A Genetic Algorithm for Ship Routing and Scheduling Problem with Time Window

Abstract

This paper develops an efficient variant of a Genetic Algorithm (GA) for a ship routing and scheduling problem (SRSP) with time-window in industrial shipping operation mode. This method addresses the problem of loading shipments for many customers using heterogeneous ships. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and ship capacities. The results of a computational investigation are presented and the solution quality and execution time are explored with respect to problem size. The proposed algorithm is compared, in terms of solution quality and computational time, with an exact method that uses Set Partitioning Problem (SPP). It is found that while the exact method solves small scale problem efficiently, treating large scale problems with the exact method becomes involved due to computational problem, a deficiency that the GA can encounter. Meantime, GA consistently returns better solution than other published work using Tabu Search method in term of solution quality.

Preventive Maintenance Scheduling for Multicogeneration Plants with Production Constraints Using Genetic Algorithms

“…This paper presents a genetic algorithm (GA) model designed to produce an optimal maintenance schedule for cogeneration plants in terms of maximizing the available number of units in each plant for a 12-month demand cycle. GA was used in many other problems and found to produce competitive results; see, for example, Liu et al [34] and Al-Hamad et al [33]. The model designed in this paper is reliable and capable of generating a good schedule for industrial sectors.…”

“…There are many techniques for crossover operation; see, for example, [39,40]. The 2-point and 4-point crossover techniques are adapted in this research; see [33,41]. To explain these two techniques, suppose two chromosomes (parents) are presented, as illustrated in Figure 6.…”

“…However, when the size of the problem increases the solution space increases greatly as a result causing the computational time to increase. Therefore, the exact solution might be sacrificed in order to reduce the computational time using other algorithms such as Tabu search [31], simulated annealing methods [32], and genetic algorithm (GA) [33,34]. This paper presents a genetic algorithm (GA) model designed to produce an optimal maintenance schedule for cogeneration plants in terms of maximizing the available number of units in each plant for a 12-month demand cycle.…”

Metaheuristic algorithm for ship routing and scheduling problems with time window

“…A comparison of the results showed the efficiency of the GA with local search. Alhamad et al (Al-Hamad, Al-Ibrahim, & Al-Enezy, 2012) also addressed SRSP using GA. The representation of each chromosome is an integer string of the number of shipments in the problem, while each gene is the integer number of a specific ship assigned to that original shipment.…”

Achieving optimal SERS through enhanced experimental design

“…This scenario relates to the optimisation process involved in identifying the shortest route required to pass through each node (city) of a tour only once. An approach that has applications in the scheduling of shipping and routing of ships, as described by Al‐Hamad …”