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Sustainability has become a key issue in the forest industry; this research aims to analyze the quantitative techniques and metrics applied to the forest-to-lumber supply chain to achieve sustainable decision-making during the last six years. The methodology used was the PRISMA systematic literature review guide, which provides a complete and updated view of the situation. A total of 724 publications were collected from the Web of Science database. Consequently, 85 papers were selected for analysis and synthesis after applying inclusion criteria. The results show a growing interest in sustainability in the forest-to-lumber supply chain, with a peak of publications in 2019. Mathematical Programming and Simulation models are on top of the quantitative techniques applied. These techniques are applied to the supply chain components, classified according to the raw material's degree of processing or transformation level in forest entities, sawmills, transportation, and other entities. The concluding remarks highlighted that 19 published works research the social dimension, 43 explore the environmental dimension, and 55 examine the economic dimension. Moreover, in the environmental dimension, there is a concentration on the use of metrics associated with greenhouse gases, and to a lesser extent, they have been concerned with soil and water. Additionally, in the social dimension, they have concentrated mainly on the workers, leaving the local communities around the supply chain in the second place. Our systematic review reports the techniques or quantitative methodologies applied in the forest-to-lumber supply chain and the metrics used to handle the dimensions of sustainability. [ABSTRACT FROM AUTHOR]

The inverse data envelopment analysis (InvDEA) technique is an applicable method in order to estimate the input/output levels of decision-making units (DMUs) to preserve predetermined technical efficiency scores. In the managerial atmosphere, the decision maker (DM) aims to merge two or more units and needs to know the input/output levels of the merged unit, while the efficiency score of the new unit is set, however, in some cases, the units have two-stage network structures. The main purpose of this paper is merging DMUs with two-stage network structures. To reach this goal, in this paper, an InvDEA method is presented in order to estimate inputs and the intermediate products of two-stage DMUs, to achieve the different predetermined efficiency scores which have been set by the DM. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to design an applied mathematical model to maximize the profits of maintenance activities in manufacturing organizations, also providing an efficient solution method for that. Design/methodology/approach: Reviewing published articles in the field of maintenance planning and then trying to model the problem to optimal decision making in this field. Findings: Maintenance optimization can be done more appropriately by the accurate use of mathematical programming. Research limitations/implications: The existence of probabilistic parameters in this problem leads to hard stochastic programming. Practical implications: Designing and organizing maintenance activities will increase productivity. This paper attempts to use mathematical models to optimize this matter. Originality/value: This paper highlights the importance of using optimization methods for maintenance planning. [ABSTRACT FROM AUTHOR]

In most project portfolio selection (PPS) situations, the presence of multiple attributes and decision-maker preference is inevitable. As Multi-criteria Decision Analysis (MCDA) methods provide a framework well-suited to deal with these challenges in PPS problems, the use of MCDA methods in real-life PPS problems has increased in recent years. This paper provides a comprehensive literature review of the use of different MCDA methods and their individual or combined utilization with other modeling techniques to support PPS problems. First, we summarize how MCDA methods are used in different modeling approaches. Second, we examine the mathematical models that are generally used to combine MCDA with mathematical programming techniques to solve PPS problems with resource constraints. Third, we present the drawbacks of combined utilization and discuss recent advances. Finally, we visualize the summary of the reviewed papers as a decision tree to assist researchers and practitioners in the use of MCDA methods in a specific PPS context and propose some future research directions. [ABSTRACT FROM AUTHOR]

In the context of project management, the attention given to project portfolio management has increased in recent years. The use of mathematical programming for portfolio management is also on the rise, because it integrates the project interactions with the multiple objectives of portfolio management into a single model. Among the possible objectives, recent studies have paid special attention to the emerging objective of future preparedness, which has not yet been incorporated into the existing mathematical models. This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. Given the importance of the last measure, the purpose of this paper is to provide a more complete model that provides the marginal contribution and the best combination of projects according to the needs of the company. The model is tested using real data from two companies, one in Brazil and one in Canada, and the results obtained are coherent with their respective practices. [ABSTRACT FROM AUTHOR]

Traditional auditing has been commonly practiced by multinational companies to monitor their suppliers for sustainability violations. Based on a collaborative supplier sustainability performance improvement program at Koninklijke (Royal) Philips N.V., we introduce a framework that offers a paradigm shift to an improvement-based proactive approach that makes use of suppliers' self-assessments. We refer to this framework as CARE, consisting of the following phases: collecting supplier sustainability data, assessing suppliers' sustainability levels, reacting to future violations proactively, and enhancing sustainability performance. The framework integrates analytics techniques to understand the link between the general characteristics of the carefully assessed suppliers—such as location, size, and sector—and their sustainability profile, enabling large-scale supplier assessment and improvement. This information is then used to leverage machine learning techniques to predict current and future sustainability levels of suppliers and to determine best actions for sustainability improvement using mathematical programming. The utilization of analytics constitutes a pivotal element in this endeavor and notably makes CARE highly scalable because it harnesses limited supplier data—namely, only general supplier information—while there is a need to support decision making concerning thousands of suppliers. Philips makes use of this framework and reports that the overall 2021 year-on-year improvement in sustainability performance was 24% for suppliers that entered the program in 2020, indicating the efficacy of the suggested approach. History: This paper was refereed. Funding: The authors gratefully acknowledge the support of TKI Dinalog–Dutch Institute for Advance Logistics on the project entitled "Supplier Sustainability Improvement" [Grant 2017-2-132TKI]. [ABSTRACT FROM AUTHOR]

