Post-optimality Analysis Research Articles

Emergency monitoring layout method for sudden air pollution accidents based on a dispersion model, fuzzy evaluation, and post-optimality analysis

Air pollutant distributions identified through monitoring form an important basis for devising adequate emergency responses against sudden air pollution accidents (SAPAs). However, studies on the optimisation of monitoring layouts for SAPAs, which can help us better understand air pollutant dispersion, are lacking. We developed an emergency monitoring layout optimisation method for SAPAs that combines an air pollutant dispersion model, a fuzzy comprehensive evaluation method (FCEM), and a post-optimality analysis technique (POAT) to identify the optimal monitoring layout scheme based on a field sulphur hexafluoride (SF6) leakage test. The impact of the number of monitoring sites on the results (coverage rate and accuracy) were investigated and analysed through a proposed comprehensive evaluation method. A monitoring result–monitoring number curve was plotted to determine the optimal number of monitoring sites. Two scenarios, i.e. with and without environmentally sensitive monitoring sites, were analysed. The results showed that the developed optimisation method can effectively consider the role of sensitive sites and obtain a feasible network for monitoring SAPAs. The coverage rates of the optimal monitoring layout schemes were 76.2% and 61.9% for the two case scenarios, respectively; the relative deviations of mean air pollutant concentrations at the sites were 14.6% and −12.2%, respectively. We investigated the effect of incorporating POAT into the method by comparing the optimal emergency monitoring layout schemes based on dispersion model–FCEM and model–FCEM–POAT. The results showed that the coverage rates of the optimised monitoring layout schemes improved when POAT was introduced. The proposed method can help determine an adequate, optimised emergency monitoring network for SAPAs.

Building-to-vehicle-to-building approach for the NZEB target at a micro-grid level: a comprehensive sensitivity and parametric post-optimality analysis

This paper focuses on a novel energy management approach for cluster of buildings connected in micro-grids by taking advantages from plug-in electric vehicles considered as vector devices for renewable energy exchanges, besides additional high-power appliances and house electricity sources. Such approach allows accelerating the development of nearly zero energy buildings and promoting the deployment of renewable energy sources at a micro grid level. To this aim, a dynamic simulation model, implemented in MatLab was developed for the building energy demands and loads assessment. To show the potentiality of the considered concept and approach, a case study based on a micro-grid consisting of a house and an office building connected by an electric vehicle is presented. The optimization of three different layouts, where electricity is alternatively produced by tilted roof or vertical façade photovoltaic panels is conducted by means of a parametric analysis performed by varying the vehicles energy use, battery capacities and solar field size. Preliminary results show that, by considering economic criteria, by exploiting the renewable energy production on and off-site, the buildings final demands decrease to values lower than those commonly considered for NZEBs.

A study on practical objectives and constraints for heat conduction topology optimization

This paper presents some practical formulations for heat conduction topology optimization problems. In post-optimization analysis, temperature metrics are often used to compare the performance of optimized structures, yet are not used generally as optimization objectives. In this article, SIMP-based topology optimization is used to explore several objective functions related to electronics applications to demonstrate clearly the impact of improper objective selection. Performance variations over 100% were observed when comparing key metrics between optimized structures. Findings here are extended to problems in electronics domains, where temperature optimization may be used in unconventional ways to capture more realistic design considerations. This includes an investigation in the combinatorial use of objectives and constraints to satisfy electronics requirements. Four case studies are presented where topology optimization methods are used to maximize system performance metrics while satisfying temperature constraints.

Optimizing the equity reassignment process: A novel application for family businesses

