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We consider Upper Domination, the problem of finding the minimal dominating set of maximum cardinality. Very few exact algorithms have been described for solving Upper Domination. In particular, no binary programming formulations for Upper Domination have been described in literature, although such formulations have proved quite successful for other kinds of domination problems. We introduce two such binary programming formulations, and show that both can be improved with the addition of extra constraints which reduce the number of feasible solutions. We compare the performance of the formulations on various kinds of graphs, and demonstrate that (a) the additional constraints improve the performance of both formulations, and (b) the first formulation outperforms the second in most cases, although the second performs better for very sparse graphs. Also included is a short proof that the upper domination number of any generalized Petersen graph P(n, k) is equal to n. [ABSTRACT FROM AUTHOR]

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Process discovery is a branch of process mining that by using event logs extracts the process model that describes the events’ behavior properly. Since, Heuristic process discovery algorithms are among the most significant and popular process discovery methods and due to the fact that the quality of outputs of these algorithms is heavily dependent on the quality of extracted dependency graph, in this paper for the first time, an approach to transform the problem of dependency graph discovery to a binary programming problem has been proposed and also, an objective function is introduced that simultaneously considers fitness and precision measures of output models. The weights dedicated to each of the measures are determined by means of a user-defined threshold. The mentioned measures are the most important metrics in assessing quality of output models of process discovery algorithms. Hence, in fact this approach focuses on improving quality metrics of output models. Moreover, by means of defining suitable constrains, the proposed approach is capable of involving domain knowledge in mining procedure, as well as guiding the result through whether the models that are more likely to be sound. This is depicted in a case study of a real company that is described in this paper. In the case study, the proposed approach has been applied to marketing event log of the mentioned company by utilizing the constrains defined according to domain knowledge and structural rules of dependency graph and at the end, the results were presented.

