Panagiotis P. Repoussis

A hybrid evolution strategy for the open vehicle routing problem

This paper presents a hybrid evolution strategy (ES) for solving the open vehicle routing problem (OVRP), which is a well-known combinatorial optimization problem that addresses the service of a set of customers using a homogeneous fleet of non-depot returning capacitated vehicles. The objective is to minimize the fleet size and the distance traveled. The proposed solution method manipulates a population of @m individuals using a (@m+@l)-ES; at each generation, a new intermediate population of @l offspring is produced via mutation, using arcs extracted from parent individuals. The selection and combination of arcs is dictated by a vector of strategy parameters. A multi-parent recombination operator enables the self-adaptation of the mutation rates based on the frequency of appearance of each arc and the diversity of the population. Finally, each new offspring is further improved via a memory-based trajectory local search algorithm, while an elitist scheme guides the selection of survivors. Experimental results on well-known benchmark data sets demonstrate the competitiveness of the proposed population-based hybrid metaheuristic algorithm.

A reactive variable neighborhood tabu search for the heterogeneous fleet vehicle routing problem with time windows

Abstract This paper presents a solution methodology for the heterogeneous fleet vehicle routing problem with time windows. The objective is to minimize the total distribution costs, or similarly to determine the optimal fleet size and mix that minimizes both the total distance travelled by vehicles and the fixed vehicle costs, such that all problem’s constraints are satisfied. The problem is solved using a two-phase solution framework based upon a hybridized Tabu Search, within a new Reactive Variable Neighborhood Search metaheuristic algorithm. Computational experiments on benchmark data sets yield high quality solutions, illustrating the effectiveness of the approach and its applicability to realistic routing problems.

Arc-Guided Evolutionary Algorithm for the Vehicle Routing Problem With Time Windows

This paper presents an arc-guided evolutionary algorithm for solving the vehicle routing problem with time windows, which is a well-known combinatorial optimization problem that addresses the service of a set of customers using a homogeneous fleet of capacitated vehicles within fixed time intervals. The objective is to minimize the fleet size following routes of minimum distance. The proposed method evolves a population of mu individuals on the basis of an (mu + lambda) evolution strategy; at each generation, a new intermediate population of lambda individuals is generated, using a discrete arc-based representation combined with a binary vector of strategy parameters. Each offspring is produced via mutation out of arcs extracted from parent individuals. The selection of arcs is dictated by the strategy parameters and is based on their frequency of appearance and the diversity of the population. A multiparent recombination operator enables the self-adaptation of the strategy parameters, while each offspring is further improved via novel memory-based trajectory local search algorithms. For the selection of survivors, a deterministic scheme is followed. Experimental results on well-known large-scale benchmark datasets of the literature demonstrate the competitiveness of the proposed method.

A web-based decision support system for waste lube oils collection and recycling

This paper presents a web-based decision support system (DSS) that enables schedulers to tackle reverse supply chain management problems interactively. The focus is on the efficient and effective management of waste lube oils collection and recycling operations. The emphasis is given on the systemic dimensions and modular architecture of the proposed DSS. The latter incorporates intra- and inter-city vehicle routing with real-life operational constraints using shortest path and sophisticated hybrid metaheuristic algorithms. It is also integrated with an Enterprise Resource Planning system allowing the utilization of particular functional modules and the combination with other peripheral planning tools. Furthermore, the proposed DSS provides a framework for on-line monitoring and reporting to all stages of the waste collection processes. The system is developed using a web architecture that enables sharing of information and algorithms among multiple sites, along with wireless telecommunication facilities. The application to an industrial environment showed improved productivity and competitiveness, indicating its applicability on realistic reverse logistical planning problems.

A well-scalable metaheuristic for the fleet size and mix vehicle routing problem with time windows

This paper presents an efficient and well-scalable metaheuristic for fleet size and mix vehicle routing with time windows. The suggested solution method combines the strengths of well-known threshold accepting and guided local search metaheuristics to guide a set of four local search heuristics. The computational tests were done using the benchmarks of [Liu, F.-H., & Shen, S.-Y. (1999). The fleet size and mix vehicle routing problem with time windows. Journal of the Operational Research Society, 50(7), 721-732] and 600 new benchmark problems suggested in this paper. The results indicate that the suggested method is competitive and scales almost linearly up to instances with 1000 customers.

