Petrol Station Replenishment Problem Research Articles

A Matheuristic Approach for the Multi-Depot Periodic Petrol Station Replenishment Problem

Planning petrol station replenishment is an important logistics activity for all the major oil companies. The studied Multi-Depot Periodic Petrol Station Replenishment problem derives from a real case in which the company must replenish a set of petrol stations from a set of depots, during a weekly planning horizon. The company must ensure refuelling according to available visiting patterns, which can be different from customer to customer. A visiting pattern predefines how many times (days) the replenishment occurs during a week and in which visiting days a certain amount of fuel must be delivered. To fulfill the weekly demand of each petrol station, one of the available replenishment plans must be selected among a given set of visiting patterns. The aim is to minimize the total distance travelled by the fleet of tank trucks during the entire planning horizon. A matheuristic approach is proposed, based on the cluster-first route-second paradigm, to solve it. The proposed approach is thoroughly tested on a set of realistic random instances. Finally, a weekly large real instance is considered with 194 petrol stations and two depots.

A MILP approach combined with clustering to solve a special petrol station replenishment problem

Vehicle routing problem is a well-known optimization problem in the logistics area. A special case of the vehicle routing problem is the station replenishment problem in which different types of fuel types have to be transported from the depots to the customers. In this paper we study the replenishment problem of a European petrol company. The problem contains several additional constraints such as time windows, different sized compartment vehicles, and restrictions on the vehicles that can serve a customer. We introduce a mixed integer linear programming model of the problem. To reduce the size complexity of the MILP model the customers are clustered and, based on the clusters, additional constraints are added to the MILP model. The resulting MILP model is tested on real problems of the company. The results show that combining the MILP model with clustering improves the effectiveness of the model.

Tabu-Based Adaptive Large Neighborhood Search for Multi-Depot Petrol Station Replenishment With Open Inter-Depot Routes

The petrol station replenishment problem (PSRP) refers to the process of transporting petroleum products from oil depots to petrol stations via tank trucks. It mainly consists of two parts: allocating petroleum products to tank trucks and planning the travel route of each truck. In this study, we examine a new variant of PSRP by considering a multi-depot vehicle routing problem with open inter-depot routes (MDVRPOI). Each depot can act as an intermediate replenishment facility, and each truck can be reloaded at any depot any number of times within the working period. Moreover, trucks can end their routes at any depot instead of making a long empty drive to the start depot. The trucks are heterogeneous with multiple load-specific compartments. We formulate the problem as a mixed-integer linear programming (MILP) model. Given the problem’s complexity, a tabu-based adaptive large neighborhood search (T-ALNS) algorithm is proposed, which integrates the tabu search approach into ALNS to solve the problem effectively. The T-ALNS executes multiple problem-tailored destroy/repair operators on the station, trip, and route levels. A local search procedure with problem-specific operators and an adaptive strategy is further embedded into T-ALNS. We use the real data of an oil company in China to evaluate our algorithm. Computational results show that our T-ALNS significantly outperforms the CPLEX solver and other algorithms in terms of solution quality and computation time. Further, it realizes an average reduction in transportation cost of about 45% compared to the company’s actual strategy.

An efficient approach for the petrol station replenishment problem: an Algerian case study

In a logistic process, the goods distribution is an important task. It is also a critical activity that needs to be optimised according to company policy. The petrol station replenishment problem (PSRP) is one of the transportation problems concerned with replenishing a set of fuel stations with the different petroleum products. This problem presents a particular case of the multi-compartment vehicle routing problem (MC-VPR). In this paper, a new bi-objective approach is proposed in order to treat the PSRP. A novel MILP model is proposed based on a real life case study (the Algerian company of fuel distribution NAFTAL). Two objectives are considered: gain maximisation and carbon emissions minimisation. The economic objective is optimised independently of the environmental one, and then they are joined by adapting a multi-objective method 'LP-metric'. Finally, computational results are presented in order to show the efficiency of our green approach for a sustainable transportation system.

Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul

With the increase in the consumption of petroleum and petroleum products, these limited resources must be provided efficiently, accurately, and with minimal damage. Therefore, the accurate and effective distribution of petroleum, and related problems with petroleum distribution have attracted much attention among the practitioners and optimization working researchers over the years. The petroleum distribution problem, as a version of the Vehicle Routing Problem (VRP), deals with the planning of petroleum distribution from the depot(s) to the petrol stations safely and quickly. In this study, the petrol station replenishment problem (PSRP) is handled and a case study is presented for a public company located in İstanbul. The problem is considered as a time-dependent VRP with time windows. A novel mixed integer mathematical model is proposed for the problem. In order to handle the proposed time-dependent problem in a more realistic way, variable tanker speeds are considered based on traffic density. The optimum route is determined in which risks such as environment and marine pollution may occur in case of possible accidents, and these risks are minimized by the proposed mathematical model considering the factors as traffic, vehicle speed, road structure, the road's proximity to the sea and living areas

An efficient approach for the Petrol Station Replenishment Problem: An Algerian case study

An efficient approach for the Petrol Station Replenishment Problem: An Algerian case study

Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment

Energy supply is an important system that affects the overall efficiency of urban transportation. To improve the system operational efficiency and reduce costs, we formulate and solve a collaborative multidepot petrol station replenishment problem with multicompartments and time window assignment by establishing a mixed-integer linear programming model. The hybrid heuristic algorithm composed of genetic algorithm and particle swarm optimization is used as a solution, and then the Shapley value method is applied to analyze the profit allocation of each petrol depot under different coalitions. The optimal membership sequence of the cooperation is determined according to the strict monotone path. To analyze and verify the effectiveness of the proposed method, a regional petrol supply network in Chongqing city in China is investigated. Through cooperation between petrol depots in the supply network, the utilization of customer clustering, time window coordination, and distribution truck sharing can significantly reduce the total operation costs and improve the efficiency of urban transportation energy supply. This approach can provide theoretical support for relevant government departments and enterprises to make optimal decisions. The implementation of the joint distribution of energy can promote the sustainable development of urban transportation.

Multi-Compartment Vehicle Routing Problem to Find the Alternative Distribution Route of Petroleum Product Delivery

Distribution is an activity that the firm has to do to deliver their product to the customers. The product has to deliver to the customers on the right condition, at the right time, and with the minimal cost of delivery. This paper focuses to solve the planning distribution problem on petrol product delivery or Petrol Station Replenishment Problem. One of the ways to solve the planning distribution is VRP (Vehicle Routing Problem). VRP aims to find the minimal total cost delivery with some constraints depend on the factory. In this research, VRP is used by the distribution center to find the optimal route of distribution of petrol products. Using Multi-Compartment Vehicle Routing Problem (MC-VRP) that considers the demand for the petrol product, tank-truck capacity, distribution cost, and the number of the compartment. In this work, a mathematical formulation for MC-VRP is developed in the Mixed Integer Linear Programming (MILP) model. Using Ilog Cplex with branch and bound algorithm to solve this distribution problem with the minimum total distribution cost. There are 10 customers with different distances, different time deliveries and different travel costs in this research. The mathematic formulation produces the best alternative that is Rp 1.898.584 to deliver products from depot to the customers.

A two-level evolutionary algorithm for solving the petrol station replenishment problem with periodicity constraints and service choice

This paper addresses the petrol station replenishment problem with periodicity constraints and introduces the frequency service choice as a decision variable. We present a mathematical optimization model for the problem and we develop first a simple heuristic method that is able to handle the complexity of the problem and then two metaheuristic approaches based on a novel two-level evolutionary algorithm. The first level deals with the periodicity and frequency selection of the visits to the petrol stations. The second level of evolution assigns the stations to the tank-trucks such that the total traveled distance is minimized. The effectiveness of the proposed approaches has been tested by means of a comprehensive experimental study by using first a set of randomly generated test cases and then a real-life problem.

Two metaheuristic approaches for solving the multi-compartment vehicle routing problem

We address in this paper a multi-compartment vehicle routing problem (MCVRP) that aims to plan the delivery of different products to a set of geographically dispatched customers. The MCVRP is encountered in many industries, our research has been motivated by petrol station replenishment problem. The main objective of the delivery process is to minimize the total driving distance by the used trucks.The problem configuration is described through a prefixed set of trucks with several compartments and a set of customers with demands and prefixed delivery. Given such inputs, the minimization of the total traveled distance is subject to assignment and routing constraints that express the capacity limitations of each truck’s compartment in terms of the pathways’ restrictions. For the NP-hardness of the problem, we propose in this paper two algorithms mainly for large problem instances: an adaptive variable neighborhood search (AVNS) and a Partially Matched Crossover PMX-based Genetic Algorithm to solve this problem with the goal of ensuring a better solution quality. We compare the ability of the proposed AVNS with the exact solution using CPLEX and a set of benchmark problem instances is used to analyze the performance of the both proposed meta-heuristics.

