Solving the Torpedo Scheduling Problem

被引:0
|
作者
Geiger, Martin Josef [1 ]
Kletzander, Lucas [2 ]
Musliu, Nysret [2 ]
机构
[1] Helmut Schmidt Univ, Univ Fed Armed Forces Hamburg, Holstenhofweg 85, D-22043 Hamburg, Germany
[2] TU Wien, Christian Doppler Lab Artificial Intelligence & O, Karlspl 13, A-1040 Vienna, Austria
来源
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH | 2019年 / 66卷
基金
奥地利科学基金会;
关键词
VEHICLE-ROUTING PROBLEM; LOCAL SEARCH; ALGORITHM; IRON;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The article presents a solution approach for the Torpedo Scheduling Problem, an operational planning problem found in steel production. The problem consists of the integrated scheduling and routing of torpedo cars, i. e. steel transporting vehicles, from a blast furnace to steel converters. In the continuous metallurgic transformation of iron into steel, the discrete transportation step of molten iron must be planned with considerable care in order to ensure a continuous material flow. The problem is solved by a Simulated Annealing algorithm, coupled with an approach of reducing the set of feasible material assignments. The latter is based on logical reductions and lower bound calculations on the number of torpedo cars. Experimental investigations are performed on a larger number of problem instances, which stem from the 2016 implementation challenge of the Association of Constraint Programming (ACP). Our approach was ranked first (joint first place) in the 2016 ACP challenge and found optimal solutions for all used instances in this challenge.
引用
收藏
页码:1 / 32
页数:32
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