This paper considers a location-allocation problem in a closed-loop supply chain (CLSC) with two extensions: first, demand and prices of new and return products are regarded as non-deterministic parameters and second, the objective function is developed from expected profit to three types of mean-risk ones. Indeed, design and planning an integrated CLSC in real-world volatile markets is an important and necessary issue. Further, risk-neutral approaches, which are considered expected values, are not efficient for such uncertain conditions. Hence, this paper, copes with the design and planning problem of a CLSC in a two-stage stochastic structure. Besides, risk criteria are considered through using three types of popular and well-behaved risk measures: mean absolute deviation, value at risk and conditional value at risk (CVaR). Consequently, three types of mean-risk models are developed as objective functions and decision-making procedures are undertaken based on the expected values and risk adversity criteria. Finally, performances of the developed mean-risk models are evaluated in various aspects. Results reveal that the inefficiencies of risk-neutral approaches can be overcome. In addition, in terms of quality of solutions, the acceptability of CVaR is proved when it is compared to other risk measures. [ABSTRACT FROM AUTHOR]

Multicriteria decision-making (MCDM) techniques have successfully been used to address a wide range of real-world decision-making issues. The best–worst method (BWM) is one of the several deterministic MCDM approaches. A recently proposed method called the multichoice best–worst method (MCBWM) takes into account several linguistic terms for pairwise comparisons of relative preferences among the criteria. It has been shown that the MCBWM approach has advantages over BWM: it reduces the calculation and determines optimal weight values by providing the choices for the optimal solution. This paper proposes a unique method for group decision-making based on MCBWM. We extended the MCBWM to solve group decision-making problems. A novel solution approach was developed and validated for multiple problems. Two examples and one case study were solved using the proposed approach to demonstrate the validity and application of the proposed method. The results were further compared with existing models to validate the proposed approach. We found that the obtained ranking order for all problems is the same and that the proposed model has a higher consistency ratio than the existing approaches. This method can be extended to other mathematical programming models for collective decision making in uncertain situations. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to spur thought-provoking practical debates on current nurse workforce staffing and scheduling systems in relation to a critical review of Ang and colleagues' (2018) article entitled "Nurse workforce scheduling in the emergency department: A sequential decision support system considering multiple objectives." Design: Discussion paper on a practical discourse in connection with the aforementioned published article. Discussion: Mathematical Programming (optimisation) (MP)-based nursing research has been published for nearly thirty years almost exclusively in industrial engineering or health business administration journals, demonstrating a widening gap between nursing research and practice. Nurse scientists' knowledge and skill of MP is insufficient, as are their interdisciplinary collaborations, setting back the advancement of nursing science. Above all, nurse scientists skilled in decision science are desperately needed for that analytic intellection which is rooted in the 'intrinsic nature and value of nursing care.' It is imperative that nurse scientists be well-prepared for the new age of the Fourth Industrial Revolution through both an education in MP and interdisciplinary collaboration with decision science experts in order to prevent potential stereotyped MP-based algorithm-driven destructive influences. Conclusions: The current global nursing shortage makes optimal nursing workforce staffing and scheduling more important. MP helps nurse executives and leaders to ensure the most efficient number of nurses with the most effective composition of nurse staffing at the right time for a reasonable cost. Nurse scientists urgently need to produce a new nursing knowledge base that is directly implementable in nursing practice. Impact Statement: Nurse scientists should take the leading role in producing the mathematical programming-integrated knowledge base that is directly implementable in practice. [ABSTRACT FROM AUTHOR]

Fuzzy set theory has been extensively employed in mathematical programming, especially in linear programming problems. As a generalization of fuzzy sets, a hesitant fuzzy set is a very useful tool in places where there are some hesitations in determining the membership of an element to a set. There are few studies on hesitant fuzzy linear programming problems; therefore, in this paper, we have studied such problems. For this purpose, at first, the motivation of this paper is explained; then, types of hesitant fuzzy linear programming models are introduced. Since it is not easy to examine all of the hesitant fuzzy models for the linear programming problems in one paper, we have restricted ourselves to symmetric and right-hand-side hesitant fuzzy linear programming problems with the flexible approach and then proposed two new approaches to solve them. Finally, to illustrate the applicability of the proposed approaches, three examples under hesitant fuzzy information are given. [ABSTRACT FROM AUTHOR]

Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area. [ABSTRACT FROM PUBLISHER]

