Showing papers on "Tardiness published in 2003"
TL;DR: This study exploits the problem structure to derive upper bounds that are independent of job duration distribution type and presents new analytical insights into the problem as well as a series of numerical experiments that illustrate properties of the optimal solution with respect to distribution type, cost structure, and number of jobs.
Abstract: This study is concerned with the determination of optimal appointment times for a sequence of jobs with uncertain durations. Such appointment systems are used in many customer service applications to increase the utilization of resources, match workload to available capacity, and smooth the flow of customers. We show that the problem can be expressed as a two-stage stochastic linear program that includes the expected cost of customer waiting, server idling, and a cost of tardiness with respect to a chosen session length. We exploit the problem structure to derive upper bounds that are independent of job duration distribution type. These upper bounds are used in a variation of the standard L-shaped algorithm to obtain optimal solutions via successively finer partitions of the support of job durations. We present new analytical insights into the problem as well as a series of numerical experiments that illustrate properties of the optimal solution with respect to distribution type, cost structure, and numbe...
338 citations
TL;DR: The MPGA is extended to scheduling problems with three objectives: makespan, TWT, and total weighted completion times (TWC), and also performs better than MOGA.
241 citations
TL;DR: In this article, the authors proposed an integrated model that simultaneously determines production scheduling and preventive maintenance planning decisions so that the total weighted tardiness of jobs is minimized, and investigated the benefits of integration through a numerical study of small problems.
Abstract: Production scheduling and preventive maintenance planning decisions are inter-dependent but most often made independently. Given that maintenance affects available production time and elapsed production time affects the probability of machine failure, this inter-dependency seems to be overlooked in the literature. We propose an integrated model that simultaneously determines production scheduling and preventive maintenance planning decisions so that the total weighted tardiness of jobs is minimized. We investigate the benefits of integration through a numerical study of small problems. We compare the integrated solution and its performance with the solutions obtained from solving the production scheduling and preventive maintenance planning problems independently. The numerical results show an average reduction of 30% in expected total weighted tardiness. Finally, we discuss the issues related to solving larger problems and extensions for future study.
163 citations
TL;DR: A heuristic is proposed to find the near-optimal schedule for the problem and is beneficial to the company, and it will be implemented in the near future.
146 citations
TL;DR: A branch-and-bound algorithm that utilizes several inherent theorems is developed to derive the optimal schedule for the problem and a heuristic to solve large-sized problems is also developed.
141 citations
TL;DR: Four search heuristics are proposed to address the problem, namely the earliest weighted due date, the shortest weighted processing time, the two-level batch scheduling heuristic, and the simulated annealing method.
Abstract: This paper presents several search heuristics and their performance in batch scheduling of parallel, unrelated machines. Identical or similar jobs are typically processed in batches in order to decrease setup times and/or processing times. The problem accounts for allotting batched work parts into unrelated parallel machines, where each batch consists of a fixed number of jobs. Some batches may contain different jobs but all jobs within each batch should have an identical processing time and a common due date. Processing time of each job of a batch is determined according to the machine group as well as the batch group to which the job belongs. Major or minor setup times are required between two subsequent batches depending on batch sequence but are independent of machines. The objective of our study is to minimize the total weighted tardiness for the unrelated parallel machine scheduling. Four search heuristics are proposed to address the problem, namely (1) the earliest weighted due date, (2) the shortest weighted processing time, (3) the two-level batch scheduling heuristic, and (4) the simulated annealing method. These proposed local search heuristics are tested through computational experiments with data from dicing operations of a compound semiconductor manufacturing facility.
124 citations
TL;DR: A two-level batch scheduling framework is suggested based on the features of batch scheduling, and existing heuristics, which show excellent performance in terms of total weighted tardiness for the single machine scheduling are extended.
122 citations
TL;DR: In this paper, the problem of scheduling a number of jobs on a single machine against a restrictive common due date is considered, and meta-heuristics, namely evolutionary strategies, simulated annealing and threshold accepting, are applied.
117 citations
TL;DR: Heuristic methods and optimizing techniques are surveyed for both types of problems in the single-machine environment and some extensions of the TT and TWT problems are given for multi-machine environments.
98 citations
TL;DR: This work addresses the one-machine problem in which the jobs have distinct due dates, earliness costs, and tardiness costs and proposes a new lower bound based on the decomposition of each job in unary operations that is then assigned to the time slots, which gives a preemptive schedule.
Abstract: We address the one-machine problem in which the jobs have distinct due dates, earliness costs, and tardiness costs. In order to determine the minimal cost of such a problem, a new lower bound is proposed. It is based on the decomposition of each job in unary operations that are then assigned to the time slots, which gives a preemptive schedule. Assignment costs are defined so that the minimum assignment cost is a valid lower bound. A branch-and-bound algorithm based on this lower bound and on some new dominance rules is experimentally tested.
