Showing papers on "Single-machine scheduling published in 2016"
TL;DR: In this paper, a new continuous-time mixed-integer linear programming (MILP) model was proposed for the problem and an efficient greedy insertion heuristic was developed to minimize the total electricity cost for processing the jobs.
111 citations
TL;DR: This work considers integrated production and batch delivery scheduling in a make-to-order production system involving two competing agents, each of which having its own job set competes to process its jobs on a shared single machine.
62 citations
TL;DR: Computational experiments show that the proposed heuristic outperforms a state-of-the-art commercial mixed integer programming solver both in terms of solution quality and computation time.
49 citations
TL;DR: Two pseudo-polynomial dynamic programming algorithms and a fully polynomial-time approximation scheme for the problem of scheduling n independent and simultaneously available jobs without preemption on a single machine, where the machine has a fixed maintenance activity.
Abstract: We consider the problem of scheduling n independent and simultaneously available jobs without preemption on a single machine, where the machine has a fixed maintenance activity. The objective is to find the optimal job sequence to minimize the total amount of late work, where the late work of a job is the amount of processing of the job that is performed after its due date. We first discuss the approximability of the problem. We then develop two pseudo-polynomial dynamic programming algorithms and a fully polynomial-time approximation scheme for the problem. Finally, we conduct extensive numerical studies to evaluate the performance of the proposed algorithms. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 172–183, 2016
45 citations
TL;DR: In this article, a branch-and-bound algorithm was proposed to minimize the number of tardy jobs in a single-machine scheduling problem with periodic maintenance, which is motivated by various industrial applications.
Abstract: In this paper, we investigate a single-machine scheduling problem with periodic maintenance, which is motivated by various industrial applications (e.g. tool changes). The pursued objective is to minimise the number of tardy jobs, because it is one of the important criteria for the manufacturers to avoid the loss of customers. The strong NP-hardness of the problem is shown. To improve the state-of-the-art exact algorithm, we devise a new branch-and-bound algorithm based on an efficient lower bounding procedure and several new dominance properties. Numerical experiments are conducted to demonstrate the efficiency of our exact algorithm.
43 citations
TL;DR: This paper presents an exact branch-and-bound method to solve the single machine scheduling problem under a weighted completion time performance metric, and shows that the algorithm is able to optimally solve instances of moderate size.
37 citations
TL;DR: In this article, the authors consider single machine scheduling problems with controllable processing time (resource allocation), truncated job-dependent learning and deterioration effects, and find the optimal sequence of jobs and the optimal resource allocation separately for minimizing a cost function containing makespan (total completion time, total absolute differences in completion times) and total resource cost.
Abstract: In this paper, we consider single machine scheduling problems with controllable processing time (resource allocation), truncated job-dependent learning and deterioration effects. The goal is to find the optimal sequence of jobs and the optimal resource allocation separately for minimizing a cost function containing makespan (total completion time, total absolute differences in completion times) and/or total resource cost. For two different processing time functions, i.e., a linear and a convex function of the amount of a common continuously divisible resource allocated to the job, we solve them in polynomial time respectively.
33 citations
TL;DR: A beam search (BS) heuristic is proposed to solve larger instances of the scheduling problem and experiments showed that the BS heuristic finds high quality solutions at low cost.
32 citations
TL;DR: This paper addresses single-machine scheduling and due-window assignment with common flow allowances and resource-dependent processing times and shows that the problem under the model where the two criteria are integrated into a single criterion is polynomially solvable.
Abstract: This paper addresses single-machine scheduling and due-window assignment with common flow allowances and resource-dependent processing times. Due-window assignment with common flow allowances means...
29 citations
TL;DR: This study proposes an exact algorithm with the computational complexity O ( n 2 ) for each of the four SMSPs, to minimize mean lateness, maximum tardiness, total flow time and mean tardyness.
28 citations
TL;DR: It is proved that the single-machine scheduling problem with truncated sum-of-processing-times-based learning effect and past-sequence-dependent job delivery times can be solved in polynomial time for some regular objective functions.
Abstract: The single-machine scheduling problem with truncated sum-of-processing-times-based learning effect and past-sequence-dependent job delivery times is considered. Each job's delivery time depends on its waiting time of processing. For some regular objective functions, it is proved that the problems can be solved by the smallest processing time first rule. For some special cases of the total weighted completion time and the maximum lateness objective functions, the thesis shows that the problems can be solved in polynomial time.
TL;DR: A mixed integer programming model is derived to obtain the optimal solution and a hybrid genetic algorithm with a two-stage dispatching heuristic represented by a simple chromosome is proposed.
