Bi-objective facility expansion and relayout considering monuments

In this paper, the unequal area facility expansion and relayout problem is studied. The facility relayout problem is important since both manufacturing and service entities must modify their layouts over time when their operational characteristics change. A bi-objective approach is proposed to solve the relayout problem for cases of both a fixed facility area and an expanded facility area. Material handling costs and relayout costs are minimized using a tabu search meta-heuristic optimizer. This heuristic randomly alternates the objective function between the two objectives of the problem in each step and, by doing so, eliminates the difficulty of weighting and scaling the two objectives. The approach is flexible in handling various aspects of the problem such as stationary portions of departments (i.e., monuments), addition of new departments, and changes in existing department and facility areas. Computational experiments show that the bi-objective tabu search approach is effective and tractable. The use of the Pareto front of designs is demonstrated by showing a few approaches to analyzing the trade-offs between initial costs (relayout cost) and ongoing expenses (material handling costs).     

Multiobjective optimization by genetic algorithms: application to safety systems

When attempting to optimize the design of engineered systems, the analyst is frequently faced with the demand of achieving several targets (e.g. low costs, high revenues, high reliability, low accident risks), some of which may very well be in conflict. At the same time, several requirements (e.g. maximum allowable weight, volume etc.) should also be satisfied. This kind of problem is usually tackled by focusing the optimization on a single objective which may be a weighed combination of some of the targets of the design problem and imposing some constraints to satisfy the other targets and requirements. This approach, however, introduces a strong arbitrariness in the definition of the weights and constraints levels and a criticizable homogenization of physically different targets, usually all translated in monetary terms.The purpose of this paper is to present an approach to optimization in which every target is considered as a separate objective to be optimized. For an efficient search through the solution space we use a multiobjective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with the complete spectrum of optimal solutions with respect to the various targets. Based on this information, the decision maker can select the best compromise among these objectives, without a priori introducing arbitrary weights.     

Cost modelling in polymer composite applications: Case study – Analysis of existing and automated manufacturing processes for a large wind turbine blade

Cost modelling is typically one of the first steps in the design and development of a new or existing product and can show clear avenues for effective manufacturing. This paper provides an overview of existing cost modelling techniques and details the process term technical cost modelling (TCM), which is a combined parametric and process flow simulation method used widely throughout the manufacturing industry where historical data is either not available or does not exist. A focus is drawn on the manufacture of a 40 m wind turbine blade with the effects of part size, production volume, materials costs and tooling being detailed. A case study is presented which investigates the cost effectiveness of a range of existing manual production methods (hand-lay prepreg, VI, LRTM) and compares this to automated manufacturing (ATL, AFP, overlay braiding). ATL and AFP are shown to reduce manufacturing costs by up to 8% despite the high capital costs associated with automated equipment. The cost centres are isolated and clearly indicate the dominance of materials and labour. Material deposition in the tool is only one of a string of labour intensive processes in the manufacture of a large wind turbine blades and a holistic automated blade manufacturing approach is perhaps required to see the true labour saving benefits.     

Optimization of biofuels production via a water–energy–food nexus framework

Biofuels have emerged as an attractive renewable alternative to satisfy the global energy demands. The large-scale production of biofuels requires the installation of biorefining systems that involve strategic decisions for the logistics and operation in the production of biofuels such as location, feedstock type(s), production capacities and interactions with the surrounding environment. This work proposes an optimization framework for the design of a biorefining system while accounting for the interactions with the surrounding watershed using a material flow analysis technique through the design of an efficient supply chain for the production and distribution of feedstocks, grains and biofuels considering the water and land requirements. The proposed model deals with the uncertainty involved in the project (e.g., prices of feedstocks and products, biofuel demands and precipitation in the watershed). A mixed-integer linear programming model is proposed to simultaneously consider the economic and environmental objectives. A case study located in Mexico is solved for a set of scenarios with the purpose of illustrating the capabilities of the proposed optimization approach. The results show strong trade-offs between the considered objectives and the impact of uncertainties.     

