A computationally efficient simulation-based optimization method with region-wise surrogate modeling for stochastic inventory management of supply chains with general network structures

2016 ◽  
Vol 87 ◽  
pp. 164-179 ◽  
Author(s):  
Wenhe Ye ◽  
Fengqi You
Keyword(s):  
Supply Chains ◽  
Inventory Management ◽  
Surrogate Modeling ◽  
Optimization Method ◽  
Network Structures ◽  
Computationally Efficient ◽  
Stochastic Inventory ◽  
Efficient Simulation ◽  
Simulation Based ◽  
General Network

scholarly journals 115 The Beef Industry in a Post-Pandemic World

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 40-40
Author(s):  
Derrell S Peel
Keyword(s):  
Supply Chains ◽  
Inventory Management ◽  
Service Sector ◽  
Food Service ◽  
Consumer Products ◽  
Labor Management ◽  
Beef Industry ◽  
Design Changes ◽  
The Face ◽  
Retail Grocery

Abstract The onset of the COVID-19 pandemic in 2020 caused unprecedented shocks and disruptions in the cattle and beef industry. The shutdown of food service in March 2020 caused an unparalleled stacking of food demand on the retail grocery sector. The rigidity and specialized nature of food service and retail grocery supply chains, compounded by a surge in consumer demand at retail grocery, resulted in temporary shortages of meat in other consumer products in supermarkets. The food service sector recovered somewhat over many weeks but remained diminished through the balance of 2020 and beyond. In April 2020, COVID-19 infections affected the labor forces of many meat packing and processing facilities and resulted in significant reductions in beef packing and further processing for eight to twelve weeks. This caused additional product shortages in retail grocery and food service sectors. These impacts have raised many questions about how the beef industry might adapt to be more resilient in the face of such profound disruptions. Possible changes include more use of multi-purpose facilities (less specialized for food service or retail grocery supply chains); design changes in new plants and retrofitting existing facilities to reduce human health impacts; changes in labor management; changes in inventory management; and changes in business supply chain management and risk assessment practices.

Simulation-Based Hybrid Optimization Method for the Digital Twin of Garment Production Lines

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Woo-Kyun Jung ◽  
Young-Chul Park ◽  
Jae-Won Lee ◽  
Eun Suk Suh
Keyword(s):  
Expert Knowledge ◽  
Structural Characteristics ◽  
Optimization Method ◽  
Hybrid Optimization ◽  
Optimization Process ◽  
Production Lines ◽  
Distribution Method ◽  
Workload Analysis ◽  
Hybrid Optimization Method ◽  
Simulation Based

AbstractImplementing digital transformation in the garment industry is very difficult, owing to its labor-intensive structural characteristics. Further, the productivity of a garment production system is considerably influenced by a combination of processes and operators. This study proposes a simulation-based hybrid optimization method to maximize the productivity of a garment production line. The simulation reflects the actual site characteristics, i.e., process and operator level indices, and the optimization process reflects constraints based on expert knowledge. The optimization process derives an optimal operator sequence through a genetic algorithm (GA) and sequentially removes bottlenecks through workload analysis based on the results. The proposed simulation optimization (SO) method improved productivity by ∼67.4%, which is 52.3% higher than that obtained by the existing meta-heuristic algorithm. The correlation between workload and production was verified by analyzing the workload change trends. This study holds significance because it presents a new simulation-based optimization model that further applies the workload distribution method by eliminating bottlenecks and digitizing garment production lines.

scholarly journals Multiscale, thermomechanical topology optimization of self-supporting cellular structures for porous injection molds

2019 ◽  
Vol 25 (9) ◽  
pp. 1482-1492
Author(s):  
Tong Wu ◽  
Andres Tovar
Keyword(s):  
Topology Optimization ◽  
Mechanical Performance ◽  
Mechanical Load ◽  
Design Method ◽  
Three Dimensional ◽  
Optimization Method ◽  
Cellular Structures ◽  
Injection Mold ◽  
Computationally Efficient ◽  
Content Type

Purpose This paper aims to establish a multiscale topology optimization method for the optimal design of non-periodic, self-supporting cellular structures subjected to thermo-mechanical loads. The result is a hierarchically complex design that is thermally efficient, mechanically stable and suitable for additive manufacturing (AM). Design/methodology/approach The proposed method seeks to maximize thermo-mechanical performance at the macroscale in a conceptual design while obtaining maximum shear modulus for each unit cell at the mesoscale. Then, the macroscale performance is re-estimated, and the mesoscale design is updated until the macroscale performance is satisfied. Findings A two-dimensional Messerschmitt Bolkow Bolhm (MBB) beam withstanding thermo-mechanical load is presented to illustrate the proposed design method. Furthermore, the method is implemented to optimize a three-dimensional injection mold, which is successfully prototyped using 420 stainless steel infiltrated with bronze. Originality/value By developing a computationally efficient and manufacturing friendly inverse homogenization approach, the novel multiscale design could generate porous molds which can save up to 30 per cent material compared to their solid counterpart without decreasing thermo-mechanical performance. Practical implications This study is a useful tool for the designer in molding industries to reduce the cost of the injection mold and take full advantage of AM.

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