Solving multi-product inventory ship routing with a heterogeneous fleet model using a hybrid cross entropy-genetic algorithm: a case study in Indonesia

Santosa, Budi; Damayanti, Rita; Sarkar, Biswajit

doi:10.1080/21693277.2016.1204961

Cited by 8 publications

(4 citation statements)

References 18 publications

(20 reference statements)

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“…If Buffero is greater than a capacity of compartment (cap) then the updating values are shown is shown in Eqs. ( 15) and (16).…”

Section: Resultsmentioning

confidence: 99%

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Hybrid Metaheuristics for Solving Vehicle Routing Problem in Multi Bulk Product Shipments with Limited Undedicated Compartments

Adhi¹,

Santosa²,

Siswanto³

2021

IJIES

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Studies and research related to the Vehicle Routing Problem (VRP) are mostly carried out on simple constraints. The study was conducted to find the shortest route that the carrier will take. Effective algorithms are sought to find better solutions. Another study will develop VRP cases to be closer to real conditions. In actual conditions, the VRP cases are very complex. Several solutions that are considered good can be implemented to solve this problem. VRP in shipping bulk products, such as oil, cements, and fertilizers is one of the daily logistical cases carried out in an archipelago country. Bulk products are shipped from the depot port city to other islands. The optimum solution is needed to make the delivery shorter distance and the products can be sent faster. The solution to the VRP case with a single product can no longer solve the problem of bulk product shipment. Bulk product shipment involves more than one product with more than one ship compartment. The number of ship compartments that are smaller than the number of products will further increase the complexity of the VRP of bulk product shipment. The combination of several metaheuristic methods and strategies of product selection to fill undedicated compartment of ship is used to find the shortest route. Modified Nawas Enscore Ham (M-NEH), Particle Swarm Optimization (PSO), and 3-Opt algorithms are combined to solve VRP. This hybrid algorithm called Hybrid Particle Swarm Optimization (HPSO) is improved and supports each other. HPSO provides a better solution than those algorithms separately and other metaheuristics namely Tabu Search (TS) and Genetic Algorithm (GA). The implementation of the algorithm to solve real data of bulk cement transportation supports the conclusion that HPSO is superior to the other algorithms. It has 0.99% more effective than the second best and about 3.42% more effective than the average total distance of five other compared metaheuristic methods.

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“…If Buffero is greater than a capacity of compartment (cap) then the updating values are shown is shown in Eqs. ( 15) and (16).…”

Section: Resultsmentioning

confidence: 99%

“…One of the cases developed in VRP is the management of shipments by sea. This routing problem determines the route of the ship to deliver products from a depot city to consumption cities to achieve minimum cost [16]. Product shipment by sea transportation is important in global trade.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristics for Solving Vehicle Routing Problem in Multi Bulk Product Shipments with Limited Undedicated Compartments

Adhi¹,

Santosa²,

Siswanto³

2021

IJIES

Self Cite

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“…In recent years, in the literature, numerous works on this topic have been presented based on swarm algorithms. These algorithms include the biological evolutionary processes, such as: genetic algorithm (GA) [10,58], Hybrid Metaheuristics Algorithms [11,12], Particle Swarm Optimization Algorithm (PSO) [33] and Harmony Search (HS) [27], Bacterial Foraging Optimization Algorithm (BFOA) [43], Artificial Bee Colony Algorithm (ABC), Central Force Optimization Algorithm (CFO) [25], Group Search Optimizer (GSO) [29], Krill Herd Algorithm (KH) [26], Optics Inspired Optimization (OIO) [31], Biogeography Based Optimization (BBO) [61], Ant Colony Optimization (ACO) [21] and Backtracking Search Optimization (BSO) [16].…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand

Jamali

Sana²,

Moghdani

2018

RAIRO-Oper. Res.

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One of the fundamental problems in supply chain management is to design the effective inventory control policies for models with stochastic demands because efficient inventory management can both maintain a high customers’ service level and reduce unnecessary over and under-stock expenses which are significant key factors of profit or loss of an organization. In this study, a new formulation of an inventory system is analyzed under discrete Markov-modulated demand. We employ simulation-based optimization that combines simulated annealing pattern search and ranking selection (SAPS&RS) methods to approximate near-optimal solutions of this problem. After determining the values of demand, we employ novel approach to achieve minimum cost of total SCM (Supply Chain Management) network. In our proposed approach, hybrid improved cuckoo search algorithm (ICS) and genetic algorithm (GA) are presented as main platform to solve this problem. The computational results demonstrate the effectiveness and applicability of the proposed approach.

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“…MIRP has special conditions compared with other kinds of transportation. The transportation in MIRP uses a huge capacity ship to deliver bulk products [7], although it can be implemented in a small capacity ship. Usually, ships have one or more compartments to bring the products.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristic for Solving Maritime Inventory Routing Problem in Bulk Product Transportation

2023

IJIES

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Maritime Inventory Routing Problem (MIRP) is an important issue in the optimization of maritime distribution and transportation. This problem is related to planned ship's routing and scheduling in delivery of goods from the depot to some demand points by minimizing associated costs such as transportation and inventory management costs. This paper discusses how to solve MIRP with multi ships and limited undedicated compartments used to deliver bulk products from the depot port to the several consumption ports. The new hybrid metaheuristics are used to find the optimal assignment routes and schedule of ships along time horizon. This research modifies several metaheuristics algorithms called Modified Hybrid Particle Swarm Optimization (MHPSO) to find the best solution for MIRP. The algorithm of this method is developed from the combination of Particle Swarm Optimization (PSO), Nahwaz-Enscore-Ham (NEH), and 3-Opt. Some metaheuristic methods such as Genetic Algorithm (GA), Tabu Search (TS), Particle Swarm Optimization (PSO), Hybrid Genetic Algorithm (HGA), and Hybrid Tabu Search (HTS) were also developed in the same way to test and compare with the proposed method. Based on the ten test data instances, it can be concluded that MHPSO provides 0.64% effectiveness better results than other metaheuristic methods.

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Solving multi-product inventory ship routing with a heterogeneous fleet model using a hybrid cross entropy-genetic algorithm: a case study in Indonesia

Cited by 8 publications

References 18 publications

Hybrid Metaheuristics for Solving Vehicle Routing Problem in Multi Bulk Product Shipments with Limited Undedicated Compartments

Hybrid Metaheuristics for Solving Vehicle Routing Problem in Multi Bulk Product Shipments with Limited Undedicated Compartments

Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand

Hybrid Metaheuristic for Solving Maritime Inventory Routing Problem in Bulk Product Transportation

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