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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.

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Nowadays most industries do not integrate product, process and energy data. Costs due to energy consumption are often considered externalities and energy efficiency is not deemed a relevant performance criterion. In energy-intensive processes, as injection moulding, the specific energy consumption, embedded inside the same products, depends on the machine–product combinations. Multi-objective scheduling, including the energy data acquired from shop floor and allocation criteria, is a valuable approach to improve energy efficiency. This paper presents the extension of a commercial detailed scheduling support system developed within a regional Italian project aiming at providing tools to manufacturing industry for improving energy efficiency. The project designed a monitoring system developed by instrumenting injection moulding presses to acquire the energy consumption for each product–machine combination. The commercial scheduling system was extended by implementing a multi-objective metaheuristic scheduling approach. The experimental assessment of the proposed approach involved a major producer of plastic dispensers. The extended algorithm simultaneously optimizes the total weighted tardiness, the total setup and the energy consumption costs. The obtained results, produced for a real test case and a set of random generated instances, show the effectiveness of the proposed approach

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In this work, we are looking at the problem of determining stowage plans for containerships. This problem, denoted in the literature as the Master Bay Plan Problem (MBPP), is computationally difficult to solve, that is NP-hard. We start from the optimal solution of subsets of bays related to independent portions of the ship, which are determined by a previously proposed decomposition approach for the MBPP; then, we look for the global ship stability of the overall stowage plan by using a tabu search (TS) meta-heuristic approach. Note that at the same time the proposed TS algorithm allows us to further reduce the handling time of the containers to be loaded on the ship. The proposed heuristics has been implemented within a software support system that helps the planning management in the visualisation of the stowage plans of each bay of the ship. Preliminary computational experimentations performed on some real-life test cases related to a terminal located at the port of Genoa, Italy are provided. Maritime Economics & Logistics (2009) 11, 98–120. doi:10.1057/mel.2008.19

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