Data Envelopment Analysis (DEA) is non-parametric mathematical programming for measuring the performance of a set of homogeneous decision-making units (DMUs). Standard DEA models usually result in several efficient units, so, picking the best unit among efficient units has been one of the most challenging subjects in DEA literature. With reference to various researchers, the common set of weights (CSW) approach has been intriguing among them. This paper discusses a mechanism for detecting a common set of weights which is managed to be always positive and prevents weights dissimilarity. Employing this common set of weights can determine the efficiency score of each unit and finally rank them based on their obtained efficiency score. Equally, the proposed model not only provides the closest targets, but also minimizes the deviations of actual DMUs and extreme efficient units. In order to verify the proposed approach an empirical example of Iranian electricity distribution companies is explained. [ABSTRACT FROM AUTHOR]

This paper proposes an interactive consensus reaching model in the group decision making for heterogeneous hesitant preference relations (i.e., hesitant fuzzy preference relations, hesitant multiplicative preference relations, hesitant fuzzy linguistic preference relations). First, the consistencies of three hesitant preference relations are defined, respectively. Then, based on their definitions, three optimization models are constructed to obtain the weight vector of alternatives, based on which an interactive consensus adjustment algorithm is established based on the direct consensus framework. This framework adopts feedback mechanism to facilitate the information correction of decision makers. After several rounds of adjustment, the decision results with satisfactory consensus level are achieved. Finally, the practicability and effectiveness of the model are illustrated through a case study of mine accident emergency decision making. [ABSTRACT FROM AUTHOR]

This Note points out a minor error in a technique developed by Soyster and Lev [1] for determining if a linear goal can be substituted for a fractional goal in a goal programming problem. A revised formulation is provided. [ABSTRACT FROM AUTHOR]

Agri-food sector performance strongly impacts global economy, which means that developing optimisation models to support the decision-making process in agri-food supply chains (AFSC) is necessary. These models should contemplate AFSC’s inherent characteristics and sources of uncertainty to provide applicable and accurate solutions. To the best of our knowledge, there are no conceptual frameworks available to design AFSC through mathematical programming modelling while considering their inherent characteristics and sources of uncertainty, nor any there literature reviews that address such characteristics and uncertainty sources in existing AFSC design models. This paper aims to fill these gaps in the literature by proposing such a conceptual framework and state of the art. The framework can be used as a guide tool for both developing and analysing models based on mathematical programming to design AFSC. The implementation of the framework into the state of the art validates its. Finally, some literature gaps and future research lines were identified. [ABSTRACT FROM AUTHOR]

This paper proposes a mathematical programming approach to construct an appropriate membership function extending our previous studies. It is important to set a membership function with both subjectivity and objectivity to obtain a reasonable optimal solution based on decision maker's feelings in real-world decision making. In order to ensure objectivity of obtained membership function as well as subjectivity, an entropy-based approach based on mathematical programming is integrated into interval estimation considered by the decision maker. As a general entropy with fuzziness, fuzzy Harvda-Charvat entropy is introduced, which is a natural extension of fuzzy Shannon entropy. In addition, qualitative and subjective evaluations based on the pairwise comparison are introduced to represent the differences between two membership values. The main step of our revised approach is to solve the proposed mathematical programming problem strictly using nonlinear programming. In this paper, the given membership function is assumed to be a piecewise linear membership function as approximation of nonlinear functions, and each intermediate value of partial linear function is optimally obtained. [ABSTRACT FROM AUTHOR]

This paper pertains to goal programming with fuzzy goals and fuzzy priorities, Hannan [1], in his paper on fuzzy goal programming, alludes to the difficulty of handling fuzzy priorities and further notes that a method that this author proposed [2] may lead to incorrect results. In this note, the general problem of goal programming with fuzzy priorities is reexamined, along with the solution to the specific example presented in my original paper[2]. It is shown that the method for handling fuzzy priorities originally proposed by this author does indeed capture the relative importance of goals. [ABSTRACT FROM AUTHOR]

The dynamic and complex environment affects the operational efficiency of automated guided vehicles (AGVs) at automated container terminals, in which artificial intelligent approaches are applied to address optimization problems in dynamic systems. This paper proposes a digital twin-based decision support approach to improve the efficiency of AGV scheduling service. Accordingly, the factors that affect AGV scheduling performance, including conflicts, failures, and battery constraints, are discussed first. Then, the framework and main steps of the proposed approach are described. The physical operation space is mapped into the virtual space, and both spaces keep synchronized to support the verification of solutions. A mathematical programming model and Q-learning algorithm are used to generate the AGV scheduling plan considering battery charging. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach. Comparisons of the digital twin-based approach, genetic algorithm (GA), and particle swarm optimization (PSO) are also made with different scale experiments. It appears that the proposed digital twin-based approach is superior to GA and PSO when solving small- and large-scale cases. A sensitivity analysis is performed concerning the battery utilization rate, task, and AGV number. Experimental results show that an optimal configuration of AGV and task can improve the battery utilization rate and reduce the completion time. • A digital twin-based decision support approach is proposed to improve scheduling. • A mathematical programming model is developed to minimize the total completion time. • Digital twin and Q-learning are used to monitor operation and obtain solutions. • Sensitivity analysis of battery utilization is made to provide equipment maintenance. • The proposed approach is superior based on the comparison of solution performances. [ABSTRACT FROM AUTHOR]

