Showing papers on "Tardiness published in 2014"
TL;DR: In this paper, a multi-objective scheduling method is developed with reducing energy consumption as one of the objectives, which can also be applied across existing legacy systems and does not require large investment.
241 citations
TL;DR: A Lagrangian relaxation (LR) approach relaxing the machine capacity constraints is presented to solve the MIP problem, which decomposes the relaxed problem into two tractable subproblems by separating the continuous variables from the integer ones.
99 citations
TL;DR: In this article, two metaheuristic algorithms are presented based on genetic algorithm, namely: Non-dominated Sorting Genetic Algorithm (NSGAII) and Multi Objective Genetic algorithm (MOGA).
84 citations
TL;DR: In this article, two multi-objective scheduling problems involving economic and environmental-related criteria are studied: (1) a batch-processing machine scheduling problem to minimize the total weighted tardiness and carbon footprint simultaneously; (2) a triple-criteria scheduling problem involving of a hybrid flow shop consisting of a batch processing machine followed by two parallel-processing machines, in which the shop attempts to minimise the total weighting of tardyness, carbon footprint and peak power.
Abstract: Firms heavily emphasise reducing carbon footprint, an area warranting further improvement. This study examines carbon footprint within the context of production scheduling. Two multi-objective scheduling problems involving economic- and environmental-related criteria are studied: (1) a batch-processing machine scheduling problem to minimise the total weighted tardiness and carbon footprint simultaneously; (2) a triple-criteria scheduling problem involving of a hybrid flow shop consisting of a batch-processing machine followed by two parallel-processing machines, in which the shop attempts to minimise the total weighted tardiness, carbon footprint and peak power. Since the above problems are treated as a true multi-objective optimisation problem, decision-makers should select a solution among the trade-off solutions provided in the Pareto-optimal set. Therefore, the non-dominated sorting-based genetic algorithm II (NSGA-II) is implemented, which identifies the set of approximate efficient schedules to both...
80 citations
TL;DR: In this article, an Iterated Local Search (ILS) heuristic was proposed to solve the single machine total weighted tardiness problem with sequence-dependent setup times (often known as problem ) where a given set of jobs to be sequenced on a single machine, where a setup time is required before each job that depends on both the preceding job and the job to be processed next.
Abstract: The single machine total weighted tardiness problem with sequence-dependent setup times (often known as problem ) requires a given set of jobs to be sequenced on a single machine, where a setup time is required before the processing of each job that depends on both the preceding job and the job to be processed next. The goal is to minimise the sum of weighted tardiness, where the tardiness of a job is zero if it is completed by its due date and is equal to its completion time minus its due date otherwise. In this paper, we develop an Iterated Local Search (ILS) heuristic and compare its performance with the state-of-the-art metaheuristic algorithms from the literature. The proposed ILS algorithm obtains high-quality solutions using computation times that is comparable to its competitors.
74 citations
21 May 2014
TL;DR: In this paper, the authors investigate three techniques for generating robust schedules based on the insertion of temporal slack and show that these techniques out-perform the existing temporal protection technique both in terms of producing schedules with low simulated tardiness and in producing schedules that better predict the level of simulated tardyiness.
Abstract: Many scheduling systems assume a static environment within which a schedule will be executed. The real world is not so stable: machines break down, operations take longer to execute than expected, and orders may be added or canceled. One approach to dealing with such disruptions is to generate robust schedules: schedules that are able to absorb some level of unexpected events without rescheduling. In this paper we investigate three techniques for generating robust schedules based on the insertion of temporal slack. Simulation-based results indicate that the two novel techniques out-perform the existing temporal protection technique both in terms of producing schedules with low simulated tardiness and in producing schedules that better predict the level of simulated tardiness.
65 citations
TL;DR: In this paper, the authors considered the problem of finding a preemptive schedule of minimum aggregate cost for a set of jobs with arbitrary release time, size, and monotone function specifying the cost incurred when the job is completed.