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

In the smart city paradigm, the deployment of Internet of Things (IoT) services and solutions requires extensive communication and computing resources to place and process IoT applications in real time, which consumes a lot of energy and increases operational costs. Usually, IoT applications are placed in the cloud to provide high-quality services and scalable resources. However, the existing cloud-based approach should consider the above constraints to efficiently place and process IoT applications. In this paper, an efficient optimization approach for placing IoT applications in a multi-layer fog-cloud environment is proposed using a mathematical model (Mixed-Integer Linear Programming (MILP)). This approach takes into account IoT application requirements, available resource capacities, and geographical locations of servers, which would help optimize IoT application placement decisions, considering multiple objectives such as data transmission, power consumption, and cost. Simulation experiments were conducted with various IoT applications (e.g., augmented reality, infotainment, healthcare, and compute-intensive) to simulate realistic scenarios. The results showed that the proposed approach outperformed the existing cloud-based approach in terms of reducing data transmission by 64% and the associated processing and networking power consumption costs by up to 78%. Finally, a heuristic approach was developed to validate and imitate the presented approach. It showed comparable outcomes to the proposed model, with the gap between them reach to a maximum of 5.4% of the total power consumption.     

Availability and cost functions for periodically inspected preventively maintained units

Unavailability and cost rate functions are developed for components whose failures can occur randomly but they are detected only by periodic testing or inspections. If a failure occurs between consecutive inspections, the unit remains failed until the next inspection. Components are renewed by preventive maintenance periodically, or by repair or replacement after a failure, whichever occurs first (age-replacement). The model takes into account finite repair and maintenance durations as well as costs due to testing, repair, maintenance and lost production or accidents. For normally operating units the time-related penalty is loss of production. For standby safety equipment it is the expected cost of an accident that can happen when the component is down due to a dormant failure, repair or maintenance. The objective of maintenance optimization is to minimize the total cost rate by proper selection of two intervals, one for inspections and one for replacements. General conditions and techniques are developed for solving optimal test and maintenance intervals, with and without constraints on the production loss or accident rate. Insights are gained into how the optimal intervals depend on various cost parameters and reliability characteristics.     

Cost and reliability based strategies for fatigue maintenance planning of floating structures

Floating structures are designed in such way that the appearance of fatigue failures cannot be avoided, implying the need for inspections during their life. Their maintenance has to be planned from an economic point of view so as to minimize maintenance costs but satisfying a minimum reliability level. A method is proposed to quantify the repair costs resulting of different reliability-based maintenance strategies. As an application of this approach a side shell structure typical of a floating production unit is analysed and the influence of different parameter with respect to the repair cost is also studied here.     

Game-theory approach for multi-objective optimal design of stationary flat-plate solar collectors

A game-theory approach has been used for the multi-objective optimum design of stationary flat-plate solar collectors. The clear-day solar-beam radiation and diffuse radiation at the location of the solar collector (Miami) are estimated. Three objectives are considered in the optimization-problem formulation: maximization of the annual average incident solar energy; maximization of the lowest month incident solar energy; and minimization of costs. The game-theory methodology is used for the solution of the three objective-constrained optimization problems to find a balanced solution. This solution represents the best compromise in terms of the super-criterion selected. Two types of sensitivity analyses are conducted on the optimum solution in this work. The sensitivity analysis with respect to the design variables indicates which design valuables are more important to different objective functions. The sensitivity analysis with respect to the solar constant shows that small fluctuations of solar constant experienced in practice affect the various objectives very little, thereby indicating that the mathematical model is robust. This work represents the first work aimed at the application of multi-objective optimization strategy, particularly the game theory approach, for the solution of the solar collector design problem.     

An Investigation into the Relationship Between Maintenance and Finance

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Maintenance supplier selection considering life cycle costs and risks: a fuzzy goal programming approach

In this study, we address a new variant of supplier selection problem named maintenance supplier selection problem faced by a manufacturer. The production system consists of different multi-component equipments whose maintenance activities require several components (parts) each of which could be provided by multiple suppliers. A multi-objective mathematical model is developed to decide about the supply base of each part as well as the purchasing quantity of each part from each selected supplier. The model accounts for the total life cycle costs of purchased parts and various risks threatening the candidate suppliers. A fuzzy/soft lexicographic goal programming approach with soft priorities between objectives is proposed to enable the decision-maker to make preferred trade-offs between objectives by which the effects of various risks in each phase of life cycle of procured parts are investigated. The capability and effectiveness of the proposed model is validated through a case study. Some sensitivity analyses are also carried out for investigating the impact of cost, risk and objectives’ priorities on the final preferred compromise solution. Finally, some managerial insights and concluding remarks are provided.     