Randomized decision making refers to the process of making decisions randomly according to the outcome of an independent randomization device, such as a dice roll or a coin flip. The concept is unconventional, and somehow counterintuitive, in the domain of mathematical programming, in which deterministic decisions are usually sought even when the problem parameters are uncertain. However, it has recently been shown that using a randomized, rather than a deterministic, strategy in nonconvex distributionally robust optimization (DRO) problems can lead to improvements in their objective values. It is still unknown, though, what is the magnitude of improvement that can be attained through randomization or how to numerically find the optimal randomized strategy. In this paper, we study the value of randomization in mixed-integer DRO problems and show that it is bounded by the improvement achievable through its continuous relaxation. Furthermore, we identify conditions under which the bound is tight. We then develop algorithmic procedures, based on column generation, for solving both single- and two-stage linear DRO problems with randomization that can be used with both moment-based and Wasserstein ambiguity sets. Finally, we apply the proposed algorithm to solve three classical discrete DRO problems: the assignment problem, the uncapacitated facility location problem, and the capacitated facility location problem and report numerical results that show the quality of our bounds, the computational efficiency of the proposed solution method, and the magnitude of performance improvement achieved by randomized decisions. Summary of Contribution: In this paper, we present both theoretical results and algorithmic tools to identify optimal randomized strategies for discrete distributionally robust optimization (DRO) problems and evaluate the performance improvements that can be achieved when using them rather than classical deterministic strategies. On the theory side, we provide improvement bounds based on continuous relaxation and identify the conditions under which these bound are tight. On the algorithmic side, we propose a finitely convergent, two-layer, column-generation algorithm that iterates between identifying feasible solutions and finding extreme realizations of the uncertain parameter. The proposed algorithm was implemented to solve distributionally robust stochastic versions of three classical optimization problems and extensive numerical results are reported. The paper extends a previous, purely theoretical work of the first author on the idea of randomized strategies in nonconvex DRO problems by providing useful bounds and algorithms to solve this kind of problems. [ABSTRACT FROM AUTHOR]

Aquaculture is an important industry in certain coastal areas. Focusing on the farming of salmon and trout, an operational planning problem arises with the goal of allocating a supply of fish to the demand that is expressed through customer orders. This paper provides a conceptual model of such a planning problem and defines a corresponding bi-objective mathematical programming model. The problem is novel with respect to the structure of fish transport and the rules for satisfying customer orders with respect to fish size, quality, certification, and health status. Computational experiments have been conducted to gain further insight into the use of the provided model to provide support for planners who are involved in operational decision-making. The results indicated that the bi-objective optimization model can be useful in situations where a supply is insufficient to cover all of the demand within a given planning horizon. [ABSTRACT FROM AUTHOR]

This paper focuses on a current topic of supply chain management and operations research which serves as a tool manufacturing enterprises to cope with pressure put on them by continuously changing market conditions and global economy itself. Paper presents results of research conducted on sample file of Slovak manufacturing enterprises. The main aim of this paper is to explore the extent of utilization of mathematical programming as optimization methods in production practice in Slovakia to analyse possible relationship between enterprise's size and used optimizing method. Representativeness of the sample file was confirmed by application of Pearson's chi-squared test (X2 - test) due to criterion of enterprise's size. The results of this research have an implication for business practice and may serve managers in their decision-making process. In managerial practice enterprises have to deal with many different problems concerning their supply chains. The majority of them can be resolved using mathematical programming. [ABSTRACT FROM AUTHOR]

The paper presents the methodological background of Multiple Criteria Decision Making/Aiding (MCDM/A) and its practical application in logistics systems. It explains why MCDM/A methodology is important when dealing with different categories of decision problems that arise in those systems. The major features and basic notions of MCDM/A methodology are presented, while different categories of MCDM/A methods are characterized and classified. Two case studies demonstrate potential applications of MCDM/A methodology in logistics. In the first case study multiple objective optimization of the distribution system is carried out and compared with single objective optimization. The decision problem is formulated as multiple criteria mathematical programming problem and solved by an extended version of MS Excel Solver - Premium Solver Plus. The second case study focuses on multiple criteria evaluation and the ranking of logistics infrastructure objects, i.e. a set of warehouses - distribution centres. The decision problem is formulated as a multiple criteria ranking problem and solved with an application of the ELECTRE III/IV method. [ABSTRACT FROM AUTHOR]

