Rita Gamberini

A multiple single-pass heuristic algorithm solving the stochastic assembly line rebalancing problem

Assembly line rebalancing is a problem companies are frequently confronted with as continuous changes in product features and volume demand caused by the volatility of modern markets result in re-definition of assembly tasks and line cycle time fluctuations. Consequently, managers are forced to adjust the balancing of their lines in order to adapt to the new conditions while trying to minimise both increases in completion costs and costs related to changes in task assignment. In particular, when modifications are made to line balancing, costs are incurred for operator training, equipment switching and moving, and quality assurance. The stochastic assembly line rebalancing problem is essentially composed of a multi-objective problem in which two joint objectives, total expected completion cost of the new line and similarity between the new and the existing line, must be optimised. Consequently, this paper presents a multiple single-pass heuristic algorithm developed for the purpose of finding the most complete set of dominant solutions representing the Pareto front of the problem. The operative parameters of the heuristic are set as a result of a great deal of experimentation. Moreover, a multi-objective genetic algorithm is developed and then compared with the proposed heuristic in order to demonstrate its effectiveness. Finally, an illustrative case study is presented.

An innovative container for WEEE collection and transport: details and effects following the adoption.

The content of hazardous components in Waste arising from Electrical and Electronic Equipment (WEEE) is a major concern that urges governments and industry to take measures to ensure proper treatment and disposal. Thus, the European Union issued directives to encourage reuse, recycling and other proper forms of recovery of such waste while companies and academics are still studying methods and technologies for optimizing recovery processes. This paper presents an analysis of the logistics process assuring the correct collection, handling, transportation and storing of WEEE. The experience comes from an Italian WEEE treatment plant (TRED Carpi S.r.l.) where a new kind of container has been introduced in order to improve the logistics system. An evaluation framework is described and used in order to compare different system configurations and assess the advantages emerging from adopting proper equipments for WEEE transport and handling.

Waste treatment: an environmental, economic and social analysis with a new group fuzzy PROMETHEE approach

Most complex decisions involve several stakeholders and therefore need to be solved using a group multi-criteria decision method. However, stakeholders or decision-makers often have divergent views, especially in the environmental sector. In order to integrate this divergence, a new group fuzzy PROMETHEE approach is introduced to combine the traditional environmental criteria of life cycle assessments with social and economic criteria. The modelling of uncertainty within the group of decision-makers using a fuzzy approach makes this method unique. The proposed fuzzy approach differs significantly from the standard one. The decision-makers express their judgments in crisp forms. In order to take into account the intrinsic dispersion of judgments within the group, a posteriori fuzzification procedure is applied. The crisp values are not simply aggregated; they are converted into a triangular fuzzy number based on the given evaluations. As a consequence, the definition of fuzzy membership functions, as required in standard fuzzy logic, is not required, which simplifies the process and makes it more reliable. The new approach is illustrated with a real case study concerning the selection of the best waste treatment solution in a natural park from among a traditional incinerator and an innovative integrated plant.

Municipal solid waste management: identification and analysis of engineering indexes representing demand and costs generated in virtuous Italian communities.

The definition and utilisation of engineering indexes in the field of Municipal Solid Waste Management (MSWM) is an issue of interest for technicians and scientists, which is widely discussed in literature. Specifically, the availability of consolidated engineering indexes is useful when new waste collection services are designed, along with when their performance is evaluated after a warm-up period. However, most published works in the field of MSWM complete their study with an analysis of isolated case studies. Conversely, decision makers require tools for information collection and exchange in order to trace the trends of these engineering indexes in large experiments. In this paper, common engineering indexes are presented and their values analysed in virtuous Italian communities, with the aim of contributing to the creation of a useful database whose data could be used during experiments, by indicating examples of MSWM demand profiles and the costs required to manage them.

An innovative model for WEEE recovery network management in accordance with the EU directives

An increased interest towards environmental respect amongst consumers, managers and researchers is registered, owing both to changing sensitivity and guidelines described in technical regulations. In this scenario, the European Union (EU) directives 2002/96/EC and 2003/108/EC, controlling the management of Wastes of Electric and Electronic Equipments (WEEE) are well-inserted. Companies producing Electric and Electronic Equipments (EEE) become responsible for end of life steps of their products. Hence, great efforts are made to optimise both recovery networks and remanufacturing and recycling processes. This paper proposes an innovative model for recovery network management. Included is a case study.