In this paper, we extend the purely dual formulation that we recently proposed for the three-dimensional assignment problems to solve the more general multidimensional assignment problem. The convex dual representation is derived and its relationship to the Lagrangian relaxation method that is usually used to solve multidimensional assignment problems is investigated. Also, we discuss the condition under which the duality gap is zero. It is also pointed out that the process of Lagrangian relaxation is essentially equivalent to one of relaxing the binary constraint condition, thus necessitating the auction search operation to recover the binary constraint. Furthermore, a numerical algorithm based on the dual formulation along with a local search strategy is presented. The simulation results show that the proposed algorithm outperforms the traditional Lagrangian relaxation approach in terms of both accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Using cross-functional team (CFT) is a suitable strategy for improving the performance of organizations. The member selection problem is an important aspect of the CFT formation. Several evidences showed the important criteria for choosing true members are: cooperation and coordination, functional expertise, individual abilities, cost, and communication. In this paper, effective features for member selection are identified and a multi-objective 0–1 nonlinear programming model is developed. This model is developed by using individual and collaborative performance. Afterward, it is converted into the linear form by changing variables to solve it more easily. The proposed model is used in the real example in Census cross-functional team in the statistical center of Iran and required data were collected by surveys and interviews. The results indicate that this proposed model has better performance compared to recommendation of experts and can be used in other fields. Introduction: Nowadays using teams is increased; it helps companies and organizations to survive in product markets’ competition, business pressure, and customers’ expectations (Proehl, 1996; Santa, Ferrer, Bretherton et al. 2010; Fan, Feng, Jiang et al, 2009). Among various teams, cross-functional team is one of the most effective strategy which is used in NPD (Wang, Yan, and Ma, 2003), lean production, TQM and continuous improvement (Love and Roper, 2009). CFT is defined by a group of members who come from different functional areas (Feng, Jiang, Fan et al, 2010) in the same hierarchy as level within an organization, or even between organizations for a limited time )Saarani and Bakri, 2012). CFT has several advantages such as positive impact on cycle time and project performance )Barczak and Wilemon, 2003(, increasing learning, processing optimization, knowledge sharing (Love and Roper, 2009), creativity, problem solving (Santa, Ferrer, Bretherton et al. 2010; Saarani and Bakri, 2012), increasing competition in organization, responding to market changes (Santa, Ferrer, Bretherton et al. 2010), spanning organizational boundaries (Love and Roper, 2009; Feng, Jiang, Fan et al, 2010), and responding quickly to environmental changes (Zhang and Zhang, 2013) The first stage of team development is forming, therefore organizations must select candidates carefully to ensure CFT’s effects and success (Feng, Jiang, Fan et al, 2010). Correct selection prevents wasting time (Feng, Jiang, Fan et al, 2010), financial losses and productivity shortcoming )Saarani and Bakri, 2012(. Recently, some researchers have attended to CFT’s formation and discuss suitable characteristics to assemble members. In Chen and Lin (2004) study, functional expertise, teamwork experience, communication skill, flexibility in job assignment, and personality traits indicated as five important characteristics of team members that build successful multifunctional team. Fitzpatrick and Askin (2005) regarded innate tendencies, interpersonal skills, and technical skills as important criteria for member selection. Wang, Yan and Ma (2003) listed the selection attributes for the creation ability, management ability, utilization rates, cooperation levels, and so forth. Jiang et al. (2010) reported the criteria for selecting members for cross-functional teams: individual performance (such as work experience, ability to solve work problems, and technical knowledge), exterior organizational collaborative performance (such as the extent of external cooperation), and interior organizational collaborative performance (for instance mutual communication among members and collaboration in solving problems). Zhang and Zhang (2013) stated that the effective NPD team should have four capabilities: expertise and experience consistent, learning and knowledge sharing, communication, and problem-solving. Kargar and Zihayat (2012) discussed requirements such as communication, cost, and skills for desired members. Several evidences showed important criteria for choosing appropriate members. These are cooperation, coordination, functional expertise, individual abilities, cost, and communication. Existing researches focus on main skills of candidates while to the best of our knowledge there is no study that notices subskills of candidates. Furthermore, there is no study which considers all the criteria simultaneously. In addition, utilizing quantitative methods for the formation of CFTs has been the topic of recent researches. Chen and Lin (2004) used chain wise AHP to evaluate sharing knowledge and selecting members who have high knowledge rating in each department. Following that, they have proposed the nonlinear quantitative model to select the appropriate candidates based on teamwork capabilities and working relationships for teams in industrial environments. The working relationships and teamwork capabilities respectively are calculated by using MBTI and AHP. Fitzpatrick and Askin (2005) formulated mathematical model based on Kolbe Conative Index (synergy, inertia, and stability) that measures interpersonal structure for multi-functional teams and then solves it by heuristic solution. Zhang and Zhang (2013) presented nonlinear multi-objective optimization for NPD and employed Multi-objective Particle Swarm Optimization to resolve it. They improved fuzzy AHP based on Fuzzy Lin PreRa. In addition, they used the model to evaluate capability and employed MBTI to measure interpersonal relationships among members of interior and exterior departments of the organization. Jiang et al (2010) offered a multi-objective 0–1 programming model for formation cross-functional teams and developed an improved non dominated sorting genetic algorithm II (INSGA-II) to solve it. However, most of the existing methods for the formation of CFTs are nonlinear and there is no simple model for developing CFTs. Therefore the multi-objective nonlinear 0–1 programming model is built based on all of the criteria. Then to solve it more easily, it is converted into a linear form by changing variables. Materials and Methods: Five main measures are considered to form cross-functional teams: cooperation and coordination, functional expertise, individual abilities, cost, and communication. The Meyers-Briggs Type Indicator (MBTI) test is used to evaluate “cooperation and coordination”. MBTI is a self-help assessment test which indicates different psychological preferences in how people perceive the world and how they make decisions. The professional interview is conducted to assess “functional expertise”. In addition, the members answered self-report skill measures which rated in the Likert scale. In order to calculate “individual abilities” two methods are used: personal assessment (the participants are asked by questionnaire) and professional assessment (using the analytical hierarchy process (AHP) method). With the aim of realizing the “cost”, the paychecks of candidates are considered. Sociometry test is used to asses “communication”. The collected data, based on the mentioned methods, are analyzed and the model is developed. The proposed model minimizes the cost and maximizes the other objective measures. Global Criteria method is used to convert the multi-objective proposed model to the one-objective model. Furthermore, this non-linear model is transformed to linear by using the Glover and Woolsey’s method. Results and Discussion: In order to examine the proposed method, the Census cross-functional team in the statistical center of Iran as the real case is considered. Four departments were selected for this test. 10 people were nominated from chosen departments (3, 3, 2, and 2 members from each department, respectively). The proposed model was applied and the results were compared to the chosen team by the head of the office. Themembers selected by the proposed model are 1, 2, 4, 5 and 9. However, 1, 2, 4, 5, and 7 members are chosen by the expert. In fact, these two results are 80% in common. Conclusion: Forming cross-functional teams improves the performance of organizations. Selecting appropriate members for the team formation is a critical decision. Therefore, in this paper, significant features to form the CFT is found and a new model to solve the CFTs formation problem is developed. The findings show the vital criteria in CFTs are: cooperation and coordination, functional expertise, individual abilities, cost, and communication. We have developed simple, linear, multi-objective model which solve forming CFTs more easily and effectively. The outcome of the proposed model is better than the expert’s decision in all goals except the “individual abilities”, this exception may happen because of considering constraints. References Barczak, G., & Wilemon, D. (2003). Team member experiences in new product development: views from the trenches. R&D Management, 33(5), 463-479. Chen, S. J., & Lin, L. (2004). Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering. IEEE Transactions on Engineering Management, 51(2), 111-124. Fan, Z. P., Feng, B., Jiang, Z. Z., & Fu, N. (2009). A method for member selection of R&D teams using the individual and collaborative information. Expert Systems with Applications, 36(4), 8313-8323. Feng, B., Jiang, Z. Z., Fan, Z. P., & Fu, N. (2010). A method for member selection of cross-functional teams using the individual and collaborative performances. European Journal of Operational Research, 203(3), 652-661. Fitzpatrick, E. L., & Askin, R. G. (2005). Forming effective worker teams with multi-functional skill requirements. Computers & Industrial Engineering, 48(3), 593-608. Kargar, M., An, A., & Zihayat, M. (2012, September). Efficient bi-objective team formation in social networks. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 483-498). Springer, Berlin, Heidelberg. Love, J. H., & Roper, S. (2009). Organizing innovation: complementarities between cross-functional teams. Technovation, 29(3), 192-203. Otero, L. D., Centeno, G., Otero, C. E., & Ruiz-Torres, A. J. (2012). A fuzzy goal programming model for skill-based personnel assignments. International Journal of Multicriteria Decision Making, 2(4), 313-337. Proehl, R. A. (1996). Enhancing the effectiveness of cross-functional teams. Leadership & Organization Development Journal, 17(5), 3-10. Saarani, C. R. B., & Bakri, N. (2012). Examining The Technical and Non Technical Member’s Participation in Cross-Functional Teams: A Case Study. Procedia-Social and Behavioral Sciences, 40, 187-196. Santa, R., Ferrer, M., Bretherton, P., & Hyland, P. (2010). Contribution of cross-functional teams to the improvement in operational performance. Team Performance Management: An international Journal, 16(3/4), 148-168. Wang, Z., Yan, H. S., & Ma, X. D. (2003). A quantitative approach to the rganization of cross-functional teams in concurrent engineering. The International Journal of Advanced Manufacturing Technology, 21(10-11), 879-888. Zhang, L., & Zhang, X. (2013). Multi-objective team formation optimization for new product development. Computers & Industrial Engineering, 64(3), 804-811.

