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Christos D Tarantilis - One of the best experts on this subject based on the ideXlab platform.

  • integrated distribution and loading planning via a compact Metaheuristic Algorithm
    European Journal of Operational Research, 2013
    Co-Authors: Emmanouil E Zachariadis, Christos D Tarantilis, C T Kiranoudis
    Abstract:

    Abstract The present article examines a vehicle routing problem integrated with two-dimensional loading constraints, called 2L-CVRP. The problem is aimed at generating the optimal route set for satisfying customer demand. In addition, feasible loading arrangements have to be determined for the transported products. To solve 2L-CVRP, we propose a Metaheuristic solution approach. The basic advantage of our approach lies at its compact structure, as in total, only two parameters affect the Algorithmic performance. To optimize the routing aspects, we propose a local-search method equipped with an effective diversification component based on the regional aspiration criteria. The problem’s loading requirements are tackled by employing a two-dimensional packing heuristic which repetitively attempts to develop feasible loading patterns. These attempts are effectively coordinated via an innovative, simple-structured memory mechanism. The overall solution framework makes use of several memory components for drastically reducing the computational effort required. The performance of our Metaheuristic development has been successfully evaluated on benchmark instances considering two distinct versions of the loading constraints. More specifically, the Algorithm managed to improve or match the majority of best known solution scores for both problem versions.

  • a guided tabu search for the vehicle routing problem with two dimensional loading constraints
    European Journal of Operational Research, 2009
    Co-Authors: Emmanouil E Zachariadis, Christos D Tarantilis, Christos T Kiranoudis
    Abstract:

    We present a Metaheuristic methodology for the Capacitated Vehicle Routing Problem with two-dimensional loading constraints (2L-CVRP). 2L-CVRP is a generalisation of the Capacitated Vehicle Routing Problem, in which customer demand is formed by a set of two-dimensional, rectangular, weighted items. The purpose of this problem is to produce the minimum cost routes, starting and terminating at a central depot, to satisfy the customer demand. Furthermore, the transported items must be feasibly packed into the loading surfaces of the vehicles. We propose a Metaheuristic Algorithm which incorporates the rationale of Tabu Search and Guided Local Search. The loading aspects of the problem are tackled using a collection of packing heuristics. To accelerate the search process, we reduce the neighbourhoods explored, and employ a memory structure to record the loading feasibility information. Extensive experiments were conducted to calibrate the Algorithmic parameters. The effectiveness of the proposed Metaheuristic Algorithm was tested on benchmark instances and led to several new best solutions.

  • minimizing makespan in permutation flow shop scheduling problems using a hybrid Metaheuristic Algorithm
    Computers & Operations Research, 2009
    Co-Authors: G I Zobolas, Christos D Tarantilis, George Ioannou
    Abstract:

    This paper proposes a hybrid Metaheuristic for the minimization of makespan in permutation flow shop scheduling problems. The solution approach is robust, fast, and simply structured, and comprises three components: an initial population generation method based on a greedy randomized constructive heuristic, a genetic Algorithm (GA) for solution evolution, and a variable neighbourhood search (VNS) to improve the population. The hybridization of a GA with VNS, combining the advantages of these two individual components, is the key innovative aspect of the approach. Computational experiments on benchmark data sets demonstrate that the proposed hybrid Metaheuristic reaches high-quality solutions in short computational times. Furthermore, it requires very few user-defined parameters, rendering it applicable to real-life flow shop scheduling problems.

  • a hybrid Metaheuristic Algorithm for the vehicle routing problem with simultaneous delivery and pick up service
    Expert Systems With Applications, 2009
    Co-Authors: Emmanouil E Zachariadis, Christos D Tarantilis, C T Kiranoudis
    Abstract:

    This article addresses a vehicle routing problem variant which considers customers to require simultaneous delivery and pick-up service (VRPSPD). The objective of this problem is to determine the optimal set of routes to totally satisfy both the delivery and pick-up demand of the customer population. VRPSPD is an NP-hard combinatorial optimization problem; therefore exact methods are incapable of dealing with large scale VRPSPD instances arising in a wide variety of practical operations. We propose a hybrid solution approach incorporating the rationale of two well-known Metaheuristics which have proven to be effective for routing problem variants, namely tabu search and guided local search. The intelligence of the proposed hybrid was designed to achieve a vast exploration of the search space, by escaping from local optima and intensifying at promising solution regions. The performance of our Metaheuristic Algorithm was tested on benchmark instances involving from 50 to 400 customers. It produced high quality results, improving several best solutions previously reported.