96 citations
TL;DR: An extensive performance analysis has shown that the proposed heuristics are computationally faster and more effective in yielding solutions of better quality than the benchmark procedures.
TL;DR: This research considers the problem of scheduling jobs on parallel machines with noncommon due dates and additional resource constraints and shows the Lagrangian relaxation approach to be equivalent to the asymmetric assignment problem.
30 Jul 2003
TL;DR: This work develops a fully polynomial time approximation scheme (FPTAS) for minimum tardiness scheduling with rejection using a geometric rounding technique on the total rejection penalty.
Abstract: We consider the problem of minimum lateness/tardiness scheduling with rejection for which the objective function is the sum of the maximum lateness/tardiness of the scheduled jobs and the total rejection penalty (sum of rejection costs) of the rejected jobs. If rejection is not considered, the problems are solvable in polynomial time using the Earliest Due Date (EDD) rule. We show that adding the option of rejection makes the problems \(\mathcal{NP}\)-complete. We give pseudo-polynomial time algorithms, based on dynamic programming, for these problems. We also develop a fully polynomial time approximation scheme (FPTAS) for minimum tardiness scheduling with rejection using a geometric rounding technique on the total rejection penalty.
TL;DR: Algorithms with O(n2 log n) running times are presented for scheduling costs involving earliness/tardiness and number of tardy jobs and computational experiments show that the algorithms can solve problems with n= 5,000 in less than a minute on a standard PC.
TL;DR: It is shown that the problem of scheduling n jobs with a common due date and proportional early and tardy penalties on m identical parallel machines is NP-hard and a dynamic programming algorithm is proposed to solve it.
TL;DR: A hybrid technique using constraint programming and linear programming is applied to the problem of scheduling with earliness and tardiness costs and shows that creating and solving a subproblem containing only the activities with direct impact on the cost function significantly increases the number of problems that can be solved to optimality while significantly decreasing the search time.
Abstract: A hybrid technique using constraint programming and linear programming is applied to the problem of scheduling with earliness and tardiness costs. The linear model maintains a set of relaxed optimal start times which are used to guide the constraint programming search heuristic. In addition, the constraint programming problem model employs the strong constraint propagation techniques responsible for many of the advances in constraint programming for scheduling in the past few years. Empirical results validate our approach and show, in particular, that creating and solving a subproblem containing only the activities with direct impact on the cost function and then using this solution in the main search, significantly increases the number of problems that can be solved to optimality while significantly decreasing the search time.
08 Apr 2003
TL;DR: In this paper, a population-based ant colony optimization approach is proposed to solve multi-criteria optimization problems where the population of solutions is chosen from the set of all nondominated solutions found so far.
Abstract: In this paper a Population-based Ant Colony Optimization approach is proposed to solve multi-criteria optimization problems where the population of solutions is chosen from the set of all nondominated solutions found so far. We investigate different maximum sizes for this population. The algorithm employs one pheromone matrix for each type of optimization criterion. The matrices are derived from the chosen population of solutions, and can cope with an arbitrary number of criteria. As a test problem, Single Machine Total Tardiness with changeover costs is used.
TL;DR: In this paper, the problem of scheduling in dynamic conventional jobshops has been extensively investigated over many years, and a survey of literature on dynamic assembly jobshop scheduling has revealed that the TWKR-RRP rule is the best one for minimizing the mean flowtime and staging delay, and the job due-date (JDD) rule was the best for minimising the mean tardiness of jobs.
TL;DR: In this article, a simulation model of a flexible manufacturing system (FMS) which subjects to minimization three performance criteria simultaneously such as mean flow time, mean tardiness, and mean earliness is presented.
TL;DR: This work proposes a polynomial algorithm assuming that all processing times and all due dates are fuzzy numbers of the L–R type with power shape functions and proves that the second problem is NP-hard.
TL;DR: In this paper, the authors proposed heuristics for scheduling jobs within a part family by identifying subfamilies and sequencing them to improve the use of machines within a cell and to reduce the tardiness as well as the number of tardy jobs.
Abstract: A key operational issue in cellular manufacturing systems is the scheduling of jobs within a family at each cell. Sequence-dependent set-up times during changeovers from one job to another afford scope for the exploitation of similarities at this stage to minimize the time spent on set-ups. This paper proposes heuristics for scheduling jobs within a part family by identifying subfamilies and sequencing them to improve the use of machines within a cell and to reduce the tardiness as well as the number of tardy jobs. The proposed heuristic, when evaluated by comparison with existing benchmark heuristics, yielded encouraging results.
TL;DR: A tabu search (TS) approach is adopted together with the optimal timing algorithm to generate job completion sequences and final schedules to minimize total tardiness in two-machine preemptive open shops.
TL;DR: The algorithm of soft computing, which is a fuzzy logic embedded Genetic Algorithm is developed to solve the problem of scheduling grouped jobs on identical parallel machines and shows that the soft computing algorithm has potential for practical applications in larger scale production systems.
