Indoor Environmental Quality (IEQ): A Comparison between TOPSIS- and PROMETHEE-Based Approaches for Indirect Eliciting of Category Weights

Indoor Environmental Quality (IEQ) has received a great deal of attention in recent years due to the relationship between worker comfort and productivity. Many academics have studied IEQ from both a building design and an IEQ assessment perspective. This latter line of research has mostly used direct eliciting to obtain weights assigned to IEQ categories such as thermal comfort, visual comfort, acoustic comfort, and indoor air quality. We found only one application of indirect eliciting in the literature. Such indirect eliciting operates without the need for imprecise direct weighing and requires only comfort evaluations, which is in line with the Industry 5.0 paradigm of individual, dynamic, and integrated IEQ evaluation. In this paper, we use a case study to compare the only indirect eliciting model already applied to IEQ, based on TOPSIS, to an indirect eliciting method based on PROMETHEE and to a classical direct eliciting method (AHP). The results demonstrate the superiority of indirect eliciting in reconstructing individual preferences related to perceived global comfort

Exposure to Air Pollution in Transport Microenvironments

People spend approximately 90% of their day in confined spaces (at home, work, school or in transit). During these periods, exposure to high concentrations of atmospheric pollutants can pose serious health risks, particularly to the respiratory system. The objective of this paper is to define a framework of the existing literature on the assessment of air quality in various transport microenvironments. A total of 297 papers, published from 2002 to 2021, were analyzed with respect to the type of transport microenvironments, the pollutants monitored, the concentrations measured and the sampling methods adopted. The analysis emphasizes the increasing interest in this topic, particularly regarding the evaluation of exposure in moving cars and buses. It specifically focuses on the exposure of occupants to atmospheric particulate matter (PM) and total volatile organic compounds (TVOCs). Concentrations of these pollutants can reach several hundreds of µg/m3 in some cases, significantly exceeding the recommended levels. The findings presented in this paper serve as a valuable resource for urban planners and decision-makers in formulating effective urban policies

Order Picking Problem: A Model for the Joint Optimisation of Order Batching, Batch Assignment Sequencing, and Picking Routing

Background: Order picking is a critical activity in end-product warehouses, particularly using the picker-to-part system, entail substantial manual labor, representing approximately 60% of warehouse work. Methods: This study develops a new linear model to perform batching, which allows for defining, assigning, and sequencing batches and determining the best routing strategy. Its goal is to minimise the completion time and the weighted sum of tardiness and earliness of orders. We developed a second linear model without the constraints related to the picking routing to reduce complexity. This model searches for the best routing using the closest neighbour approach. As both models were too complex to test, the earliest due date constructive heuristic algorithm was developed. To improve the solution, we implemented various algorithms, from multi-start with random ordering to more complex like iterated local search. Results: The proposed models were tested on a real case study where the picking time was reduced by 57% compared to single-order strategy. Conclusions: The results showed that the iterated local search multiple perturbation algorithms could successfully identify the minimum solution and significantly improve the solution initially obtained with the heuristic earliest due date algorithm

Order Picking Systems: A Queue Model for Dimensioning the Storage Capacity, the Crew of Pickers, and the AGV Fleet

Designing an order picking system can be very complex, as several interrelated control variables are involved. We address the sizing of the storage capacity of the picking bay, the crew of pickers, and the AGV fleet, which are the most important variables from a tactical viewpoint in a parts-to-pickers system. Although order picking is a widely explored topic in the literature, no analytical model that can simultaneously deal with these variables is currently available. To bridge this gap, we introduce a queue model for Markovian processes, which enables us to jointly optimise the aforementioned control variables. A discrete-event simulation is then used to validate our model, and we then test our proposal with real data under different operative scenarios, with the aim of assessing the usefulness of the proposal in real settings

Age-based preventive maintenance with multiple printing options

In today's economic context, production systems must be readily available and machinery downtime kept to a minimum. Maintenance and spare parts inventory management play a vital role in achieving these goals, and preventive maintenance has increasingly been considered in maintenance policies. Additive manufacturing (AM) has recently been combined with preventive maintenance, and thus represents an emerging research direction. However, few studies have as yet been conducted in this research stream, and we intend to fill this gap. Our study makes three main contributions. First, we address the main limitations of two current models (i.e., assuming that no failure occurs during the replenishment lead time of the spare parts). Second, we propose a new maintenance policy that considers two printing options with different levels of reliability and unitary purchase costs. Third, we develop a decision support system (DSS) to assist managers in deciding whether to implement a preventive maintenance policy that includes AM or conventional manufacturing (CM) parts. We take an interdisciplinary approach to conducting a parametrical analysis where we consider real data on the reliability of CM and AM parts, in addition to the impact of post-processing operations and optimization routines. We find that AM-based preventive maintenance policies are favored when the MTTF and the backorder costs are low and when the failure and maintenance costs are high. These findings have been incorporated into the DSS, which provides thresholds for every parameter to guide practitioners in choosing between AM and CM parts for preventive maintenance, without requiring time-expensive calculations

Resizing the Workforce for Picking Activity: Application in the Fashion Sector

Order picking is one of the most critical activities in warehouses as being the most labor intensive with costs that can be up to 55% of total warehouse expenses. In this context the right sizing of picking workforce is decisive and has to guarantee a satisfactory service level. In this paper, workforce resizing for warehouse picking activities, was investigated in the light of the growth of receptivity required by one of the commissioning firms. Given the high labour intensity in the picking activities, the first phase of our analytical framework for the workforce resizing incl udes a statistical validation of the law of diminishing returns, which can be viewed as an effect of the free-rider behaviour, and then (i.e., second phase) a fitting approach of the said law; the curve that best fits the historical data is used in the third phase to forecast the future productivity. The last phase is made of an analytical procedure to derive the average future required number of ordinary and overtime pickers. We applied our framework in a real warehouse for a firm in the fashion sector, results highlighted a necessity for workforce increase, compared to the “as-is” scenario; this will allow the firm to strategically identify future workforce size requirements, from a cost-based perspective

On demand printing with Additive Manufacturing (AM) for spare parts: scenarios for the insourcing of a 3D Printer

Additive Manufacturing (AM) has become a promising technique for spare parts management. The reduced lead time of AM compared to Classical Manufacturing (CM) has attracted the interest of researchers and many applications of AM to spare parts management have been introduced in the literature. However, the high production and equipment costs obscure the advantages of AM to spare parts management to practitioners and academics. The recent literature on spare parts management with AM have two main limitations which we address in this work. The first is that AM spare parts are mistakenly assumed to be less reliable than CM ones, which has been refuted by the recent literature on the mechanical characteristics of AM parts. Secondly, the external supply of AM parts that excludes the investment cost of the equipment. Our model overcomes these limitations by taking into account a spare part installed on a fleet of systems which failures are based on failure data from recent literature. In addition, we consider an insourced 3D printer, and account for the purchasing cost. We propose several scenarios for the insourcing of a 3D printing, considering a future cost reduction and constrained stock systems, individuating constrained stock system with high lead times for the CM part, ideal for in-house printing. The work has been supported by the project SUPERCRAFT, funded by the Emilia-Romagna Region (Italy) with European funds (POR FESR)