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Cellular manufacturing systems (CMS) are essential for achieving efficient production processes, and their success relies on effective cell formation and layout design. This research paper presents a novel nonlinear mathematical programming model that employs the rectilinear distance notion to define the layout within a continuous space. The proposed model incorporates the stochastic nature of machine failures, considering the unreliability with the inclusion of a stochastic time between failures. With a bi-objective approach, the model aims to minimize inter and intra-cell movements of parts, as well as the associated costs related to exceptional elements (EEs), cell reconfigurations, and machine failures. To optimize the proposed model, several multi-objective meta-heuristic algorithms, including the multi-objective particle swarm optimization (MOPSO), multi-objective taboo search (MOTS), and non-dominated sorting genetic algorithm (NSGA-II), are introduced. The effectiveness of these algorithms is validated through numerical instances and a real case study. The results indicate that the MOPSO algorithm exhibits superior performance in optimizing various multi-objective criteria. The presented mathematical model and algorithms provide valuable tools for manufacturers to optimize their CMS, resulting in reduced costs, enhanced production efficiency, and increased competitiveness. By considering the simultaneous effects of time and cost associated with machine failures, this research offers practical insights and solutions for improving the performance of cellular manufacturing systems. [ABSTRACT FROM AUTHOR]

With the expansion of power grid, unaffordable computational cost and time will pose serious challenges of time-efficient scheduling in unit commitment problem (UCP). However, existing optimization methods, i.e., mathematical programming methods and meta-heuristic algorithms, are powerless and time-consuming to handle computationally expensive UCP (CEUCP). Thus, reinforcement learning methods with strong inference and time-saving performances are motivated to solve the computationally expensive challenges in tackling CEUCPs. In this paper, a novel expert knowledge data-driven based actor–critic (AC) reinforcement learning methodology is proposed for solving CEUCPs. Specifically, in the proposed AC reinforcement learning methodology, expert knowledge, data-driven surrogate model, and improved meta-heuristic algorithm are integrated for further performance enhancement. Firstly, a novel action selection mechanism (based on the expert knowledge of thermal units characteristic) is integrated into AC to improve the efficiency of network training. Secondly, an improved extreme learning machine (ELM) data-driven surrogate model is proposed to build reward function in AC. In detail, original cost function in reward is replaced by a lightweight ELM model. Shape distance is integrated into ELM for enhancing accuracy. Finally, original marine predators algorithm (MPA) is improved for obtaining optimal dispatching decisions and rewards of AC method quickly and correctly. Original search pattern is replaced by quantum based representation for boosting convergence. The excellent performances of the proposed AC framework are verified by simulations of 10-units, 100-units, and 100-units with wind energy test systems. • Computationally expensive unit commitment (UC) is modeled by Markov process. • Use reinforcement learning (RL) method for obtaining more economical unit status. • Propose an expert knowledge-based action selection method for rapid convergence. • Improve a time-saving extreme learning machine model to build the reward function. • Improve marine predator algorithm to get dispatch decisions quickly and accurately. [ABSTRACT FROM AUTHOR]

This paper investigates the finite termination of the optimal solution sequence in parametric optimization as a subproblem of the equilibrium problems. By introducing an augmented mapping on the solution set of the equilibrium problems, we establish the concept of augmented weak sharpness for this set relative to the sequence of optimal solutions in parametric optimization. Augmented weak sharpness is an extension of the concepts of weak sharpness and strongly non-degeneracy. Under the condition that the solution set of the equilibrium problems is augmented weak sharpness, sufficient conditions for the finite termination of the sequence of optimal solutions in parametric optimization are provided. For some special cases of the equilibrium problems (Mathematical Programming (MP), Variational Inequality Problems (VIP), etc.), these results extend the corresponding results under weak sharpness or strongly non-degeneracy conditions in the existing literature. Additionally, they also provide weaker sufficient conditions for the finite termination of many optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the C-logit stochastic user equilibrium (SUE) problem on a bimodal transportation network with road and rail travel modes. The C-logit model captures the overlapping effect among the different paths via commonality factors; sequentially, it has ability to obtain a more realistic traffic flow distribution pattern. In this paper, when we redefine the link travel cost functions and employ a binary Logit model for the mode split, the bimodal C-logit SUE model can be simplified into an unconstrained nonlinear mathematical programming formulation. Such model is verified to satisfy the bimodal C-logit SUE conditions at its stationary point and can be solved by existing algorithms. So, the simplified model can be convenient to be used on the general bimodal transportation network. [ABSTRACT FROM AUTHOR]

• Scheduling optimization of activities in low-volume low-variety production systems. • A novel multi-objective mixed-integer mathematical programming formulation. • Flexible mathematical model compatible to single or multi-parallel work centers with flexible resource profiles. • Multi-parallel work center scheduling problems with shared or dedicated resources. • Model validation through a real-world case study in the aerospace industry. A new methodology for modeling large-scale scheduling problems in low-volume low-variety production systems is proposed through this paper. Such scheduling problems are constrained by limited time and resources, where each work center is assigned a unique statement of work, to be completed on-time with the budgeted number of resources. Products assembled in low-volume low-variety production systems are processed through a series of stations referred to as work centers, where varying levels and classifications of resources are deployed onto the product. Aircraft, heavy aero-structures, and heavy military equipment are examples of products assembled in low-volume low-variety production systems. To ensure products are delivered on-time and on-budget, it is crucial to execute to a detailed schedule, such that all precedence, resource, zonal, and other constraints and characteristics inherent in such production systems are successfully satisfied. Despite the criticality of detailed schedules in delivering products on-time and on-budget, limited research is reported on mixed-integer programming approaches for scheduling optimization of activities in low-volume low-variety production systems. The discrete-time linear mixed-integer mathematical programming model developed in this paper fills the gap in the current literature with a direct impact on the organizations' service levels and bottom line. The proposed mathematical programming models are validated through a real-world case-study of the assembly process of a narrow body aircraft to ensure compatibility in the modeling of large-scale industrial problems. [ABSTRACT FROM AUTHOR]

