The human-centric approach of Industry 5.0 underscores the integration of advanced technologies and information systems to enhance worker well-being, productivity, and safety. Significant progress has been made in automation and digitalization; however, the high prevalence of work-related musculoskeletal disorders (WRMSDs) remains a critical challenge, translating into a significant socioeconomic burden. Nevertheless, industrial practice still predominantly relies on observational ergonomic assessment methods and reactive ergonomic strategies, creating an urgent need for flexible, proactive, individualized, and easy-to-implement risk mitigation approaches. This paper addresses this gap by proposing an intelligent decision-support system based on a multi-objective optimization model that integrates heterogeneous information - workers’ anthropometric measures, task requirements, and personal habits - within an expert-system architecture for industrial applications. The method relies on a convex integer program that can be embedded in machine controllers to compute the relative position between the product and the operator, minimizing ergonomic risks. Key innovations include the adoption of ergonomic principles without complex inverse kinematics, the explicit involvement of workers to account for their preferences, and the joint consideration of tasks involving both visual and physical interaction with the product. Experimental validation was conducted in a virtual environment simulating typical manufacturing scenarios, with diverse users and products. Results showed significant reductions in ergonomic risks with optimized positions, especially for smaller products, whereas larger ones posed challenges due to their size and task distribution. Statistical analyses validated these findings, highlighting the model’s potential to reduce the REBA (Rapid Entire Body Assessment) risk index and enhance operational efficiency. Overall, the proposed system provides actionable set-points for workstation configuration and practical guidance for implementation, thus supporting human-centric manufacturing in Industry 5.0.

In our partnership with a leading company specializing in automatic cutting machines for reinforcement processes, we address the management of a work center whose optimization calls for the solution of four distinct subproblems. Focusing on the third one, the subproblem asks for the effective packing of items on the identical buffers of a stocking area. The items arrive divided into subgroups (i.e., patterns), are associated with orders, and have time windows. We devise an SVC heuristic that efficiently determines feasible packing solutions while simultaneously minimizing the number of used buffers, lowering operations and fragmented orders. The SVC incorporates the idea of reachable points to restrict the location sets on the buffers. The experimental campaign highlights the SVC’s effectiveness in achieving optimality for small realistic instances, with a specific emphasis on reducing fragmented orders. Additionally, the approach showcased its ability to explore the multi-objective space and demonstrated scalability in solving practical instances.

In this paper we deal with the problem of deciding the best assortment and cut of defective bidimensional stocks. The problem, originating in a glass manufacturing process, can arise in various industrial contexts. We propose a novel bilevel programming approach describing a competition between two decision makers with contrasting objectives: one aims at fulfilling production requirements, the other at generating defects that, damaging the products, reduce yield as much as possible. By exploiting nice properties of adversarial optimal solutions, the bilevel program is rewritten as a one-level 0-1 linear program. Computational results achieved on random instances with realistic features are discussed, showing the quality and the benefits of the proposed approach in reducing the yield loss from defective material in a worst-case perspective.

Modern energy system are evolving due to the opportunities and challenges that new technologies pose in the energy sector. These changes create the requirements of decision tools able to effectively sustain the processes of design and retrofit of energy systems. In this paper a multi-energy system management problem is taken into account and a mixed integer linear programming (MILP) formulation is proposed to model both the design and the resource scheduling of energy districts. However, since the size of the formulation restricts its applicability to small cases far from the application of interest, a matheuristic based on constraint relaxations and variable fixing has been designed. Preliminary computational results show that the proposed solution strategy is able to achieve good solutions (i.e., solutions with small optimality gaps) on restricted random instances, and to solve in reasonable times instances derived from a real case study.

Cutting operations in manufacturing are characterized by practical requirements and utility criteria that usually increase the complexity of formulations or, even worse, are difficult to be modeled in terms of mathematical programming. However, disregarding or just simplifying those requirements often leads to solutions considered not attractive or even useless by the manufacturer. In this paper we consider a rich two-dimensional cutting stock problem that covers the whole specification of a family of wood cutting machines produced by a worldwide leader in industrial machinery manufacturing. A sequential value correction heuristic is implemented to minimize the employed stock area while reducing additional objective functions.

The analysis of non-functional properties of Service-Based Systems (SBSs) is a complex task, mostly because it requires models that encompass the composition of service properties into architectural properties. For example, the reliability of a SBS is given by the composition of service and interconnection reliabilities. Although several approaches have been introduced in the last few years to address these issues, the tradeoff analysis among non-functional properties of software services has not yet been studied enough. The goal of this paper is to introduce a set of optimization models that allow quantifying the costs of service failure repair/mitigation actions aimed at keeping the whole SBS reliability over a certain threshold. On the basis of our previous work in this area, we first introduce an optimization model aimed at selecting either in-house built or provided services with the goal of minimizing the SBS cost while guaranteeing a certain level of reliability. Thereafter we strengthen the reliability constraints, and we build two different optimization models that aim to solve the same problem under new constraints, where one model starts from the solution obtained in the original model and tries to improve it, while the other one looks for an optimal solution in the whole search space. Finally, we introduce a fourth model, based on stochastic optimization, with the goal of rather searching for solutions that explicitly take into account the stochastic nature of the problem and search for new repair/mitigation actions cheaper than the ones identified by the other models. Each optimization model has been experimented on about 300 variations of a nominal model. The experimental results show the efficacy of our optimization models to quantify the costs of different failure repairing/mitigation actions in different contexts.

Abstract We discuss two formulations of the pattern minimization problem: (1) introduced by Vanderbeck, and (2) obtained adding setup variables to the cutting stock formulation by Gilmore‐Gomory. Let z (u) be the bound given by the linear relaxation of (i) under a given vector u of parameters. We show that z (u) ≥ z (u) and provide a class of instances for which the inequality holds strict. We observe that the linear relaxation of both formulations can be solved by the same column generation procedure and discuss the critical role of parameter u. The article is completed by a numerical test comparing the lower bounds obtained through (1) and (2) for different values of u. © 2011 Wiley Periodicals, Inc. NETWORKS, 2011

Mass customization requires us to select a few types of resources to produce heterogeneous classes of products. In the assortment problem addressed here, a resource unit of type j yields, at a cost cij, a batch of aij product units of type i. The problem, a generalization of the p-median, calls for (i) choosing a restricted subset of resource types and (ii) assigning resource units to products so as to fulfill a given demand vector at a minimum cost. For this problem, we develop a branch-and-price scheme that can either be used to find optimal solutions, or tuned by choosing columns in a suitable class so as to get approximate solutions. The solutions obtained in the second case approach the optimum by a ratio that asymptotically reduces to zero as the demand of the least-required product increases. A comparative analysis of the features of the algorithm is discussed for a wide set of large problem instances.

In a component-based development process the selection of components is an activity that takes place over multiple lifecycle phases that span from requirement specifications through design to implementation-integration. Automated tool support for component selection would be very helpful in each phase. In this paper we introduce a framework that supports the selection of COTS components in the requirements phase. The framework lays on a tool that builds and solves an optimization model, whose solution provides the optimal COTS component selection. The selection criterion is based on cost minimization of the whole system while assuring a certain degree of satisfaction of the system requirements. The output of the model solution indicates the optimal combination of single COTS components and assemblies of COTS that satisfy the requirements while minimizing costs