Davide Anghinolfi

A new discrete particle swarm optimization approach for the single-machine total weighted tardiness scheduling problem with sequence-dependent setup times

In this paper we present a new Discrete Particle Swarm Optimization (DPSO) approach to face the NP-hard single machine total weighted tardiness scheduling problem in presence of sequence-dependent setup times. Differently from previous approaches the proposed DPSO uses a discrete model both for particle position and velocity and a coherent sequence metric. We tested the proposed DPSO mainly over a benchmark originally proposed by Cicirello in 2003 and available online. The results obtained show the competitiveness of our DPSO, which is able to outperform the best known results for the benchmark. In addition, we also tested the DPSO on a set of benchmark instances from ORLIB for the single machine total weighted tardiness problem, and we analysed the role of the DPSO swarm intelligence mechanisms as well as the local search intensification phase included in the algorithm.

Parallel machine total tardiness scheduling with a new hybrid metaheuristic approach

This work proposes a hybrid metaheuristic (HMH) approach which integrates several features from tabu search (TS), simulated annealing (SA) and variable neighbourhood search (VNS) in a new configurable scheduling algorithm. In particular, either a deterministic or a random candidate list strategy can be used to generate the neighbourhood of a solution, both a tabu list mechanism and the SA probabilistic rule can be adopted to accept solutions, and the dimension of the explored neighbourhood can be dynamically modified. The considered class of scheduling problems is characterized by a set of independent jobs to be executed on a set of parallel machines with non-zero ready times and sequence dependent setups. In particular, the NP-hard generalized parallel machine total tardiness problem (GPMTP) recently defined by Bilge et al. [A tabu search algorithm for parallel machine total tardiness problem. Computers & Operations Research 2004;31:397-414], is faced. Several alternative configurations of the HMH have been tested on the same benchmark set used by Bilge et al. The results obtained highlight the appropriateness of the proposed approach. Algorithms based on metaheuristics have been quite extensively used to face scheduling problems and they are a valuable tool to provide high quality solutions. A metaheuristic describes principles and features that need to be tailored on the specific problem under concern to define a customized approach. This work aims to evaluate the possibility of defining a hybrid customizable neighbourhood search algorithm for combinatorial problems as a combination of a subset of concepts and features from three main metaheuristics of reference, i.e., the TS, the SA and the VNS. The proposed HMH approach is applied to the difficult problem of minimizing the total tardiness in parallel machines scheduling; in particular, a generalized version of such a problem has been considered, which makes it closer to several real industrial applications since it takes into account non-zero ready times, distinct due dates, uniform machines and setup times, recently proposed by Bilge et al. (A tabu search algorithm for parallel machine total tardiness problem. Computers & Operations Research 2004;31:397-414). To evaluate the effectiveness of the HMH for the generalized parallel machine total tardiness scheduling problem, a relevant benchmark available from the literature has been considered.

A dynamic optimization model for solid waste recycling

Recycling is an important part of waste management (that includes different kinds of issues: environmental, technological, economic, legislative, social, etc.). Differently from many works in literature, this paper is focused on recycling management and on the dynamic optimization of materials collection. The developed dynamic decision model is characterized by state variables, corresponding to the quantity of waste in each bin per each day, and control variables determining the quantity of material that is collected in the area each day and the routes for collecting vehicles. The objective function minimizes the sum of costs minus benefits. The developed decision model is integrated in a GIS-based Decision Support System (DSS). A case study related to the Cogoleto municipality is presented to show the effectiveness of the proposed model. From optimal results, it has been found that the net benefits of the optimized collection are about 2.5 times greater than the estimated current policy.

An experimental comparison of different heuristics for the master bay plan problem

Different heuristics for the problem of determining stowage plans for containerships, that is the so called Master Bay Plan Problem (MBPP), are compared. The first approach is a tabu search (TS) heuristic and it has been recently presented in literature. Two new solution procedures are proposed in this paper: a fast simple constructive loading heuristic (LH) and an ant colony optimization (ACO) algorithm. An extensive computational experimentation performed on both random and real size instances is reported and conclusions on the appropriateness of the tested approaches for the MBPP are drawn.

A hybrid particle swarm optimization approach for the sequential ordering problem

The sequential ordering problem is a version of the asymmetric travelling salesman problem where precedence constraints on vertices are imposed. A tour is feasible if these constraints are fulfilled, and the objective is to find a feasible solution with minimum cost. A particle swarm optimization approach hybridized with a local search procedure is discussed in this paper. The method is shown to be very effective in guiding a sophisticated local search previously introduced in the literature towards high quality regions of the search space. Differently from standard particle swarm algorithms, the proposed hybrid method tends to fast convergence to local optima. A mechanism to self-adapt a parameter and to avoid stagnation is therefore introduced. Extensive experimental results, where the new method is compared with the state-of-the-art algorithms, show the effectiveness of the new approach.