This paper is devoted to theoretical aspects in nonlinear optimization, in particular, duality relations for some mathematical programming problems. In this paper, we introduce a new generalized class of second-order multiobjective symmetric G-Wolfe-type model over arbitrary cones and establish duality results under G f -bonvexity/ G f -pseudobonvexity assumptions. We construct nontrivial numerical examples which are G f -bonvex/ G f -pseudobonvex but neither η-bonvex/η-pseudobonvex nor η-invex/η-pseudoinvex. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of partitioning a complete weighted graph into complete subgraphs, each having the same number of vertices, with the objective of minimizing the sum of edge weights of the resulting subgraphs. This NP-complete problem arises in many applications such as assignment and scheduling-related group partitioning problems and micro-aggregation techniques. In this paper, we present a mathematical programming model and propose a complementary column generation approach to solve the resulting model. A dual based lower bounding feature is also introduced to curtail the notorious tailing-off effects often induced when using column generation methods. Computational results are presented for a wide range of test problems. [ABSTRACT FROM AUTHOR]

Energy-aware scheduling in a manufacturing environment with real-time energy pricing is a challenging problem. The purpose of this paper is to study a nonpreemptive scheduling problem on a single-machine to minimize the total tardiness and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multi-objective mathematical programming model. To achieve these objectives, we develop several new holistic genetic algorithms. The proposed model is solved via several methods including weighted sum method and multiobjective genetic algorithms based on dominance rank (GA-1), weighted sum aggregation (GA-2), dominance ranking procedure and crowding distance comparison (GA-3), and heuristic approach (GA-H). This paper illustrates that operations management, which is typically observed as a significant technique that allows manufacturing to operate in challenging data enabled environment in an industrial internet of things ecosystem, can also carefully optimize a production process to improve energy cost in order to manage environmental challenges. Thus, the findings provide information on when to start each job. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we illustrate how the results of the multiobjective model could be utilized in decision making using the technique for order preference by similarity to ideal solution method. [ABSTRACT FROM AUTHOR]

The prominence of managing municipal solid waste (MSW) in an efficient and effective manner is increasing from day to day. In this paper, the solid waste management (SWM) system of İstanbul is analyzed by applying the techniques from mathematical programming methodology. In this manner, the solutions of the two optimization problems which aim to minimize the total cost and the environmental effects of SWM, respectively, are presented in this study. Additionally, a sensitivity analysis is performed and a multi-objective problem that combines two problems is presented. In this regard, the application of five MSW management technologies which are currently in use in İstanbul on six waste components is analyzed; and the optimal solution regarding the best mixture of these technologies is developed on a given waste composition. Besides, this optimal solution is compared with the current practice in İstanbul; and recommendations are presented about possible future investments for the policymakers. The results of the study emphasize the importance of material recovery and incineration facilities to improve profitability and to minimize environmental side effects. In particular, material recovery facility (MRF) should be expanded to be able to treat all of metal, paper and plastic from a cost management perspective. Incineration (INC) facility should also be expanded in order to treat plastics or organic waste from a Greenhouse Gas (GHG) minimization perspective. In addition to this, landfill appears to be the most prominent treatment technique according to the current problem parameters. However, regarding the waste composition, the amount of organic waste must be decreased by more than 37% for other waste streams to be treated in different facilities other than landfill. Anaerobic digestion and composting facilities need to be more cost-effective for becoming economically feasible. The methodology represented in this study can be extended and generalized to other cities around the world once the correct problem parameters are specified. Image 105510 • Applying multi-objective optimization to solid waste management problem. • Importance of material recovery and incineration to improve profitability. • Landfill appears to be the most prominent treatment technique. • Must decrease the operating costs of composting and anaerobic digestion processes. • Applicable method to other cities after specifying the correct problem parameters. [ABSTRACT FROM AUTHOR]

This paper provides an introduction to Reinforcement Learning (RL) technology, summarizes recent developments in this area, and discusses their potential implications for the field of process control, and more generally, of operational decision-making. The paper begins with an introduction to RL that allows an agent to learn, through trial and error, the best way to accomplish a task. We then highlight new developments in RL that have led to the recent wave of applications and media interest. A comparison of the key features of RL and mathematical programming based methods (e.g., model predictive control) is then presented to clarify their similarities and differences. This is followed by an assessment of several ways that RL technology can potentially be used in process control and operational decision applications. A final section summarizes our conclusions and lists directions for future RL research that may improve its relevance for the process systems engineering field. [ABSTRACT FROM AUTHOR]