This paper deals with the problems faced with the designing and planning of a sporting fixture considering correct referee assignments. A non-linear binary program model is proposed to solve the problems, which aims to minimize the sums of the differences that exist between the requirements of each match and the quality of the referee assigned achieving the design of the most adequate referee for each match. The efficiency of the proposed model is proved using some real data obtained from various fixtures for sports such as soccer, volleyball, and basketball. The mathematical model is solved by using CPLEX 12.7.0., which allows the automatic linearization of the problems. The results obtained demonstrate the efficiency of the proposed methodology for tackling problems, as well as its extension to other sporting disciplines, which require a similar type of planning. Similarly, given the robust nature of the proposed model, it is possible to implement other objective functions in accordance with the requirements of each league.

The secure domination problem, a variation of the domination problem with some important real-world applications, is considered. Very few algorithmic attempts to solve this problem have been presented in literature, and the most successful to date is a binary programming formulation which is solved using CPLEX. A new binary programming formulation is proposed here which requires fewer constraints and fewer binary variables than the existing formulation. It is implemented in CPLEX, and tested on certain families of graphs that have previously been considered in the context of secure domination. It is shown that the runtime required for the new formulation to solve the instances is significantly less than that of the existing formulation. An extension of our formulation that solves the related, but further constrained, secure connected domination problem is also given; to the best of the authors' knowledge, this is the first such formulation in literature. [ABSTRACT FROM AUTHOR]