TL;DR: This paper addresses the single machine weighted number of on-time jobs scheduling problem where the machine is unavailable during one or more maintenance periods and the jobs share a common due date, and offers an alternative proof that the problem is NP-Complete in the strong sense.
TL;DR: In this article, the authors use interval number theory for renewable energy in uncertainty modelling and propose two interval single-machine scheduling problems, and derive Pareto-optimal solutions of the bi-objective optimisation problem,, using the lexicographic-weighted Tchebycheff method.
Abstract: Carbon dioxide (CO2) in particular is by far the primary driver of global warming. One of the most effective ways to reduce CO2 emissions is to increase the amount of power from renewable energy. A key challenge in utilising renewable energies, such as wind and solar, is their uncertainty in terms of when and to what degree and force renewable energies will become available next time. This study uses interval number theory for renewable energy in uncertainty modelling and proposes two novel interval single-machine scheduling problems, and . A solution procedure is formulated to optimise these problems with interval numbers using interval arithmetic. Additionally, this study derives Pareto-optimal solutions of the bi-objective optimisation problem, , using the lexicographic-weighted Tchebycheff method. Some managerial implications are obtained by parameter analysis. Analytical results offer decision-makers an intuitive view of how these factors impact scheduling results and provide practical guidelines for...
TL;DR: A single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T), cost, and energy consumption simultaneously simultaneously in a multi-objective scheduling problem.
Abstract: Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T), cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA) is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multiobjective Particle Swarm Optimization (OMOPSO), and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.
TL;DR: In this paper, a discrete artificial bee colony algorithm for a single machine earliness-tardiness scheduling problem is presented, where instead of using a single search operator to generate neighbor solutions, random selection from an operator pool is employed.
Abstract: This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on ...
TL;DR: This work considers bi-criteria scheduling problems on a single machine with release dates and rejections and both the makespan and the total rejection cost have to be minimized and provides for several cases improved approximation algorithms and FPTASs.
Abstract: We consider bi-criteria scheduling problems on a single machine with release dates and rejections and both the makespan and the total rejection cost have to be minimized. We consider three scenarios: (1) minimize the sum of the two objectives: makespan and total rejection cost, (2) minimize the makespan subject to a bound on the total rejection cost and (3) minimize the total rejection cost subject to a bound on the makespan. We summarize the results obtained in the literature and provide for several cases improved approximation algorithms and FPTASs.
TL;DR: A recursive decomposition algorithm is developed and applied to solving the single machine scheduling problem to achieve the best possible running time.
Abstract: In this paper, we study a scheduling problem on a single machine, provided that the jobs have individual release dates and deadlines, and the processing times are controllable. The objective is to find a feasible schedule that minimizes the total cost of reducing the processing times. We reformulate the problem in terms of maximizing a linear function over a submodular polyhedron intersected with a box. For the latter problem of submodular optimization, we develop a recursive decomposition algorithm and apply it to solving the single machine scheduling problem to achieve the best possible running time.
TL;DR: A pseudo-polynomial dynamic programming algorithm is introduced, based on a number of properties of an optimal schedule, that can solve problems of hundreds of jobs in very reasonable time and proves NP-hardness in the ordinary sense.
TL;DR: It is shown that the 2-approximation algorithm developed by Zhang et al. 6 can be implemented in O ( n log ? n ) time and a new exact algorithm with an improved complexity for the special case with identical job processing times is developed.
TL;DR: A branch-and-bound algorithm is developed that is effective in solving medium-sized instances of the single-machine scheduling problem, and it compares favorably with existing methods for special cases of the problem.
Abstract: We study a single-machine scheduling problem that is a generalization of a number of problems for which computational procedures have already been published. Each job has a processing time, a release date, a due date, a deadline, and a weight representing the penalty per unit-time delay beyond the due date. The goal is to schedule all jobs such that the total weighted tardiness penalty is minimized and both the precedence constraints as well as the time windows (implied by the release dates and the deadlines) are respected. We develop a branch-and-bound algorithm that solves the problem to optimality. Computational results show that our approach is effective in solving medium-sized instances, and that it compares favorably with existing methods for special cases of the problem.
TL;DR: This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance using multi-objective particle swarm optimization (MOPSO) algorithm and confirmed the supremacy of MOPSO to the other algorithms.
Abstract: This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms’ parameters and a multiple criterion decision making technique based on the technique for order of preference by similarity to ideal solution is applied to choose the best algorithm. Comparison results confirmed the supremacy of MOPSO to the other algorithms.
