A rolling horizon optimisation model for consolidated hump yard operational planning. (May 2019)

• Record Type: Journal Article
• Authors: Raut, Sumit; Sinha, Sudhir Kumar; Khadilkar, Harshad; Salsingikar, Shripad
• Is Part Of: Journal of rail transport planning & management. Volume 9(2019)
• Page Start: 3
• Page End: 21
• Publication Date: 2019-05
• Subjects: Logistics -- Railway hump yard operations -- Mixed integer linear programming -- Rolling horizon; Railroads -- Periodicals; Railroads -- Planning -- Periodicals; Railroads -- Management -- Periodicals; Railroads; Railroads -- Management; Railroads -- Planning; Periodicals; 385.068
• DOI: 10.1016/j.jrtpm.2018.09.002
• Languages: English
• ISSNs: 2210-9706

Abstract

Abstract: This paper presents an optimisation formulation for consolidated planning of railway hump yard operations. The objective is to minimise the average dwell time of railway cars in the yard, while satisfying constraints related to (i) incoming (outgoing) train arrival (departure) times and composition, (ii) car movement sequencing, and (iii) the limited capacity and number of classification tracks. We present a mixed-integer linear programming formulation that combines three stages of decision making; inbound train humping, railcar classification and outbound train construction. The consolidated approach enables a natural linking between capacity constraints at various stages of the system. However, the scale and complexity of the resulting problem requires some relaxation, with the optimisation model providing a high-level plan, and a low-level heuristic handling the detailed implementation. The high-level optimiser determines hump schedule of the inbound trains, block-to-classification track assignment, and pull-out schedule at coarse level by dividing the planning horizon into discrete time intervals and trains into groups of consecutive cars (segments). The low-level heuristic converts the resulting instructions into fine-grained decisions spatially (at the individual car level) and temporally (the actual duration required for each action). The proposed approach is implemented on a rolling horizon basis, and is used to solve a 42-day, 52246-car example.