Computing the frustration index of a signed graph is a key step toward solving problems in many fields including social networks, political science, physics, chemistry, and biology. The frustration index determines the distance of a network from a state of total structural balance. Although the definition of the frustration index goes back to the 1950s, its exact algorithmic computation, which is closely related to classic NP‐hard graph problems, has only become a focus in recent years. We develop three new binary linear programming models to compute the frustration index exactly and efficiently as the solution to a global optimization problem. Solving the models with prioritized branching and valid inequalities in Gurobi, we can compute the frustration index of real signed networks with over 15 000 edges in less than a minute on inexpensive hardware. We provide extensive performance analysis for both random and real signed networks and show that our models outperform all existing approaches by large factors. Based on resolution time, algorithm output, and effective branching factor we highlight the superiority of our models to both exact and heuristic methods in the literature. [ABSTRACT FROM AUTHOR]

• A hybrid binary particle swarm optimization is proposed. • Applying an adaptive penalty function as fitness function. • Using a tabu search procedure to improve solution quality. • Presenting a tabu based mutation procedure to achieve diversification. • We find new best solutions for 28 out of 30 instances. The set-union knapsack problem (SUKP) is a generalization of the standard 0–1 knapsack problem. It is NP-hard, and has several industrial applications. Several approximation and heuristic approaches have been previously presented for solving the SUKP. However, the solution quality still needs to be enhanced. This work develops a hybrid binary particle swarm optimization with tabu search (HBPSO/TS) to solve the SUKP. First, an adaptive penalty function is utilized to evaluate the quality of solutions during the search. This method endeavours to explore the boundary of the feasible solution space. Next, based on the characteristics of the SUKP, a novel position updating procedure is designed. The newly generated solutions obtain the good structures of previously found solutions. Then, a tabu based mutation procedure is introduced to lead the search to enter into new hopeful regions. Finally, we design a tabu search procedure to improve the exploitation ability. Furthermore, a gain updating strategy is employed to reduce the solution time. The HBPSO/TS is tested on three sets of 30 benchmark instances, and comparisons with current state-of-the-art algorithms are performed. Experimental results show that HBPSO/TS performs much better than the other algorithms in terms of solution quality. Moreover, HBPSO/TS improves new best results at 28 out of the 30 instances. The impact of the main parts of the HBPSO/TS is also experimentally investigated. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel convex dual approach to the three dimensional assignment problem, which is an NP-hard binary programming problem. It is shown that the proposed dual approach is equivalent to the Lagrangian relaxation method in terms of the best value attainable by the two approaches. However, the pure dual representation is not only more elegant, but also makes the theoretical analysis of the algorithm more tractable. In fact, we obtain a sufficient and necessary condition for the duality gap to be zero, or equivalently, for the Lagrangian relaxation approach to find the optimal solution to the assignment problem with a guarantee. Also, we establish a mild and easy-to-check condition, under which the dual problem is equivalent to the original one. In general cases, the optimal value of the dual problem can provide a satisfactory lower bound on the optimal value of the original assignment problem. Furthermore, the newly proposed approach can be extended to higher dimensional cases and general assignment problems. [ABSTRACT FROM AUTHOR]

We introduce new diversification methods for zero-one optimization that significantly extend strategies previously introduced in the setting of metaheuristic search. Our methods incorporate easily implemented strategies for partitioning assignments of values to variables, accompanied by processes called augmentation and shifting which create greater flexibility and generality. We then show how the resulting collection of diversified solutions can be further diversified by means of permutation mappings, which equally can be used to generate diversified collections of permutations for applications such as scheduling and routing. These methods can be applied to non-binary vectors using binarization procedures and by diversification-based learning procedures that provide connections to applications in clustering and machine learning. Detailed pseudocode and numerical illustrations are provided to show the operation of our methods and the collections of solutions they create. [ABSTRACT FROM AUTHOR]

We present a computational method for performing structural translation, which has been studied recently in the context of analyzing the steady states and dynamical behavior of mass-action systems derived from biochemical reaction networks. Our procedure involves solving a binary linear programming problem where the decision variables correspond to interactions between the reactions of the original network. We call the resulting network a reaction-to-reaction graph and formalize how such a construction relates to the original reaction network and the structural translation. We demonstrate the efficacy and efficiency of the algorithm by running it on 508 networks from the European Bioinformatics Institutes' BioModels database. We also summarize how this work can be incorporated into recently proposed algorithms for establishing mono- and multistationarity in biochemical reaction systems. [ABSTRACT FROM AUTHOR]