Decision support system for light petroleum products supply chain

This paper describes a decision support system that has been developed to manage petroleum deliveries from a bulk terminal to petrol stations using a fleet of petrol tanker trucks. The problem is defined as a petrol station replenishment problem with customer demands determined using a prediction algorithms based on time series of daily sales. The DSS employs a user-defined road network as the underlying transport to meet governmental hazardous goods transport regulations. The system was implemented and has been in operation for over 18 months. Delivery costs per litre delivered have decreased 15% in comparison with those of previous years.

Planning Retail Distribution of Fuel Oils

In this paper, we study the Periodic Petrol Station Replenishment Problem (PPSRP), an important real problem relative to logistics in the fuel oil distribution. It consists, under suitable assumptions and subject to certain operational constraints, of determining the fuel oil procurement plans of a set of petrol stations and planning the delivering routes of petrol products to the stations along a certain planning horizon. The considered problem belongs to the class of Inventory Routing Problems (IRP), of which it is a particularization. In particular, it draws its concepts from two different classes of distribution problems, the Periodic Vehicle Routing Problem (PVRP) and the Petrol Station Replenishment Problem (PSRP). We provide a mathematical formulation of the problem, and due to the large size of the real instances which in general an oil company has to deal with we heuristically solve it. We propose different heuristic strategies based on a common partitioning-then-routing paradigm, and in order to assess their performances we test them on a set of scenarios coming from Italian fuel oil distribution real cases and compare the results with those obtained by using other known heuristic approaches.

A petrol station replenishment problem: new variant and formulation

One of the most important problems in the petroleum industry is the well-known petrol station replenishment problem with time windows, which calls for the determination of optimal routes by using a fleet of tank trucks to serve a set of petrol stations over a given planning horizon. In this paper, we introduce a model and solve a specific problem that originates from a real-life application arising in the fuel distribution where specific attention is paid to tank trucks with compartments and customers with different types of products and time windows. Literally, we call the resulting problem the multi-compartment vehicle routing problem with time windows (MCVRPTW). To solve the MCVRPTW, we begin by describing the problem, providing its mathematical formulation and discussing the sense of its constraints. As the problem is NP-hard, we propose an efficient tabu search algorithm for its solution. We introduce the Kolmogorov–Smirnov statistic into the framework of the tabu search to manage the neighbourhood size. We evaluate the performance of the algorithm on a set of vehicle routing problems with time windows instances as well as other realistic instances. Our results are compared to CPLEX, to the heuristics reported in the literature and also to those extracted from the company plans.

Optimisation techniques for planning the petrol replenishment to retail stations over a multi-period horizon

The problem of planning the petrol station replenishment problem (PSRP) consists in making simultaneously several decisions, such as determining the minimum number of trucks required, assigning the stations to the available trucks, defining a feasible route for each tank-truck, etc. The objective to be achieved is usually defined as the minimisation of the travelled distance by the tank-trucks to serve all of the distribution stations. Traditional studies in the literature model and solve this problem over a time period of one single day. Only few works have recognised the fact that extending the time horizon to several days may yield important savings for the delivering company. The goal of this paper is to survey the optimisation techniques that support the petrol companies in improving their delivery performance over a multi-period planning horizon. We present the mathematical optimisation models that have been developed for both the t-day and periodic variants of the problem and discuss the heuristic methods so far developed for their solution.

Optimisation techniques for planning the petrol replenishment to retail stations over a multi-period horizon

The problem of planning the petrol station replenishment problem (PSRP) consists in making simultaneously several decisions, such as determining the minimum number of trucks required, assigning the stations to the available trucks, defining a feasible route for each tank-truck, etc. The objective to be achieved is usually defined as the minimisation of the travelled distance by the tank-trucks to serve all of the distribution stations. Traditional studies in the literature model and solve this problem over a time period of one single day. Only few works have recognised the fact that extending the time horizon to several days may yield important savings for the delivering company. The goal of this paper is to survey the optimisation techniques that support the petrol companies in improving their delivery performance over a multi-period planning horizon. We present the mathematical optimisation models that have been developed for both the t-day and periodic variants of the problem and discuss the heuristic methods so far developed for their solution.

