Bi-objective optimization of a single machine batch scheduling problem with energy cost consideration
Bi-objective optimization of a single machine batch scheduling problem with energy cost consideration
复制标题
考虑能源成本的单机批量调度问题双目标优化
DOI：
10.1016/j.jclepro.2016.07.206
复制
发表时间：
2016
影响因子：
11.1
通讯作者：
Chengbin Chu
中科院分区：
文献类型：
--
作者：
Shijin Wang;Ming Liu;Feng Chu;Chengbin Chu
随着能源价格的不断上涨、电力需求的快速增长以及可持续发展面临的严峻挑战，节能调度对于电力密集型制造业，特别是批量生产企业来说变得越来越重要。本文研究了具有不同作业规模、不同使用时间（TOU）电价和不同机器能耗率的双目标单机批量调度问题。第一个目标是最大限度地缩短完工时间，第二个目标是通过考虑机器利用率和经济成本来最大限度地降低总能源成本。该问题被表述为整数规划模型。然后，采用精确的ε约束方法来获得精确的Pareto前沿。为了处理大规模问题，基于分解思想，开发了两种启发式方法来获得近似帕累托前沿。对随机生成实例的计算实验表明了该方法的有效性。还对一家现实世界的玻璃制造公司进行了研究案例，表明所提出的方法具有实际应用前景。
With the increasing energy price, the rapid growth of electricity demand and severe challenges for sustainable development, energy-efficient scheduling is becoming more and more important for power-intensive manufacturing industry, especially for batch production companies. This paper investigates a bi-objective single machine batch scheduling problem with non-identical job sizes, the time-of-use (TOU) electricity prices, and different energy consumption rates of the machine. The first objective is to minimize the makespan and the second is to minimize the total energy costs, by considering both the machine utilization and the economic cost. The problem is formulated as an integer programming model. Then, an exact ε-constraint method is adapted to obtain the exact Pareto front. To deal with large scale problems, based on decomposition ideas, two heuristic methods are developed to obtain approximate Pareto fronts. Computational experiments on randomly generated instances show the effectiveness of the methods. A study case of a real-world glass manufacturing company is also conducted to show that the proposed methods are promising for practical usages.