Highlights • To put flexibility in the non-radial DEA models. • Presenting the flexible model as a mixed integer second order conic programming problem. • The new model gives higher efficiency score compared to the similar recent models in the literature. Abstract The role of some factors is not completely clear as an input or an output in many real applications of Data Envelopment Analysis (DEA). In other words, a factor can be used as an input by some Decision-Making Units (DMUs) while it may play an output role in other DMUs. This type of factors are called flexible factors. To improve the models for determining the type of flexible factors, two models were presented in this article in which flexible factors are added to be used in the best situation. In this way, factors in both output and input roles will optimize productivity with maximum efficiency without any change in the productivity level. The proposed models showed more consistency to achieve the desired goals in DEA as compared to similar models. Eventually, the proposed models were compared with existing models through a numerical example. [ABSTRACT FROM AUTHOR]

The cluster analysis is a traditional tool for multi-varietal data processing. Using the k-means method, we can split a pattern set into a given number of clusters. These clusters can be used for the final classification of known output classes. This paper focuses on various approaches that can be used for an optimal union of hidden classes. The resulting tasks include binary programming or convex optimization ones. Another possibility of obtaining hidden classes is designing imperfect classifier system. Novel context out learning approach is also discussed as possibility of using simple classifiers as background of the system of hidden classes which are easy to union to output classes using the optimal algorithm. All these approaches are useful in many applications, including econometric research. There are two main methodologies: supervised and unsupervised learning based on given pattern set with known or unknown output classification. Preferring supervised learning, we can combine the context out learning with optimal union of hidden classes to obtain the final classifier. But if we prefer unsupervised learning, we will begin with cluster analysis or another similar approach to also obtain the hidden class system for future optimal unioning. Therefore, the optimal union algorithm is widely applicable for any kind of classification tasks. The presented techniques are demonstrated on an artificial pattern set and on real data related to crisis prediction based on the clustering of macroeconomic indicators. [ABSTRACT FROM AUTHOR]

In the recent decade, special attention is paid to reverse logistic due to economic benefits of recovery and recycling of used products as well as environmental legislation and social concerns. On the other hand، many researches claim that separately and sequential planning of forward and reverse logistic causes sub-optimality. Effective transport activities are also one of the most important components of a logistic system and it needs an accurate planning. In this study, a mixed integer linear programming model is proposed for integrated forward / reverse supply chain as well as vehicles routing. Logistic network which is used in this paper is a multi-echelon integrated forward /reverse logistic network which is comprised capacitated facility, common facilities of production/recovery and distribution/collection, disposal facilities and customers. The proposed model is multi-period and multi-product with the ability to consider several facilities in each level. Various types of vehicle routing models are also included such as multi-period routing, multi-depot, multi-products, routing with simultaneous delivery and pick-up, flexible depot assignment and split delivery. The model results present the product flow between the various facilities in forward and reverse direction throughout the planning horizon with the objective minimization of total cost. Numerical example for solving the model using GAMS shows that the proposed model could reach the optimal solution in reasonable time for small and medium real world’s problems.

The nature of health care services is very complex and specific, thus delays and organizational imperfections can cause serious and irreversible consequences, especially when dealing with emergency medical services. Therefore, constant improvements in various aspects of managing and organizing provision of emergency medical services are vital and unavoidable. The main goal of this paper is the development and application of a binary programming model to support decision making process, especially addressing scheduling workforce in organizations with stochastic demand. The necessary staffing levels and human resources allocation in health care organizations are often defined ad hoc, without empirical analysis and synchronization with the demand for emergency medical services. Thus, irrational allocation of resources can result in various negative impacts on the financial result, quality of medical services and satisfaction of both patients and employees. We start from the desired staffing levels determined in advance and try to find the optimal scheduling plan that satisfies all significant professional and regulatory constraints. In this paper a binary programming model has been developed and implemented in order to minimize costs, presented as the sum of required number of ambulance crews. The results were implemented for staff rostering process in the Ambulance Service Station in Subotica, Serbia. Compared to earlier scheduling done ad hoc at the station, the solution of the formulated model provides a better and equable engagement of crews. The developed model can be easily modified and applied to other organizations with the same, stochastic, nature of the demand.

