Context (archaeology)

Network planning and design

Algorithm

Engineering

Transport engineering

Biology

Environmental economics

Transshipment (information security)

Archaeology

Operating system

Telecommunications

Economics

Computer science

Shore

Integer programming

Geology

Oceanography

Paleontology

Operations research

Geography

Artificial intelligence

Heuristic

Software deployment

Transportation Research Part B Methodological

The Emission Control Areas (ECAs) established by the International Maritime Organization are beneficial to reduce the sulphur emissions in maritime transportation but bring a significant increase in operating cost for shipping liners. Low sulphur emissions are required when ships berth or sail within ECAs. It is an irreversible trend that green technologies such as scrubbers and shore power will be implemented in maritime shipping industry. However, the literature lacks a quantitative decision methodology on green technology adoption for fleet deployment in a shipping network in the context of ECAs. Given a shipping network with multiple routes connected by transshipment hubs, this study proposes a nonlinear mixed integer programming model to optimally determine fleet deployment along routes (including green technology adoption), sailing speeds on all legs, timetables, cargo allocation among routes for each origin-destination pair, and berth allocation considering the availability of shore power at different berths in order to minimize total five types of cost. A three-phase heuristic is also developed to solve this problem. Numerical experiments with real-world data are conducted to validate the effectiveness of the proposed model and the efficiency of the three-phase heuristic. Some managerial implications are also outlined on the basis of the numerical experiments.

Green technology adoption for fleet deployment in a shipping network