Data stored in information systems are often erroneous. Duplicate data are one of the typical error type. To discover and handle duplicates, the so-called deduplication methods are applied. They are complex and time costly algorithms. In data deduplication, pairs of records are compared and their similarities are computed. For a given deduplication problem, challenging tasks are: (1) to decide which similarity measures are the most adequate to given attributes being compared and (2) defining the importance of attributes being compared, and (3) defining adequate similarity thresholds between similar and not similar pairs of records. In this paper, we summarize our experience gained from a real R&D project run for a large financial institution. In particular, we answer the following three research questions: (1) what are the adequate similarity measures for comparing attributes of text data types, (2) what are the adequate weights of attributes in the procedure of comparing pairs of records, and (3) what are the similarity thresholds between classes: duplicates, probably duplicates, non-duplicates? The answers to the questions are based on the experimental evaluation of 54 similarity measures for text values. The measures were compared on five different real data sets of different data characteristic. The similarity measures were assessed based on: (1) similarity values they produced for given values being compared and (2) their execution time. Furthermore, we present our method, based on mathematical programming, for computing weights of attributes and similarity thresholds for records being compared. The experimental evaluation of the method and its assessment by experts from the financial institution proved that it is adequate to the deduplication problem at hand. The whole data deduplication pipeline that we have developed has been deployed in the financial institution and is run in their production system, processing batches of over 20 million of customer records. [ABSTRACT FROM AUTHOR]

This paper introduces an interactive framework to guide decision-makers in a multi-criteria supplier selection process. State-of-the-art multi-criteria methods for supplier selection elicit the decision-maker's preferences among the criteria by processing pre-collected data from different stakeholders. We propose a different approach where the preferences are elicited through an active learning loop. At each step, the framework optimally solves a combinatorial problem multiple times with different weights assigned to the objectives. Afterwards, a pair of solutions among those computed is selected using a particular query selection strategy, and the decision-maker expresses a preference between them. These two steps are repeated until a specific stopping criterion is satisfied. We also introduce two novel fast query selection strategies, and we compare them with a myopically optimal query selection strategy. Computational experiments on a large set of randomly generated instances are used to examine the performance of our query selection strategies, showing a better computation time and similar performance in terms of the number of queries taken to achieve convergence. Our experimental results also show the usability of the framework for real-world problems with respect to the execution time and the number of loops needed to achieve convergence. [ABSTRACT FROM AUTHOR]

Disassembly is one of the steps of the recovery activities. Since it includes expensive processes, disassembly shoul be performed with the system that provides efficient and effective outputs. A disassembly line is the most suitable system for disassembly of the returned products. A disassembly line balancing problem (DLBP) is assigning disassembly tasks to consecutive workstations by satisfying a series of constraints and optimizing one or more than one goal. In this paper, the DLBP with multiple conflicting goals which takes into account the negative zone (DLBP-Z) constraints has been proposed. Negative zone constraint is related to hazardous parts. If there are hazardous part/parts in the product and they need to be removed, they may damage the othetr parts and disassembly line. Therefore, these parts must be assigned to different stations from the other parts. Goal programming (GP) and fuzzy goal programming (FGP) approaches have been proposed in order to optimize three conflicting goals, namely total net recovery profit value, the number of parts to be removed for recycling and cycle time. Through a numerical example, the proposed approaches have been tested and goal programming formulations have been shown to be valid and useful. To the best of the authors knowledge, the proposed GP and FGP models are the first multi citeria decision making (MCDM) approaches for DLBP-Z. [ABSTRACT FROM AUTHOR]

• A simple analytical tool is proposed for the evaluation of the shear resistance of RC circular columns subjected to axial force, bending moment and shear force. • The proposed method is formulated for either solid or hollow circular columns. • The method can be easily embedded in structural programs. • The method predicts with satisfying accuracy the shear resistance of members with medium–high shear span ratio. The paper proposes a simple analytical tool for the evaluation of the shear strength of reinforced concrete circular, solid or hollow, slender columns endowed with shear reinforcement and subjected to axial force, bending moment and shear force. The proposed method considers the sole beam action by means of simplified stress fields and simultaneously satisfies the equilibrium under the above combined internal forces. The paper identifies basic points in the N-M-V ultimate interaction curve and describes the procedures for the evaluation of the internal forces of these points as well as the procedures for the evaluation of the internal forces of the points in between. The method is first applied to a set of circular solid columns with different geometric and mechanical properties and a comparison is drawn between the shear resistance resulting from the simplified method and that from a more complex non-linear mathematical programming problem proposed in the past by one of the authors. Then, to demonstrate the value of the method and define the field of its accurate application, the proposed method is applied to circular (solid or hollow) columns tested in laboratory by other researchers. Finally, the results of the proposed method are compared with those of some code formulations. [ABSTRACT FROM AUTHOR]

This paper studies joint algorithms of order picking and distribution in new retail enterprises. The problem will consider many factors, such as the type of goods, picking time, batch capacity of distribution, distribution time, and distribution cost. First of all, the research problems are summarized as mathematical programming problems. Then, a genetic algorithm and comparison algorithms are proposed. Finally, the rationality of the model and the effectiveness of the algorithms are verified by computational experiments, and management enlightenments are revealed. [ABSTRACT FROM AUTHOR]

This paper studies the energy efficiency optimization problem for coordinated multipoint (CoMP)-enabled and backhaul-constrained ultra-dense small-cell networks (UDNs). Energy efficiency is an eternal topic for future wireless communication networks; however, taking actual bottleneck of the backhaul link and the coordinated network architecture into consideration, it is difficult to find an effective way to improve the energy efficiency of the network. Aiming at this problem, we propose to combine cell association, subchannel allocation, backhaul resource allocation, and sleep/on of the cells together to develop an optimization algorithm for energy efficiency in UDN and then solve the formulated energy efficiency optimization problem by means of improved modified particle swarm optimization (IMPSO) and linear programming in mathematics. Simulation results indicate that nearly 13 % energy cost saving and 21 % energy efficiency improvement can be obtained by combining IMPSO with linear programming, and the backhaul link data rate can be improved by 30 % as the number of small cells increases. From the results, it can be found that by combining IMPSO with linear programming, the proposed algorithm can improve the network energy efficiency effectively at the expense of limited complexity. [ABSTRACT FROM AUTHOR]