Abstract: We consider the following general scheduling problem. The input consists of $n$ jobs, each with an arbitrary release time, size, and monotone function specifying the cost incurred when the job is completed at a particular time. The objective is to find a preemptive schedule of minimum aggregate cost. This problem formulation is general enough to include many natural scheduling objectives, such as total weighted flow time, total weighted tardiness, and sum of flow time squared. We give an $O(\log \log P )$ approximation for this problem, where $P$ is the ratio of the maximum to minimum job size. We also give an $O(1)$ approximation in the special case of identical release times. These results are obtained by reducing the scheduling problem to a geometric capacitated set cover problem in two dimensions.
64 citations
TL;DR: Computational experiments and comparisons show that the proposed NSGA-II + VNS algorithm generates better or competitive results than the existing NS GA-II and SPEA-II for the no-wait flexible flow shop scheduling problem with sequence-dependent setup times to simultaneous minimizing the makespan and mean tardiness criterion.
Abstract: We address the no-wait k-stage flexible flowshop scheduling problem where there are m identical machines at each stage. The objectives are to schedule the available n jobs so that makespan and mean tardiness of n jobs are minimized. Sequence-dependent setup times are treated in this problem as one of the prominent practical assumptions. This problem is NP-hard, and therefore we present a new multiobjective ap- proach for solving the mentioned problem. The proposed meta-heuristic is evaluated based on randomly generated data in comparison with two well-known multiobjective algorithm including NSGA-II and SPEA-II. Due to sensitivity of our proposed algorithm to parameter values, a new approach for tackling of this issue was designed. Our proposed method includes Taguchi method (TM) and multiobjective decision making (MODM). We have chosen six measures into two groups. Qualitative metrics including number of Pareto solu- tions (NPS), diversity metric (DM) as well as the spread of non-dominance solution (SNS) and quantitative metrics in- cluding the rate of achievement to two objectives simulta- neously (RAS), mean ideal distance (MID) and quality metric (QM)toevaluatethe performanceofour proposedalgorithms. Computational experiments and comparisons show that the proposed NSGA-II+VNS algorithm generates better or competitive results than the existing NSGA-II and SPEA-II for the no-wait flexible flow shop scheduling problem with sequence-dependent setup times to simultaneous minimizing the makespan and mean tardiness criterion.
64 citations
TL;DR: In this paper, a mixed integer programming model that can find optimal solutions for the studied problem is presented. But the problem of scheduling unrelated parallel machines with sequence-and machine-dependent setup times and ready times to minimize total weighted tardiness (TWT) is not considered.
Abstract: We consider the problem of scheduling unrelated parallel machines with sequence- and machine-dependent setup times and ready times to minimise total weighted tardiness (TWT). We present a mixed integer programming model that can find optimal solutions for the studied problem. We also propose a heuristic (ATCSR_Rm) and an iterated hybrid metaheuristic (IHM) that can find optimal or nearly optimal solutions for the studied problem within a reasonable time. The proposed IHM begins with effective initial solutions, and then improves the initial solutions iteratively. The IHM integrates the principles of the attraction–repulsion mechanism within electromagnetism-like algorithms with local search. If the search becomes trapped at a local optimum, an elite search procedure is developed to help the search escape. We have compared our proposed IHM with two existing metaheuristics, tabu search (TS) and ant colony optimisation (ACO). Computational results show that the proposed IHM outperforms TS and ACO in terms of...
52 citations
TL;DR: In this article, the problem of multiple due windows assignment scheduling and controllable processing times on a single machine was studied and shown to be polynomially solvable in O(n 3 ) time, where n is the total number of jobs.
51 citations
TL;DR: This paper provides a polynomial-time algorithm to find the optimal job sequence,Due date values, and resource allocations that minimize an integrated objective function, which includes earliness, tardiness, due date assignment, and total resource consumption costs.
Abstract: In this paper, we consider a single-machine earliness-tardiness scheduling problem with due-date assignment, in which the processing time of a job is a function of its position in a sequence and its resource allocation. The due date assignment methods studied include the common due date, and the slack due date, which reflects equal waiting time allowance for the jobs. For each combination of due date assignment method and processing time function, we provide a polynomial-time algorithm to find the optimal job sequence, due date values, and resource allocations that minimize an integrated objective function, which includes earliness, tardiness, due date assignment, and total resource consumption costs.
