A data-driven robust optimization method for the assembly job-shop scheduling problem under uncertainty

As a result of increasingly serious environmental pollution, it is vital to reduce carbon emissions to achieve green and sustainable development for manufacturing processes. Customer satisfaction, as an important factor affecting enterprise profits, is of great importance in the promotion of sustainable development. Because an accurate delivery time and high delivery rate improve customer satisfaction and enhance an enterprise’s competitive advantage in the market, this paper proposes a new optimization method for achieving low carbon emissions, a high delivery rate, and a low cost for a job-shop scheduling problem. The computational results show the negative correlation between assembly cost and carbon emissions, and the positive correlation between assembly cost and delivery time by Pareto optimization. The proposed method, which takes into consideration carbon emissions, greatly supports the objective of achieving a green and sustainable development.

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OPTIMIZATION METHOD FOR A JOB - SHOP SCHEDULING PROBLEM WITH ALTERNATIVE MACHINES IN THE BATCH PROCESS

Journal of Mechanical Engineering ◽

10.3901/jme.2004.04.036 ◽

2004 ◽

Vol 40 (04) ◽

pp. 36 ◽

Cited By ~ 9

Author(s):

Quanke Pan

Keyword(s):

Job Shop ◽

Job Shop Scheduling ◽

Batch Process ◽

Optimization Method ◽

Scheduling Problem ◽

Job Shop Scheduling Problem ◽

Shop Scheduling ◽

Alternative Machines

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Research of Multi-Objective Optimization Study for Job Shop Scheduling Problem Based on Grey Ant Colony Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.308-310.1033 ◽

2011 ◽

Vol 308-310 ◽

pp. 1033-1036 ◽

Cited By ~ 2

Author(s):

Ya Dong Fang ◽

Fang Wang ◽

Hui Wang

Keyword(s):

Ant Colony Algorithm ◽

Job Shop ◽

Job Shop Scheduling ◽

Optimization Method ◽

Ant Colony ◽

Scheduling Problem ◽

Job Shop Scheduling Problem ◽

Shop Scheduling ◽

Multi Objective ◽

Processing Order

In order to resolve Multi-objective job shop scheduling problem, an optimization method of many goals scheduling based on grey relation theory and ant colony algorithm is proposed. Firstly, this paper introduces the relevant mathematical theory. AHP and Grey relational analysis, and they are combined to solve the choice of pre-processing equipment under the multi-objective conditions. What's more, ant colony algorithm is discussed to solve problem of processing order for machine. The effectiveness of multi-objective algorithm for job shop scheduling problem is verified through applying example.

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Two-Level Robust Optimization for Uncertain Job Shop Scheduling Problem

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1039.514 ◽

2014 ◽

Vol 1039 ◽

pp. 514-521

Author(s):

Bing Wang ◽

Xiao Yan Li ◽

He Xia Meng

Keyword(s):

Robust Optimization ◽

Optimization Model ◽

Job Shop ◽

Job Shop Scheduling ◽

Scheduling Problem ◽

Job Shop Scheduling Problem ◽

Shop Scheduling ◽

Processing Times ◽

Robustness Measure ◽

Expected Performance

This paper proposes a two-level robust optimization model in the context of job shop scheduling problem. The job shop scheduling problem optimizes the makespan under uncertain processing times, which are described by a set of scenarios. In the first-level optimization, a traditional stochastic optimization model is conducted to obtain the optimal expected performance as a standard performance, on which a concept of bad-scenario set is defined. In the second-level optimization, a robustness measure is given based on bad-scenario set. The objective function for the second robust optimization model is to combine expected performance and robustness measure. Finally, an extensive experiment was conducted to investigate the advantages of the proposed robust optimization model. The computational results show that the two-level model can achieve a better compromise between average performance and robustness than the existing robust optimization models.

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