Multi-Stage Simultaneous Lot-Sizing and Scheduling

Seeanner, Florian

doi:10.1007/978-3-658-02089-7

Cited by 8 publications

(5 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computational experiments were conducted on three groups of test instances (i.e., N(5,10,20)-T5-M9, N20-T(5,7,10)-M9, and N20-T10-M (9,15,20), representing different problem scales, where the numbers represent products, periods, and machines, respectively). These groups are similar to the data structure of Experiment 1.…”

Section: Equipment Number K Product Number Imentioning

confidence: 99%

“…In Experiment 3, the optimal value of different scales takes the minimum objective value of multiple solutions as Formula (15). The average optimal value is the expected value of various solutions, which can be expressed by Formula (12).…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…Depending on whether the planning of the lot size is affected by the production capacity of the production resources, the lot-sizing problem can be divided into two groups: uncapacitated and capacitated. Lot-sizing problems can be divided into single level and multilevel according to the structure of the bill of material (BOM), which consists of subassembly, pieces, raw material, and finished products, and is a structured material list that describes the assembly relationship of components [13][14][15]. Since the single-level uncapacitated lot-sizing problem, known as the Wagner-Whitin problem, was proposed, there have been single-level capacitated lot-sizing problems, multilevel uncapacitated lotsizing problems, and multilevel capacitated lot-sizing problems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Genetic Algorithm Based on Fuzzy Reasoning for the Multilevel Capacitated Lot-Sizing Problem with Energy Consumption in Synchronizer Production

et al. 2022

View full text Add to dashboard Cite

The multilevel capacitated lot-sizing problem (MLCLSP) is a vital theoretical problem of production planning in discrete manufacturing. An improved algorithm based on the genetic algorithm (GA) is proposed to solve the MLCLSP. Based on the solution results, the distribution of energy consumption in a synchronous production case is analyzed. In the related literature, the GA has become a much-discussed topic in solving these kinds of problems. Although the standard GA can make up for the defects of the traditional algorithm, it will lead to the problems of unstable solution results and easy local convergence. For these reasons, this research presents an adaptive genetic algorithm based on fuzzy theory (fuzzy-GA) to solve the MLCLSP. Firstly, the solving process of the MLCLSP with the fuzzy-GA is described in detail, where algorithms for key technologies such as the capacity constraint algorithm and the algorithm of solving fitness value are developed. Secondly, the auto-encoding of decision variables for MLCLSPs is studied; within this, the decision variables of whether to produce or not are encoded into a hierarchical structure based on the bill of material; combined with external demand, the decision variables of lot-sizing are constructed. Thirdly, the adaptive optimization process of parameters of the GA for the MLCLSP based on fuzzy theory is expounded, in which membership function, fuzzy rule, and defuzzification of the MLCLSP is mainly presented. Experimental studies using the processed dataset collected from a synchronizer manufacturer have demonstrated the merits of the proposed approach, in which the energy consumption distribution of the optimized production plan is given. The optimal lot-sizing is closer to the average value of the optimal value compared with the standard GA, which indicates that the proposed fuzzy-GA approach has better convergence and stability.

show abstract

Section: Equipment Number K Product Number Imentioning

confidence: 99%

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Genetic Algorithm Based on Fuzzy Reasoning for the Multilevel Capacitated Lot-Sizing Problem with Energy Consumption in Synchronizer Production

et al. 2022

View full text Add to dashboard Cite

show abstract

“…These hybrid models allow the production of multiple products per time period while preserving setup states across periods by carrying over setup states to one or more periods and, consequently, providing sequencing information of (ordered) items. Such models are the subject of extensive research; for more details see Babaei et al (2013) Seeanner (2013). For excellent reviews on scheduling with lot-sizing and/or sequence-(in)dependent setup times and costs see Copil et al (2017).…”

Section: Incorporating Simultaneous Lot Sizing and Schedulingmentioning

confidence: 99%

Integrated Production Lot Size and Distribution Planning with Shared Warehouses

Pahl

2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Collaboration in supply chains (SCs) is steadily improving especially due to advanced information systems that provide transparency over resource allocation, utilization, and dynamic demands. We are examining the case of shared inventory spaces in SCs where utilization of storage spaces depend on all SC partners. Decisions that need to be made are when to produce how much of multiple products and where and how long to store them in order to fulfill known customer demands. Therefore, we take production, inventory control and placement, as well as distribution (logistics) decisions into consideration. Special emphasis is made on setup considerations for detailed production sequencing. We analyze this integrated approach that leads to reduced overall SC costs and extends the literature to the view of shared resources that become increasingly prominent with the developments of Industry 4.0. solutions.

show abstract

“…While scheduling determines the sequence of the production on a machine. If sequence-dependent setup times occur during production, a simultaneous lot-sizing and scheduling is essential because the sequence of the lots has an effect on the available capacity, and the capacity impacts the sizes of the lots [15].…”

mentioning

confidence: 99%

Multiple-stage multiple-machine capacitated lot-sizing and scheduling with sequence-dependent setup: A case study in the wheel industry

Deeratanasrikul¹,

Mizuno²

2017

Journal of Industrial &Amp; Management Optimization

View full text Add to dashboard Cite

This paper studies a real-world problem of simultaneous lot-sizing and scheduling in a capacitated flow shop. The problem combines two significant characteristics in production which are multiple-stage production with heterogeneous multiple machines and sequence-dependent setup time. Setup time does not hold the triangle inequality, thus there may be a setup for a product without actual production. Consequently, a novel mixed integer programming (MIP) formulation is proposed and tested on real data sets of wheel production. Exact approaches cannot find a feasible solution for the model in a reasonable time, so MIP-based heuristics are developed to solve the model more quickly. Test results show that the formulation is able to contain the problem requirements and the heuristics are computationally effective. Moreover, the obtained solution can improve on a real practice at the plant.

show abstract

Multi-Stage Simultaneous Lot-Sizing and Scheduling

Cited by 8 publications

References 65 publications

Adaptive Genetic Algorithm Based on Fuzzy Reasoning for the Multilevel Capacitated Lot-Sizing Problem with Energy Consumption in Synchronizer Production

Adaptive Genetic Algorithm Based on Fuzzy Reasoning for the Multilevel Capacitated Lot-Sizing Problem with Energy Consumption in Synchronizer Production

Integrated Production Lot Size and Distribution Planning with Shared Warehouses

Multiple-stage multiple-machine capacitated lot-sizing and scheduling with sequence-dependent setup: A case study in the wheel industry

Contact Info

Product

Resources

About