TL;DR: Computational results demonstrate that VNS is a very fast heuristic that quickly leads to high-quality solutions, whereas the GRASP is slightly outperformed by the VNS approach.
TL;DR: Experimental results show that in terms of both quality and diversity of solutions, ε-AGA outperforms NSGA-II for same computation time limit as the stopping criteria, and several interesting observations are made.
Abstract: The quantity of carbon dioxide (CO2) emissions is one of the most widely recognised measures of environmental sustainability Given the mounting concern about climate change and global warming, managers are facing growing pressure to reduce CO2 emissions In practice, other than CO2 emissions, managers may be concerned with other objectives when making a scheduling decision This work develops the e-archived genetic algorithm (e-AGA) to examine two batch scheduling problems with the goal of minimising CO2 emissions and the traditional due date-based objective of minimising total weighted tardiness (TWT) Experimental results show that in terms of both quality and diversity of solutions, e-AGA outperforms NSGA-II for same computation time limit as the stopping criteria Several interesting observations are made (1) These two objectives conflict with each other; (2) jobs that arrive soon after each other reduce makespan, and so reduce CO2 emissions; (3) given a set of m identical batching machines, the due
TL;DR: This paper focuses on the single machine scheduling problem, with sequence dependent setup times, and proposes a specific metaheuristic based on the harmony search algorithm, which integrates harmony search and genetic algorithms.
TL;DR: In this paper, an iterated Pareto greedy (IPG) algorithm is proposed to solve a RHFSP with the bi-objective of minimising makespan and total tardiness.
Abstract: The multi-objective reentrant hybrid flowshop scheduling problem (RHFSP) exhibits significance in many industrial applications, but appears under-studied in the literature. In this study, an iterated Pareto greedy (IPG) algorithm is proposed to solve a RHFSP with the bi-objective of minimising makespan and total tardiness. The performance of the proposed IPG algorithm is evaluated by comparing its solutions to existing meta-heuristic algorithms on the same benchmark problem set. Experimental results show that the proposed IPG algorithm significantly outperforms the best available algorithms in terms of the convergence to optimal solutions, the diversity of solutions and the dominance of solutions. The statistical analysis manifestly shows that the proposed IPG algorithm can serve as a new benchmark approach for future research on this extremely challenging scheduling problem.
TL;DR: Experimental results demonstrate that the hybrid procedure is a proficient method and could efficiently solve such complicated problems as well as efficiently solve medium-to-large size cases.
TL;DR: This paper forms a scenario-based mixed-integer program formulation for minimizing CVaR for general scheduling problems and demonstrates its application for the single machine total weighted tardiness problem, for which it is presented both a specialized l-shaped algorithm and a dynamic programming-based heuristic procedure.
Abstract: This paper introduces the use of conditional-value-at-risk (CVaR) as a criterion for stochastic scheduling problems. This criterion has the tendency of simultaneously reducing both the expectation and variance of a performance measure, while retaining linearity whenever the expectation can be represented by a linear expression. In this regard, it offers an added advantage over traditional nonlinear expectation-variance-based approaches. We begin by formulating a scenario-based mixed-integer program formulation for minimizing CVaR for general scheduling problems. We then demonstrate its application for the single machine total weighted tardiness problem, for which we present both a specialized l-shaped algorithm and a dynamic programming-based heuristic procedure. Our numerical experimental results reveal the benefits and effectiveness of using the CVaR criterion. Likewise, we also exhibit the use and effectiveness of minimizing CVaR in the context of the parallel machine total weighted tardiness problem. We believe that minimizing CVaR is an effective approach and holds great promise for achieving risk-averse solutions for stochastic scheduling problems that arise in diverse practical applications.
