50,811 research outputs found
A Dynamic Heuristic for the Stochastic Unrelated Parallel Machine Scheduling Problem
This paper addresses the problem of batch scheduling in an unrelated parallel machine environment with sequence dependent setup times and an objective of minimizing the total weighted mean completion time. The jobs\u27 processing times and setup times are stochastic for better depiction of the real world. This is a NP-hard problem and in this paper, new heuristics are developed and compared to existing ones using simulation. The results and analysis obtained from the computational experiments proved the superiority of the proposed algorithm PMWP over the other algorithms presented
A Neighborhood Search for Sequence-dependent Setup Time in Flow Shop Fabrics Making of Textile Industry
Abstract
This paper proposes a neighborhood search to solve scheduling for fabrics making in a textile industry.
The production process consists of three production stages from spinning, weaving, and dyeing. All
stages have one processor. Setup time between two consecutive jobs with different color is considered.
This paper also proposes attribute’s decomposition of a single job to classify available jobs to be
processed and to consider setup time between two consecutive jobs. Neighborhood search (NS) algorithm
is proposed in which the permutation of set of jobs with same attribute and the permutation among set of
jobs is conducted. Solution obtained from neighborhood search, which might be trapped in local solution,
then is compared with other known optimal methods
Solving two production scheduling problems with sequence-dependent set-up times
In today�s competitive markets, the importance of good scheduling strategies in manufacturing companies lead to the need of developing efficient methods to solve complex scheduling problems. In this paper, we studied two production scheduling problems with sequence-dependent setups times. The setup times are one of the most common complications in scheduling problems, and are usually associated with cleaning operations and changing tools and shapes in machines. The first problem considered is a single-machine scheduling with release dates, sequence-dependent setup times and delivery times. The performance measure is the maximum lateness. The second problem is a job-shop scheduling problem with sequence-dependent setup times where the objective is to minimize the makespan. We present several priority dispatching rules for both problems, followed by a study of their performance. Finally, conclusions and directions of future research are presented.Production-scheduling, set-up times, priority dispatching rules
Capacity Planning and Leadtime management
In this paper we discuss a framework for capacity planning and lead time management in manufacturing companies, with an emphasis on the machine shop. First we show how queueing models can be used to find approximations of the mean and the variance of manufacturing shop lead times. These quantities often serve as a basis to set a fixed planned lead time in an MRP-controlled environment. A major drawback of a fixed planned lead time is the ignorance of the correlation between actual work loads and the lead times that can be realized under a limited capacity flexibility. To overcome this problem, we develop a method that determines the earliest possible completion time of any arriving job, without sacrificing the delivery performance of any other job in the shop. This earliest completion time is then taken to be the delivery date and thereby determines a workload-dependent planned lead time. We compare this capacity planning procedure with a fixed planned lead time approach (as in MRP), with a procedure in which lead times are estimated based on the amount of work in the shop, and with a workload-oriented release procedure. Numerical experiments so far show an excellent performance of the capacity planning procedure
Minimizing sum of completion times on a single machine with sequence-dependent family setup times
This paper presents a branch-and-bound (B&B) algorithm for minimizing the sum of completion times in a singlemachine scheduling setting with sequence-dependent family setup times. The main feature of the B&B algorithm is a new lower bounding scheme that is based on a networkformulation of the problem. With extensive computational tests, we demonstrate that the B&B algorithm can solve problems with up to 60 jobs and 12 families, where setup and processing times are uniformly distributed in various combinations of the [1,50] and [1,100] ranges
Minimizing weighted total earliness, total tardiness and setup costs
The paper considers a (static) portfolio system that satisfies adding-up contraints and the gross substitution theorem. The paper shows the relationship of the two conditions to the weak dominant diagonal property of the matrix of interest rate elasticities. This enables to investigate the impact of simultaneous changes in interest rates on the asset demands.
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
In this paper we show that reporting a single performance score is
insufficient to compare non-deterministic approaches. We demonstrate for common
sequence tagging tasks that the seed value for the random number generator can
result in statistically significant (p < 10^-4) differences for
state-of-the-art systems. For two recent systems for NER, we observe an
absolute difference of one percentage point F1-score depending on the selected
seed value, making these systems perceived either as state-of-the-art or
mediocre. Instead of publishing and reporting single performance scores, we
propose to compare score distributions based on multiple executions. Based on
the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we
present network architectures that produce both superior performance as well as
are more stable with respect to the remaining hyperparameters.Comment: Accepted at EMNLP 201
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