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This paper aims to determine the effect of exposure of underground electrical cables to chemically contaminated water.

Visual inspection and photography were carried out to record the appearance of electrical cables. Failed and un-failed cable samples were collected and analyzed using light microscopy, scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy and Fourier transform infrared spectroscopy. Sand and water samples were chemically tested for contaminants.

Underground low-voltage 0.6/1-kV cross-linked polyethene insulated cables belonging to a chemical production plant suffered failure after four years of service. Excavation of the cable trench revealed that the cables were buried in sand polluted with chemically contaminated water. The cables were discolored and covered with corrosion deposits. Experimental results indicated that the cable insulation was heavily degraded and the outer jacket of polyvinyl chloride exhibited cracks that had penetrated through its thickness. Water and sand surrounding the cable were found to have high concentrations of ammonia. Mechanical testing of the cables indicated high values of stiffness that could contribute to the formation of cracks at the surface.

It was concluded that contamination in the water had degraded the cable, resulting in the development of a network of branched cracks within the cable insulation through which water could permeate, leading to eventual failure of the cable. Accelerated degradation took place due to exposure to the contaminated environment, which promoted aging and brittleness. Continued exposure of electric cables to contamination would lead to power failures and plant shutdowns.

This paper provides an account of a failure investigation of low-voltage electrical cable buried underground. It discusses the role of contaminated environment in the eventual failure of electrical cable due to corrosion. This information will be useful for plant engineers and project managers working in any industry that makes use of chemicals.

This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.

The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.

In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.

This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.

This paper proposes a two-level supply chain including suppliers and manufacturers. The purpose of this paper is to design a resilient fuzzy risk-averse supply portfolio selection approach with lead-time sensitive manufacturers under partial and complete supply facility disruption in addition to the operational risk of imprecise demand to minimize the mean-risk costs. This problem is analyzed for a risk-averse decision maker, and the authors use the conditional value-at-risk (CVaR) as a risk measure, which has particular applications in financial engineering.

The methodology of the current research includes two phases of conceptual model and mathematical model. In the conceptual model phase, a new supply portfolio selection problem is presented under disruption and operational risks for lead-time sensitive manufacturers and considers resilience strategies for risk-averse decision makers. In the mathematical model phase, the stages of risk-averse two-stage fuzzy-stochastic programming model are formulated according to the above conceptual model, which minimizes the mean-CVaR costs.

In this paper, several computational experiments were conducted with sensitivity analysis by GAMS (General algebraic modeling system) software to determine the efficiency and significance of the developed model. Results show that the sensitivity of manufacturers to the lead time as well as the occurrence of disruption and operational risks, significantly affect the structure of the supply portfolio selection; hence, manufacturers should be taken into account in the design of this problem.

The study proposes a new two-stage fuzzy-stochastic scenario-based mathematical programming model for the resilient supply portfolio selection for risk-averse decision-makers under disruption and operational risks. This model assumes that the manufacturers are sensitive to lead time, so the demand of manufacturers depends on the suppliers who provide them with services. To manage risks, this model also considers proactive (supplier fortification, pre-positioned emergency inventory) and reactive (revision of allocation decisions) resilience strategies.

Process mining helps organizations improve their business processes in today’s data-rich environment. However, these processes can change over time due to factors like policy shifts or process trends, impacting model performance. This study examines process behavior in event logs and uses machine learning to detect concept drift.

The trace clustering and change mining techniques have been implemented on two processes, namely loan payment and temporary identity creation, to detect drifts. We use the bag-of-activities and edit distance methods, along with K-Mode and agglomerative hierarchical clustering techniques.

This study makes two important findings: trace clustering is a popular choice for detecting drifts, and the bag-of-activities method using K-Mode clustering and hamming distance proved highly effective at spotting drifts in various event logs. It also identifies different types of drifts occurring simultaneously in the processes.

The drifts discovered in different processes provide a real-world example of concept drift in the domains of loans and university administrations. This contributes to improving operational efficiency and overall organizational performance based on these detected drifts and assists in enhancing the process design.

This study is the first to employ a hybrid method of trace clustering and change mining to detect process changes. It is also the first to simultaneously detect sudden and recurring drift in the field of trace clustering in process mining. Furthermore, it stands out for investigating and comparing the performance of multiple clustering methods, in contrast to prior research that used a single technique. Additionally, it is pioneering in applying machine learning methods to detect drift in the domain of loan processes.