This paper studies joint algorithms of order picking and distribution in new retail enterprises. The problem will consider many factors, such as the type of goods, picking time, batch capacity of distribution, distribution time, and distribution cost. First of all, the research problems are summarized as mathematical programming problems. Then, a genetic algorithm and comparison algorithms are proposed. Finally, the rationality of the model and the effectiveness of the algorithms are verified by computational experiments, and management enlightenments are revealed. [ABSTRACT FROM AUTHOR]

This paper studies joint algorithms of order picking and distribution in new retail enterprises. The problem will consider many factors, such as the type of goods, picking time, batch capacity of distribution, distribution time, and distribution cost. First of all, the research problems are summarized as mathematical programming problems. Then, a genetic algorithm and comparison algorithms are proposed. Finally, the rationality of the model and the effectiveness of the algorithms are verified by computational experiments, and management enlightenments are revealed. [ABSTRACT FROM AUTHOR]

In this paper, the authors define a new generic class of functions involving a certain modified Fox-Wright function. A useful identity using fractional integrals and this modified Fox-Wright function with two parameters is also found. Applying this as an auxiliary result, we establish some Hermite-Hadamard-type integral inequalities by using the above-mentioned class of functions. Some special cases are derived with relevant details. Moreover, in order to show the efficiency of our main results, an application for error estimation is obtained as well. [ABSTRACT FROM AUTHOR]

The internationalisation of the manufacturing operations process includes decision-making about new facility implementation (NFI) and global supplier network development (GSND), whose first step is to analyse the situation of a company and its environment. The purpose of this paper is to investigate the optimal design of a manufacturing production and distribution network for global small- and medium-sized enterprises (SMEs). This research uses a mixed-integer linear programming (MILP) model to support decision-making in the analysis stage of the internationalisation of manufacturing operations for global SMEs. A realworld case study is presented to illustrate the application of the proposed model. Different scenarios were evaluated not only to identify the strengths and limitations of the mathematical programming model, but to also provide support for the next strategic decisions that the examined company has to make in the near future. [ABSTRACT FROM AUTHOR]

There is a vivid debate in cities all over the world on how to distribute the restricted space in urban areas among stakeholders. Urban design movements such as new pedestrianism or Copenhagenization advocate that too much space is attributed to cars. In this context, our research investigates the optimization of parking lots with the help of mathematical programming. For the given ground plot of a parking lot, we maximize the number of parking spaces each reachable via a driving lane, so that the urban space attributed to the parking of cars is efficiently used. Based on a grid of squares in which we rasterize the ground plot, this paper presents mixed-integer programs based on three different resolutions for orthogonal parking. Our computational study explores the tradeoff between the additional parking spaces promised by a higher resolution and the increased computational effort because of the larger solution space (and vice versa). We compare our optimization approaches with a sample of 177 real-world parking lots and show that optimization can be a serviceable car park design tool with the help of a case study. [ABSTRACT FROM AUTHOR]

Background: This paper deals with the location of emergency medical stations where ambulances waiting to be dispatched are parked. The literature reports a lot of mathematical programming models used to optimize station locations. Most studies evaluate the models only analytically applying the same simplifying assumptions that were used in the modelling phase. In addition, they concentrate on systems operating one type of emergency units in homogeneous urban areas. The goal of our study is to identify which optimization criterion the emergency medical service (EMS) outcomes benefit from the most and which model should be used to design tiered systems in large urban–rural areas. Methods: A bi-criteria mathematical programming model is proposed. The criteria include the accessibility of high-priority patients within a short time limit and average response time to all patients. This model is being compared to the p-median model with a single response time objective and to a hierarchical pq-median model that considers two different vehicle types. A detailed computer simulation model is used to evaluate the solutions. The methodology is verified in the conditions of the Slovak Republic using real historical data on 149,474 ambulance trips performed in 2015. Results: All mathematical models improve EMS performance by relocating some stations compared to the current distribution. The best results are achieved by the hierarchical median-type model. The average response time is reduced by 58 s, the number of calls responded to within 15 min is increased by 5% and the number of high-priority calls responded to within 8 min by 6%. Conclusions: The EMS systems operating in heterogeneous areas should be designed to minimize response times, and not to maximize the number of calls served within a given time limit. [ABSTRACT FROM AUTHOR]

In decision making with interval-valued intuitionistic fuzzy preference relations (IVIFPRs), the consistency and its improvement are key issues. The aim of this paper is to investigate a new multiplicative consistency of IVIFPRs based on interval arithmetic, and propose goal programming approach to improve consistency and consensus. Based on analysis of the drawbacks of the existing consistency definitions of IVIFPR, new definition for multiplicative consistent IVIFPR is introduced. Based on the proposed multiplicative consistency and analysis of the relationship between interval-valued intuitionistic fuzzy weights and multiplicative consistent IVIFPR, goal programming-based model is developed for deriving interval-valued intuitionistic fuzzy weights from IVIFPR. To measure the multiplicative consistency degree of IVIFPRs, a consistency index is introduced and then an acceptable consistency is defined. For an IVIFPR with unacceptable consistency, a mathematical programming approach is proposed to improve its consistency. By considering the experts' subjective and consensus weights, the experts' comprehensive weights are determined. Some numerical examples illustrate the proposed models. [ABSTRACT FROM AUTHOR]

