Christos D. Tarantilis

A threshold accepting metaheuristic for the heterogeneous fixed fleet vehicle routing problem

Abstract The purpose of this paper is to present a new metaheuristic, termed the backtracking adaptive threshold accepting algorithm, for solving the heterogeneous fixed fleet vehicle routing problem (HFFVRP). The HFFVRP is a variant of the classical vehicle routing problem (VRP) and has attracted much less attention in the operational research (OR) literature than the classical VRP. It involves the design of a set of minimum cost routes, originating and terminating at a depot, for a fleet with fixed number of vehicles of each type, with various capacities, and variable costs to service a set of customers with known demands. The numerical results show that the proposed algorithm is robust and efficient. New best solutions are reported over a set of published benchmark problems.

BoneRoute: An Adaptive Memory-Based Method for Effective Fleet Management

This paper presents an adaptive memory-based method for solving the Capacitated Vehicle Routing Problem (CVRP), called BoneRoute. The CVRP deals with the problem of finding the optimal sequence of deliveries conducted by a fleet of homogeneous vehicles, based at one depot, to serve a set of customers. The computational performance of the BoneRoute was found to be very efficient, producing high quality solutions over two sets of well known case studies examined.

A meta-heuristic algorithm for the efficient distribution of perishable foods

Abstract A fast and robust algorithm for solving the fresh milk distribution problem for one of the biggest diary companies in Greece was developed. This particular problem was formulated as a Heterogeneous Fixed Fleet Vehicle Routing Problem (HFFVRP) for which, due to its high computational complexity, no exact algorithm ever has been used to solve it. In this study, a threshold-accepting based algorithm was developed aiming to satisfy the needs of the company that plans to use this methodology repeatedly to schedule their distribution many times a week. For this purpose, the proposed formulation was implemented in an efficient and reliable computer code. The algorithm manages to provide practical solutions and the early findings indicate considerable improvements in the operational performance of the company.

Dispatching of small containers via coastal freight liners: The case of the Aegean Sea

Abstract This work investigates the introduction of small containers, an important new technology, in an effort to reengineer coastal freight shipping in the Aegean Sea in Greece. Infrastructure problems of island ports are documented and the advantages of introducing small containers are discussed. The problem, hereby referred to as Coastal Freight Shipping Problem (CFSP), is dealt with under two dimensions. At first, strategic planning is analyzed by appropriately introducing an LP formulation for the determination of vessel traffic under known supply and demand constraints where total fuel costs and port dues are minimized. Subsequently, the operational dimension of the problem is analyzed by introducing a vehicle routing problem (VRP) formulation corresponding to periodic needs for transportation using small containers. For the planning case, a pilot network of 13 ports (including a depot port) and 25 sea links were taken into consideration. The problem was represented by a constrained formulation of current vessel routes and a more relaxed one utilizing the full network. Results show that current shipping practices of determining freight shipping routes according to passenger traffic demands are not optimal and at least 5.1% cost savings may be realized by redesigning island links while sensitivity analysis shows that total cost decreases with increasing vessel capacity. The utilization of a VRP formulation for the operational needs of a ship fleet allows exploration of problems of higher dimensions with respect to fleet size, demands sites and loads and gives a more comprehensive account with respect to cost and fleet efficiency and utilization.

A list based threshold accepting metaheuristic for the heterogeneous fixed fleet vehicle routing problem

In real life situations most companies that deliver or collect goods own a heterogeneous fleet of vehicles. Their goal is to find a set of vehicle routes, each starting and ending at a depot, making the best possible use of the given vehicle fleet such that total cost is minimized. The specific problem can be formulated as the Heterogeneous Fixed Fleet Vehicle Routing Problem (HFFVRP), which is a variant of the classical Vehicle Routing Problem. This paper describes a variant of the threshold accepting heuristic for the HFFVRP. The proposed metaheuristic has a remarkably simple structure, it is lean and parsimonious and it produces high quality solutions over a set of published benchmark instances. Improvement over several of previous best solutions also demonstrates the capabilities of the method and is encouraging for further research.

Using a spatial decision support system for solving the vehicle routing problem

Recent technological advances in Operational Research and Information Technology have enabled the development of high quality spatial decision support systems (SDSS). They constitute a new scientific area of information systems applications developed to support semi-structured or unstructured decisions, paying much attention to the spatial dimension of data to be analyzed, such as the location and shape of, and relationships among, geographic features. This paper presents a SDSS to coordinate and disseminate tasks and related information for solving the vehicle routing problem (VRP) using a metaheuristic method termed: backtracking adaptive threshold accepting (BATA). Its architecture involves an integrated framework of geographical information system (GIS) and a relational database management system (RDBMS) equipped with interactive communication capabilities between peripheral software tools. The SDSS was developed for Windows 98 platforms, focusing on the detailed road network of Athens.

Combination of geographical information system and efficient routing algorithms for real life distribution operations

Abstract This paper presents a decision support system (DSS) employing a metaheuristic algorithm called BoneRoute, for solving the open vehicle routing problem (OVRP). The OVRP deals with the problem of finding a set of vehicle routes, for a fleet of capacitated vehicles to satisfy the delivery requirements of customers, without returning to the distribution centre. The computational performance of the BoneRoute algorithm for the OVRP was found to be very efficient, producing new best solutions over a set of well-known published case studies examined. Technical and managerial issues aroused from the ad hoc connections between the geographical information system (GIS), the routing technique used for calculating shortest paths and the BoneRoute algorithm for finding the optimal sequence of customers, were faced successfully.

A List Based Threshold Accepting Algorithm for the Capacitated Vehicle Routing Problem

The aim of this study is to describe a new stochastic search meta-heuristic algorithm for solving the Capacitated Vehicle Routing Problem (CVRP), termed as the List Based Threshold Accepting (LBTA) algorithm. The main advantage of this algorithm over the majority of other meta-heuristics is that it produces quite satisfactory solutions in reasonable amount of time by tuning only one parameter of the algorithm. This property makes this algorithm a reliable and a practical tool for every decision support system designed for solving real life vehicle routing problems.

Using the vehicle routing problem for the transportation of hazardous materials

Transportation of hazardous materials (hazmats) is a decision problem that has been attracted much attention due to the risk factor involved. A considerable amount of models have been developed that employ single or multiple objective shortest path algorithms minimising the risks for a given origin-destination pair. However in many real life applications (i.e. transportation of gas cylinders), transportation of hazmats calls for the determination of a set of routes used by a fleet of trucks to serve a set of customers, rather than determination of a single optimal route as shortest path algorithms produce. In this paper, we focus on population exposure risk mitigation via production of truck-routes by solving a variant of the Vehicle Routing Problem. For this purpose we employ a single parameter metaheuristic algorithm. A case study of this approach is also demonstrated.

A backtracking adaptive threshold accepting algorithm for the vehicle routing problem

The aim of this study is to describe a new stochastic search metaheuristic algorithm for solving the capacitated Vehicle Routing Problem, termed as the Backtracking Adaptive Threshold Accepting (BATA) algorithm. Our effort focuses on developing an innovative method, which produces reliable and high quality solutions in a reasonable amount of time, without requiring substantial parameter tuning. BATA belongs to the class of threshold accepting algorithms. Its main difference over a typical threshold-accepting algorithm is that during the optimization process, the value of the threshold not only is lowered but also raised, or backtracked, depending on the success of the inner loop iterations to provide an acceptable new configuration (set of routes) replacing the previous one. This adaptation of the value of the threshold, plays an important role in finding the high quality solutions demonstrated in computational results presented in this study.