A Vessel Schedule Recovery Problem at the Liner Shipping Route with Emission Control Areas

Abstract

Liner shipping is a vital component of the world trade. Liner shipping companies usually operate fixed routes and announce their schedules. However, disruptions in sea and/or at ports affect the planned vessel schedules. Moreover, some liner shipping routes pass through the areas, designated by the International Maritime Organization (IMO) as emission control areas (ECAs). IMO imposes restrictions on the type of fuel that can be used by vessels within ECAs. The vessel schedule recovery problem becomes more complex when disruptions occur at such liner shipping routes, as liner shipping companies must comply with the IMO regulations. This study presents a novel mixed-integer nonlinear mathematical model for the green vessel schedule recovery problem, which considers two recovery strategies, including vessel sailing speed adjustment and port skipping. The objective aims to minimize the total profit loss, endured by a given liner shipping company due to disruptions in the planned operations. The nonlinear model is linearized and solved using CPLEX. A number of computational experiments are conducted for the liner shipping route, passing through ECAs. Important managerial insights reveal that the proposed methodology can assist liner shipping companies with efficient vessel schedule recovery, minimize the monetary losses due to disruptions in vessel schedules, and improve energy efficiency as well as environmental sustainability.Energies 2019, 12, 2380 2 of 28 arrival, departure, and waiting times of vessels at ports, sailing speed, and service frequency are planned at the tactical level (i.e., every 3-6 months) [3].However, in practice, many disruptions may occur that will further affect the planned vessel schedules. Some of the disruptions include severe weather, port congestion, mechanical failures, port strikes, and natural hazards. Brouer et al. [4] highlighted piracy and crew strikes on vessels as some of the unusual causes of disruptions in liner shipping. Notteboom [5] found that about 70%-80% of liner shipping routes recorded late arrivals at one or multiple port(s) of call along their routes. The latter affected the planned vessel schedules, and liner shipping companies faced the challenge of recovering the planned vessel schedules. Disruptions in vessel schedules usually result in late arrivals of vessels at the consecutive ports of call. In some cases, the whole network could be affected by the delay at one of the segments of the liner shipping route (which can be a voyage leg or a port). Disruptions in the planned schedule of vessels can significantly increase the total route service cost for the liner shipping company, and, consequently, reduce the desired profit or even result in monetary losses.In the event of a disruption in the planned vessel schedule, the liner shipping company has to decide on the most viable action that could be taken in order to recover the schedule and ensure timely delivery of cargo or at least minimize the cargo delivery delays. If a disruption occurs in sea and/or at ports,...

Holistic tactical-level planning in liner shipping: an exact optimization approach

“…The vessel sailing speed was restricted to vary within 15 knots and 25 knots (Wang and Meng, 2012a,b). The container demand at each port of call (QC PORT rp ; r∈R; p∈P r ) was assumed to be within the range of 200 TEUs to 2000 TEUs (Dulebenets, 2018a;Abioye et al, 2019). The coefficients of container demand (α dem rp , β dem rp ; r∈R; p∈P r ) were set as U[400; 1,800] TEUs and U[1,250; 3,000] TEUs, respectively, where U denotes uniformly distributed pseudorandom numbers with the lower and upper bounds, presented in square brackets.…”

“…U½ϑ min ;ϑ max ∀r∈R; p∈P r ; t∈T rp (hours) (Dulebenets, 2018a;Abioye et al, 2019). Moreover, the arrival TW duration at each port under each port rotation was restricted to U[12; 24] hours.…”

“…The unit handling cost for vessel type v at port p of port rotation r during TW t under HR h was estimated as follows: c hand rpthv ¼ U½200; 500∀r∈R; p∈P r ; t∈T rp ; h∈H rpt ; v∈V (USD/TEU). The unit late arrival cost at ports ( c late rp ; r∈R; p∈P r ) was set to U[5,000; 8,000] USD/hour (Dulebenets, 2018a-b;Abioye et al, 2019). The unit fuel cost (c fuel ) was assumed to be 200 USD/ton (Wang and Meng, 2012a,b).…”

“…numerical data were obtained from the liner shipping and freight terminal operations literature (Wang and Meng, 2012a-c;Psaraftis and Kontovas, 2013;Dulebenets et al, 2018;Abioye et al, 2019;Cheaitou and Cariou, 2019;CMA, 2019a,b;Kavoosi et al, 2019a,b;Theophilus et al, 2019). Two types of vessels were selected for the computational experiments (i.e., n 3 = 2).…”

Development of Algorithms for Effective Resource Allocation among Highway–Rail Grade Crossings: A Case Study for the State of Florida

“…Smart city operations are significantly influenced by a variety of elements, including water, gas, electricity, buildings, services, transportation networks, and others [1][2][3][4][5][6][7][8][9]. Transportation networks play a critical role for the populations inhabiting smart cities.…”