PurposeThis paper aims to support the equity reassignment process of large family business conglomerates, which can be complex due to both the nature and number of companies involved and specific owner requirements. Addressing these issues is especially important in the context of family businesses, since a successful reassignment might resolve and prevent family conflicts. Design/methodology/approachThe paper presents a model that determines the optimal reassignment in terms of a specific owner's preferences. This model can also handle different types of requirements, including accounting for equity and intra-loan partition between owners and controlling for liquidity, capital structure, and transaction costs. The model also considers risk diversification for each member's fortune by considering the uncertainty involved in the future value of each firm, which can change at any point depending on industry and market conditions. The methodology not only finds the optimal solution in terms of a specific target, but it allows for post-optimal analysis so that owners can obtain important insights in terms of the costs involved in adding each requirement to the model. Findings/ResultsThe model was successfully applied in a real case study. The tool played a primary role in identifying a new equity distribution for a family holding structure composed of 4 members and 26 companies. In the first step, the model derived an optimal solution in terms of the target chosen by the owners, but it did not fully satisfy all members. However, owners were able to come to a decision regarding final reassignment after doing a sensible post-optimal analysis. Originality/valuePrevious research has focused on analyzing the special characteristics of family-run businesses and how they differ with respect to non-family-run businesses in terms of performance, governance, and management, among other things. However, this paper is the first referring to the process of ownership reassignment and to use an optimization model in its methodology. It is also the first study that bridges the gaps between the disciplines of portfolio optimization, corporate finance, and family business.

Integer linear programming-based multi-objective scheduling for scientific workflows in multi-cloud environments

Scientific communities are motivated to schedule the data-intensive scientific workflows in multi-cloud environments, where considerable diverse resources are provided by multiple clouds and resource limitation imposed by individual clouds is overcome. However, this scheduling involves two conflicting objectives: minimizing cost and makespan. In general, dealing with such conflicting criteria is a difficult task. But fortunately recent efficient methods for solving multi-objective optimization problems motivated us to provide a multi-objective model considering minimization of cost and makespan as objectives. For solving this model, we use different scalarization procedures such as weighted-sum, Benson's scalarization and weighted min–max under different scenarios. Moreover, we investigate the stability of obtained solutions and propose a new approach for determining the most stable solution related to weighted-sum and weighted min–max as post-optimality analysis. Results indicate that our proposed weighted-sum approach outperforms the previously developed methods in terms of hypervolume.

Compact representation of near-optimal integer programming solutions

It is often useful in practice to explore near-optimal solutions of an integer programming problem. We show how all solutions within a given tolerance of the optimal value can be efficiently and compactly represented in a weighted decision diagram. The structure of the diagram facilitates rapid processing of a wide range of queries about the near-optimal solution space, as well as reoptimization after changes in the objective function. We also exploit the paradoxical fact that the diagram can be reduced in size if it is allowed to represent additional solutions. We show that a “sound reduction” operation, applied repeatedly, yields the smallest such diagram that is suitable for postoptimality analysis, and one that is typically far smaller than a tree that represents the same set of near-optimal solutions. We conclude that postoptimality analysis based on sound-reduced diagrams has the potential to extract significantly more useful information from an integer programming model than was previously feasible.

Evaluating the quality of solutions in project portfolio selection

Assessing the quality of decisions while selecting project portfolios becomes an inherent part of the decision-making process when the project parameters are inaccurate or uncertain. Small adjustments to the initial parameters can lead to situations where the preferred portfolio no longer reflects the investor’s requirements. The paper studies the post-optimal analysis of the Pareto optimal portfolios chosen by Savage’s risk criteria. Stability characteristic, such as the stability function, is considered. Using the stability function, we evaluate the quality of feasible portfolios. This function indicates the robustness of portfolios to any changes in the initial data. Using the stability function the formula for calculating the optimality threshold is obtained, which determines the level of risk reduction when the selected Pareto optimal portfolio can obtain optimal properties. The performances of the stability function and the optimality threshold are shown in the case study using global risk assessments for projects participating in the Belt and Road Initiative. The computation results demonstrate the ability through the stability function to evaluate the quality and optimal properties of feasible project portfolios.

Active BIM Approach to Optimize Work Facilities and Tower Crane Locations on Construction Sites with Repetitive Operations

This paper presents an active building information modeling (BIM) approach for work facilities and the optimal positioning of tower cranes on construction sites with repetitive operations. In this context, the metamorphosis of a passive BIM approach into an active approach is described. Here, the enhancement of the construction-ready BIM model starts with the export of the optimization input parameters, such as the 3D coordinates of the building, perimeter of the construction site, space for feasible solutions, relevant segment of the building with repetitive works, etc. Depending on the complexity of the problem, the user selects a suitable optimization approach and formulates the tower crane positioning optimization problem with the objective of minimizing the total duration of the operation’s cycle. Similarly, according to the model formulation, the user also chooses the optimization tool, including the search algorithm. The final step involves the post-optimal analysis and importing of the optimal solution into the BIM. An application example is demonstrated at the end of the paper to show the advantages of the proposed approach in which the optimization model has significantly improved the initial solution of the crane and depot positions.

