A constructive heuristic for the multi-compartment vehicle routing problem: an approach for a fuel distribution company

In this paper, we suggest a Decision Support System (DSS) for route planning. This DSS was developed in a real context in a fuel distribution company. This company distributes three types of fuel through multi-compartment vehicles with limited capacity, and satisfying delivery time windows imposed by the customers. Two approaches for this problem were considered: The first one considers the route planning without replenishments while the second one considers the multi-trip case. The solution method is based on a Clarke and Wright savings algorithm incorporating all the constraints referred to above. Both approaches were tested using real instances. The savings for the company, in terms of the distribution process, were identified in the computational results. The DSS may provide an effective support to a complex decision problem.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

A procedure for optimizing hazardous materials transportation including road infrastructures' characteristics and door-to-door attempted deliveries

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Optimising the periodic distribution of gas cylinders with customers priority

none3siTriki, Chefi; Akil, Jamila; Azri, Nasser AlTriki, Chefi; Akil, Jamila; Azri, Nasser A

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 solutio

Optimization Approach for Multi-Period Fuel Replenishment

This paper proposes mathematical models and solution approaches for solving the multi-period fuel replenishment planning problem. The model aims to search for a set of routes, determining the quantity of several petroleum products to be loaded on individual vehicle compartments, and specifying the quantity to be discharged to customer tanks over a given planning horizon in which multiple constraints are satisfied. The objective function is to minimize the transportation unit cost, equal to the total transportation cost divided by the sum of replenished quantity. As the model size grows exponentially when the number of customers, vehicles, and time period increases, an exact algorithm is not feasible. Hence, in this study, we propose two heuristic approaches: two-phase method (2PM) and three-phase method (3PM). The 2PM is primarily designed for solving small problems whereas the 3PM adopts a similar approach but has the ability to solve larger problems. The proposed solutions were tested using a real-life scenario and randomly generated test instance. The results showed that our solution outperforms the solution constructed by experienced planners and also proved that considering multiple periods when devising the fuel replenishment plan, gives superior results in comparison to single periods

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 how much fuel must be delivered. To fulfil 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. We provide a math-heuristic approach, based on cluster-first route-second paradigm, to solve it. We thoroughly experiment our approach on a set of realistic random instances. Finally, we consider a weekly large real instance with 194 petrol stations and 2 depots

Risk based, multi objective vehicle routing problem for hazardous materials: a test case in downstream fuel logistics

Abstract The paper analyses a practical case of study related to the distribution of fuels for the Total Erg Oil Company to the service stations located in the Province of Rome (Italy). The problem is formulated as a capacitated vehicle routing problem with time windows, where several heuristic procedures have been tested, considering both static and dynamic travel times. With respect to the standard operational costs used typically, a multivariable objective function has been proposed which takes into account also a new risk index. The risk index proposed is function of the population density of the zones covered by each path and of the estimated number of road accidents on each road link. In such a way, we take into account the population's exposure to the risk associated with an incidental event involving a fuel tank. The obtained output is the set of planned routes with minimum service cost and minimum risk. Results demonstrate how an accurate planning of the service saves up to 3 hours and 30 km on a daily basis compared to a benchmark. Moreover, the distribution company can parameterize the configuration of the service, by varying the weight adopted in order to include the risk index. Including the risk index may bring to a higher safety route planning, with an increase of the operating costs of only 2%

The heterogeneous fleet vehicle routing problem with light loads and overtime: Formulation and population variable neighbourhood search with adaptive memory

In this paper we consider a real life Vehicle Routing Problem inspired by the gas delivery industry in the United Kingdom. The problem is characterized by heterogeneous vehicle fleet, demand-dependent service times, maximum allowable overtime and a special light load requirement. A mathematical formulation of the problem is developed and optimal solutions for small sized instances are found. A new learning-based Population Variable Neighbourhood Search algorithm is designed to address this real life logistic problem. To the best of our knowledge Adaptive Memory has not been hybridized with a classical iterative memoryless method. In this paper we devise and analyse empirically a new and effective hybridization search that considers both memory extraction and exploitation. In terms of practical implications, we show that on a daily basis up to 8% cost savings on average can be achieved when overtime and light load requirements are considered in the decision making process. Moreover, accommodating for allowable overtime has shown to yield 12% better average utilization of the driver's working hours and 12.5% better average utilization of the vehicle load, without a significant increase in running costs. We also further discuss some managerial insights and trade-offs