Serial batching scheduling of deteriorating jobs in a two-stage supply chain to minimize the makespan

Pei, Jun; Pardalos, Pãnos M.; Liu, Xinbao; Fan, Wenjuan; Yang, Shanlin

doi:10.1016/j.ejor.2014.11.034

Cited by 87 publications

(21 citation statements)

References 29 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All jobs are available at time t 0 , where t 0 > 0. In this paper, we continue our previous model [13] and extend Lee's model [26] in the serial-batching scheduling problem, and the deteriorating and learning effects are both considered in this model. The actual processing time of J i is indicated as a function of its starting time t such that…”

Section: Notation and Problem Descriptionmentioning

confidence: 96%

See 1 more Smart Citation

Serial-batching scheduling with time-dependent setup time and effects of deterioration and learning on a single-machine

et al. 2015

Self Cite

View full text Add to dashboard Cite

This paper deals with serial-batching scheduling problems with the effects of deterioration and learning, where time-dependent setup time is also considered. In the proposed scheduling models, all jobs are first partitioned into serial batches, and then all batches are processed on a single serial-batching machine. The actual job processing time is a function of its starting time and position. In addition, a setup time is required when a new batch is processed, and the setup time of the batches is time-dependent, i.e., it is a linear function of its starting time. Structural properties are derived for the problems of minimizing the makespan, the number of tardy jobs, and the maximum earliness. Then, three optimization algorithms are developed to solve them, respectively.

show abstract

Section: Notation and Problem Descriptionmentioning

confidence: 96%

“…However, the processing times may change in many real-world situations Table 1 Differences between group and serial-batching scheduling problems [13] Factors…”

Section: Introductionmentioning

confidence: 99%

Serial-batching scheduling with time-dependent setup time and effects of deterioration and learning on a single-machine

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…In most situations, a scheduling problem with batch availability assumption is more difficult than the corresponding problem with job availability assumption. Recently, Pei et al [13] studied a serial batching scheduling of deteriorating jobs in a two-stage supply chain to minimize the makespan. In this problem, it was assumed that the machine and the vehicle have the same capacity on the number of the jobs.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Woeginger [20] considered a similar problem on parallel machines without considering release dates. Hoogeveen and Vestjen [6] investigated the on-line version of the problem studied by Potts [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integrated production and delivery scheduling on a serial batch machine to minimize the makespan

Zhang

Wan

2015

Theoretical Computer Science

View full text Add to dashboard Cite

In this paper, we study the integrated production and delivery scheduling on a serial batch machine. The objective is to minimize the makespan, i.e., the maximum delivery completion time of the jobs. We consider four distinct problems which depend on whether split is allowed in the production or delivery of the jobs. We present a polynomial-time algorithm for the first problem and show that other three problems are strongly NPhard. Furthermore, we provide effective approximation algorithms for the three NP-hard problems.

show abstract

Energy‐efficient and sustainable supply chain in the manufacturing industry

Beheshtinia

Fathi

2022

Energy Science & Engineering

View full text Add to dashboard Cite

This study aims at reducing energy consumption in supply chain networks by providing optimal integrated production and transportation scheduling. The considered supply chain consists of one main manufacturing center, multiple production units (i.e., suppliers), and multiple heterogeneous vehicles as the transportation fleet. To schedule this complex supply chain network in an energy-efficient way, several decisions should be made concerning the assignment of orders to suppliers and determining their production sequence, splitting orders, assigning orders to vehicles, and assigning delivery priority to orders. To cope with the problem, a mixed-integer linear programming model is presented. Due to the complexity of the problem, a novel development of the genetic algorithm named the Multiple Reference Group Genetic Algorithm (MRGGA) is also proposed. Four objectives are considered to be optimized to meet both suitability and energy-efficiency aspects in the supply chain network. These optimization objectives are to minimize the total orders' delivery times to the manufacturing center, fuel consumption by the vehicles, energy consumption at supplies, and maximize orders' quality. To analyze the performance of the proposed algorithm, a real case and a set of generated instances are solved. The results obtained by the proposed algorithm are compared with an existing genetic algorithm in the literature. Moreover, the results are also compared with the optimal solutions obtained from the mathematical model for small-size problems. The results of the comparisons show the efficiency of the proposed MRGGA in finding energy-efficient solutions for the considered supply chain network.

show abstract

Serial batching scheduling of deteriorating jobs in a two-stage supply chain to minimize the makespan

Cited by 87 publications

References 29 publications

Serial-batching scheduling with time-dependent setup time and effects of deterioration and learning on a single-machine

Serial-batching scheduling with time-dependent setup time and effects of deterioration and learning on a single-machine

Integrated production and delivery scheduling on a serial batch machine to minimize the makespan

Energy‐efficient and sustainable supply chain in the manufacturing industry

Contact Info

Product

Resources

About