Fuel and Energy System Control at Large-Scale Damages. IV. A Priori Estimates of Structural and Functional Vulnerability

The paper continues the study of the possibilities of a multiparametric model that provides an aggregated description of the operation of spatially distributed fuel and energy systems using a single-commodity network. An approach to obtaining the characteristics of the simulated system that reflect possible changes in the supply of energy resources after large-scale accidents is proposed. Scenarios involving the simultaneous failure of a set of arcs corresponding to the minimum cut for an arbitrary sink vertex, where each sink vertex simulates the regional consumption of some commodity, are considered. The maximum energy flows for all sink vertices are calculated to determine the damage caused in these scenario assumptions. Different damage indices are computed and the expected damage diagrams are constructed based on them. A comparison of the introduced damage characteristics and the structure of the indicated sets makes it possible to trace the changes in the quantity and trace of energy flows with the change in the localization, severity, and type of damage. Postoptimal analysis makes it possible to identify spatial interdependence and mutual influence, as well as the technological features of the interchange and interconnection of energy resource production. The computational problems arising in the model calculations are solved effectively by flow methods.

Sustainable evaluation of environmental and occupational risks scheduling flexible job shop manufacturing systems

Sustainability has become a challenge of relevant importance for organizational management. Companies should not only achieve appropriate levels of economic efficiency, but also of environmental and social efficiency. In this sense, the inclusion of sustainability principles in the scheduling of flexible job shop systems (FJS) has focused on the evaluation of energy consumption and key economic indicators (makespan, total weighted tardiness …). An industrial case (Medellín, Colombia) from the metal-mechanic sector is used to illustrate the simultaneous evaluation of the three sustainability dimensions: economic, environmental and social. The makespan is the indicator associated with the economic performance; CO2 equivalent emissions, water consumption, metal waste, and chrome waste are the indicators to evaluate the environmental performance; and weight manipulated at workstations, noise, ambient temperature, and vibrations are the indicators associated with the social performance. The measurement, normalization, and weighted sum of the previous indicators, allow for the estimation of the performance of each sustainability dimension. The NSGA-II and NSGA-III methods are used in order to carry out the multi-objective evaluation process, based on the values estimated for the three general objectives (i.e., environmental, social, and economic). Consequently, in the industrial case analyzed, the applied methods identify the corresponding structure of the Pareto optimal fronts. The post-optimal analysis of the solutions shows that the solutions can be directly associated with market demand tendencies, and their implementation can be detected in advance. Solutions that balance economic, environmental, and social efficiency, could be potentially selected according to the market trend, reaching a win-win scenario for all actors involved.

Routing problems in agricultural cooperatives: a model for optimization of transport vehicle logistics

AbstractThis study considers the problem of optimizing the routes of vehicles used in an agricultural cooperative that distributes animal feed to its customers. Different peculiarities distinguish our problem from the general Vehicle Routing Problem because there exists a type of time window constraint, truck capacity is limited, trucks are compartmentalized, there are access restrictions for trucks to some farms and our problem has the main objective of maximizing the amount of feed distributed every day and an auxiliary criterion of minimizing the transport costs. We present a mathematical programming formulation of the model that represents the logistics management problem. As solving the exact model is computationally expensive, we opt for a hybrid heuristic approach that first uses an insertion heuristic algorithm to obtain an initial solution and then improvement processes are performed using the so-called simulated annealing metaheuristic. We have built a graphical interface that allows easy use of the system and, in particular, efficient post-optimality analysis. Moreover, this interface can easily interface with other management tools. The utility of our model is shown with real and simulated data sets.