A template-based Tabu Search algorithm for the Consistent Vehicle Routing Problem

This paper presents a generic template-based solution framework and its application to the so-called Consistent Vehicle Routing Problem (ConVRP). The ConVRP is an NP-hard combinatorial optimization problem and involves the design of a set of minimum cost vehicle routes to service a set of customers with known demands over multiple days. Customers may receive service either once or with a predefined frequency; however frequent customers must receive consistent service, i.e., must be visited by the same driver over approximately the same time throughout the planning period. The proposed solution framework adopts a two-level master-slave decomposition scheme. Initially, a master template route schedule is constructed in an effort to determine the service sequence and assignment of frequent customers to vehicles. On return, the master template is used as the basis to design the actual vehicle routes and service schedules for both frequent and non-frequent customers over multiple days. To this end, a Tabu Search improvement method is employed that operates on a dual mode basis and modifies both the template routes and the actual daily schedules in a sequential fashion. Computational experiments on benchmark data sets illustrate the competitiveness of the proposed approach compared to existing results.

Adaptive Path Relinking for Vehicle Routing and Scheduling Problems with Product Returns

This paper deals with one-to-many-to-one vehicle routing and scheduling problems with pickups and deliveries and studies the effect of various backhauling strategies. Initially, focus is given on problem instances with clustered backhauls where all delivery customers must be visited before pickup customers. Afterward, operational settings with mixed backhauls and varying visiting sequence restrictions with respect to the capacity of the vehicles are examined. The proposed solution method evolves a set of reference solutions on the basis of a novel Adaptive Path Relinking framework. The latter encompasses an adaptive multisolution recombination procedure to generate provisional solutions based on the recurrence of particular solution attributes. On return, these solutions are used as guiding points for performing search trajectories from initial reference solutions via tunneling. Computational results on benchmark data sets of the literature illustrate the competitiveness and robustness of the proposed approach compared to state-of-the-art solution methods for well-known vehicle routing and scheduling problems. Finally, various experiments are also reported to demonstrate the economic effect of different mixing levels and densities of linehaul and backhaul customers.

A reactive greedy randomized variable neighborhood tabu search for the vehicle routing problem with time windows

This paper presents a hybrid metaheuristic to address the vehicle routing problem with time windows (VRPTW). The VRPTW can be described as the problem of designing least cost routes from a depot to geographically dispersed customers. The routes must be designed such that each customer is visited only once by exactly one vehicle without violating capacity and time window constraints. The proposed solution method is a multi-start local search approach which combines reactively the systematic diversification mechanisms of Greedy Randomized Adaptive Search Procedures with a novel Variable Neighborhood Tabu Search hybrid metaheuristic for intensification search. Experimental results on well known benchmark instances show that the suggested method is both efficient and robust in terms of the quality of the solutions produced.

An Adaptive Memory Programming Framework for the Robust Capacitated Vehicle Routing Problem

We present an adaptive memory programming (AMP) metaheuristic to address the robust capacitated vehicle routing problem under demand uncertainty. Contrary to its deterministic counterpart, the robust formulation allows for uncertain customer demands, and the objective is to determine a minimum cost delivery plan that is feasible for all demand realizations within a prespecified uncertainty set. A crucial step in our heuristic is to verify the robust feasibility of a candidate route. For generic uncertainty sets, this step requires the solution of a convex optimization problem, which becomes computationally prohibitive for large instances. We present two classes of uncertainty sets for which route feasibility can be established much more efficiently. Although we discuss our implementation in the context of the AMP framework, our techniques readily extend to other metaheuristics. Computational studies on standard literature benchmarks with up to 483 customers and 38 vehicles demonstrate that the proposed approach is able to quickly provide high-quality solutions. In the process, we obtain new best solutions for a total of 123 benchmark instances.

The Capacitated Team Orienteering Problem: A Bi-Level Filter-and-Fan Method

This paper focuses on vehicle routing problems with profits and addresses the so-called Capacitated Team Orienteering Problem. Given a set of customers with a priori known profits and demands, the objective is to find the subset of customers, for which the collected profit is maximized, and to determine the visiting sequence and assignment to vehicle routes assuming capacity and route duration restrictions. The proposed method adopts a hierarchical bi-level search framework that takes advantage of different search landscapes. At the upper level, the solution space is explored on the basis of the collected profit, using a Filter-and-Fan method and a combination of profit oriented neighborhoods, while at the lower level the routing of customers is optimized in terms of traveling distance via a Variable Neighborhood Descent method. Computational experiments on benchmark data sets illustrate the efficiency and effectiveness of the proposed approach. Compared to existing results, new upper bounds are produced with competitive computational times.