A closed-loop supply chain (CLSC) network is composed of both forward and reverse flows. An essential issue to be considered in designing any supply chain network is determination of number and locations of facilities in each layer of the network. Such a problem is a challenging job, since it contains sub-problems which are proven to be nondeterministic polynomial time complete. This paper proposes a CLSC distribution network design problem in which reverse flows are imported into forward model proposed by Selim and Ozkarahan (Int J Adv Manuf Technol 36:401-418, ). Such a model is considered assuming forward covering (model I) and backward covering (model II) objectives, and then results are compared against the model incorporating covering of both forward and backward networks (model III). Our aim is to accentuate on the role of considering backward parameters in design of a CLSC network and to show how results differ from considering sub-problems separately. To model and solve the problem, a fuzzy goal programming approach is developed for network design in an interactive manner between decision maker and the model. To validate the presented model and the proposed solution approach, a test problem is presented and comparison of results is made using this problem. The results show that the proposed model can solve the CLSC problems in a manageable time. Moreover, outputs of the three models differ significantly. Therefore, the role of incorporating backward flows into the network design problem has been shown using our experiments. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to develop a distributed task allocation method for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs) based on the consensus algorithm and the online cooperative strategy.Design/methodology/approach In this paper, the allocation process is conducted in a distributed framework. The cooperative task allocation problem is proposed with constraints and uncertainties in a real mission. The algorithm based on the consensus algorithm and the online cooperative strategy is proposed for this problem. The local chain communication mode is adopted to restrict the bandwidth of the communication link among the UAVs, and two simulation tests are given to test the optimality and rapidity of the proposed algorithm.Findings This method can handle both continuous and discrete uncertainties in the mission space, and the proposed algorithm can obtain a feasible solution in allowable time.Research limitations/implications This study is only applied to the case that the total number of the UAVs is less than 15.Practical implications This study is expected to be practical for a real mission with uncertain targets.Originality/value The proposed algorithm can go beyond previous works that only deal with continuous uncertainties, and the Bayesian theorem is adopted for estimation of the target. [ABSTRACT FROM AUTHOR]

Goal programming provides an efficient technique to deal with decision making problems with multiple conflicting objectives. This paper joins the streams of research on goal programming by providing a so-called uncertain random goal programming to model the multi-objective optimization problem involving uncertain random variables. Several equivalent deterministic forms are derived on the condition that the set of parameters consists of uncertain variables and random variables. Finally, an example is given to illustrate the application of the approach. [ABSTRACT FROM AUTHOR]

To reduce water conservancy projects’ negative effects on the ecological environment, in this paper, a method is proposed to assess the impact of hydropower stations on the environment in the processes of the flood discharge and energy dissipation. It utilizes the hesitant fuzzy linguistic information to describe the problem's uncertainty and fuzziness, portrays decision maker's satisfaction degree with the increasing marginal utility, and applies the hyperplane to establish a mathematical programming model. Then, the undetermined parameter in the model is discussed, and a decision-making procedure for solving the considered problem is provided. Furthermore, an experiment is designed to verify the availability and reasonability of the proposed method by comparing it with an existing one. Finally, the method is applied to assess the impact of giant hydropower stations’ flood discharge and energy dissipation on the environment in Sichuan, China. [ABSTRACT FROM AUTHOR]

This paper presents a new decision-making model of harden grey target based on a measure transformation method, to study the problem of grey decision with interval numbers in which the decision-maker has certain preference information. First, a measure transformation method for normalized treatment of interval numbers is proposed, in order to select the harden decision-making interval through minimizing the measure of the interval of situation effect, which can improve the decision quality. Then, programming model is established based on minimizing the square sum of distance, and index weights are confirmed by constructing the lagrange function. The model of harden grey target based on interval number with preference information is derived. The validity and practicability are illustrated with an example. [ABSTRACT FROM AUTHOR]

In this paper, the shortcoming of multi-objective mixed-model assembly line scheduling problem (MMSP) is pointed out firstly, and then grey relational analysis (GRA) is applied to address multi-objective MMSP. The methodology, solving steps and an instance are given. The result shows that GRA is a scientific and practical method of multi-objective decision of MMSP. [ABSTRACT FROM AUTHOR]

The dynamic production scheduling is a very complex process that may arise from the occurrence of unpredictable situations such as the arrival of new orders besides the ones already accepted. As a consequence, companies may often encounter several difficulties to make decisions about the new orders acceptance and sequencing along with the production of the existing ones. With this recognition, a mathematical programming model for the regenerative scheduling problem with deterministic processing times is formulated in the present paper to evaluate the economic advantage of accepting a new order in an engineer to order (ETO) manufacturing organization. The real case of an Italian ETO company which produces hydraulic marine and offshore cranes is afterwards presented. [ABSTRACT FROM AUTHOR]

Hybrid manufacturing technology has enabled manufacturers to combine advantages of mainly subtractive and additive manufacturing technologies. A single machine supports producing products with complex geometry, at high quality, and with a high degree of automation. To benefit from these advantages, decisions taken in the process planning stage of such a sophisticated manufacturing system should be optimized. The objective of this paper is to determine the optimal process plan considering both the engineering and manufacturing aspects of the hybrid technology. A comprehensive process planning model is proposed. The model specifies the optimal sequence of additive and subtractive features that minimizes the production cycle time. In addition, the model sets the optimal part orientations such that the time needed for building support structures, performing post-processing and inspection operations, changing cutting tools and printing nozzles, and unclamping the part is minimized. The model is comprehensive as it considers productive and non-productive times, precedence, technological, quality, and manufacturing restrictions imposed on hybrid manufacturing systems. The proposed model is nonlinear; due to this nonlinearity, the model is intractable. A linearization scheme is applied to formulate an equivalent linear model that is solvable to optimality by commercial solvers. Case studies on test and industrial parts are provided to evaluate the computational performance of the proposed model. Integrating the proposed model in hybrid manufacturing (HM) systems ensures adopting the HM technology in its optimal direction. HM technology is an enabler of establishing a smart manufacturing system which is one of the pillars of Industry 4.0. [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]

