TY - JOUR
T1 - A unified framework for rich routing problems with stochastic demands
AU - Markov, Iliya
AU - Bierlaire, Michel
AU - Cordeau, Jean François
AU - Maknoon, Yousef
AU - Varone, Sacha
PY - 2018
Y1 - 2018
N2 - We introduce a unified framework for rich vehicle and inventory routing problems with complex physical and temporal constraints. Demands are stochastic, can be non-stationary, and are forecast using any model that provides the expected demands and their error term distribution, which can be any theoretical or empirical distribution. We offer a detailed discussion on the modeling of demand stochasticity, focusing on the probabilities and cost effects of undesirable events, such as stock-outs, breakdowns and route failures, and their associated recourse actions. Tractability is achieved through the ability to pre-compute or at least partially pre-process the stochastic information, which is possible under mild assumptions for a general inventory policy. We integrate the stochastic aspect into a mixed integer non-linear program, illustrate applications to various problem classes, and show how to model specific problems through the lens of inventory routing. The case study is based on two sets of realistic instances, representing a waste collection inventory routing problem and a facility maintenance problem, respectively. We analyze the effects of our assumptions on modeling realism and tractability, and demonstrate that our framework significantly outperforms deterministic policies in its ability to limit the number of undesirable events for the same routing cost.
AB - We introduce a unified framework for rich vehicle and inventory routing problems with complex physical and temporal constraints. Demands are stochastic, can be non-stationary, and are forecast using any model that provides the expected demands and their error term distribution, which can be any theoretical or empirical distribution. We offer a detailed discussion on the modeling of demand stochasticity, focusing on the probabilities and cost effects of undesirable events, such as stock-outs, breakdowns and route failures, and their associated recourse actions. Tractability is achieved through the ability to pre-compute or at least partially pre-process the stochastic information, which is possible under mild assumptions for a general inventory policy. We integrate the stochastic aspect into a mixed integer non-linear program, illustrate applications to various problem classes, and show how to model specific problems through the lens of inventory routing. The case study is based on two sets of realistic instances, representing a waste collection inventory routing problem and a facility maintenance problem, respectively. We analyze the effects of our assumptions on modeling realism and tractability, and demonstrate that our framework significantly outperforms deterministic policies in its ability to limit the number of undesirable events for the same routing cost.
KW - Forecasting
KW - Recourse
KW - Rich routing problem
KW - Stochastic demand
KW - Tractability
KW - Unified framework
UR - http://www.scopus.com/inward/record.url?scp=85048382315&partnerID=8YFLogxK
U2 - 10.1016/j.trb.2018.05.015
DO - 10.1016/j.trb.2018.05.015
M3 - Article
AN - SCOPUS:85048382315
SN - 0191-2615
VL - 114
SP - 213
EP - 240
JO - Transportation Research Part B: Methodological
JF - Transportation Research Part B: Methodological
ER -