The purchase cost of chain convenience stores accounts for a large proportion of the total cost. Enterprises are facing the problem of how to reasonably reduce the purchase cost while ensuring the quality of service. This paper first considers the uniqueness of chain convenience stores, draws on existing research results, and establishes an evaluation index system for chain convenience store suppliers through field research. Then, we use the principal component analysis method of selection to determine the weight of each index and make a preliminary ranking of the importance of chain convenience store suppliers. Secondly, according to the relevant weights determined by the network hierarchy method, the collected data are substituted into the multiobjective mathematical programming model to analyze the distribution of supplier procurement and calculate the procurement cost. The level of procurement expenditures accounted for by suppliers confirms the importance of suppliers. The results show that through the evaluation and ranking of suppliers and the effective management of suppliers according to the ranking results, not only the goal of cost minimization is achieved but also the reasonable service level is guaranteed, which is scientific. [ABSTRACT FROM AUTHOR]

• Presenting a novel mixed-integer programming model for designing a sustainable lateral resupply blood supply chain network. • Addressing a blood inventory-routing problem with supply and demand uncertainties. • Considering ABO and Rh factor and the interchangeability of blood products. • Developing a scenario-based stochastic optimization model and revised multi-choice goal programming approach to solve. The vibrant subject of blood banking and blood distribution is addressed where lateral resupply of blood products is allowed. In this paper, we studied a blood supply chain network design consisting of four conventional levels each of which corresponding to one entity in the real world: donors, blood collection facilities, blood centers, and hospitals. The blood supply chain network considered two important properties: ABO-Rh factors and shelf lives of blood products. Moreover, an integrated inventory system for sharing hospitals' inventory levels is considered to investigate the two performance indicators of the network in terms of cross-matching and outdated units. This type of inventory system is known as "Lateral Resupply" permitting a hospital to satisfy its demand by the other hospitals' inventories in the absence of the required product at the blood center and its excess in any hospital. The proposed mixed-integer mathematical programming model entails three objectives referring to the three underlying concepts of sustainability, additionally, to cope with uncertainties and multi objectiveness, a scenario-based optimization approach and revised multi-choice goal programming technique were employed, respectively. Finally, the results confirmed that the lateral resupply virtue improved the performance indicators. [ABSTRACT FROM AUTHOR]

• An introduction to optimization under uncertainty is presented. • Recent advances in data-driven optimization under uncertainty are reviewed. • Future perspective on a "closed-loop" data-driven optimization framework is given. • Potential application of deep learning to data-driven optimization is discussed. This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. A comprehensive review and classification of the relevant publications on data-driven distributionally robust optimization, data-driven chance constrained program, data-driven robust optimization, and data-driven scenario-based optimization is then presented. This paper also identifies fertile avenues for future research that focuses on a closed-loop data-driven optimization framework, which allows the feedback from mathematical programming to machine learning, as well as scenario-based optimization leveraging the power of deep learning techniques. Perspectives on online learning-based data-driven multistage optimization with a learning-while-optimizing scheme are presented. [ABSTRACT FROM AUTHOR]

Abstract Exposure to an aggressive marine atmosphere can seriously harm reinforced concrete (RC) structures and affects a building's service life. In particular, buildings close to the sea are at high risk of suffering damage, mainly due to the effects of chloride. The scientific and technical communities are interested in studying new parameters related to building degradation, including "exposure to aggressive environments" in order to predict a building's service life and schedule maintenance and monitoring systems. This paper proposes a marine exposure index aimed at quantifying potential risks and damage to RC building structures. The definition of the index includes three synergistically related techniques: i) the Analytic Hierarchy Processes to analyze the complexity of the problem and the parameters involved in marine exposure; ii) a state-of-the-art technique to evaluate the degree of building degradation; iii) an optimization calibration procedure by setting a Mathematical Programming problem. The resulting index can be extensively and fruitfully applied by researchers and practitioners in order to assess the building's exposure to an aggressive marine atmosphere. Architects and engineers could apply the proposed index in any phase of the building process, from the project to the maintenance and management phases. The effectiveness of the proposal was tested in a survey of 1816 buildings on the Valencia coast (Spain). Graphical abstract Unlabelled Image Highlights • Marine atmosphere can seriously harm reinforced concrete structures. • This paper proposes a new marine exposure index. • The definition of the index includes three synergistically related techniques. • The index can be used to assess a building's exposure to a marine atmosphere. • The index has been calibrated by surveying close to two thousand buildings. [ABSTRACT FROM AUTHOR]

Abstract The mathematical essence of structural topology optimization is large-scale nonlinear integer programming. To overcome its huge computational burden, a popular way is to relax the 0–1 variable constraints and transform the integer programming problem into a continuous variable programming problem which can be solved by using gradient based mathematical programming methods. To cope with the variable transformation, the well-known SIMP (Solid Isotropic Material with Penalty) method introduces interpolation schemes for the material properties versus design variables with penalty and achieves great success and popularity. However, there is no doubt that directly tackling the large-scale nonlinear integer programming is very important. This paper solves the structural topology optimization problems with single or multiple constraints by applying the Canonical Dual Theory (CDT) by Gao and Ruan (2010) together with Sequential Approximate Programming approach under the classic structural topology optimization formulations. Following the Sequential Approximate Programming (SAP) frame from the structural optimization, the present paper firstly utilizes sensitivity information to construct the explicit and separable approximate Sequential Quadratic Integer Programming (SQIP) or Sequential Linear Integer Programming (SLIP) subproblems for the topology optimization. And then, the subproblems are solved by applying the Canonical relaxation algorithm based on CDT theory. Their special mathematical structures are exploited to develop analytic solution of Kuhn–Tucker condition of the dual programming. Numerical experiments of two linear and quadratic integer programming problems with random coefficients assert that the Canonical relaxation algorithm can obtain approximate solutions with good properties very efficiently and the dual gap is negligible when the number of design variables increases. Because move limit strategies play a key role in many search algorithms of structural optimization, this paper combines one of two different move limit strategies within the new method. The new method first solves a set of classic topology optimization problems with only material usage constraint, including minimum structural compliance design under constant load, maximum heat transfer efficiency for the heat conduction problem. And then we apply the method to the topology optimization problems with multiple constraints, including minimum structural compliance design under an additional local displacement constraint and minimum structural compliance design under infill constraints. The results of these problems demonstrate that the new method can efficiently solve the discrete variable structural topology optimization problems with multiple nonlinear constraints or many local linear constraints in a unified and systematic way. Beyond that, the new method can achieve integer solutions when combined with the move limit strategy of controlling volume fraction parameter. It can deal with much more design variables than the general branch-and-bound method and makes no use of any sensitivity threshold or heuristic stabilization scheme during the iterative process in comparison with BESO method. Finally, the new method in this paper can be further developed as a general solver for these large-scale discrete variable structural topology optimization problems. [ABSTRACT FROM AUTHOR]