In this paper, we consider a bi-criteria approach to address the problem of portfolio construction and selection, taking into account the weaknesses of Markowitz’ Mean-Variance portfolio selection. Portfolio theory has many drawbacks, multiple criteria modelling and optimisation is time consuming and generally very demanding from the viewpoint of necessary hardware resources, and every investor is characterised by different preferences. To effectively address these issues, this paper presents an integrated and innovative methodological approach, within the frame of bi-objective mathematical programming for constructing and selecting portfolios. The validity of the proposed approach is tested through an illustrative application in the New York Stock Exchange. Moreover, we evaluate the differences and similarities between the efficient frontier of the proposed model and the classical Mean-Variance. We show that, having the individual investor’s preferences incorporated, the proposed utility-based model provides more suitable efficient portfolios which differ markedly from the classical Mean-Variance efficient portfolios. [ABSTRACT FROM PUBLISHER]

A frequent problem for decision makers (DMs) analysing decisions involving multiple objectives is the identification and selection of the most preferred option from the set of non-dominated solutions. Two techniques, weighted sum optimization and reference point optimization, have been developed to address this problem for multiobjective linear programming problems (MOLP), In this paper, we examine the relationship between these two techniques. We demonstrate that the values of the dual variables associate with auxiliary constraints of the reference point technique are equal to the weight values used to compute the same non-dominated solution via the weighted sum technique. This insight will enable the development of new interactive solution procedures for MOLPs which allow the DM to readily switch from one method to the other during the search for the most preferred non-dominated solution. The advantages of the approach are discussed in the paper. [ABSTRACT FROM AUTHOR]

Fuzzy game theory has been applied in many decision-making problems. The matrix game with interval-valued intuitionistic fuzzy numbers (IVIFNs) is investigated based on Archimedean t-conorm and t-norm. The existing matrix games with IVIFNs are all based on Algebraic t-conorm and t-norm, which are special cases of Archimedean t-conorm and t-norm. In this paper, the intuitionistic fuzzy aggregation operators based on Archimedean t-conorm and t-norm are employed to aggregate the payoffs of players. To derive the solution of the matrix game with IVIFNs, several mathematical programming models are developed based on Archimedean t-conorm and t-norm. The proposed models can be transformed into a pair of primal–dual linear programming models, based on which, the solution of the matrix game with IVIFNs is obtained. It is proved that the theorems being valid in the exiting matrix game with IVIFNs are still true when the general aggregation operator is used in the proposed matrix game with IVIFNs. The proposed method is an extension of the existing ones and can provide more choices for players. An example is given to illustrate the validity and the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm which is based on many things: the notions well-known of the simplex network method, Ford Fulkerson's algorithm and our new idea, which is << the gain cycles >>, applied on a bi-objective minimum cost flow problem. This algorithm permits us to have a good order of many criteria in a rapid and an efficient way; because this classification permits us to structure the optimal area, in which we can choose the best action among the others which exist in the objective space. From this one, we distinguish, that the resolution of this problem comes to find an under set of good actions, among which the decider can select an action of best compromise, or make a decision, in the case where reference indications of the deciders may change. A didactic example is done to illustrate our algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an exhaustive literature review on theories and applications in the field of multi-choice programming (MCP). The increasing competition in the business world has given rise to the situations where decision-makers are offered with multiple options/information to optimally decide on a single task. Under such circumstances a number of decision-making problems are falling into the scenario of MCPs. Thus, ever-increasing applicability of the MCPs is making more and more Operations Research practitioners to focus on them. This paper analyses all the related work and presents the same by classifying the research problems into two broad categories: theories and applications. As an aid to assist future researchers and practitioners, key insights on the evolution of various variants of MCPs have also been systematically delineated. The paper also throws light on some research gaps and concludes with the scope of future research in this area. [ABSTRACT FROM AUTHOR]

Large construction and infrastructure projects are a billion-dollar business, but few studies have addressed the integrated operations in this unique domain of the construction supply chain (CSC). The comparison between the CSC and a conventional supply chain enables us to examine its framework and establish a quantitative optimization model for the CSC. To introduce the integrated operations concept into the CSC, many uncertainties need to be first dealt with, for which a multiobjective uncertain optimization model is developed. As the optimization of the owner and fabricator's costs and the service level are the main objectives, a hybrid genetic algorithm with fuzzy-random method is developed to solve the optimization model. An integrated multiobjective purchasing and production planning model is then constructed and applied to a hydropower construction project in Southwest China. The results illustrate that efficient integrated operations are critical for the CSC performance. The optimization results also indicate that considering of uncertain rush orders and delay times can be vital for optimum CSC performance. With this proposed method, construction managers can quickly respond to changing uncertain demand. This paper has highlighted that project managers need to collaborate with other stakeholders to ensure optimal CSC performance. [ABSTRACT FROM AUTHOR]