A Stochastic Two-Echelon Supply Chain Model for the Petrol Station Replenishment Problem

In this paper, a new mathematical model is developed to optimize replenishment policies and inventory costs of a two-echelon supply chain system of kerosene product under demand uncertainty. The system consists of a fuel depot at the upper echelon and four petrol stations at the lower echelon. The petrol stations face stochastic stationary demand where inventory replenishment periods are uniformly fixed over the echelons. Adopting a Markov decision process approach, the states of a Markov chain represent possible states of demand for the inventory item. The replenishment cost, holding cost and shortage costs are combined with demand and inventory positions in order to generate the inventory cost matrix over the echelons. The matrix represents the long run measure of performance for the decision problem. The objective is to determine in each echelon of the planning horizon an optimal replenishment policy so that the long run inventory costs are minimized for a given state of demand. Using weekly equal intervals, the decisions of when to replenish additional units are made using dynamic programming over a finite period planning horizon. A numerical example demonstrates the existence of an optimal state-dependent replenishment policy and inventory costs over the echelons. DOI:10.12660/joscmv8n2p67-76URL: http://dx.doi.org/10.12660/joscmv8n2p67-76

Solution Methods for the Periodic Petrol Station Replenishment Problem

In this paper we introduce the Periodic Petrol Station Replenishment Problem (PPSRP) over a T-day planning horizon and describe four heuristic methods for its solution. Even though all the proposed heuristics belong to the common partitioning-then-routing paradigm, they differ in assigning the stations to each day of the horizon. The resulting daily routing problems are then solved exactly until achieving optimalization. Moreover, an improvement procedure is also developed with the aim of ensuring a better quality solution. Our heuristics are tested and compared in two real-life cases, and our computational results show encouraging improvements with respect to a human planning solution

Heuristics for the multi-depot petrol station replenishment problem with time windows

In the multi-depot petrol station replenishment problem with time windows (MPSRPTW), the delivery of petroleum products stored in a number of different petroleum depots to a set of petrol distribution stations has to be optimized. Each depot has its own fleet of heterogeneous and compartmented tank trucks. Stations specify their demand by indicating the minimum and maximum quantities to be delivered for each ordered product and require the delivery within a predetermined time window. Several inter-related decisions must be made simultaneously in order to solve the problem. For this problem, the set of feasible routes to deliver all the demands, the departure depot for each route, the quantities of each product to be delivered, the assignment of these routes to trucks, the time schedule for each trip, and the loading of the ordered products to different tanks of the trucks used need to be determined. In this paper, we propose a mathematical model that selects, among a set of feasible trips, the subset that allows the delivery of all the demands while maximizing the overall daily net revenue. If this model is provided with all possible feasible trips, it determines the optimal solution for the corresponding MPSRPTW. However, since the number of such trips is often huge, we developed a procedure to generate a restricted set of promising feasible trips. Using this restricted set, the model produces a good but not necessarily optimal solution. Thus the proposed solution process can be seen as a heuristic. We report the results of the extensive numerical tests carried out to assess the performance of the proposed heuristic. In addition, we show that, for the special case of only one depot, the proposed heuristic outperforms a previously published solution method.

Petrol Delivery Assignment with Multi-Product, Multi-Depot, Split Deliveries and Time Windows

The purpose of this study is to solve a planning problem faced by Pertamina, a state-owned Oil and Gas company in Indonesia, dealing with the delivery of petrol products. Based on Petrol Station Replenishment Problem (PSRP), a fleet of tank-trucks with different capacities has been assigned to deliver two petrol products from two depots with split deliveries and time windows to a set of petrol stations in the working area. The Tabu Search (TS) algorithm has been used to solve this problem. The problem consists of jointly determining the fleet assignment and of designing delivery routes to petrol stations to satisfy the orders using the available resources with the minimum total travel cost for delivery.

The petrol station replenishment problem with time windows

The petrol station replenishment problem with time windows