Deep reinforcement learning approach to solve dynamic vehicle routing problem with stochastic customers

National Research Foundation (NRF) Singapore under Corp Lab @ University scheme; Fujitsu Lt

Combinatorial approaches for hard problems in manpower scheduling

Abstract Manpower scheduling is concerned with the construction of a workers ' schedule which meets demands while satisfying given constraints. We consider a manpower scheduling problem, called the Change Shift Assignment Problem(CSAP). In previous work, we proved that CSAP is NP-hard and presented greedy methods to solve some restricted versions. In this paper, we present ~ombinat~orial algorithms to solve more general and realistic versions of CSAP which are unlikely solvable by greedy methods. First, we model CSAP as a fixed-charge network and show that a feasible schedule can be obtained by finding disjoint paths in the network, which can be derived from a minimum-cost flow. Next, we show that if the schedule is tableau-shaped, then such disjoint paths can be derived from an optimal path cover, which can be found by a polynomial-time algorithm. Finally, we show that if all constraints are monotonzc, then CSAP may be solved by a pseudo-polynomial backtracking algorithm which has a good run-time performance for random CSAP instances.

A New Approach for Weighted Constraint Satisfaction

10.1023/A:1015157615164Constraints72151-165CNST

A genetic algorithm to minimise number of vehicles in an electric vehicle routing problem

National Research Foundation (NRF) Singapore under Corp Lab @ University scheme; Fujitsu Lt

Decision making for improving maritime traffic safety using constraint programming

A*STAR-Fujitsu-SMU Urban Computing and Engineering Centre of Excellence; Singapore National Research Foundatio