SUSCAPE: A framework for the optimal design of SUStainable ChemicAl ProcEsses incorporating data envelopment analysis

Developing computer aided tools for process design is of paramount importance in the transition toward a more sustainable chemical industry. In this work, we present a framework to incorporate sustainability principles in the design of chemical processes that combines a palette of tools, including life cycle assessment, surrogate modeling, objective reduction, multi-objective optimization and data envelopment analysis (DEA). The latter methodology facilitates the post-optimal analysis of the Pareto front by narrowing down the number of designs and ranking them without the need to define weights in an explicit manner. DEA provides in turn improvement targets for the suboptimal alternatives that if attained would make them optimal, thereby guiding retrofit efforts toward the most effective actions based on benchmarking them against the best technologies available. The capabilities of the framework are demonstrated in a case study based on the production of methanol from CO2 and hydrogen.

Evolutionary multi-objective optimization for bulldozer and its blade in soil cutting

ABSTRACTThe paper targets an optimal soil cutting operation by considering economic and productive aspects. Three realistic objectives and three problem-specific constraints are developed, and the optimization problem is solved using a hybrid evolutionary multi-objective optimization (EMO) technique. In this technique, a set of non-dominated solutions is generated by using an existing EMO technique, and then a few of them are selected for local search using the -constraint method. These selected solutions are used for starting independent local searches using fmincon solver of Matlab. Results demonstrate that the local searches have improved the non-dominated solutions a little, thereby suggesting a closeness of evolved solutions from EMO technique with true Pareto-optimal (PO) solutions. The PO solutions are further validated using experimental data from the literature. Overall, this study offers a platform to choose an appropriate solution from the set of PO solutions. Moreover, the post-optimal analysis demonstrates the commonality principle of few decision variables, which is followed by all PO solutions. The rest of the decision variables decipher important relationships that are responsible for trade-off among the PO solutions. The relationships are later used for preparing guidelines for a practitioner in selecting an appropriate solution for the optimal operation.

Integer linear programming-based cost optimization for scheduling scientific workflows in multi-cloud environments

Given that multi-cloud environments contain considerably diverse resources, scheduling workflows in these environments significantly reduces financial costs and overcomes the resource limitations imposed by commercial cloud providers. Accordingly, this study addressed the problem of scientific workflow scheduling in multi-cloud settings under deadline constraint to minimize associated financial costs. To this end, we proposed integer linear programming models that can be solved in a reasonable time by available solvers. In a mathematical model, the objective of a problem and real and physical constraints or restrictions are formulated using exact mathematical functions. Such formulation enabled us to comprehensively understand the system under evaluation, consider secondary preferences and post-optimality analysis and apply useful revisions to inappropriately selected input data. We analyzed the treatment of optimal cost under variations in deadline and workflow size. As part of the post-optimality analysis, sensitivity analysis and deadline revision were implemented. Results indicated that our proposed approach outperforms previously developed methods in terms of financial cost reduction.

Reverse logistics network design for product recovery and remanufacturing

• A mathematical model for the network design of multi-echelon reverse logistics is developed. • A hybrid genetic algorithm is proposed to solve the problem. • The amount of remanufactured products depends on the critical and the most valuable modules. • The model results produce less CO 2 and reduce the environmental impact. • The results show the proposed model performs better than current reverse logistics operating in the real city. Due to environmental concerns, reverse logistics now is becoming an important strategy to increase customer satisfaction. This research develops a generic mixed integer nonlinear programming model (MINLP) for reverse logistics network design. This is a multi-echelon reverse logistics model. It maximizes total profit by handling products returned for repair, remanufacturing, recycling, reuse, or incineration/landfill. A hybrid genetic algorithm (GA) is proposed to solve the problem. The designed model is validated and tested by using a real-life example of recycling bulk waste in Taoyuan City, Taiwan. Sensitivity analyses are conducted on various parameters to illustrate the capabilities of the proposed model. Post-optimality analysis and comparison show that the proposed model performs better than current reverse logistic operations and the proposed hybrid GA demonstrates the efficiency of solving the complex reverse logistics problem.

Dynamic Spectrum Sharing Optimization and Post-Optimization Analysis With Multiple Operators in Cellular Networks

Dynamic spectrum sharing aims to provide flexible spectrum usage and improve spectrum efficiency for cellular and noncellular networks. We propose two optimization models using stochastic optimization algorithms in which the secondary operator: 1) spends the minimal cost to achieve the target grade of service (GoS) assuming unrestricted budget or 2) gains the maximal profit to achieve the target GoS assuming restricted budget. We assume that there are spectrum resources available for secondary operators to borrow under a merchant mode . Results obtained from each model are then compared with results derived from algorithms in which spectrum borrowings are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimization framework is significantly higher than heuristic counterparts. Second, post-optimization performance analysis of the operators in the form of blocking probability in various scenarios is investigated to determine the probable performance gain and degradation of the secondary and primary operators, respectively. We mathematically model the sharing agreement scenario and derive the closed-form solution of blocking probabilities for each operator. Results show how the secondary operator performs in terms of blocking probability under various offered loads and sharing capacity.