TL;DR: An approach consisting of meta-heuristic algorithms such as a tabu search and a genetic algorithm was proposed to generate proper schedules to meet demands of customers just in time whilst total penalty costs of earliness and tardiness were minimized.
TL;DR: A novel definition for single machine scheduling problem with flexible periodic availability constraints has been provided and a heuristic algorithm with the time complexity of and a branch-and-bound algorithm accompanied with several lemmas and efficient dominance rules are proposed in order to solve the problems optimally.
Abstract: In many scheduling problems, machines can face availability constraints and as a result, they may stop for a while. In this paper, a novel definition for single machine scheduling problem with flexible periodic availability constraints has been provided. According to this definition, in each period, the duration of unavailability corresponding to the continuous working time of the machine changes in a discrete manner and it can adopt two different values. Therefore, such availability constraints are called bimodal availability constraints. The objective has been to minimise the total completion time. By considering the complexity issues through a mathematical model, a heuristic algorithm with the time complexity of and a branch-and-bound algorithm accompanied with several lemmas and efficient dominance rules are proposed in order to solve the problems optimally. Computational results for 1680 sample problems are employed to demonstrate that the branch-and-bound algorithm is able to solve problems up to 22 jobs and the mean average error for the heuristic algorithm is 1.05%.
TL;DR: In this paper, a general framework for solving single machine scheduling problems with the ordered weighted averaging (OWA) criterion is proposed and some positive and negative computational results for two basic single-machine scheduling problems are provided.
Abstract: In this paper a class of single machine scheduling problems is discussed. It is assumed that job parameters, such as processing times, due dates, or weights are uncertain and their values are specified in the form of a discrete scenario set. The ordered weighted averaging (OWA) aggregation operator is used to choose an optimal schedule. The OWA operator generalizes traditional criteria used in decision making under uncertainty, such as the maximum, average, median, or Hurwicz criterion. It also allows us to extend the robust approach to scheduling by taking into account various attitudes of decision makers towards a risk. In this paper, a general framework for solving single machine scheduling problems with the OWA criterion is proposed and some positive and negative computational results for two basic single machine scheduling problems are provided.
TL;DR: This paper shows that the single machine scheduling of minimizing total weighted tardiness with generalized due dates is unary NP-hard.
TL;DR: In this article, the problem of single-machine group scheduling with group-dependent multiple due windows assignment is considered and it is shown that the problem is solvable in time, where n is the total number of jobs.
Abstract: We consider single-machine group scheduling with group-dependent multiple due windows assignment. In the group technology environment, the jobs are divided into groups in advance according to their processing similarities, and all the jobs of the same group are processed consecutively in order to improve production efficiency. A sequence-independent machine set-up time precedes the processing of the first job of each group. Each group has group-dependent multiple due windows. The objective is to find the optimal job sequence, the set of jobs assigned to each due window sequence, the optimal group sequence, and the optimal due window assignment to minimise a total cost that comprises the earliness and tardiness penalties and the due window starting time and due window size costs. For the case where the number of jobs assigned to each due window in each group is given in advance, we show that the problem is solvable in time, where n is the total number of jobs. For the case where the number of jobs assigned...
TL;DR: Three hybrid greedy algorithms and a genetic algorithm are introduced to solve the test problems generated for the generalised problem while Taguchi experimental design method is used to find the best level of parameters for each algorithm.
Abstract: This paper focuses on earliness and tardiness minimisation of a special case of single machine scheduling problem with common fuzzy due-date. The problem arises from a cable manufacturing system where cables are produced in different sizes and colours. The problem is generalised by considering two attributes for each product job and different levels for each attribute. Setup time between a pair of jobs is different when the level of one attribute or both attributes is changed, as is the case in this study. Three hybrid greedy algorithms and a genetic algorithm are introduced to solve the test problems generated for the generalised problem while Taguchi experimental design method is used to find the best level of parameters for each algorithm. Finally, the comparisons are employed to select the best method.
TL;DR: The two problems are proved to be NP-complete, and two dynamic programming algorithms are proposed to solve the problems, and it is shown that the problems under study are solvable in polynomial time if the processing loads of all jobs are uniformly bounded.
TL;DR: This paper reviews the problems of Boolean non-linear programming related to the half-product problem and focuses on the development of fully polynomial-time approximation schemes, especially of those with strongly polynometric time, and on their applications to various scheduling problems.
Abstract: This paper reviews the problems of Boolean non-linear programming related to the half-product problem. All problems under consideration have a similar quadratic non-separable objective function. For these problems, we focus on the development of fully polynomial-time approximation schemes, especially of those with strongly polynomial time, and on their applications to various scheduling problems.