Bulk ship scheduling in industrial shipping with stochastic backhaul canvassing demand. (November 2018)

• Record Type: Journal Article
• Authors: Wu, Lingxiao; Pan, Kai; Wang, Shuaian; Yang, Dong
• Is Part Of: Transportation research. Volume 117(2018)Part A
• Page Start: 117
• Page End: 136
• Publication Date: 2018-11
• Subjects: Industrial shipping -- Bulk ship scheduling -- Stochastic optimization -- Dynamic programming -- Benders decomposition; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2018.08.016
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: Consider a stochastic bulk ship scheduling problem in industrial shipping that has never been addressed in the literature but has real impact in the shipping market. Develop a two-step solution scheme consisting of a dynamic programming algorithm and a tailored Benders decomposition method to solve the problem effectively. Conduct extensive numerical experiments to demonstrate that proposed solution method can well solve the considered problem in various and practical sizes. Abstract: This paper studies a ship scheduling problem for an industrial corporation that manages a fleet of bulk ships under stochastic environments. The considered problem is an integration of three interconnected sub-problems from different planning levels: the strategic fleet sizing and mix problem, the tactical voyage planning problem, and the operational stochastic backhaul cargo canvassing problem. To obtain the optimal solution of the problem, this paper provides a two-step algorithmic scheme. In the first step, the stochastic backhaul cargo canvassing problem is solved by a dynamic programming (DP) algorithm, leading to optimal canvassing strategies for all feasible voyages of all ships. In the second step, a mixed-integer programming (MIP) model that jointly solves the fleet sizing and mix problem and the voyage planning problem is formulated using the results from the first step. To efficiently solve the proposed MIP model, this paper develops a tailored Benders decomposition method. Finally, extensive numerical experiments are conducted to demonstrate the applicability and efficiency of the proposed models and solution methods for practical instances.