Abstract: A model for scheduling grouped jobs on identical parallel machines is addressed in this paper. The model assumes that a set-up time is incurred when a machine changes from processing one type of component to a different type of component, and the objective is to minimize the total earliness-tardiness penalties. In this paper, the algorithm of soft computing, which is a fuzzy logic embedded Genetic Algorithm is developed to solve the problem. The efficiency of this approach is tested on several groups of random problems and shows that the soft computing algorithm has potential for practical applications in larger scale production systems.
TL;DR: In the analysis, several solution properties are characterized and based upon these properties, three efficient polynomial time algorithms are developed for minimizing the due date related measures.
TL;DR: This research focuses on scheduling jobs with varying processing times and distinct due dates on a single machine subject to earliness and tardiness penalties, and finds application in a just-in-time (JIT) production environment.
TL;DR: An attempt is made to develop efficient dispatching rules by incorporating the relative costs of holding and tardiness of jobs in the form of scalar weights, and the proposed rules are effective in minimizing the means and maximums of the primary measure.
Abstract: Most studies on scheduling in dynamic job-shops assume that the holding cost of a job is given by the flowtime of the job and that the tardiness cost of a job is given by the tardiness of the job. In other words, unit holding and unit tardiness costs of a job are assumed. However, in reality, such an assumption need not hold, and it is quite possible that there are different costs for holding and tardiness for different jobs. In addition, most studies on job-shop scheduling assume that jobs are independent and that no assembly operations exist. The current study addresses the problem of scheduling in dynamic assembly job-shops (manufacturing multilevel jobs) with the consideration of different holding and tardiness costs for different jobs. An attempt is made to develop efficient dispatching rules by incorporating the relative costs of holding and tardiness of jobs in the form of scalar weights. The primary objective of scheduling considered here is the minimization of the total scheduling cost consisting...
08 Dec 2003
TL;DR: The use of GP to learn single-machine predictive scheduling heuristics with stochastic breakdowns is investigated, where both tardiness and stability objectives in face of machine failures are considered.
Abstract: Genetic programming (GP) has been rarely applied to scheduling problems. In this paper the use of GP to learn single-machine predictive scheduling (PS) heuristics with stochastic breakdowns is investigated, where both tardiness and stability objectives in face of machine failures are considered. The proposed bi-tree structured representation scheme makes it possible to search sequencing and idle time inserting programs integratedly. Empirical results in different uncertain environments show that GP can evolve high quality PS heuristics effectively. The roles of inserted idle time are then analysed with respect to various weighting objectives. Finally some guides are supplied for PS design based on GP-evolved heuristics.
TL;DR: In this article, a two-step mixed-integer linear programming (MILP) approach is developed for such a class of problems with real and complex features such as limited shelf-lives of intermediate products, batch splitting at the storage, a batch filling multiple orders, and general product specifications.
Abstract: Many commonly occurring multistage facilities in noncontinuous chemical industries can be modeled as a two-stage process with intermediate storage. A two-step mixed-integer linear programming (MILP) approach is developed for such a class of problems with real and complex features such as limited shelf-lives of intermediate products, batch splitting at the storage, a batch filling multiple orders, and general product specifications. Step 1 forms optimal lots and batches to meet the orders, while step 2 schedules them. Twelve new formulations based on several novel constraint sets to assign consecutive orders to a unit are proposed and evaluated using several examples involving up to 90 batches. These sets are useful even for multistage scheduling problems other than the present problem. For this problem, minimizing setups and tardiness together seems easier and better than minimizing tardiness alone.
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TL;DR: A function is developed that maps some instance statistics into an appropriate value for the lookahead parameter of the Apparent Tardiness Cost (ATC) heuristic that is compared with some fixed values that have been previously used.
Abstract: The Apparent Tardiness Cost (ATC) heuristic is one of the best performing dispatch rules for the weighted tardiness scheduling problem This heuristic uses a lookahead parameter whose value must be specified In this paper we develop a function that maps some instance statistics into an appropriate value for the lookahead parameter This function is compared with some fixed values that have been previously used The computational results show that the ATC heuristic performs better when the lookahead parameter value is determined by the proposed function
TL;DR: In this paper, an ant colony system (ACSACS) approach is presented to continuously improve the constructive heuristics, and a computational study is conducted on the single machine total weighted tardiness problem.
Abstract: Over the past 50 years, researchers have developed many simple constructive heuristics for the scheduling problem. A major defect of these heuristics is the non-robustness of their solutions. An ant colony system (ACS) approach is presented to continuously improve the constructive heuristics. To verify the developed ACS approach, a computational study is conducted on the single machine total weighted tardiness problem. The results show that the proposed approach can effectively improve the robustness of various constructive heuristics, and outperform the existing heuristics for a well-known benchmark problem set. From the viewpoints of both the solution quality and computational expenses, the proposed ACS approach is an efficient and effective method for scheduling problems.