No-wait two-stage flowshop problem with multi-task flexibility of the first machine

Dong, Jianming; Pan, Hong; Ye, Cunkui; Tong, Weitian; Hu, Jueliang

doi:10.1016/j.ins.2020.06.052

Cited by 15 publications

(5 citation statements)

References 34 publications

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“…Its application scope is broader, and computational efficiency is higher compared to 23 . Furthermore, Dong et al 24 considered a no-wait two-stage flow shop scheduling problem with the first stage having multi-task flexibility. The objective is to minimize the maximum completion time of all jobs.…”

Section: Literature Reviewmentioning

confidence: 99%

A Q-learning Algorithm for Two-Stage Hybrid Flow Shop Scheduling

Lu,

Liu,

Zhang

2024

Preprint

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With the increasing demand for customization, flow shop scheduling tends to process multi-variety small batch products. Thus, there are frequent switches between batches. Frequent switchovers between different sets are required during batch changeover, where machine setups may be necessary. This paper investigates scheduling optimization and work-in-process inventory optimization problems in a hybrid flow shop with rolling processing requirements. Developing continuous processing conditions to meet rolling processing requirements presents a significant challenge. In this work, continuous processing conditions are derived. Subsequently, a linear programming model is designed to find an optimal solution for this scheduling problem. Formulas for calculating work-in-process inventory are presented. To enhance work-in-process storage, a greedy algorithm with bubble sort and a Q-learning algorithm with a modified ε-policy are proposed to find an optimized product sequence and reduce work-in-process inventory, respectively. Numerical experiments evaluate the effectiveness and efficiency of the proposed methods.

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Section: Literature Reviewmentioning

confidence: 99%

A Q-learning Algorithm for Two-Stage Hybrid Flow Shop Scheduling

Lu,

Liu,

Zhang

2024

Preprint

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“…A multi-objective hybrid ant colony algorithm is constructed by [17] to trade off the makespan and total energy costs for a two-stage blocking permutation flow-shop. For a no-waiting TSFS, [18] design a linear-time combinatorial algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-Step Approach to Scheduling a Class of Two-Stage Flow Shops in Automotive Glass Manufacturing

Qiao

et al. 2023

Machines

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Driven from real-life applications, this work aims to cope with the scheduling problem of automotive glass manufacturing systems, that is characterized as a two-stage flow-shop with small batches, inevitable setup time for different product changeover at the first stage, and un-interruption requirement at the second stage. To the best knowledge of the authors, there is no report on this topic from other research groups. Our previous study presents a method to assign all batches to each machine at the first stage only without sequencing the assigned batches, resulting in an incomplete schedule. To cope with this problem, if a mathematical programming method is directly applied to minimize the makespan of the production process, binary variables should be introduced to describe the processing sequence of all the products, not only the batches, resulting in huge number of binary variables for the model. Thus, it is necessary and challenging to search for a method to solve the problem efficiently. Due to the mandatory requirement that the second stage should keep working continuously without interruption, solution feasibility is essential. Therefore, the key to solve the addressed problem is how to guarantee the solution feasibility. To do so, we present a method to determine the minimal size of each batch such that the second stage can continuously work without interruption if the sizes of all batches are same. Then, the conditions under which a feasible schedule exists are derived. Based on the conditions, we are able to develop a two-step solution method. At the first step, an integer linear program (ILP) is formulated for handling the batch allocation problem at the first stage. By the ILP, we need then to distinguish the batches only, greatly reducing the number of variables and constraints. Then, the batches assigned to each machine at the first stage are optimally sequenced at the second step by an algorithm with polynomial complexity. In this way, by the proposed method, the computational complexity is greatly reduced in comparison with the problem formulation without the established feasibility conditions. To validate the proposed approach, we carry out extensive experiments on a real case from an automotive glass manufacturer. We run ILP on CPLEX for testing. For large-size problems, we set 3600 s as the longest time for getting a solution and a gap of 1% for the lower bound of solutions. The results show that CPLEX can solve 96.83% cases. Moreover, we can obtain good solutions with the maximum gap of 4.9416% for the unsolved cases.

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“…Objective function Solution methodology [11] no-wait FS Makespan Greedy algorithm [12] no-wait PFS Tardiness & Energy Artificial bee colony & GA [13] HFS Total flowtime & Makespan Particle swarm optimization [14] HFS Makespan & Energy Artificial bee colony [15] HFS Inventory holding & Setup Fruit fly algorithm [16] HFFS Makespan Iterated greedy algorithm [10] DFS Makespan Artificial bee colony [17] no-wait DFS Makespan Artificial bee colony [18] DPFS Total flowtime Iterated greedy algorithm [19] DHFS Makespan Artificial bee colony…”

Section: Article Categorymentioning

confidence: 99%