Abstract
This study proposes a job scheduling model and its heuristics for an automated container terminal with an overhead shuttle crane (OS) to reduce the total tardiness time of flatcars and external trucks by considering the separation of each job into a main job, and a premarshaling or remarshaling job. The OS is busy or idle according to the fluctuations in the processing times of different pieces of equipment. We identify the OS job sequencing problem considering job separation (OSJSPS) as a mixed-integer programming (MIP) model, which simultaneously sequences a set of jobs and searches for their possible separation into premarshaling and remarshaling jobs. We present a two-stage genetic algorithm (TGA) based on two local improvement procedures: an iterative local search procedure and an opportunistic job separation procedure. We conclude that the two-stage genetic algorithm reduces the total tardiness time of the container terminal’s flatcars and external trucks as the number of OS jobs increases.
Highlights
Increased trade volume carried by container ships has motivated container terminal operators and engineers to develop more sophisticated strategies and container terminal designs [2]–[5] for improving throughput and storage capacity [6]–[8]
(2) We develop a two-stage genetic algorithm based on an iterative local search procedure and an opportunistic job separation procedure
This article presented a job sequencing problem, considering job separation, and developed a two-stage genetic algorithm based on an iterative local search procedure and an opportunistic job separation procedure for sequencing a set of overhead shuttle crane (OS) jobs in a rail-based automated container terminal (RACT) with a single OS traveling on a rail in a bay
Summary
Increased trade volume carried by container ships has motivated container terminal operators and engineers to develop more sophisticated strategies and container terminal designs [2]–[5] for improving throughput and storage capacity [6]–[8]. We develop a two-stage genetic algorithm (TGA) based on an iterative local search procedure (iLS) and an opportunistic job separation procedure (OSJSPS). The procedure assigns each of the resulting auxiliary jobs from the selected separable job to a time slot that minimizes the total tardiness of all jobs. After identifying all time slots and all separable jobs, the procedure replaces all separable jobs j (original job) with the resulting storage or retrieval job (main job) j + N and an auxiliary job j + 2N. 1) LINEAR ORDER CROSSOVER We adopt the popular crossover operator, linear order crossover (LOX) mentioned in.Pinedo [69] for searching main job sequences It generates offspring from two random crossover points which can be applied to a single machine scheduling problem. If more than one auxiliary job is assigned to the same position, the auxiliary jobs are re-sequenced randomly
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