Single machine scheduling with controllable processing times to minimize total tardiness and earliness

Kayvanfar, Vahid; Mahdavi, Iraj; Komaki, G.M.

doi:10.1016/j.cie.2011.08.019

Cited by 36 publications

(18 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SMSP is classified as NP hard (Salmasnia et al, 2014) and is one of the most studied problems in production scheduling because of its complexity and practical importance . Recently, problems considering earliness-tardiness penalties have attracted much attention due to the reception of Just-In-Time philosophies in industry (Kayvanfar et al, 2013). However, according to even good solutions to the deterministic problem, lead to poor performance measures on real environments because of the variability of problem parameters, such as processing times, due dates, and arrival times.…”

Section: Methodsmentioning

confidence: 99%

Performance evaluation of a GRASP-based approach for stochastic scheduling problems

Duarte¹,

Cepeda²,

González-Neira³

et al. 2017

10.5267/j.uscm

View full text Add to dashboard Cite

Stochastic scheduling addresses several forms of uncertainty to represent better production environments in the real world. Stochastic scheduling has applications on several areas such as logistics, transportation, production, and healthcare, among others. This paper aims to evaluate the performance of various greedy functions for a GRASP-based approach, under stochastic processing times. Since simulation is used for estimating the objective function, two simulation techniques, Monte Carlo simulation and Common Random Numbers (CRN), are used to compare the performance of different greedy (utility) functions within the GRASP. In order to validate the proposed methodology, the expected total weighted tardiness minimization for a single machine problem was taken as case study. Results showed that both, CRN and Monte Carlo, are not statistically different regarding the expected weighted tardiness results. However, CRN showed a better performance in terms of simulation replications and the confidence interval size for the difference between means. Furthermore, the statistical analysis confirmed that there is a significant difference between greedy functions.Growing Science Ltd. All rights reserved. 7

show abstract

Section: Methodsmentioning

confidence: 99%

Performance evaluation of a GRASP-based approach for stochastic scheduling problems

Duarte¹,

Cepeda²,

González-Neira³

et al. 2017

10.5267/j.uscm

View full text Add to dashboard Cite

show abstract

“…There are a few literatures on the JIT scheduling problem with controllable processing times. For example, Kayvanfar et al [3] proposed a single machine scheduling problem with controllable processing times to minimize the total E/T. They presented an efficient heuristic called NBC-NBE approach for the JIT problem.…”

Section: Jit Production In Scheduling Problemmentioning

confidence: 99%

“…Just-in-time (JIT) production is another key issue because most companies are concerned not only with reducing inventory cost but also with delivering on time. It is noted that there is a remarkable relation between JIT scheduling problems and controllable processing times, since the scheduling environment can approach JIT philosophy by controlling the process times [3]. The controllable processing times mean that each operation can be processed in a reasonable shorter or longer time by allocating the available resources like fuel, equipment, catalyzes, manpower and so on.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Yin

et al. 2016

Sustainability

View full text Add to dashboard Cite

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.

show abstract

“…Hamidinia et al [4] proposed a genetic algorithm for minimizing total tardiness/earliness of weighted jobs in a batched delivery system [4]. A single machine scheduling problem with controllable processing times without inserted idle time to minimize total tardiness and earliness is presented by Kayvanfar et al [5]. Kaweegitbundit developed a memetic algorithm for minimizing earliness and tardiness penalties [6] Kedad Sidhoum and Sourd proposed a neighborhood search algorithm for the single machine earliness-tardiness which jobs have distinct due dates [7].…”

Section: Introductionmentioning

confidence: 99%

Invasive Weed Optimization Algorithm for Minimizing Total Weighted Earliness and Tardiness Penalties on a Single Machine under Aging Effect

Yazdani¹,

Ghodsi

2017

IRATJ

View full text Add to dashboard Cite

In this paper, minimizing total weighted tardiness and earliness criteria on a single machine is considered. Job processing time is a linear function of its starting time and each job has a distinct due date. In this study an Invasive Weed Optimization (IWO) algorithm is proposed for machine scheduling problem. Because the local search operator in IWO algorithm is designed for continuous problem only, a simple and efficient coding and decoding technique is applied to map the discrete feasible space of the permutation to a number. Then, this number is used to produce the solution. The computational results show that the performance of the proposed algorithm is much better than the GA algorithm for this problem in finding the best solutions.

show abstract

Single machine scheduling with controllable processing times to minimize total tardiness and earliness

Cited by 36 publications

References 41 publications

Performance evaluation of a GRASP-based approach for stochastic scheduling problems

Performance evaluation of a GRASP-based approach for stochastic scheduling problems

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Invasive Weed Optimization Algorithm for Minimizing Total Weighted Earliness and Tardiness Penalties on a Single Machine under Aging Effect

Contact Info

Product

Resources

About