On the Problem of the Automated Design of Large-Scale Robot Skin

This paper describes automated procedures for the design and deployment of artificial skin for humanoid robots. This problem is challenging under different perspectives: on the one hand, different robots are characterized by different shapes, thereby requiring a high degree of skin customization; on the other hand, it is necessary to define optimal criteria specifying how the skin must be placed on robot parts. This paper addresses the problem of optimally covering robot parts with tactile sensors, discussing possible solutions with reference to a specific artificial skin technology for robots, which has been developed in the past few years. Results show that it is possible to automate the majority of the required steps, with promising results in view of a future complete automation of the process.

Production planning of mixed-model assembly lines: a heuristic mixed integer programming based approach

Process technology capabilities are becoming increasingly important as flexible manufacturing continues to be more prevalent, and as competition compels companies to provide expanded variety, at ever lower cost, so introducing plant and processes technological constraints. Model flexibility can also benefit from an appropriate production planning process, especially concerning mixed-model assembly lines, since it can facilitate master scheduling and line balancing activities, which are essential aspects of flexibility. Robust and practical planning approaches have to take into account two different aspects: the first consists in ensuring that the elaborated aggregate plan can be disaggregated into at least one detailed feasible plan for the realised demand, whereas the second in ensuring that this detailed plan is feasible at the operational level. This article faces the model flexibility challenge, reviewing and discussing the planning problem of a real world assembly manufacturing system, producing high volume and a variety of agricultural tractors and machines, analysing and resolving some important issues related to technological, organisational and managerial constraints. This article illustrates the implementation of an Advanced Planning System integrated with a mixed integer-programming model, which is solved by a new iterative heuristic approach capable of achieving interesting planning improvements for model-flexibility management.

Freight transportation in railway networks with automated terminals: A mathematical model and MIP heuristic approaches

In this paper we propose a planning procedure for serving freight transportation requests in a railway network with fast transfer equipment at terminals. We consider a transportation system where different customers make their requests (orders) for moving boxes, i.e., either containers or swap bodies, between different origins and destinations, with specific requirements on delivery times. The decisions to be taken concern the route (and the corresponding sequence of trains) that each box follows in the network and the assignment of boxes to train wagons, taking into account that boxes can change more than one train and that train timetables are fixed. The planning procedure includes a pre-analysis step to determine all the possible sequences of trains for serving each order, followed by the solution of a 0-1 linear programming problem to find the optimal assignment of each box to a train sequence and to a specific wagon for each train in the sequence. This latter is a generalized assignment problem which is NP-hard. Hence, in order to find good solutions in acceptable computation times, two MIP heuristic approaches are proposed and tested through an experimental analysis considering realistic problem instances.

Heuristic approaches for the optimal wiring in large scale robotic skin design

This paper faces the problem of optimizing the wiring and the connections in a tactile skin for robots. The robotic skin is a device composed of a network of tactile sensors, whose wiring can be very complex: the control of this complexity is a key problem. In the considered robotic skin, skin elements are grouped into skin patches, which output tactile data that have to be read by a micro-controller. The logical connections between the sensors must be defined in order to route signals through the network. A finite set of micro-controllers is given and a set of constraints is imposed on the given assignment and routing. The considered problem has a combinatorial nature and it can be formulated as a Minimum Constrained Spanning Forest problem with costs on arcs that cannot be a priori defined as they are solution-dependent. The problem is NP-hard. The paper introduces a mathematical formulation and then proposes a Multi-Start Heuristic algorithm and an Ant Colony Optimization approach whose effectiveness is evaluated through experimental tests performed on both real and synthetically generated instances.

Performance Evaluation of an Adaptive Ant Colony Optimization Applied to Single Machine Scheduling

We propose a self-adaptive Ant Colony Optimization (AD-ACO) approach that exploits a parameter adaptation mechanism to reduce the requirement of a preliminary parameter tuning. The proposed AD-ACO is based on an ACO algorithm adopting a pheromone model with a new global pheromone update mechanism. We applied this algorithm to the single machine total weighted tardiness scheduling problem with sequence-dependent setup times and we executed an experimental campaign on a benchmark available in literature. Results, compared with the ones produced by the ACO algorithm without adaptation mechanism and with those obtained by recently proposed metaheuristic algorithms for the same problem, highlight the quality of the proposed approach.