A GRASP algorithm for the concrete delivery problem

In the location science, environmental effect becomes a new main consideration for site selection. For the unwanted facility location selection, decision makers should consider the cost of resolving the environmental conflict. We introduced the negative influence cost for the facility which was inversely proportional to distance between the facility and residents. An unwanted facility location problem was suggested to minimize the sum of the negative influence cost and the transportation cost. The objective cost function was analyzed as nonlinear type and was neither convex nor concave. Three GRASP (Greedy Randomized adaptive Search Procedure) methods as like Random_GRASP, Epsilon_GRASP and GRID_GRASP were developed to solve the unwanted facility location problem. The Newton's method for nonlinear optimization problem was used for local search in GRASP. Experimental results showed that quality of solution of the GRID_GRASP was better than those of Random_GRASP and Epsilon_GRASP. The calculation time of Random_GRASP and Epsilon_GRASP were faster than that of Grid_GRASP.

GRASP for traffic grooming and routing with simple path constraints in WDM mesh networks

A greedy randomized adaptive search procedure (GRASP) for minimum 2-fold connected dominating set problem

This paper presents a greedy randomized adaptive search procedure (GRASP) for scheduling n jobs on m nonhomogeneous parallel machines with time windows. An additional feature of the problem is that each job falls into one of ρ priority classes. The objective is to maximize the number of jobs scheduled, where a job in a higher priority class has infinitely more value than a job in a lower priority class. The GRASP consists of two phases. The first phase produces feasible solutions by ranking each job with a greedy function and then selecting one at random from a restricted candidate list. The process is repeated until no more jobs can be scheduled. The second phase seeks a local optimum by searching over a series of neighborhoods defined by job insertions and exchanges. The algorithm is compared to a dynamic-programming heuristic that sequentially schedules the jobs in each priority class. Extensive computational results are presented based on data drawn from an application involving the use of communications relay satellites.

Multicast routing problem is a well know optimization problem for transmitting real-time multimedia applications in telecommunication networks. As the underpinning mathematical model, the constrained minimum Steiner tree problem in graphs is a well-known NP-complete problem. In this paper we investigate a new hybrid GRASP (Greedy Randomized Adaptive Search Procedure) approach where a pilot method is applied to further enhance the search for the Delay-Constrained Least-Cost (DCLC) multicast routing problem. Experimental results demonstrate the efficiency of the hybrid GRASP algorithm and the contributions of the post-processing pilot method to better solutions in most cases. The proposed GRASP approach is a competitive approach in solving the DCLC multicast routing problem.

This paper presents a new hybrid algorithm, which is based on the concepts of Particle Swarm Optimization (PSO) and Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm is a two phase algorithm which combines a Multi-Swarm Constriction Particle Swarm Optimization algorithm for the solution of the feature selection problem and a GRASP algorithm for the solution of the clustering problem. In this paper in PSO, multiple swarms are used in order to give to the algorithm more exploration and exploitation abilities as the different swarms have the possibility to explore different parts of the solution space and, also, a constriction factor is used for controlling the behaviour of particles in each swarm. The performance of the algorithm is compared with other popular metaheuristic methods like classic genetic algorithms, tabu search, GRASP, ant colony optimization and particle swarm optimization. In order to assess the efficacy of the proposed algorithm, this methodology is evaluated on datasets from the UCI Machine Learning Repository. The high performance of the proposed algorithm is achieved as the algorithm gives very good results and in some instances the percentage of the corrected clustered samples is very high and is larger than 98%.KeywordsParticle Swarm OptimizationGreedy Randomized Adaptive Search ProcedureClustering Analysis

A greedy randomized adaptive search procedure (GRASP) is an iterative multistart metaheuristic for difficult combinatorial optimization. Each GRASP iteration consists of two phases: a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed solution is sought. Since 1989, GRASP has been applied to a wide range of combinatorial optimization problems, ranging from scheduling and routing to drawing.In this article, we cover the literature where GRASP is applied to scheduling, routing, logic, partitioning, location, graph theory, assignment, manufacturing, transportation, telecommunications, biology and related fields, automatic drawing, power systems, and VLSI design.

Greedy Randomized Adaptive Search Procedures (GRASP) are among the most popular metaheuristics for the solution of combinatorial optimization problems. While GRASP is a relatively simple and efficient framework to deal with deterministic problem settings, many real-life applications experience a high level of uncertainty concerning their input variables or even their optimization constraints. When properly combined with the right metaheuristic, simulation (in any of its variants) can be an effective way to cope with this uncertainty. In this paper, we present a simheuristic algorithm that integrates Monte Carlo simulation into a GRASP framework to solve the permutation flow shop problem (PFSP) with random processing times. The PFSP is a well-known problem in the supply chain management literature, but most of the existing work considers that processing times of tasks in machines are deterministic and known in advance, which in some real-life applications (e.g., project management) is an unrealistic assumption.

Satisfiability (SAT) and maximum satisfiability (MAX-SAT) are difficult combinatorial problems that have many important real-world applications. In this paper we investigate the performance of the dynamic convexized method based heuristics on the weighted MAX-SAT problem. We first present an auxiliary function which is constructed based on a penalty function, and minimize the function by a local search method which can escape successfully from previously converged local minimizers by increasing the value of a parameter. Two algorithms of the approach are implemented and compared with the Greedy Randomized Adaptive Search Procedure (GRASP) and the GRASP with Path Relinking (GRASP + PR). Experimental results illustrate efficient and faster convergence of our two algorithms.