In public policy decision making and in capital planning fractional criterion functions occur. For a given set of desirable target values (goals) τi, this paper develops an algorithm for solving a nonconvex programming problem of the type: Minx∈S Maxi{&phii(fi(x)/gi(x) - τi), i = 1,..., m} where fi are convex functions, gi, are concave functions over the convex subset S of Rn and φi are nondecreasing gauge functions. Here φi(&middot) is the penalty incurred whenever the fractional objective fi/gi deviates from the target value τi, the problem is then to choose an x that minimizes the maximum penalty incurred. [ABSTRACT FROM AUTHOR]

An earlier paper and subsequent commentaries in Decision Sciences described purportedly new methods for formulating and solving goal programming problems with fuzzy goals. This note suggests that much of what these articles propose is but a rediscovery of well-known results. [ABSTRACT FROM AUTHOR]

In this paper hierarchical multicriteria optimization problems are addressed in a convex programming framework It IS assumed that the criteria are aggregated into a nonlinear function, which renders the problem nonseparable. in general. The projection of the problem onto the criteria space is used to obtain an equivalent separable problem, solved through a relaxation procedure implemented on basis of a multilevel structure. At the upper level of the structure, the decision making process involves the solution of a multicriteria problem formulated in the criteria space. The solution encountered at the upper level originates a lower level parametric optimization problem with separable structure that can be treated by standard coordination-decomposition techniques. The convergence of the overall procedure is ensured. The paper includes an application of the approach proposed for the control of dynamic systems with linear quadratic structure. [ABSTRACT FROM AUTHOR]

This paper shows that the satisficing trade-off method (STOM), one of the interactive multiobjective programming techniques, can be applied effectively to the formulation of livestock rations. Nutrient requirements are considered as soft constraints whose right-hand sides are flexible to some extent. This is easily done by regarding the values of the right-hand sides of the constraints as aspiration levels of the decision maker for objective functions. In this way a well-balanced solution can be obtained by STOM with automatic trade-off analysis. In STOM, moreover, because the objective functions and the constraint functions are interchangeable, decision makers are not required to consider the role of objective and constraint as fixed from the beginning. The authors' experience is that the method makes ration formulation very easy, rapid and flexible. In addition, this paper shows how effectively STOM can be applied not only to diet planning but also to nutritional diagnosis. [ABSTRACT FROM AUTHOR]

This paper investigates decision-making in multi-echelon serial supply chain management in the presence of imprecision or uncertainty arising from human reasoning, emphasising the computational resolution. The proposed analysis method is based on a combination of the extension principle and the alpha-representation in fuzzy theory and optimisation theory. The problem is first formulated as a fuzzy optimisation model with several fuzzy parameters. To conserve the fuzziness of the input information of the supply chain, such as forecast market demands and inventory costs, a pair of two-level mathematical programs is proposed to identify the lower and upper bounds of the fuzzy performance at different possibility levels, so that the complete membership function can be described. Four example scenarios are solved to demonstrate the validity of the proposed analysis method. The proposed methodology is widely applicable with different types of membership functions for fuzzy parameters, positive lead times or other more complicated cases. The managerial implications are also discussed for reference by decision-makers. [ABSTRACT FROM AUTHOR]

Geographic information system (GIS) users rely heavily on the versatile operations of GIS software and the abundant variety of geospatial data from different resources to satisfy their application requirements. However, the convenient use of GIS software has resulted in users easily ignoring the threat of data misuse because of the lack of understanding of data quality. Here we argue that data quality considerations must be coherently assimilated into the GIS operation design to visually present helpful information and ensure the accuracy of data for decision making. Data completeness is selected in this paper to demonstrate how the use of data quality information opens a new dimension to the design of future GIS software. We propose a new model for the representation, analysis, and visualization of data completeness information. With the brand new quantitative measures and informative visual approach, understanding of the data completeness of the illustrated contents in the map interface is enhanced, and inappropriate dataset selection can be effectively prevented. Thus, this paper presents an innovative, integrated and geospatial concept of future GIS operation design, where users are constantly aware of the continuously changing status of data quality based on formalized and quantitative data quality theories. [ABSTRACT FROM PUBLISHER]

