计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2014年
5期
196-202
,共7页
岸桥调度%集卡调度%约束规划%集装箱码头%最优化%启发式算法%混合整数规划
岸橋調度%集卡調度%約束規劃%集裝箱碼頭%最優化%啟髮式算法%混閤整數規劃
안교조도%집잡조도%약속규화%집장상마두%최우화%계발식산법%혼합정수규화
quay crane scheduling%yard truck scheduling%Constraint Programming(CP)%container terminal%optimization%heuristic algorithm%Mixed Integer Programming(MIP)
针对进口集装箱卸船的岸桥与集卡集成调度问题,分别提出混合整数规划(MIP)模型和约束规划(CP)模型,目标是使得卸船完工时间最短,该问题是NP难题。通过OPL语言设计约束规划模型,利用其为调度问题提供的特殊构造,如区间变量、序列变量等进行建模,并采用“扩展操作任务”的概念来定义区间变量以提升求解效率。为评价解的质量,设计一个新的下界求解方法。使用不同规模的实例对约束规划模型和MIP模型进行测试,结果表明,在小规模实例中,CP模型求解性能略差于MIP模型,但对于中大规模实例,MIP模型无法在设定时限内找到解,而CP模型则能以较快的收敛速度得到高质量的解,目标距离下界的差距控制在2.19%~8.28%。
針對進口集裝箱卸船的岸橋與集卡集成調度問題,分彆提齣混閤整數規劃(MIP)模型和約束規劃(CP)模型,目標是使得卸船完工時間最短,該問題是NP難題。通過OPL語言設計約束規劃模型,利用其為調度問題提供的特殊構造,如區間變量、序列變量等進行建模,併採用“擴展操作任務”的概唸來定義區間變量以提升求解效率。為評價解的質量,設計一箇新的下界求解方法。使用不同規模的實例對約束規劃模型和MIP模型進行測試,結果錶明,在小規模實例中,CP模型求解性能略差于MIP模型,但對于中大規模實例,MIP模型無法在設定時限內找到解,而CP模型則能以較快的收斂速度得到高質量的解,目標距離下界的差距控製在2.19%~8.28%。
침대진구집장상사선적안교여집잡집성조도문제,분별제출혼합정수규화(MIP)모형화약속규화(CP)모형,목표시사득사선완공시간최단,해문제시NP난제。통과OPL어언설계약속규화모형,이용기위조도문제제공적특수구조,여구간변량、서렬변량등진행건모,병채용“확전조작임무”적개념래정의구간변량이제승구해효솔。위평개해적질량,설계일개신적하계구해방법。사용불동규모적실례대약속규화모형화MIP모형진행측시,결과표명,재소규모실례중,CP모형구해성능략차우MIP모형,단대우중대규모실례,MIP모형무법재설정시한내조도해,이CP모형칙능이교쾌적수렴속도득도고질량적해,목표거리하계적차거공제재2.19%~8.28%。
A Mixed Integer Programming(MIP) model and a Constraint Programming(CP) model to tackle the integrated quay crane and yard truck scheduling problem for inbound containers are proposed, which aims to minimize the makespan of unloading process. The CP model is developed with OPL modeling language and employs OPL’s special constructs designed for scheduling problems, e.g., interval variables sequence variable etc. To improve solving efficiency, a special concept called extended operation task is proposed which is used to define interval variables. Besides, a new lower bound is given to evaluate the quality of solutions. Computational experiments on varied scales of instances are carried out to test the CP model and the MIP model. The results indicate that the CP model does not outperform the MIP model for small instances. For medium and large instances, the MIP model can not be solved within time limit, whereas the CP model is effective for finding high-quality solutions and can efficiently solve large problems with fast convergence rate. On average, the gap between the objective values of the CP model and the lower bounds is 2.19%~8.28%.