The weighted maximal planar graph (WMPG) is practically important in the laying out of facilities in modern manufacturing environments. Given a weighted complete graph, the WMPG seeks to find a sub-graph such that it is planar—it can be embedded on the plane without any arcs intersecting, and it is maximal—no additional arc can be added to the sub-graph without destroying its planarity, and it also has the maximal sum of arc weights. In this paper, an integer linear programming (ILP) model is newly introduced for the problem. Two meta-heuristics are then derived from the ILP relaxation. The first meta-heuristic considers all variables with fractional values greater than half in the ILP relaxation to build an initial sub-graph from which a planar sub-graph is extracted using greedy random adaptive search procedure (GRASP) and augmented by triangulation of faces. The second meta-heuristic considers only arcs with integer values in the ILP relaxation. The remaining arcs are then sorted in descending order of their weights, for selection and insertion with a planarity testing procedure, to obtain a feasible solution using GRASP. Computational results are reported on a set of 100 test instances of size varying from 20 to 100 facilities. The computational results demonstrate the tightness of the new upper bound when compared to the classical one as well as the good performance of the proposed metaheuristics when compared to the best-known procedures in the literature in terms of solution quality and computational requirement. Finally, the paper presents a successful integration of GRASP with classical optimisation approaches and should be attempted for other optimisation problems.

Natural and triggered-disasters, have devastating and profound negative effects on human lives that require a speedy declaration of an emergency in order to minimize their severe consequences. Hence, a prompt disaster response, in addition to effective measures such as informed decision making, organized evacuation plan, right hospital selection, proper rescue vehicles, efficient resources assignment and timely vehicle scheduling are critical actions needed to organize successful secured operations that could, if well prepared, save many injured bodies and lessen the human distress. To reach this ultimate goal, a complicated procedure should be in place and any failure can potentially increase the number of causalities, thus a complete alertness and full caution should be exercised. In this paper, we treat the Integrated Problem of Ambulance Scheduling and Resource Assignment (IPASRA) in the case of a sudden disaster. The main resources to be assigned are the ambulances and the hospitals. While, the hospitals serving capacities might be considered or not according to the extent of disaster and particularly to the wounded bodies’ total number. We formulate the (IPASRA) as a linear model, furthermore a novel hybrid algorithm based on Tabu Search (TS) and Greedy Randomized Adaptive Search Procedure (GRASP) is offered to tackle this complex problem. Simulation tests are also presented to prove the efficiency of our modelling and resolution approaches.

Greedy Randomised Adaptive Search Procedure (GRASP) is one of the best-known metaheuristics to solve complex combinatorial optimisation problems (COPs). This paper proposes two extensions of the typical GRASP framework. On the one hand, applying biased randomisation techniques during the solution construction phase enhances the efficiency of the GRASP solving approach compared to the traditional use of a restricted candidate list. On the other hand, the inclusion of simulation at certain points of the GRASP framework constitutes an efficient simulation–optimisation approach that allows to solve stochastic versions of COPs. To show the effectiveness of these GRASP improvements and extensions, tests are run with both deterministic and stochastic problem settings related to flow shop scheduling, vehicle routing, and facility location.

This paper considers a production–distribution problem that consists of defining the flow of produced products from manufacturing plants to clients (markets) via a set of warehouses. The problem also consists of defining the location of such warehouses that have unlimited storage capacity. This problem is known in the literature as the three-echelon uncapacitated facility location problem (TUFLP), and is known to be NP-hard when the objective function is to minimize the total cost of warehouse location and production and distribution of products. This paper proposes a Greedy Randomized Adaptive Search Procedure (GRASP) to solve the multi-item version of the TUFLP. Computational experiments are conducted using known instances from the literature. Solutions obtained using GRASP are compared against both optimal solutions and lower bounds obtained using mathematical programming. Results show that proposed algorithm performs well, obtaining good solutions (and even the optimal values) in less computational time than the mixed-integer linear programming model.

This study investigates the capacitated planar multi-facility location-allocation problem by considering various capacity constraints. The problem is also known as the capacitated multi-source Weber problem, where the number of facilities to be located is specified and each of which has a capacity constraint. An efficient greedy randomised adaptive search procedure (GRASP) is proposed to deal with the problem. A scheme that applies the furthest distance rule (FDR) and self-adjusted threshold parameters to generate initial facility locations that are situated sparsely within GRASP framework is also presented. The construction of the restricted candidate list (RCL) within GRASP is also guided by applying a concept of restricted regions that prevents new facility locations to be sited too close to the previous selected facility locations. The performance of the proposed GRASP heuristics is tested using benchmark data sets from literature. The computational experiments show that the proposed methods provide encouraging solutions when compared to recently published papers. Some future research avenues on the subject are also briefly highlighted.

In recent years, statistics show that the percentage of household consumption in GDP is getting higher and higher. One of the subsequent influences is that the transport demand for packages has increased sharply. Hence it is very necessary to propose diverse transport organization modes in accordance with the different situation in different countries. A kind of new transportation mode, multi-package multi-train pickup and delivery problem with time window (MPMT-PDPTW) is proposed in this paper. It aims to achieve express package transportation with minimum cost and maximize utilization of luggage car of a passenger train at the same time. Considering the multiple constraints, such as limited carrying capacity, fixed departing and accepting time, we define the quality of service (QoS) as the evaluation criteria for package express transportation by the passenger train. A kind of meta-heuristic algorithm, greedy randomized adaptive search procedure (GRASP), is designed to make full use of the redundant capacity of railway luggage car. An example is designed according to the railway passenger train timetable released by China Railway Corporation (CRC), the assignment schemes of packages at different railway stations reveal that the new transportation mode proposed in this paper is feasible and practical in package express transportation, and also has advantages in speed comparing with the highway and in transport cost over the airline.