TL;DR: An Iterated Local Search (ILS) algorithm for solving the single-machine scheduling problem with sequence-dependent setup times to minimize the total weighted tardiness and its computational efficiency is comparable with the state-of-the-art exact algorithm by Tanaka and Araki.
Abstract: We present an Iterated Local Search (ILS) algorithm for solving the single-machine scheduling problem with sequence-dependent setup times to minimize the total weighted tardiness. The proposed ILS algorithm exhibits several distinguishing features, including a new neighborhood structure called Block Move and a fast incremental evaluation technique, for evaluating neighborhood solutions. Applying the proposed algorithm to solve 120 public benchmark instances widely used in the literature, we achieve highly competitive results compared with a recently proposed exact algorithm and five sets of best solutions of state-of-the-art metaheuristic algorithms in the literature. Specifically, ILS obtains the optimal solutions for 113 instances within a reasonable time, and it outperforms the previous best-known results obtained by metaheuristic algorithms for 34 instances and matches the best results for 82 instances. In addition, ILS is able to obtain the optimal solutions for the remaining seven instances under a relaxed time limit, and its computational efficiency is comparable with the state-of-the-art exact algorithm by Tanaka and Araki (Comput Oper Res 40:344---352, 2013). Finally, on analyzing some important features that affect the performance of ILS, we ascertain the significance of the proposed Block Move neighborhood and the fast incremental evaluation technique.
TL;DR: A multi-objective scheduling model aiming at minimizing total operation costs, finishing time and tardiness of all logistic tasks in a 4PL is proposed and an improved nondominated sorting genetic algorithm (NSGA-II) is presented to solve the model.
TL;DR: The obtained results substantially improve the former method used in the company in terms of minimising average tardiness and other important benefits are obtained, including significant saving in the time spent on scheduling, simplicity of use of the proposed procedure, robustness against unexpected events, reduction of idle times, improvement of decision-making information, and improvement of on-time delivery performance.
Abstract: In this study, we present a dispatching algorithm to solve a real-world case of the flexible job-shop scheduling problem with transfer batches and the objective of minimising the average tardiness of production orders. The proposed algorithm considers two variants: (i) an ordered variant, where the priority dispatching rules are applied in a predefined order, and (ii) a randomised variant, where the user can assign probabilities (weights) to the priority rules. Using the information of the number of units and due dates requested by the only customer, the algorithm provides the sequence of operations that must be performed on each machine, as well as the start and completion times of operations. In order to reduce the impact of unexpected events on a generated schedule, several robustness rules are considered. The obtained results substantially improve the former method used in the company in terms of minimising average tardiness. Additionally, other important benefits are obtained, including significant s...
TL;DR: In this paper, a Binary Particle Swarm Vehicle Heuristic Algorithm (BPSVHA) for simultaneous scheduling of machines and AGVs adopting Rebust factor function and minimization of mean tardiness.
TL;DR: It is argued that the proposed schedulers, such as G-FL, should replace G-EDF for SRT applications and is proposed a particular scheduling algorithm, namely the global fair lateness (G-FL) algorithm, to minimize maximum absolute lateness bounds.
Abstract: In prior work on soft real-time (SRT) multiprocessor scheduling, tardiness bounds have been derived for a variety of scheduling algorithms, most notably, the global earliest-deadline-first (G-EDF) algorithm. In this paper, we devise G-EDF-like (GEL) schedulers, which have identical implementations to G-EDF and therefore the same overheads, but that provide better tardiness bounds. We discuss how to analyze these schedulers and propose methods to determine scheduler parameters to meet several different tardiness bound criteria. We employ linear programs to adjust such parameters to optimize arbitrary tardiness criteria, and to analyze lateness bounds (lateness is related to tardiness). We also propose a particular scheduling algorithm, namely the global fair lateness (G-FL) algorithm, to minimize maximum absolute lateness bounds. Unlike the other schedulers described in this paper, G-FL only requires linear programming for analysis. We argue that our proposed schedulers, such as G-FL, should replace G-EDF for SRT applications.