Component choice for managing risk in engineered systems with generalized risk/cost functions

The constrained optimization of resource allocation to minimize the probability of failure of an engineered system relies on a probabilistic risk analysis of that system, and on ‘risk/cost functions’. These functions describe, for each possible improvement of the system's robustness, the corresponding gain of reliability given the considered component or management factor to be upgraded. These improvements can include, for example, the choice of components of different robustness levels (at different costs), addition of redundancies, or changes in operating and maintenance procedures. The optimization model is generally constrained by a maximum budget, a schedule deadline, or a maximum number of qualified personnel. A key question is thus the nature of the risk/cost function linking the costs involved and the corresponding failure-risk reduction. Most of the methods proposed in the past have relied on continuous, convex risk/cost functions reflecting decreasing marginal returns. In reality, the risk/cost functions can be simple step functions (e.g. a discrete choice among possible components), discontinuous functions characterized by continuous segments between points of discontinuity (e.g. a discrete choice among components that can be of continuously increasing levels of robustness), or continuous functions (e.g. exponentially decreasing failure risk with added resources).This paper describes a general method for the optimization of the robustness of a complex engineered system in which all three risk/cost function types may be relevant. We illustrate the method with a satellite design problem. We conclude with a discussion of the complexity of the resolution of this general type of optimization problem given the number and the types of variables involved.     

Guidelines for corrective replacement based on low stochastic structure assumptions

This paper presents corrective replacement decisions, e.g. for machines in a production process or other technical systems. In an attempt to base decisions on observed failure times only, some guidelines are provided for replacing failed machines. The method does not provide an optimal strategy in all situations, indicating that sometimes more information or assumptions are needed. The optimal policy indicates how to act if the low assumptions model recommends action. If the model does not strongly indicate an action, more data need to be collected or more sophisticated modelling is needed. Further modelling would require additional assumptions or input from expert judgements, and could be an expensive exercise. A method that gives clear guidelines if the data are strongly indicative may save time and money. This paper presents the model in an elementary form and is intended as a first step towards modelling more realistic maintenance situations. © 1997 John Wiley & Sons, Ltd.     

Establishing sensitivity requirements for environmental analyses from project data quality objectives.

This article proposes a simple strategy for establishing sensitivity requirements (quantitation limits) for environmental chemical analyses when the primary data quality objective is to determine if a contaminant of concern is greater or less than an action level (e.g., an environmental "cleanup goal," regulatory limit, or risk-based decision limit). The approach assumes that the contaminant concentrations are normally distributed with constant variance (i.e., the variance is not significantly dependent upon concentration near the action level). When the total or "field" portion of the measurement uncertainty can be estimated, the relative uncertainty at the laboratory's quantitation limit can be used to determine requirements for analytical sensitivity. If only the laboratory component of the total uncertainty is known, the approach can be used to identify analytical methods or laboratories that will not satisfy objectives for sensitivity (e.g., when selecting methodology during project planning).     

Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization

Optimization of testing and maintenance activities performed in the different systems of a complex industrial plant is of great interest as the plant availability and economy strongly depend on the maintenance activities planned. Traditionally, two types of models, i.e. deterministic and probabilistic, have been considered to simulate the impact of testing and maintenance activities on equipment unavailability and the cost involved. Both models present uncertainties that are often categorized as either aleatory or epistemic uncertainties. The second group applies when there is limited knowledge on the proper model to represent a problem, and/or the values associated to the model parameters, so the results of the calculation performed with them incorporate uncertainty. This paper addresses the problem of testing and maintenance optimization based on unavailability and cost criteria and considering epistemic uncertainty in the imperfect maintenance modelling. It is framed as a multiple criteria decision making problem where unavailability and cost act as uncertain and conflicting decision criteria. A tolerance interval based approach is used to address uncertainty with regard to effectiveness parameter and imperfect maintenance model embedded within a multiple-objective genetic algorithm. A case of application for a stand-by safety related system of a nuclear power plant is presented. The results obtained in this application show the importance of considering uncertainties in the modelling of imperfect maintenance, as the optimal solutions found are associated with a large uncertainty that influences the final decision making depending on, for example, if the decision maker is risk averse or risk neutral.     

Probability Constrained Optimization as a Tool for Functional Design for Six Sigma

An important up-stream activity in the overall design of a system is the so-called functional design wherein the means and tolerances of the design variables are determined with respect to the competing demands of quality and cost. In this article probability constrained optimization is invoked to produce a functional design that focuses on the goal of design for Six Sigma (i.e., improved customer satisfaction, robustness, and predictable cost levels). Herein, a maximum system probability of nonconformance is obtained from a prescribed defect rate that in turn provides the primary design constraint. The production cost provides the objective function to be minimized in order to allocate the design parameters. All three quality metrics (e.g., target/larger/smaller-is-best) and robustness are inherent in the approach. The design of an electro-mechanical servo system serves as a case study wherein three responses are related to three control variables and two noise variables by mechanistic models. Designs for selected defect rates show the practicality and potential of the approach.     