The supply chain ecosystem has become increasingly volatile due to a host of different factors. In an unpredictable environment, the factors influencing decision-making fluctuate constantly over time, and the effect of volatility on supply chain parameters such as cost, revenue, and working capital must be evaluated periodically. This paper seeks to develop a set of mathematical programming models for exploring the impact of price fluctuation of raw materials on the cost of goods sold (COGS) of end products relative to targets in manufacturing companies. The mathematical programming problems proposed in this paper rationally minimize the COGS of end products while satisfying a set of constraints regarding forecasting and physical conditions. The optimal solutions of the proposed models provide guidelines on the optimal price at which to purchase all raw materials, leading to the minimum the overall COGS value for the enterprise. We also introduce a special type of sensitivity analysis, in which we explore the conditions where the optimal purchasing price of a raw material used to manufacture an end product changes within its feasibility range, and the relative effect on how much the minimum COGS of end products fluctuates. Finally, an empirical example presented in this paper illustrates the practicality and usefulness of the proposed approach in practice. [ABSTRACT FROM AUTHOR]

This paper proposes a solution approach for the multicommodity capacitated fixed-charge network design problem with uncertain demand modeled as a two-stage stochastic program. The proposed learning-based matheuristic combines heuristic search techniques with mathematical programming. It provides a systematic approach to identifying structures of good-quality solutions by gradually considering scenarios and their influences on design decisions. Extensive computational experiments illustrate the efficiency of the proposed matheuristic in obtaining high-quality solutions with limited computational efforts. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study the Manufacturers pallet-loading problem (MPLP), by loading identical small boxes onto a rectangle pallet to maximise the pallet utilization percentage while reducing the Complexity of loading. Design/methodology/approach: In this research a Block approach is proposed using a Mixed integer linear programming (MILP) model that generates layouts of an improved structure, which is very effective due to its properties in grouping boxes in a certain orientation along the X and Y axis. Also, a novel complexity index is introduced to compare the complexity for different pallet loading, which have the same pallet size but different box arrangements. Findings: The proposed algorithm has been tested against available data-sets in literature and the complexity measure and graphical layout results clearly demonstrate the superiority of the proposed approach compared with literature Manufacturers pallet-loading problem layouts. Originality/value: This study aids real life manufactures operations when less complex operations are essential to reduce the complexity of pallet loading. [ABSTRACT FROM AUTHOR]

This paper deals with approximate solutions of an optimization problem with interval-valued objective function. Four types of approximate solution concepts of the problem are proposed by considering the partial ordering LU on the set of all closed and bounded intervals. We show that these solutions exist under very weak conditions. Under suitable constraint qualifications, we derive Karush-Kuhn-Tucker necessary and sufficient optimality conditions for convex interval-valued optimization problems. [ABSTRACT FROM AUTHOR]

In a society where computers have become ubiquitous, it is necessary to develop a broader understanding of the nature of computing and programming, not just from a technical viewpoint but also from a historical and philosophical perspective. Computers and computer programs do not exist in a vacuum. Instead, they are a part of a rich socio-technological context that provides ways for understanding computers and reasoning about programs. This includes not only formal logic, mathematics, sciences, and technology but also cognitive sciences and sociology. The focus of this special issue is on questions that arise when we consider computing and programming in a wider context. In particular, the papers in this special issue explore the interplay between computing or programming and mathematics, formal logic, sciences, technology, and society. [ABSTRACT FROM AUTHOR]

The importance of linear programming, and especially mixed-integer linear programming, has increased over time as computers have gotten more capable, algorithms have improved and more software solutions have become available. This approach has been successfully applied for solving hard problems in operations research (like traveling salesman problem, routing problems and telecommunications network design problems). In the eighteenth paper, "Exact method for the two-machine flow-shop problem with time delays" Mkadem, Moukrim and Serairi propose an exact method for solving the two-machine flow-shop problem with time delays. [Extracted from the article]

