Trip packing in petrol stations replenishment

Boctor, Fayez F.; Renaud, Jacques; Cornillier, Fabien

doi:10.1016/j.omega.2010.03.003

Cited by 25 publications

(9 citation statements)

References 23 publications

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“…In a follow-up study, Cornillier et al (2009Cornillier et al ( , 2012 considered the time window and multiple depots to make the problem more realistic. Boctor et al (2011) proposed a heuristic algorithm to solve the problem considering different types of petrol trucks and their time-based work limits. Wang et al (2018) studied petrol truck scheduling problems considering multipath selection and congestion and proposed a heuristic approach.…”

Section: Literature Reviewmentioning

confidence: 99%

A metaheuristic method for the multireturn-to-depot petrol truck routing problem with time windows

et al. 2019

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The petrol truck routing problem is an important part of the petrol supply chain. This study focuses on determining routes for distributing petrol products from a depot to petrol stations with the objective of minimizing the total travel cost and the fixed cost required to use the trucks. We propose a mathematical model that considers petrol trucks returning to a depot multiple times and develop a heuristic algorithm based on a local branch-and-bound search with a tabu list and the Metropolis acceptance criterion. In addition, an approach that accelerates the solution process by adding several valid inequalities is presented. In this study, the trucks are homogeneous and have two compartments, and each truck can execute at most three tasks daily. The sales company arranges the transfer amount and the time windows for each station. The performance of the proposed algorithm is evaluated by comparing its results with the optimal results. In addition, a real-world case of routing petrol trucks in Beijing is studied to demonstrate the effectiveness of the proposed approach.

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Section: Literature Reviewmentioning

confidence: 99%

A metaheuristic method for the multireturn-to-depot petrol truck routing problem with time windows

et al. 2019

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“…Ma et al [13] proposed a mathematical model with the shortest driving path and high full load rate as the optimization goals for the secondary distribution of refined oil, and the improved genetic algorithm was used to solve the model. Bocto et al [14] designed several heuristic algorithms to solve the mathematical model for the problem of replenishment at gas stations, the simulation and real data were applied to verify the effectiveness of the algorithms respectively. Zhen et al [15] studied the distribution of refined oil from the perspective of third-party logistics and introduced a combinatorial auction mechanism to construct a mixed-integer programming model with the objective of minimizing costs.…”

Section: Refined Oil Distribution Optimizationmentioning

confidence: 99%

“…Constraint (13) indicates the balance of oil tank trucks that entering and leaving each gas station. All oil tank trucks start from the oil depot and return to the oil depot after the distribution tasks are completed, and their operation is shown in (14). Constraint (15) represents that the delivery volume is limited by the remaining stock of the oil depot.…”

Section: Lcirp Model Settingmentioning

confidence: 99%

Optimization of Inventory Routing Problem in Refined Oil Logistics with the Perspective of Carbon Tax

2018

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Abstract:In order to solve the optimization problem of the refined oil distribution system from the perspectives of low-carbon and environmental protection, this paper focuses on the characteristics of the secondary distribution of refined oil and combines it with the integrated optimization concept of refined oil distribution network, where a low-carbon inventory routing problem (LCIRP) model is constructed with the minimum total costs as the objective function on the basis of considering carbon emissions. An adaptive genetic algorithm combined with greedy algorithm is designed to solve the model, and an example is given to verify the effectiveness of the algorithm. Then, this paper solves the model with two parts by introducing a practical numerical example: in the first part, the LCIRP models with different carbon tax values are solved, which verifies the effectiveness of the model and proves that carbon tax policies can effectively reduce the carbon emissions in the secondary distribution network of refined oil; in the second part, the LCIRP models with the different maximum load capacity of oil tank trucks are solved, which provides the economic and environmentally friendly distribution schemes for refined oil distribution enterprises under the premise of carbon tax policies and load limitation. Finally, the emission reduction proposals that take into account both economic and environmental benefits are given respectively from the aspect of government environmental protection agencies and from the aspect of refined oil distribution enterprises.

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“…Tankers have a capacity of around 30,000 litres; assuming dedicated deliveries, standalone sites must be resupplied on average once every five days, and supermarket sites daily. Resupplying stations more frequently with partial tanker-loads can reduce inventory requirements somewhat; Boctor et al [24] describe several techniques for solving the resulting trip-packing problem.…”

Section: Uk Petroleum Retailingmentioning

confidence: 99%

Fuel Panics: Insights From Spatial Agent-Based Simulation

Upton

Nuttall

2014

IEEE Trans. Intell. Transport. Syst.

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The United Kingdom has twice suffered major disruption as a result of fuel panics first in September 2000 coincident with a wave of fuel protests and more recently in March 2012 following politcal warnings of possible future supply chain disruption. In each case the disruption and economic consequences were serious. Fuel distribution is an example of a supply chain. Approaches to supply-chain planning based on linear programming are poorly suited to modelling non-equilibrium effects, while coarse-grained system dynamics models often fail to capture local phenomena which contribute to the evolution of global demand. In this Paper, we demonstrate that agent-based techniques offer a powerful framework for cosimulation of supply chains and consumers under conditions of transient demand. In the case of fuel panic crisis, we show that even a highly abstract model can reproduce a range of transient phenomena seen in the real world, and present a set of practical recommendations for policymakers faced with panic-buying.

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Trip packing in petrol stations replenishment

Cited by 25 publications

References 23 publications

A metaheuristic method for the multireturn-to-depot petrol truck routing problem with time windows

A metaheuristic method for the multireturn-to-depot petrol truck routing problem with time windows

Optimization of Inventory Routing Problem in Refined Oil Logistics with the Perspective of Carbon Tax

Fuel Panics: Insights From Spatial Agent-Based Simulation

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