A Comparative Study on Swarm-based Algorithms to Solve the Stochastic Optimization Problem in Container Terminal Design

Crow search algorithm for binary optimization (BinCSA) is currently used in some ideal models of the uncapacitated facility location problem (UFLP), but studies on its use in real-world supply chain cases remain limited. Therefore, this study aimed to address the gap by introducing a hybrid method that combined the BinCSA with an exact method to solve a CLSC problem, including location allocation, transportation, and supplier selection challenges. The initial sections of the study included theoretical foundations and experimental results of the BinCSA. Subsequently, how the BinCSA works in the proposed hybrid method was discussed, and the computational results were showed to evaluate the performance of the proposed method.

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Method for The Closed-loop Supply Chain to Minimize Total Logistics Costs

Xu,

Luis,

Yuce

2023

“…The resulting scheme achieves better load balancing performance in terms of the balance network index for Long Term Evolution-Advanced (LTE-A) networks. In addition, PSO is also used in optimizing the design of container terminal capacity (Zukhruf et al, 2020). Figure 3 shows the PSO approach to find the optimal combination booking capacity solution in this study.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Carrier and Freight Forwarders Strategies to Utilize the Immobile Shipping Capacity of Freight Forwarders and Maximize Profits

Wirjodirdjo¹,

Budianto²,

Widjanarka³

et al. 2021

Carriers and freight forwarders (FFs) play several roles in ensuring the effective flow of goods delivery. They are tasked with accommodating the shippers' needs in transporting goods via containers, following the carrier's ship destination plan. In practice, FFs often experience overbook and underbook capacity toward the capacity limit for shipping goods. This has consequently increased FF costs. However, for the carrier, this will increase profits. The aim of this study is to develop strategies for carriers and FFs using a mathematical model approach to obtain the optimal quantity of booking shipping capacity; thus, overbooks or underbooks can be minimized. More broadly, this study also proposes several strategies to increase the profits of all parties, both for FFs through collaboration and for carriers by directly selling marketing shipping capacity to shippers. Optimum booking quantity for goods delivery from each FF is performed through the particle swarm optimization (PSO) approach. Using four FF collaboration scenarios, the model test results yield a profit of $121,270, 2.14% more than the non-collaboration scenarios with a profit of $119,169. The carrier generated an average profit of $39,926 when the FF did not collaborate. Conversely, when the FFs collaborated, the carrier experienced a decline of -1.88% on average profit, which was $39,175. However, if the carrier responds with direct selling, the profit will increase by 9.36%, which is $42,840. It is concluded that collaboration can increase the profits of FFs but reduce the profits of carriers, while direct selling can increase the carrier's profits.

“…Mathematical techniques deployed to tackle uncomplicated tasks like assignments, workshop tasks, or other discrete and combinatorial optimization problems, can be considered conventional means to resolve production planning problems (Lee, Kim, and Kim 2023;Bengio, Lodi, and Prouvost, 2021). Such methods include linear optimization methods (Yazdani, Khezri, and Benyoucef, 2021), the Hungarian algorithm (Laha and Gupta, 2016), the branch-andbound algorithm (Li and Qi, 2022), probability theory and simulation methods (Costas et al, 2023); stochastic optimization methods, for instance, swarm-based algorithms, are applicable to certain specific tasks (Zukhruf, Frazila, and Widhiarso, 2020). However, given the complexity of the problem within the framework presented, none of these methods is appropriate.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Approach to Planning Complex Multi-Stage Production Systems

Pospelov,

Vatamaniuk,

Lundaeva

et al. 2023

The paper describes an algorithm for finding a quasi-optimal production plan for complex production systems that involve moving products through a chain of linked processes with varying resource sets. The general problem of optimizing a network of manufacturing enterprises is considered: a set of enterprises producing homogeneous products is modeled during a process consisting of a set of sequential operations arranged in a strict sequence. Standard methods for solving production planning problems are considered. According to the analytical review, most planning tasks in such systems are resolved using original techniques. For this reason, a universal heuristic algorithm was proposed. An algorithm with two branches is also proposed for two cases: for the case of a known constraint in the system and for an alternative case. The algorithm is focused on application in production systems in which the acquisition of empirical data is complicated by the large volume, heterogeneity, and limited reliability of data. In such systems, quasi-optimization in accordance with the proposed algorithm will allow for obtaining a satisfactory result with the permissible and required computing power. The algorithm can be classified as a greedy algorithm. It is partly based on local optimization and performs well for production with a long cycle and a small number of products. For this reason, the approach is recommended for heavy industry, shipbuilding, aircraft manufacturing, and other productions with a long cycle.