  • solving the open vehicle routeing problem via a single parameter Metaheuristic Algorithm
    Journal of the Operational Research Society, 2005
    Co-Authors: Christos D Tarantilis, C T Kiranoudis, George Ioannou, Gregory P Prastacos
    Abstract:

    In this paper, we consider the open vehicle routeing problem (OVRP), in which routes are not sequences of locations starting and ending at the depot but open paths. The problem is of particular importance for planning fleets of hired vehicles, a common practice in the distribution and service industry. In such cases, the travelling cost is a function of the vehicle open paths. To solve the problem, we employ a single-parameter Metaheuristic method that exploits a list of threshold values to guide intelligently an advanced local search. Computational results on a set of benchmark problems show that the proposed method consistently outperforms previous approaches for the OVRP. A real-world example demonstrates the applicability of the method in practice, demonstrating that the approach can be used to solve actual problems of routing large vehicle fleets.

John W Sutherland - One of the best experts on this subject based on the ideXlab platform.

  • a hybrid Metaheuristic Algorithm for a profit oriented and energy efficient disassembly sequencing problem
    Robotics and Computer-integrated Manufacturing, 2020
    Co-Authors: Yaping Ren, Hongyue Jin, Leilei Meng, Chaoyong Zhang, John W Sutherland
    Abstract:

    Abstract Value recovery from end-of-life products plays a key role in sustainability and circular economy, which starts with disassembly of products into components for reuse, remanufacturing, or recycling. As the process is often complex, a disassembly sequencing problem (DSP) studies how to optimally disassemble products considering the physical constraints between subassemblies/disassembly tasks for maximum profit. With a growing attention on energy conservation, this paper addresses a profit-oriented and energy-efficient DSP (PEDSP), whereby not only the profit is maximized, but also energy consumption is accounted as an important decision criterion. In this work, a disassembly AND/OR graph (DAOG) is used to model a disassembly diagram for a product, in which the ‘AND’ and ‘OR’ relations illustrate precedence relationships between subassemblies. Based on the DAOG, we propose a hybrid multi-objective Metaheuristic that integrates an artificial bee colony Algorithm, a non-dominated sorting procedure, and a variable neighborhood search approach to solve the PEDSP for Pareto solutions. The proposed method is applied to real-world cases (i.e., a simple ballpoint pen and a relatively complex radio) and compared with other multi-objective Algorithms. The results indicate that our method can quickly produce a Pareto front that outperforms the alternative approaches.

Emmanouil E Zachariadis - One of the best experts on this subject based on the ideXlab platform.

  • integrated distribution and loading planning via a compact Metaheuristic Algorithm
    European Journal of Operational Research, 2013
    Co-Authors: Emmanouil E Zachariadis, Christos D Tarantilis, C T Kiranoudis
    Abstract:

    Abstract The present article examines a vehicle routing problem integrated with two-dimensional loading constraints, called 2L-CVRP. The problem is aimed at generating the optimal route set for satisfying customer demand. In addition, feasible loading arrangements have to be determined for the transported products. To solve 2L-CVRP, we propose a Metaheuristic solution approach. The basic advantage of our approach lies at its compact structure, as in total, only two parameters affect the Algorithmic performance. To optimize the routing aspects, we propose a local-search method equipped with an effective diversification component based on the regional aspiration criteria. The problem’s loading requirements are tackled by employing a two-dimensional packing heuristic which repetitively attempts to develop feasible loading patterns. These attempts are effectively coordinated via an innovative, simple-structured memory mechanism. The overall solution framework makes use of several memory components for drastically reducing the computational effort required. The performance of our Metaheuristic development has been successfully evaluated on benchmark instances considering two distinct versions of the loading constraints. More specifically, the Algorithm managed to improve or match the majority of best known solution scores for both problem versions.

