The economic lot-size scheduling problem with equally sized batch shipment policy and stochastic demands

2015 ◽  
Vol 9 (2) ◽  
pp. 195 ◽  
Author(s):  
Wen Tsung Ho ◽  
Yu Chi Tseng ◽  
Yu Cheng Hsiao
Keyword(s):  
Scheduling Problem ◽  
Lot Size ◽  
Stochastic Demands

A genetic algorithm for solving a fuzzy economic lot-size scheduling problem

2006 ◽  
Vol 102 (2) ◽  
pp. 265-288 ◽  
Author(s):  
Ping-Teng Chang ◽  
Ming-Jong Yao ◽  
Shih-Fen Huang ◽  
Chia-Tsung Chen
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Problem ◽  
Lot Size

Job Shop Scheduling Optimization Using Genetic Algorithm With Global Criterion Technique

2019 ◽  
Author(s):  
Ke Xu ◽  
Souran Manoochehri
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Technique ◽  
Scheduling Problem ◽  
Lot Size ◽  
Total Cost ◽  
Shop Scheduling ◽  
Global Criterion ◽  
Machine Utilization

Abstract The Job Shop Scheduling Problem (JSSP) is a method which assigns multiple jobs to various machines. The large dimension of JSSP and the dynamic manufacturing environment have always been a difficult problem to optimize due to its size and complexity. In this study, three objective functions are selected namely, minimizing makespan, minimizing total cost and maximizing machine utilization. Genetic Algorithm (GA) is used to solve this scheduling problem. Lot size optimization technique is investigated for the potential of optimizing the makespan, total cost, and machine utilization objectives. Global Criterion (GC) Technique is implemented which can optimize multiple objectives all at once and obtain the best schedule. Finally, a case study is presented.

scholarly journals Application of the surrogate gradient method for a multi-item single-machine dynamic lot size scheduling problem

2021 ◽  
Vol 3 (7) ◽  
Author(s):  
Minoru Kobayashi
Keyword(s):  
Single Machine ◽  
Gradient Method ◽  
Optimal Solution ◽  
Constraint Equation ◽  
Circuit Board ◽  
Simultaneous Optimization ◽  
Scheduling Problem ◽  
Lot Size ◽  
Setup Cost ◽  
Dynamic Lot Size

AbstractThis study treats a multi-item single-machine dynamic lot size scheduling problem with sequence-independent setup cost and setup time. This problem has various heterogeneous decision features, such as lot sizing and lot sequencing. Traditionally, the problem has been treated by putting artificial constraints on the other feature in order to determine one of them. The proposed model is a Lagrange decomposition and coordination method that aims at simultaneous optimization of these decision features; however, smooth convergence to a feasible near-optimal solution has been a problem. So, in this paper, we propose a model that improves the constraint equation of the existing model and showed that it satisfies the Karush–Kuhn–Tucker (KKT) condition when we obtained a feasible solution. In addition, by applying the surrogate gradient method, which has never been applied to this problem before, it was shown that smoother convergence than before can be achieved through actual example of printed circuit board.

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