TL;DR: These procedures for scheduling identical parallel machines with family setups when the objective is to minimize total tardiness are presented and it is shown that genetic algorithms are the most effective algorithms for the problem.
TL;DR: A branch and bound algorithm is developed to find optimal solutions to the single-machine stochastic scheduling problem, and it is found that surprisingly good performance can be achieved by a list schedule followed by an adjacent pairwise interchange procedure.
TL;DR: A single-machine common due-window assignment scheduling problem, in which the processing time of a job is a function of its position in a sequence and its resource allocation, is considered.
Abstract: We consider a single-machine common due-window assignment scheduling problem, in which the processing time of a job is a function of its position in a sequence and its resource allocation. The window location and size, along with the associated job schedule that minimizes a certain cost function, are to be determined. This function is made up of costs associated with the window location, window size, earliness, and tardiness. For two different processing time functions, we provide a polynomial time algorithm to find the optimal job sequence and resource allocation, respectively.
TL;DR: Nationwide benchmarking can be applied to identify and measure the effectiveness of interventions to reduce first-case tardiness in a university hospital OR environment and the implemented interventions in 4 centers were successful in significantly reducing first- case tardness.
Abstract: BACKGROUND: First-case tardiness is still a common source of frustration. In this study, a nationwide operating room (OR) Benchmark database was used to assess the effectiveness of interventions implemented to reduce tardiness and calculate its economic impact. METHODS: Data from 8 University Medical Centers over 7 years were included: 190,295 elective inpatient first cases. Data were analyzed with SPSS statistics and multidisciplinary focus-group study meetings. Analysis of variance with contrast analysis measured the influence of interventions. RESULTS: Seven thousand ninety-four hours were lost annually to first-case tardiness, which has a considerable economic impact. Four University Medical Centers implemented interventions and effectuated a significant reduction in tardiness, eg providing feedbacks directly when ORs started too late, new agreements between OR and intensive care unit departments concerning ‘‘intensive care unit bed release’’ policy, and a shift in responsibilities regarding transport of patients to the OR. CONCLUSIONS: Nationwide benchmarking can be applied to identify and measure the effectiveness of interventions to reduce first-case tardiness in a university hospital OR environment. The implemented interventions in 4 centers were successful in significantly reducing first-case tardiness.
TL;DR: A bi-level algorithm to minimize two criteria, namely makespan, and sum of the earliness and tardiness, simultaneously is proposed to solve a bi-objective hybrid flowshop scheduling problems with fuzzy tasks’ operation times, due dates and sequence-dependent setup times.
TL;DR: The goal is to find the optimal sequence for both the groups and jobs, together with the optimal due window assignment, to minimize the total cost that comprises the earliness and tardiness penalties, and the due window starting time and due window size costs.
01 Jan 2014
TL;DR: In this paper, a robust optimization approach for the SCAP with uncertain surgery duration is proposed, which allows to exploit the potentialities of a mathematical programming model without the necessity of generating scenarios.
Abstract: This paper deals with the Surgical Case Assignment Problem (SCAP) taking into account the variability pertaining patient surgery duration. In particular, given a surgery waiting list, a set of Operating Room (OR) blocks and a planning horizon, the decision herein addressed is to determine the subset of patients to be scheduled in the considered time horizon and their assignment to the available OR block times. The aim is to minimize a penalty associated to waiting time, urgency and tardiness of patients. We propose a robust optimization approach for the SCAP with uncertain surgery duration, which allows to exploit the potentialities of a mathematical programming model without the necessity of generating scenarios. Tests on a set of real-based instances are carried on in order to evaluate the solutions obtained solving different versions of the problem. Besides the value of the penalty objective function, the solution quality is also evaluated with regards to the number of patients operated and their tardiness. Furthermore, assuming lognormal distribution for the surgery times, we use a set of randomly generated scenarios in order to assess the performance of the proposed solutions in terms of OR utilization rate and number of cancelled patients.
TL;DR: This work considers the problem of scheduling a set of jobs on a single machine against a common and restrictive due date and presents a mixed integer linear model and proves that this model can be reduced to a polynomial-time model.