Increasing number of cancer survivors besides effective medications increase the demand for cancer care services. Therefore, managers have to investigate new ways to enhance the operational performance of the outpatient chemotherapy clinics (OCCs). However, the management process is complex due to significant variability in treatment times as a result of the different cancer types and accordingly different chemotherapy protocols and scarce resources such as nurses, chemotherapy chairs/beds, and pharmacists. In this paper, we address two problems in OCCs. First, in the planning problem, the objective is assigning the optimum first day to start the treatment for a set of new patients, and computing the required number of nurses and pharmacists given the limited resources. Second, the operational problem of scheduling the patients' appointments. In this problem, the objective is to set the best appointment schedules for all patients, new and existing to improve the operational performance of the clinic. As the two problems are highly interrelated, we propose a two-phase solution approach starting by a mixed integer programming model that assigns the starting day of treatment for new patients and finds the optimum number of needed nurses and pharmacists to fulfill two objectives. Then, in the second phase, a discrete event simulation model is used to generate patient appointment schedules that minimise the treatment delay for patients and the total completion times of treatments in each day under resources availability constraints, including two new constraints covering the drug availability and pharmacists working-hours. Finally, the proposed simulation model is applied for evaluating the operations performance of a current case study and finding the best scheduling rule for patient appointment times to achieve a minimum wait time in the OCC. [ABSTRACT FROM AUTHOR]

The presence of symmetries in the solution set of mathematical programs requires the exploration of symmetric subtrees during the execution of Branch-and-Bound type algorithms and yields increases in computation times. When some of the solution symmetries are evident in the formulation, it is possible to deal with them as a preprocessing step. In this sense, implementation-wise, one of the simplest approaches is to break symmetries by adjoining Symmetry-Breaking Constraints (SBCs) to the formulation. Designed to remove some of the symmetric global optima, these constraints are generated from each orbit of the action of the symmetries on the variable index set. Incompatible SBCs, however, might make all of the global optima infeasible. In this paper we introduce and test a new concept of Orbital Independence designed to address this issue. We provide necessary conditions for characterizing independent sets of orbits and also prove that such sets embed sufficient conditions to exploit symmetries from two or more distinct orbits concurrently. The theory developed is used to devise an algorithm that identifies the largest independent set of orbits of any mathematical program. Extensive numerical experiments are provided to validate the theoretical results. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have been widely deployed in the last decades to provide various services, like environmental monitoring or object tracking. Such a network is composed of a set of sensor nodes which are used to sense and transmit collected information to a base station. To achieve this goal, two properties have to be guaranteed: (i) the sensor nodes must be placed such that the whole environment of interest (represented by a set of targets) is covered, and (ii) every sensor node can transmit its data to the base station (through other sensor nodes). In this paper, we consider the Minimum Connected k-Coverage (MCkC) problem, where a positive integer k ≥ 1 defines the coverage multiplicity of the targets. We propose two mathematical programming formulations for the MCkC problem on square grid graphs and random graphs. We compare them to a recent model proposed by Rebai et al. (Comput Oper Res 59:11–21, 2015). We use a standard mixed integer linear programming solver to solve several instances with different formulations. In our results, we point out the quality of the LP-bound of each formulation as well as the total CPU time or the proportion of solved instances to optimality within a given CPU time. [ABSTRACT FROM AUTHOR]

The value and overall efficacy of a satellite mission are directly affected by its task scheduling strategy and the associated amount of work performed in orbit. Despite subject to many constraints, task scheduling is ultimately restricted by the amount of power available at any given moment. In this paper, we propose a mathematical integer programming (IP) formulation designed to maximize the number of tasks to be executed by a satellite, constrained to the amount of power available at any moment along the course of an orbit. The optimization model is formulated to contemplate task priority, minimum and maximum number of task activation, minimum and maximum execution time, minimum and maximum period of a given task and execution window. The variant power input vector was calculated based on the solar cells efficiency, and also on an analytical model used to estimate the irradiance field according to parameters of orbit and attitude. To demonstrate the applicability of our methodology, we conduct several experiments considering three satellite sizes with different orbits and task parameters. The results show that the proposed offline scheduling algorithm generates an optimal energy effective scheduling plan, allowing the best possible use of available energy resources while ensuring the quality of service (QoS). • We present an optimal task scheduling strategy for small satellites. • Test results under representative scenarios for different task parameters are presented. • The proposed offline scheduling algorithm generates an optimal energy effective scheduling plan. [ABSTRACT FROM AUTHOR]

Due to the vast and complex nature of the power systems, the study of distributed solution to multi-objective OPF (MO-OPF) problem is vital. However, the existing distributed MO-OPF approaches are based on mathematical programming techniques. The tedious repetition of algorithmic executions is inevitable. Moreover, they can hardly solve the non-differential problem. In this paper, the coevolutionary multi-objective evolutionary algorithm (MOEA), combining the idea of decomposition, is introduced to solve the distributed MO-OPF problem. The decomposition first occurs on decision variables. After segmenting, multiple subpopulations can coevolve in a distributed manner under the support of a new proposed distributed fitness evaluation method. Further, an objective decomposition (OBD) method is applied to the proposed distributed MO-OPF approach. The problem is decomposed again based on objective functions. By this OBD method, the fitness assignment problem is effectively alleviated and a more extensively covered pareto front can be obtained. The experimental results on two systems of different scales first show the effectiveness of the proposed distributed approach and then demonstrate the excellence of the OBD method in large-scale system. [ABSTRACT FROM AUTHOR]

• We develop a mathematical programming model for scheduling turnaround maintenance activities for a network of plants in a supply chain. • We propose a heuristic algorithm for solving the model when the network has large number of plants. • A case study petrochemical supply chain is used to demonstrate the utilization of the model and the effectiveness of the heuristic. Turnaround (TAM) is a periodic maintenance in which plants are shutdown to conduct inspections, repairs, preventive maintenance and overhauls. The shutdown periods range from three to four weeks and prolonging this period is expensive and therefore requires effective planning. The primary purpose of this paper is to develop a mathematical programming model to coordinate and plan TAM for several plants in an integrated supply chain network. A secondary objective is to develop a heuristic algorithm for solving the model when the network has large number of plants. The model and the algorithm are developed, tested and applied on a case study petrochemical supply chain. The exact algorithm takes 8.3 times the time of the heuristic. In addition the heuristic algorithm provides near optimal solution in much shorter time. The developed model provides a tool to plan TAM for several plants in an integrated supply in an optimal fashion. [ABSTRACT FROM AUTHOR]