Designing production and service systems using queuing theory: principles and application to an airport passenger security screening system

Queues of people, products and machines frequently occur in many production and service systems, resulting in significant inefficiencies. This paper discusses the significant impact on these problems of the queuing theory introduced by Erlang and Kendall. A methodology based on the M/M/m queuing model (including a validation phase through a goodness-of-fit test) is proposed. This methodology makes parametric analyses of system performance according to the different possible ranges of input parameters. It helps solve several typical problems found in production systems (e.g., resource design, traffic and logistics analysis) and services (e.g., optimal design and management). There is a good tradeoff between the robustness of the results, coherence with real industrial systems and mathematical complexity. A real-world application involving the design optimisation of a passenger security screening system in an international airport is presented. In particular, the optimal number of security gates in the design is discussed.

A learning model for the allocation of training hours in a multistage setting

In line with the continuous improvement theory, the learning phenomenon is often incorporated into models for predicting the evolution of the unitary quality costs. In this paper, the quality metric predicted is the rate of supplied non-conforming units through a learning process with autonomous and induced sources of experience. The former is simply learning by doing, i.e. supplying, whilst the latter is driven by the allocation of training hours to suppliers. A revised learning model with time-varying learning rates is proposed for embracing both these effects into a multistage assembly/production setting. A single-period prevention–appraisal–failure cost function is achieved, and the sample inspection rates adopted among suppliers are also considered in order to evaluate their effect. If these sample rates are given, the goal of allocating the training hours among suppliers is pursued by means of integer linear programming. Otherwise, a mixed-integer quadratic problem arises for the concurrent allocation of training hours and inspection sample rates among suppliers. A case study is finally carried out for demonstrating the applicability of the model, as well as for providing managerial insights.

A revised FMEA with application to a blow moulding process

Purpose – The purpose of this paper is to present a modified failure mode and effects analysis (FMEA) in order to make the assignment of the scores for the occurrence factor more robust, and to link the FMEA chart directly to the maintenance activities. Design/methodology/approach – A well-known clustering algorithm (i.e. K-means), along with a normalisation approach, are applied and compared for the assignment of the occurrence scores. Subsequently, the relationship between failures and maintenance operations is made explicit by a correlation matrix. Finally, the K-means algorithm is applied to the maintenance operations again in order to sort them into priority classes. Findings – It is found that this revised FMEA approach improves the standard one due to its more rigorous mathematical formulation and lean applicability in real operating environments. Research limitations/implications – The novel approach may be improved by a deeper statistical analysis and/or applying the fuzzy theory. Practical impli...

A simulative approach for evaluating alternative feeding scenarios in a kanban system

In accordance with the lean production philosophy, an assembly line may be supplied by means of a kanban system, which regulates and simplifies the flow of materials between the lines and the warehouses. This paper focuses on evaluation of feeding policies that differ from each other in term of the number of kanbans managed per feeding tour. A pure cost-based approach is thus proposed, which considers both inline inventories along with handling costs proportionate to the number of operators involved in the parts-feeding process. A multi-scenario simulative approach is applied in order to establish the number of operators required to avoid inline shortages. The scenario minimising total cost is then selected. The innovation introduced is a model for describing kanban arrivals and their requests for feeding, improving the potential of the simulation to describe real-life environments. Lastly, a case study from the automotive industry is presented in order to highlight the applicability of the proposed approach as well and the effects of alternative feeding policies on the total cost incurred.

An innovative approach for job pre-allocation to parallel unrelated machines in the case of a batch sequence dependent manufacturing environment

The problem of allocating jobs to a set of parallel unrelated machines in a make to stock manufacturing system is studied. The items are subdivided into families of similar products. Sequence-dependent setups arise when products belonging both to the same family and to different families are sequenced. Restrictions on the number of available setups should be considered. The availability of planning batch production exists. Nevertheless, batch size is not known a priori. Hence, a solving approach considering both a pre-assignment procedure and a scheduling algorithm is proposed. Specifically, the focus of the article is on the pre-assignment methodology: a pre-assignment model (solved by a commercial solver) and two heuristics are presented and compared, in order to minimise the average idle residual capacity during the planning horizon, while considering pejorative factors related with the split volumes of the same product on different machines, unsatisfied demand along with demand produced in advance in each time period. The application to a case study is finally described in order to assess the performance of the proposed approach.