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Apinanthana Udomsakdigool
Department of Production Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bang Mod, Thung Khru, Bangkok 10140, Thailand

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Journal article
Published: 25 May 2021 in Applied Sciences
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This paper presents a set covering model based on route representation to solve the green ship routing and scheduling problem (GSRSP) with berth time-window constraints for multiple bulk ports. A bi-objective set covering model is constructed with features based on the minimization of the total CO2 equivalent emissions and the total travel time subject to a limited number of berths in each port, berthing time windows, and the time window for each job. The solutions are obtained using the ε-constraint method, after which a Pareto frontier is plotted. This problem is motivated by the operations of feeder barges and terminals, where the logistics control tower is used to coordinate the routing and berthing time of its barges. We show that the proposed method outperforms the weighted sum method in terms of the number of Pareto solutions and the value of the hypervolume indicator.

ACS Style

Apichit Maneengam; Apinanthana Udomsakdigool. A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry. Applied Sciences 2021, 11, 4840 .

AMA Style

Apichit Maneengam, Apinanthana Udomsakdigool. A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry. Applied Sciences. 2021; 11 (11):4840.

Chicago/Turabian Style

Apichit Maneengam; Apinanthana Udomsakdigool. 2021. "A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry." Applied Sciences 11, no. 11: 4840.

Journal article
Published: 19 November 2019 in Computers and Electronics in Agriculture
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Crop production planning (CPP) is a complex task as it often encompasses numerous decision variables and uncertainties. This paper presents a robust crop production model applied to an agricultural cooperative. The objective of the model is to maximize the total revenue of the cooperative. The social aspect is addressed through a similarity in farmer revenues. Moreover, crop yield uncertainty, which results from the difference in skills of the farmers, is integrated in the robust model. The crop production plan must satisfy the market demand for each crop. Consequently, crop production that exceeds the demand is penalized. The models are solved by an optimization software, and the results indicate that when the model robustness increases, the value of the objective function decreases. However, the decision maker can make a decision tradeoff between the probability of constraint satisfaction and the value of the objective function. Finally, the managerial recommendations are discussed.

ACS Style

Peerapong Pakawanich; Apinanthana Udomsakdigool; Charoenchai Khompatraporn. Robust production allocation model for an agricultural cooperative with yield uncertainty and similar revenue constraints. Computers and Electronics in Agriculture 2019, 168, 105090 .

AMA Style

Peerapong Pakawanich, Apinanthana Udomsakdigool, Charoenchai Khompatraporn. Robust production allocation model for an agricultural cooperative with yield uncertainty and similar revenue constraints. Computers and Electronics in Agriculture. 2019; 168 ():105090.

Chicago/Turabian Style

Peerapong Pakawanich; Apinanthana Udomsakdigool; Charoenchai Khompatraporn. 2019. "Robust production allocation model for an agricultural cooperative with yield uncertainty and similar revenue constraints." Computers and Electronics in Agriculture 168, no. : 105090.