Back-pressure scheduling has been considered as a promising strategy for resource allocation in wireless multi-hop networks. However, there still exist some problems preventing its wide deployment in practice. One of the problems is its poor end-to-end (E2E) delay performance. In this paper, we study how to effectively use inter-flow network coding to improve E2E delay and also throughput performance of back-pressure scheduling. For this purpose, we propose an efficient network coding based back-pressure algorithm (NBP), and accordingly design detailed procedure regarding how to consider coding gain in back-pressure based weight calculation and how to integrate it into next hop decision making in the NBP algorithm. We theoretically prove that NBP can stabilize the networks. Simulation results demonstrate that NBP can not only improve the delay performance of back-pressure algorithm, but also achieve higher network throughput. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, based on a merit function of the split feasibility problem (SFP), we present a Newton projection method for solving it and analyze the convergence properties of the method. The merit function is differentiable and convex. But its gradient is a linear composite function of the projection operator, so it is nonsmooth in general. We prove that the sequence of iterates converges globally to a solution of the SFP as long as the regularization parameter matrix in the algorithm is chosen properly. Especially, under some local assumptions which are necessary for the case where the projection operator is nonsmooth, we prove that the sequence of iterates generated by the algorithm superlinearly converges to a regular solution of the SFP. Finally, some numerical results are presented. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a satisfactory degree method by using nonlinear programming for solving multiattribute decision making (MADM) problems in which ratings of alternatives on attributes is expressed via interval-valued intuitionistic fuzzy (IVIF) sets and preference information on attributes is incomplete. Concretely, a nonlinear programming model is firstly explored to determine the satisfactory degree which is the ratio of the square of the weight Euclidean distance between an alternative and the IVIF negative ideal solution (IVIFNIS) to the sum of the square of the weight Euclidean distance between the IVIF negative ideal solution (IVIFNIS) and the IVIF positive ideal solution (IVIFPIS). Another nonlinear programming model is also developed to obtain satisfactory intuitionistic fuzzy sets, and then the general satisfactory degrees of the satisfactory intuitionistic fuzzy sets are used to generate the ranking order of the alternatives. Finally, a real example is employed to verify the applicability of the proposed approach and illustrate its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

During the last decade, the stringent pressures from environmental and social requirements have spurred an interest in designing a reverse logistics (RL) network. The success of a logistics system may depend on the decisions of the facilities locations and vehicle routings. The location-routing problem (LRP) simultaneously locates the facilities and designs the travel routes for vehicles among established facilities and existing demand points. In this paper, the location-routing problem with time window (LRPTW) and homogeneous fleet type and designing a multi-echelon, and capacitated reverse logistics network, are considered which may arise in many real-life situations in logistics management. Our proposed RL network consists of hybrid collection/inspection centers, recovery centers and disposal centers. Here, we present a new bi-objective mathematical programming (BOMP) for LRPTW in reverse logistic. Since this type of problem is NP-hard, the non-dominated sorting genetic algorithm II (NSGA-II) is proposed to obtain the Pareto frontier for the given problem. Several numerical examples are presented to illustrate the effectiveness of the proposed model and algorithm. Also, the present work is an effort to effectively implement the ε-constraint method in GAMS software for producing the Pareto-optimal solutions in a BOMP. The results of the proposed algorithm have been compared with the ε-constraint method. The computational results show that the ε-constraint method is able to solve small-size instances to optimality within reasonable computing times, and for medium-to-large-sized problems, the proposed NSGA-II works better than the ε-constraint. [ABSTRACT FROM AUTHOR]

Evolutions in the power system challenge the manner in which power system reliability is managed. In particular, currently used reliability criteria, typically the deterministic N-1 criterion, are increasingly inadequate. Moving to an alternative approach is difficult as quantifying benefits is hard in a multifaceted environment and system operators are reluctant to move away from the easy and transparent existing criterion. This paper presents a generic framework to evaluate and compare socio-economic and reliability performance of power system reliability criteria, focussing on the short term decision making process of transmission system operators (TSO). The framework can also be used to tune the parameters of reliability criteria. Short term operational planning and real time operation TSO decision making processes are simulated considering various reliability criteria. The framework is applied to a 5 node test system and the 24 node IEEE reliability test system, showing that the applied probabilistic reliability criterion outperforms deterministic N-0 and N-1 approaches in those systems in terms of expected reliability and socio-economic indicator values. The effect is larger in the bigger system with more operational flexibility. [ABSTRACT FROM PUBLISHER]

In this paper, we study the multiattribute decision-making (MADM) problem with intuitionistic fuzzy values that represent information regarding alternatives on the attributes. Assuming that the weight information of the attributes is not known completely, we use an approach that utilizes the relative comparisons based on the advantage and disadvantage scores of the alternatives obtained on each attribute. The relative comparison of the intuitionistic fuzzy values in this research use all the three parameters, namely membership degree (“the more the better”), nonmembership degree (“the less the better”), and hesitancy degree (“the less the better”), thereby leading to the tradeoff values of all the three parameters. The score functions (advantage and disadvantage scores) used for this purpose are based on the positive contributions of these parameters, wherever applicable. Furthermore, these scores are used to obtain the strength and weakness scores leading to the satisfaction degrees of the alternatives. The optimal weights of the attributes are determined using a multiobjective optimization model that simultaneously maximizes the satisfaction degree of each alternative. The optimal solution is used for ranking and selecting the best alternative on the basis of the overall attribute values. To validate the proposed methodology, we present a numerical illustration of a real-world case. The methodology is further extended to treat MADM problem with interval-valued intuitionistic fuzzy information. Finally, a thorough comparison is done to demonstrate the advantages of the solution methodology over the existing methods used for the intuitionistic fuzzy MADM problems. [ABSTRACT FROM PUBLISHER]