Novel context out learning approach is discussed as possibility of using simple classifiers which is background of hidden class system. There are two ways how to perform final classification. Having a lot of hidden classes we can build their unions using binary optimization task. Resulting system has the best possible sensitivity over all output classes. Another way is to perform second level linear classification as referential approach. The presented techniques are demonstrated on traditional iris flower task.

Using cross-functional team (CFT) is a suitable strategy for improving the performance of organizations. The member selection problem is an important aspect of the CFT formation. Several evidences showed the important criteria for choosing true members are: cooperation and coordination, functional expertise, individual abilities, cost, and communication. In this paper, effective features for member selection are identified and a multi-objective 0–1 nonlinear programming model is developed. This model is developed by using individual and collaborative performance. Afterward, it is converted into the linear form by changing variables to solve it more easily. The proposed model is used in the real example in Census cross-functional team in the statistical center of Iran and required data were collected by surveys and interviews. The results indicate that this proposed model has better performance compared to recommendation of experts and can be used in other fields. [ABSTRACT FROM AUTHOR]

High-tech companies are rapidly growing in the world. Research and development (hereafter R&D) department strength is the main asset that allows a firm to achieve a competitive advantage in high-tech businesses. The allocated budget to this sector is finite; thus, integration, human resource, risk and budget limitations should be considered to choose the most valuable project in the best portion of time. This paper investigates a case study from a high-tech company in Iran to prioritize the most attractive technologies for the R&D department. The case consists of twenty three technology options and the goal is to find the most attractive projects to sort them out for implementation in the R&D department. In this research, scholars proposed the best-worst method (henceforth BWM) to find the weight of the criteria of the attractive technologies in first step and utilize the newly developed method total area based on orthogonal vectors (henceforward TAOV) to sort the selected technologies based upon the identified criteria. Project integration is one of the least-noticed subjects in scientific papers; therefore, the researchers presented a zero or one linear programming (ZOLP) model to optimize and schedule the implementation procedure on the project risk, budget and time limitation simultaneously. The results indicate that starting few but attractive projects in the first years and postponing the rest to the future, helps a firm to manage funds and gain profit with the least amount of risk. [ABSTRACT FROM AUTHOR]

The construction and application of a multiple nitrogen liquefier unit (NLU) optimal scheduling tool is discussed. Constrained by customer demands and subject to electricity spot market prices over a week-ahead horizon, a retrospective optimiser (RO) determines the minimum scheduling costs. Plant start-up penalties and inter-site optimisation capabilities are incorporated into the optimisation model to emulate realistic operational flexibilities and costs. Using operational data, actual process schedules are compared to the RO results leading to improved process scheduling insights; such as increasing afternoon NLU operation during the spring to utilise lower power pricing caused by high solar generation. The RO is used to output a trackable load management key performance indicator to quantify potential and achieved scheduling improvements. Subsequently, correlations between renewable energy generation and spot market power prices are developed. Forecast pricing is used within a predictive optimiser (PO) to automatically generate an optimal schedule for the week ahead to meet projected customer demands. The RO provides potential hindsight savings of around 11%, and the PO up to 8% (representing significant cost savings for such energy intensive processes). [ABSTRACT FROM AUTHOR]

The planning of outbound baggage handling at international airports is challenging. Outgoing flights have to be assigned and scheduled to handling facilities at which the outgoing baggage is loaded into containers. To avoid disruptions of the system the objective is to minimize workload peaks over the entire system. The resource demand of the jobs that have to be scheduled depends on the arrival process of the baggage. In this paper we present a time-indexed mathematical programming formulation for planning the outbound baggage. We propose an innovative decomposition procedure in combination with a column generation scheme to solve practical problem instances. The decomposition significantly reduces the symmetry effect in the time-indexed formulation and also speeds up the computational time of the corresponding Dantzig-Wolfe formulation. To further improve our column generation algorithm we propose state-of-the-art acceleration techniques for the primal problem and the pricing problem. Computational results based on real data from a major European airport show that the proposed procedure reduces the maximal workloads by more than 60% compared to the current assignment procedure used. The online appendix is available at . [ABSTRACT FROM AUTHOR]