Modelling and simulation of adhesive curing processes in bonded piezo metal composites

This work deals with the modelling and simulation of curing phenomena in adhesively bonded piezo metal composites (PMC) which consist of an adhesive layer, an integrated piezoelectric module and two surrounding metal sheet layers. In a first step, a finite strain modelling framework for the representation of polymer curing phenomena is proposed. Based on this formulation, a concretised model is deduced and applied to one specific epoxy based adhesive. Here, appropriate material functions are provided and the thermodynamic consistency is proved. Regarding the finite element implementation, a numerical scheme for time integration and a new approach for maintaining a constant initial volume at arbitrary initial conditions are provided. Finally, finite element simulations of a newly proposed manufacturing process for the production of bonded PMC structures are conducted. Thereby, a representative deep drawing process is analysed with respect to the impact of the adhesive layer on the embedded piezoelectric module.     

Stochastic ordering for populations of manufactured items

We develop a theory of stochastic orders for the age and the residual (remaining) lifetime for populations of manufactured identical items. The obtained theoretical results can be used by manufacturers or users for the justified decisions with respect to, e.g., the increase or decrease in the production rate or with respect to the necessary maintenance actions. Specifically, we show that if the random age of a population is smaller (resp. larger) in some stochastic sense than the defined equilibrium age, then it is also smaller (resp. larger) than the corresponding residual lifetime with respect to different stochastic orders. We discuss various stochastic comparisons between the random age and the residual lifetime for one or more populations. Some ageing properties of the random age and the residual lifetime have also been studied.     

A maintenance driven scheduling cockpit for integrated production and maintenance operation schedule

The production and maintenance functions have objectives that are often in contrast and it is essential for management to ensure that their activities are carried out synergistically, to ensure the maximum efficiency of the production plant as well as the minimization of management costs. The current evolution of ICT technologies and maintenance strategies in the industrial field is making possible a greater integration between production and maintenance. This work addresses this challenge by combining the knowledge of the data collected from physical assets for predictive maintenance management with the possibility of dynamic simulate the future behaviour of the manufacturing system through a digital twin for optimal management of maintenance interventions. The paper, indeed, presents a supporting digital cockpit for production and maintenance integrated scheduling. The tool proposes an innovative approach to manage health data from machines being in any production system and provides support to compare the information about their remaining useful life (RUL) with the respective production schedule. The maintenance driven scheduling cockpit (MDSC) offers, indeed, a supporting decision tool for the maintenance strategy to be implemented that can help production and maintenance managers in the optimal scheduling of preventive maintenance interventions based on RUL estimation. The simulation is performed by varying the production schedule with the maintenance tasks involvement; opportune decisions are taken evaluating the total costs related to the simulated strategy and the impact on the production schedule.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00380-z     

Developing an optimum maintenance policy by life cycle cost analysis – a case study

This paper focuses on developing maintenance policies for critical assets to improve the production performance based on life cycle cost (LCC) analysis. A general approach is adopted for conducting the LCC analysis. The investigation is based on a case study to demonstrate how an optimum maintenance policy is determined. The relevant LCC structure in the case study is defined for the decision process which involves determination of the optimum life, repair limit and selection of materials, and trade-off between repair and replacement. The LCC analysis is based on statistical data modelling which facilitates decision-making on the optimal replacement of an asset and its remaining life. Based on the optimization and remaining life criterion, the optimal maintenance policy can be made. The results obtained from this case study include selection of the best lining material for use, determination of the optimal time for refractory lining replacement, the hot repair sequence required for maintaining the optimum condition and the repair limit for doing cold repairs before replacement, for one type of electric arc furnaces used in the steel industry.     

Optimizing Transportation Problems with Multiple Objectives

Virtually all models developed for transportation problems have focused upon the optimization of a single objective criterion, namely the minimization of total transportation costs. They have generally neglected or often ignored the multiple conflicting objectives involved in the problem, the priority structure of these objectives, various environmental constraints, unique organizational values of the firm, and bureaucratic decision structures. However, in reality these are important factors which greatly influence the decision process of transportation problems. In this study the goal programming approach is utilized in order to allow for the optimization of multiple conflicting goals while permitting an explicit consideration of the existing decision environment.