A multicut L-shaped based algorithm to solve a stochastic programming model for the mobile facility routing and scheduling problem
被引:36
作者:
Lei, Chao
论文数: 0引用数: 0
h-index: 0
机构:
Tsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China
Tsinghua Univ, Grad Sch Shenzhen, Res Ctr Modern Logist, Shenzhen 518055, Peoples R ChinaTsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China
Lei, Chao
[1
,3
]
Lin, Wei-Hua
论文数: 0引用数: 0
h-index: 0
机构:
Univ Arizona, Dept Syst & Ind Engn, Tucson, AZ 85721 USATsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China
Lin, Wei-Hua
[2
]
Miao, Lixin
论文数: 0引用数: 0
h-index: 0
机构:
Tsinghua Univ, Grad Sch Shenzhen, Res Ctr Modern Logist, Shenzhen 518055, Peoples R ChinaTsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China
Miao, Lixin
[3
]
机构:
[1] Tsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China
[2] Univ Arizona, Dept Syst & Ind Engn, Tucson, AZ 85721 USA
[3] Tsinghua Univ, Grad Sch Shenzhen, Res Ctr Modern Logist, Shenzhen 518055, Peoples R China
Facilities planning and design;
Routing;
Fleet management;
Mobile facility;
ORIENTEERING PROBLEM;
NETWORK DESIGN;
PATH PROBLEM;
LOCATION;
TOUR;
DEMAND;
RELOCATION;
TRAVEL;
D O I:
10.1016/j.ejor.2014.04.024
中图分类号:
C93 [管理学];
学科分类号:
12 ;
1201 ;
1202 ;
120202 ;
摘要:
This paper considers the mobile facility routing and scheduling problem with stochastic demand (MFRSPSD). The MFRSPSD simultaneously determines the route and schedule of a fleet of mobile facilities which serve customers with uncertain demand to minimize the total cost generated during the planning horizon. The problem is formulated as a two-stage stochastic programming model, in which the first stage decision deals with the temporal and spatial movement of MFs and the second stage handles how MFs serve customer demands. An algorithm based on the multicut version of the L-shaped method is proposed in which several lower bound inequalities are developed and incorporated into the master program. The computational results show that the algorithm yields a tighter lower bound and converges faster to the optimal solution. The result of a sensitivity analysis further indicates that in dealing with stochastic demand the two-stage stochastic programming approach has a distinctive advantage over the model considering only the average demand in terms of cost reduction. (C) 2014 Elsevier B.V. All rights reserved.