Engineering management plays a significant part in project construction, and it is a process involved with making decision under uncertainty. When decision makers are hesitant to assess investment projects, fuzzy set theory offers an effective tool for dealing with uncertainty. In this paper, a decision-making method is put forward for handling engineering management problems, where dual hesitant fuzzy set is adopted to model the uncertainty. Firstly, we raise an improved dual hesitant fuzzy correlation coefficient and its weighted form. Compared with the current dual hesitant fuzzy correlation coefficients, the presented one has more desirable properties. It not only can manifest the positive or negative correlation between dual hesitant fuzzy sets, but also can guarantee the dual hesitant fuzzy correlation coefficient is 1 (−1) if and only if the two dual hesitant fuzzy sets are identical (complementary). Secondly, a mathematical programming model, which integrates within-attribute and between-attribute variation information, is designed to derive the attribute weights. More importantly, it can fully utilize the decision information. Finally, the weighted correlation coefficients are calculated, and an illustrative example concerning engineering management is offered to demonstrate the rationality of the proposed technique. • Decision-making method is presented for engineering management problems. • Dual hesitant fuzzy correlation coefficient is proposed. • A mathematical model is presented to derive the attribute weights. • A case study from the engineering management cost system is taken to illustrate the approach. • The effectiveness and efficiency of our algorithm are provided. [ABSTRACT FROM AUTHOR]

• A new mathematical approach to tackle the design and planning problem of Closed Loop Supply Chains in the carpet industry is proposed. • Different recovery processes involving dissimilar activities and flows of recovered materials are considered. • The reverse network facilities and logistics are completely integrated with the configuration of the forward network structure. • An illustrative example based on an industrial case that involves the recovery of raw materials from end-of-life carpets is presented. This paper introduces a mixed-integer linear programming model to tackle the design and planning problem of Closed Loop Supply Chains in the carpet industry. A distinctive feature of the approach is handling different recovery processes involving dissimilar activities and flows of recovered materials. Moreover, the reverse network facilities and logistics are completely integrated with the forward network structure, in order to obtain a high level of customer supply while considering economic goals. In addition, the economies of scale for the recycling facilities are also explicitly handled. The model allows to determine the products and quantities to be manufactured, transported, stored, and recycled to cover the demand of a set of customers. The objective function is to maximize the profit of the whole life-cycle of products. An example based on an industrial case that involves the recovery of raw materials is presented to show the relevance of the method. [ABSTRACT FROM AUTHOR]

During the last decades, the banking industry has been facing multiple challenges, from changes in regulations and legal systems, to the irruption of new technologies. A constant adaptation to the rapidly changing business ecosystem is therefore required. This paper deals with one type of efficiency improvement that is required from traditional banking operations as a result of the current shift in the industry towards more use of technology. That is, we deal with the decision-making process related to the redesign of existing ATM networks, which is currently being considered by many banks worldwide. This redesign is mainly a consequence of an increased use of electronic payment methods and card transactions, which have made the demand for cash decrease significantly. We describe a multi-objective mathematical programming model to be used as a tool when making strategic decisions regarding the bank ATM network redesign. Total costs and the coverage of customers and turnover are considered as conflicting performance attributes and analyzed through several multi-criteria techniques, namely the weighted sum and Tchebycheff methods and Archimedean goal programming. Among other insights, the results on a realistic case study show that the potential for cost reductions is high and that the consideration of both customer coverage and turnover coverage is crucial for the performance of the network. [ABSTRACT FROM AUTHOR]

Linear programming problems in fuzzy environment have been investigated by many researchers in the recent years. Some researchers have solved these problems by using primal-dual method with linear and exponential membership functions. These membership functions are particular form of the reference functions. In this paper, we introduce a pair of primal-dual PPs in Atanassov's intuitionistic fuzzy environment (AIFE) in which the membership and non-membership functions are taken in the form of the reference functions and prove duality results in AIFE by using an aspiration level approach with different view points, viz., optimistic, pessimistic and mixed. Since fuzzy and AIF environments cause duality gap, we propose to investigate the impact of membership functions governed by reference functions on duality gap. This is specially meaningful for fuzzy and AIF programming problems, when the primal and dual objective values may not be bounded. Finally, the duality gap obtained by the approach has been compared with the duality gap obtained by existing approaches. [ABSTRACT FROM AUTHOR]

This paper proposes to address the problem of the simultaneous optimization of the liner shipping route and ship schedule designs by incorporating port time windows. A mathematical programming model was developed to minimize the carrier's total operating cost by simultaneously optimizing the port call sequence, ship arrival time per port of call, and sailing speed per shipping leg under port time window constraints. In view of its structure, the nonlinear nonconvex optimization model is further transformed into a mixed-integer linear programming model that can be efficiently solved by extant solvers to provide a global optimal solution. The results of the numerical experiments performed using a real-world case study indicated that the proposed model performs significantly better than the models that handle the design problems separately. The results also showed that different time windows will affect the optimal port call sequence. Moreover, port time windows, bunker price, and port efficiency all affect the total operating cost of the designed shipping route. [ABSTRACT FROM AUTHOR]