  • a guided tabu search for the vehicle routing problem with two dimensional loading constraints
    European Journal of Operational Research, 2009
    Co-Authors: Emmanouil E Zachariadis, Christos D Tarantilis, Christos T Kiranoudis
    Abstract:

    We present a Metaheuristic methodology for the Capacitated Vehicle Routing Problem with two-dimensional loading constraints (2L-CVRP). 2L-CVRP is a generalisation of the Capacitated Vehicle Routing Problem, in which customer demand is formed by a set of two-dimensional, rectangular, weighted items. The purpose of this problem is to produce the minimum cost routes, starting and terminating at a central depot, to satisfy the customer demand. Furthermore, the transported items must be feasibly packed into the loading surfaces of the vehicles. We propose a Metaheuristic Algorithm which incorporates the rationale of Tabu Search and Guided Local Search. The loading aspects of the problem are tackled using a collection of packing heuristics. To accelerate the search process, we reduce the neighbourhoods explored, and employ a memory structure to record the loading feasibility information. Extensive experiments were conducted to calibrate the Algorithmic parameters. The effectiveness of the proposed Metaheuristic Algorithm was tested on benchmark instances and led to several new best solutions.

  • a hybrid Metaheuristic Algorithm for the vehicle routing problem with simultaneous delivery and pick up service
    Expert Systems With Applications, 2009
    Co-Authors: Emmanouil E Zachariadis, Christos D Tarantilis, C T Kiranoudis
    Abstract:

    This article addresses a vehicle routing problem variant which considers customers to require simultaneous delivery and pick-up service (VRPSPD). The objective of this problem is to determine the optimal set of routes to totally satisfy both the delivery and pick-up demand of the customer population. VRPSPD is an NP-hard combinatorial optimization problem; therefore exact methods are incapable of dealing with large scale VRPSPD instances arising in a wide variety of practical operations. We propose a hybrid solution approach incorporating the rationale of two well-known Metaheuristics which have proven to be effective for routing problem variants, namely tabu search and guided local search. The intelligence of the proposed hybrid was designed to achieve a vast exploration of the search space, by escaping from local optima and intensifying at promising solution regions. The performance of our Metaheuristic Algorithm was tested on benchmark instances involving from 50 to 400 customers. It produced high quality results, improving several best solutions previously reported.

C T Kiranoudis - One of the best experts on this subject based on the ideXlab platform.

  • integrated distribution and loading planning via a compact Metaheuristic Algorithm
    European Journal of Operational Research, 2013
    Co-Authors: Emmanouil E Zachariadis, Christos D Tarantilis, C T Kiranoudis
    Abstract:

    Abstract The present article examines a vehicle routing problem integrated with two-dimensional loading constraints, called 2L-CVRP. The problem is aimed at generating the optimal route set for satisfying customer demand. In addition, feasible loading arrangements have to be determined for the transported products. To solve 2L-CVRP, we propose a Metaheuristic solution approach. The basic advantage of our approach lies at its compact structure, as in total, only two parameters affect the Algorithmic performance. To optimize the routing aspects, we propose a local-search method equipped with an effective diversification component based on the regional aspiration criteria. The problem’s loading requirements are tackled by employing a two-dimensional packing heuristic which repetitively attempts to develop feasible loading patterns. These attempts are effectively coordinated via an innovative, simple-structured memory mechanism. The overall solution framework makes use of several memory components for drastically reducing the computational effort required. The performance of our Metaheuristic development has been successfully evaluated on benchmark instances considering two distinct versions of the loading constraints. More specifically, the Algorithm managed to improve or match the majority of best known solution scores for both problem versions.

  • a hybrid Metaheuristic Algorithm for the vehicle routing problem with simultaneous delivery and pick up service
    Expert Systems With Applications, 2009
    Co-Authors: Emmanouil E Zachariadis, Christos D Tarantilis, C T Kiranoudis
    Abstract:

    This article addresses a vehicle routing problem variant which considers customers to require simultaneous delivery and pick-up service (VRPSPD). The objective of this problem is to determine the optimal set of routes to totally satisfy both the delivery and pick-up demand of the customer population. VRPSPD is an NP-hard combinatorial optimization problem; therefore exact methods are incapable of dealing with large scale VRPSPD instances arising in a wide variety of practical operations. We propose a hybrid solution approach incorporating the rationale of two well-known Metaheuristics which have proven to be effective for routing problem variants, namely tabu search and guided local search. The intelligence of the proposed hybrid was designed to achieve a vast exploration of the search space, by escaping from local optima and intensifying at promising solution regions. The performance of our Metaheuristic Algorithm was tested on benchmark instances involving from 50 to 400 customers. It produced high quality results, improving several best solutions previously reported.

