operations. The aim is to determine an optimal sequence for processing jobs under the objective of minimizing the total earliness/tardiness cost and the total energy consumption. Since the problem is NP -hard, an improved multi-objective particle swarm optimization algorithm enhanced by a local search strategy (MOPSO-LS) is proposed. We draw on the idea of k-opt neighborhoods and modify the neighborhood operations adaptively for the production scheduling problem. We consider two types of k-opt operations and implement the one without overlap in our local search. Three different values of k have been tested. We compare the performance of MOPSO-LS and MOPSO (excluding the local search function completely). Besides, we also compare MOPSO-LS with the well-known multi-objective optimization algorithm NSGA-II. The experimental results have verified the effectiveness of the proposed algorithm. The work of this paper will shed some light on the fast-growing research related to sustainable production scheduling.
TL;DR: A binary integer programming model based on a heuristic based on two special properties is proposed to address the single-machine scheduling problem with new maintenance activities in wafer manufacturing of semiconductor, with the research problem being NP-hard.
Abstract: A single-machine scheduling problem with new maintenance activities is examined in this paper. In the scheduling literature, it is often assumed that the interval between maintenance activities is ...
TL;DR: In this article, the authors considered the single-machine scheduling problem with job-dependent machine deterioration and proved that the problem in the partial maintenance case is binary NP-hard, thus settling the open problem; they then designed a 2-approximation algorithm and a branch-and-bound exact search algorithm.
Abstract: We consider the single-machine scheduling problem with job-dependent machine deterioration. In the problem, we are given a single machine with an initial nonnegative maintenance level, and a set of jobs each with a non-preemptive processing time and a machine deterioration. Such a machine deterioration quantifies the decrement in the machine maintenance level after processing the job. To avoid a machine breakdown, one should guarantee a nonnegative maintenance level at any time point, and whenever necessary, a maintenance activity must be allocated for restoring the machine maintenance level. The goal of the problem is to schedule the jobs and the maintenance activities such that the total completion time of jobs is minimized. There are two variants of maintenance activities: In the partial maintenance case, each activity can be allocated to increase the machine maintenance level to any level not exceeding the maximum; in the full maintenance case, every activity must be allocated to increase the machine maintenance level to the maximum. In a recent work, the problem in the full maintenance case was proven NP-hard; several special cases of the problem in the partial maintenance case were shown to be solvable in polynomial time, but the complexity of the general problem was left open. In this paper we first prove that the problem in the partial maintenance case is binary NP-hard, thus settling the open problem; we then design a 2-approximation algorithm and a branch-and-bound exact search algorithm. Computational experiments are conducted for the two algorithms to examine their practical performance.