Evolutionary Bi-objective Optimization for Bulldozer and Its Blade in Soil Cutting

An evolutionary optimization approach is adopted in this paper for simultaneously achieving the economic and productive soil cutting. The economic aspect is defined by minimizing the power requirement from the bulldozer, and the soil cutting is made productive by minimizing the time of soil cutting. For determining the power requirement, two force models are adopted from the literature to quantify the cutting force on the blade. Three domain-specific constraints are also proposed, which are limiting the power from the bulldozer, limiting the maximum force on the bulldozer blade and achieving the desired production rate. The bi-objective optimization problem is solved using five benchmark multi-objective evolutionary algorithms and one classical optimization technique using the e-constraint method. The Pareto-optimal solutions are obtained with the knee-region. Further, the post-optimal analysis is performed on the obtained solutions to decipher relationships among the objectives and decision variables. Such relationships are later used for making guidelines for selecting the optimal set of input parameters. The obtained results are then compared with the experiment results from the literature that show a close agreement among them.

Best resolution and post-optimal analysis in co-operation of multi-objective optimization and multi-objective evolutionary algorithm (An application to multiple car structures design toward energy and material conservation)

Best resolution and post-optimal analysis in co-operation of multi-objective optimization and multi-objective evolutionary algorithm (An application to multiple car structures design toward energy and material conservation)

Postoptimization of the model of water supply for urban and industrial agglomeration

A case study of the tools used by an analyst of the economic aspects of the operation of the water supply network has been undertaken in this paper. All issues discussed here are formulated by using degenerated linear programming models ( PL ). Below, it is noted that the linear dependence of binding constraints ( CO ) distorts standard postoptimization procedures in PL. This observed fact makes postoptimization analysis mostly unhelpful for an average analyst due to problems with the int erpretation of ambiguous sensitivity reports which are obtained from popular computer packages. In standard postoptimization methods, changes to single parameters of the right-hand vector CO are analyzed or referred to parametric linear programming that unfortunately requires prior knowledge of mathematically and economically justified vectors of changes of right-hand sides CO. Therefore, it is suggested that modifications are introduced to some of the postoptimization procedures in this work. For issues in the field of hydrology, the following were presented: interpretation and methods of generating justified vectors of changes of right-hand sides of limiting conditions. And so, the procedure of generating infinitely many solutions of the dual issue based on certain vectors orthogonal to the vector of right-hand sides of constraint conditions was demonstrated. Furthermore, the same orthogonal vectors were used to obtain nodal solutions of the dua0l model and the corresponding vectors of changes of the entire right-hand sides of the constraint conditions. Then, managerial interpretation was applied to this way of proceeding. The methods presented in the work serve to improve the functioning of the system of water supply.

Modelling and Bi-objective Optimization of Soil Cutting and Pushing Process for Bulldozer and its Blade

Bulldozer is an earth moving machine, which is mainly used for cutting and pushing soil. The process of soil cutting and pushing involves various decisions making to make it optimum. The decisions are generally made based on the experience of practitioners that may not be optimum for different working conditions. In this paper, a bi-objective optimization problem is modelled so that the optimum values of decision variables can be determined. The objective functions are proposed to make the process economic and productive by minimizing the cutting force on a bulldozer blade and maximizing the blade capacity. A constraint is also developed on the power requirement from a bulldozer to overcome resistance. The problem is solved using e-constraint method and multi-objective genetic algorithm. The approximate Pareto-optimal solutions and their perturbation analysis are presented. Various relationships are evolved from the post-optimal analysis that can be used for making guidelines for decision making for the process. The originality of this paper lies in developing the bi-objective formulation and in presenting various relationships by the post-optimal analysis, which has sparingly done in the domain literature.