  • solving the open vehicle routeing problem via a single parameter Metaheuristic Algorithm
    Journal of the Operational Research Society, 2005
    Co-Authors: Christos D Tarantilis, C T Kiranoudis, George Ioannou, Gregory P Prastacos
    Abstract:

    In this paper, we consider the open vehicle routeing problem (OVRP), in which routes are not sequences of locations starting and ending at the depot but open paths. The problem is of particular importance for planning fleets of hired vehicles, a common practice in the distribution and service industry. In such cases, the travelling cost is a function of the vehicle open paths. To solve the problem, we employ a single-parameter Metaheuristic method that exploits a list of threshold values to guide intelligently an advanced local search. Computational results on a set of benchmark problems show that the proposed method consistently outperforms previous approaches for the OVRP. A real-world example demonstrates the applicability of the method in practice, demonstrating that the approach can be used to solve actual problems of routing large vehicle fleets.

Gaige Wang - One of the best experts on this subject based on the ideXlab platform.

  • moth search Algorithm a bio inspired Metaheuristic Algorithm for global optimization problems
    Memetic Computing, 2018
    Co-Authors: Gaige Wang
    Abstract:

    Phototaxis, signifying movement of an organism towards or away from a source of light, is one of the most representative features for moths. It has recently been shown that one of the characteristics of moths has been the propensity to follow Levy flights. Inspired by the phototaxis and Levy flights of the moths, a new kind of Metaheuristic Algorithm, called moth search (MS) Algorithm, is developed in the present work. In nature, moths are a family insects associated with butterflies belonging to the order Lepidoptera. In MS method, the best moth individual is viewed as the light source. Some moths that are close to the fittest one always display an inclination to fly around their own positions in the form of Levy flights. On the contrary, due to phototaxis, the moths that are comparatively far from the fittest one will tend to fly towards the best one directly in a big step. These two features correspond to the processes of exploitation and exploration of any Metaheuristic optimization method. The phototaxis and Levy flights of the moths can be used to build up a general-purpose optimization method. In order to demonstrate the superiority of its performance, the MS method is further compared with five other state-of-the-art Metaheuristic optimization Algorithms through an array of experiments on fourteen basic benchmarks, eleven IEEE CEC 2005 complicated benchmarks and seven IEEE CEC 2011 real world problems. The results clearly demonstrate that MS significantly outperforms five other methods on most test functions and engineering cases.

  • earthworm optimisation Algorithm a bio inspired Metaheuristic Algorithm for global optimisation problems
    International Journal of Bio-inspired Computation, 2015
    Co-Authors: Gaige Wang, Suash Deb, Leandro Dos Santos Coelho
    Abstract:

    Earthworms can aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired Metaheuristic Algorithm, called earthworm optimisation Algorithm (EWA), is proposed in this paper. The EWA method is inspired by the two kinds of reproduction (Reproduction 1 and Reproduction 2) of the earthworms. Reproduction 1 generates only one offspring by itself. Reproduction 2 is to generate one or more than one offspring at one time, and this can successfully be done by nine improved crossover operators. In addition, Cauchy mutation (CM) is added to EWA method. Nine different EWA methods with one, two and three offsprings based on nine improved crossover operators are respectively proposed. The results show that EWA23 performs the best and it can find the better fitness on most benchmarks than others.

  • test sheet composition using analytic hierarchy process and hybrid Metaheuristic Algorithm ts bbo
    Mathematical Problems in Engineering, 2012
    Co-Authors: Hong Duan, Gaige Wang, Wei Zhao, Xuehua Feng
    Abstract:

    Due to the shortcomings in the traditional methods which dissatisfy the examination requirements in composing test sheet, a new method based on tabu searchTSand biogeography- based optimizationBBOis proposed. Firstly, according to the requirements of the test-sheet composition such as the total score, test time, chapter score, knowledge point score, question type score, cognitive level score, difficulty degree, and discrimination degree, a multi constrained multiobjective model of test-sheet composition is constructed. Secondly, analytic hierarchy process � AHPis used to work out the weights of all the test objectives, and then the multiobjective model is turned into the single objective model by the linear weighted sum. Finally, an improved biogeography-based optimization—TS/BBO is proposed to solve test-sheet composition problem. To prove the performance of TS/BBO, TS/BBO is compared with BBO and other population-based optimization methods such as ACO, DE, ES, GA, PBIL, PSO, and SGA. The experiment illustrates that the proposed approach can effectively improve composition speed and success rate.