Hybridizing Basic Variable Neighborhood Search With Particle Swarm Optimization for Solving Sustainable Ship Routing and Bunker Management Problem

De, Arijit; Wang, Junwei; Tiwari, Manoj Kumar

doi:10.1109/tits.2019.2900490

Cited by 60 publications

(36 citation statements)

References 19 publications

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“…Later, He et al [21] relax this assumption and suggest a consumption function model which is a general continuously differentiable and strictly convex function, but without a concrete form of variable impacts, causing varying costs per unit of distance traveled by the ship. [22], carbon emission Qi and Song [24] Liner Scheduling Speed Uncertain port times De et al [25] Liner Routing & bunkering Speed Port time windows, emission Reinhardt et al [26] Liner Scheduling Speed Schedule robustness Andersson et al [27] RoRo Fleet deployment Speed Xia et al [28] Liner Fleet deployment Speed, payload Du et al [29] Liner Berth allocation Speed Departure delay Venturini et al [30] Liner Berth allocation Speed Carbon emission Yao et al [31] Liner Bunkering Speed Empirical consumption function Kim et al [32] Liner Bunkering Speed Carbon emission Aydin et al [33] Liner Bunkering Speed Extension of [31], port time windows De et al [34] Liner Bunkering Speed Disruption recovery Zhao and Yang [35] Liner Maintenance Speed Dockyard choice…”

Section: Literature Reviewmentioning

confidence: 99%

Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping

et al. 2020

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Enhancing environmental sustainability in maritime shipping has emerged as an important topic for both firms in shipping-related industries and policy makers. Speed optimization has been proven to be one of the most effective operational measures to achieve this goal, as fuel consumption and greenhouse gas (GHG) emissions of a ship are very sensitive to its sailing speed. Existing research on ship speed optimization does not differentiate speed through water (STW) from speed over ground (SOG) when formulating the fuel consumption function and the sailing time function. Aiming to fill this research gap, we propose a speed optimization model for a fixed ship route to minimize the total fuel consumption over the whole voyage, in which the influence of ocean currents is taken into account. As the difference between STW and SOG is mainly due to ocean currents, the proposed model is capable of distinguishing STW from SOG. Thus, in the proposed model, the ship’s fuel consumption and sailing time can be determined with the correct speed. A case study on a real voyage for an oil products tanker shows that: (a) the average relative error between the estimated SOG and the measured SOG can be reduced from 4.75% to 1.36% across sailing segments, if the influence of ocean currents is taken into account, and (b) the proposed model can enable the selected oil products tanker to save 2.20% of bunker fuel and reduce 26.12 MT of CO2 emissions for a 280-h voyage. The proposed model can be used as a practical and robust decision support tool for voyage planners/managers to reduce the fuel consumption and GHG emissions of a ship.

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Section: Literature Reviewmentioning

confidence: 99%

Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping

et al. 2020

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“…In addition, metaheuristics have been presented for the LRP due to fast and simple features; they provide feasible results within reasonable processing times. Examples of the recognized metaheuristics are simulated annealing (SA) [21,[33][34][35][36], iterated local search (ILS) [37], variable neighborhood search (VNS) [38,39], and adaptive large neighborhood search (ALNS).…”

Section: Literature Reviewmentioning

confidence: 99%

A New Optimization Technique for the Location and Routing Management in Agricultural Logistics

Theeraviriya

Pitakaso

Sethanan

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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This paper aims to solve the location and routing problem (LRP) in the agricultural sector with the objective function of fuel cost minimization. Many farmers may have problems when transporting and selling products because of high costs and unfair prices. The proper location of standardized procurement centers and suitable routes will relieve farmers’ problems. This paper includes a realistic constraint that a farm can be visited to collect product more than once. A mathematical model was formulated to be solved by Lingo software, but when the problem size was larger, Lingo was unable to solve the problem within a reasonable processing time. The variable neighborhood strategy adaptive search (VaNSAS) is proposed to solve this LRP. The main contributions of this paper are a real case study problem and the first introduction of VaNSAS. Furthermore, the different combinations of the solution approach are proposed to prove which combination is the best algorithm. The computational results show that VaNSAS can find the solutions for all problem sizes in much less processing time compared to Lingo. In medium and large-sized instances, the VaNSAS can reduce processing times by 99.91% and 99.86%, respectively, from solutions obtained by Lingo. Finally, the proposed VaNSAS has been deployed in a case study problem to decide the best locations and transportation routes with the lowest fuel cost.

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“…According to [22], GSCM practices should cover all SC activities, from green purchasing to integrating lifecycle management, through manufacturers and customers, to the close of the cycle with reverse logistics. Several product-level green practices are described in the literature, including eco-design [7,8], product recycling design [23], reduction of carbon emissions in logistics activities [24,25] and reverse logistics [26][27][28].…”

Section: Integrating Gscm Practices With Suppliers and Customersmentioning

confidence: 99%

Integrating Green Practices into Operational Performance: Evidence from Brazilian Manufacturers

2019

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The pressures exerted by the market, society, regulators and/or clients on organisational environmental responsibility have required companies to adopt environmental management practices. Within this process, integration with suppliers and customers is important to enable companies to meet these demands, and at the same time achieve their organisational goals. Using empirical results from 117 respondents on Green Supply Chain Management (GSCM) practices among Brazilian manufacturers, we examined the impact of the adoption of green practices on operational performance. To do so, we developed a questionnaire to collect the variables on environmental practices and operational performance in manufacturing firms in Brazil, an emerging economy in which this sector accounts for 25% of its Gross Domestic Product (GDP). The results show that the adoption of GSCM between suppliers and/or customers has a positive effect on operational performance. This means companies can benefit from a green supply chain by cooperating with upstream suppliers of environmentally responsible production technology and by exchanging environmental information with them, as well as considering the views of customers and green consumers in their production processes. This study provides empirical support for managers promoting environmental practices that may lead to operational performance and sustainable growth.

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Hybridizing Basic Variable Neighborhood Search With Particle Swarm Optimization for Solving Sustainable Ship Routing and Bunker Management Problem

Cited by 60 publications

References 19 publications

Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping

Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping

A New Optimization Technique for the Location and Routing Management in Agricultural Logistics

Integrating Green Practices into Operational Performance: Evidence from Brazilian Manufacturers

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