A discrete bilevel brain storm algorithm for solving a sales territory design problem: a case study

This chapter presents a case study to organize the sales territories for a company with 11 sales managers to be assigned to 111 sales coverage units in Mexico. The assignment problem is modeled as a mathematical program with two objective functions. One objective minimizes the maximum distance traveled by the manager, and the other objective minimizes the variation of the sales growth goals with respect to the national average. To solve the bi-objective non-linear mixed-integer program, a weights method is selected. Some instances are solved using commercial software with long computational times. Also, a heuristic and a metaheuristic based on simulated annealing were developed. The design of the heuristic generates good solutions for the distance objective. The metaheuristic produces better results than the heuristic, with a better balance between the objectives. The heuristic and the metaheuristic are capable of providing good results with short computational times.

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Solving the layout design problem by simulation-optimization approach–A case study on a sortation conveyor system

Simulation Modelling Practice and Theory ◽

10.1016/j.simpat.2020.102192 ◽

2021 ◽

Vol 106 ◽

pp. 102192

Author(s):

Tzu-Li Chen ◽

James C. Chen ◽

Chien-Fu Huang ◽

Ping-Chen Chang

Keyword(s):

Design Problem ◽

Simulation Optimization ◽

Layout Design ◽

Optimization Approach ◽

Conveyor System

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Using Bayesian Optimization With Knowledge Transfer for High Computational Cost Design: A Case Study in Photovoltaics

Volume 2A: 45th Design Automation Conference ◽

10.1115/detc2019-98111 ◽

2019 ◽

Author(s):

Mine Kaya ◽

Shima Hajimirza

Keyword(s):

Solar Cells ◽

Design Problem ◽

Cost Optimization ◽

Computational Cost ◽

Design Procedure ◽

Bayesian Optimization ◽

Optimization Framework ◽

Previous Design ◽

High Computational Cost

Abstract Engineering design is usually an iterative procedure where many different configurations are tested to yield a desirable end performance. When the design objective can only be measured by costly operations such as experiments or cumbersome computer simulations, a thorough design procedure can be limited. The design problem in these cases is a high cost optimization problem. Meta model-based approaches (e.g. Bayesian optimization) and transfer optimization are methods that can be used to facilitate more efficient designs. Transfer optimization is a technique that enables using previous design knowledge instead of starting from scratch in a new task. In this work, we study a transfer optimization framework based on Bayesian optimization using Gaussian Processes. The similarity among the tasks is determined via a similarity metric. The framework is applied to a particular design problem of thin film solar cells. Planar multilayer solar cells with different sets of materials are optimized to obtain the best opto-electrical efficiency. Solar cells with amorphous silicon and organic absorber layers are studied and the results are presented.

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