In the Generalized Assignment Problem (GAP), tasks must be allocated to machines with limited resources, in order to minimize processing costs. This problem has several industrial applications and often appears as a substructure in other combinatorial optimization problems. We propose a hybrid method inspired by Scatter Search metaheuristic, that efficiently generates a pool of solutions using a Tabu list criteria and an Ejection Chain mechanism. Common characteristics are extracted from the pool and solutions are combined by exploring a restricted search space, as a Binary Programming (BP) model. This method was implemented as a parallel approach to run in a Graphics Processing Unit (GPU). Experimental results show that the proposed method is very competitive to the algorithms found in the literature. On average, a gap of 0.09% is obtained over a set of 45 instances, when compared to lower bounds. Due to the integration of the method with an exact BP solver, it was capable of proving the optimality of small size instances, also finding new best known solutions for 21 instances. In terms of computational times, the proposed method performs on average 8 times faster than literature, also indicating that the proposed approach is scalable and robust for practical applications. [ABSTRACT FROM AUTHOR]

When surgical patients leave an operating room, they go to the post-anesthesia care unit (PACU). The PACU is shared with two or more operating rooms. It has a limited capacity for holding patients. A problem for the operating room scheduler is to schedule operations such that the PACU is not requested to take on more patients than it can handle. The solution is to develop work tours for operating time/PACU time pairs spread throughout the day and employ a binary integer programming formulation that not only obeys the PACU capacity constraint but also handles other scheduling constraints. [ABSTRACT FROM AUTHOR]

High-tech companies are rapidly growing in the world. Research and development (hereafter R&D) department strength is the main asset that allows a firm to achieve a competitive advantage in high-tech businesses. The allocated budget to this sector is finite; thus, integration, human resource, risk and budget limitations should be considered to choose the most valuable project in the best portion of time. This paper investigates a case study from a high-tech company in Iran to prioritize the most attractive technologies for the R&D department. The case consists of twenty three technology options and the goal is to find the most attractive projects to sort them out for implementation in the R&D department. In this research, scholars proposed the best–worst method (henceforth BWM) to find the weight of the criteria of the attractive technologies in first step and utilize the newly developed method total area based on orthogonal vectors (henceforward TAOV) to sort the selected technologies based upon the identified criteria. Project integration is one of the least-noticed subjects in scientific papers; therefore, the researchers presented a zero or one linear programming (ZOLP) model to optimize and schedule the implementation procedure on the project risk, budget and time limitation simultaneously. The results indicate that starting few but attractive projects in the first years and postponing the rest to the future, helps a firm to manage funds and gain profit with the least amount of risk.

The adjacent channel interference (ACI) resulting from imperfect filtering can severely degrade the performance of any wireless communication system. Despite this fact, most of previously proposed medium access control (MAC) protocols for cognitive radio networks (CRNs) were designed while ignoring the effects of ACI (assuming ideal filtering). The effect of ACI can be reduced by introducing guard bands (GBs). However, this solution comes at the expense of degrading spectrum efficiency. In this paper, we develop an efficient GB-aware MAC protocol that attempts at maximizing network throughput while improving fairness in CRNs. Unlike most of previously proposed GB-aware MAC protocols that perform channel assignment sequentially, our MAC performs the channel assignment for multiple CR links simultaneously (the so-called batch method). Batching enables concurrent channel assignment for multiple CR links, which consequently allows for concurrent data transmissions. Batching can be realized by introducing an admission control phase for CR users to share their control information. The batch method can effectively provide distributed decisions that achieve better throughput while reducing the number of GBs. Our MAC also allows the CR users to utilize the reserved GBs of primary networks under predefined FCC power constraints. Simulation results indicate that our protocol achieves significant performance improvement compared to previous GB-aware protocols. [ABSTRACT FROM AUTHOR]

In this paper, a newvertex-coloring problem of a fuzzy graph with crisp vertices and fuzzy edges is studied. Membership degree of a fuzzy edge is interpreted as incompatibility degree of its associated incident vertices. This interpretation can be used to define the concept of total incompatibility. Here, unlike the traditional graph coloring problems, two adjacent vertices can receive same colors; these type of vertices and their associated edge are named incompatible vertices and incompatible edge, respectively. In proposed coloring methodology, the total incompatibility of a vertex-coloring is defined as the sum of incompatibility degrees of all incompatible edges. Then, based on the minimum possible degree of total incompatibilities, fuzzy chromatic number of a fuzzy graph is introduced. In order to find an optimal k-coloring, with minimum degree of total incompatibly, firstly a binary programming problem is formulated. Then, a hybrid local search genetic algorithm is designed to solve the large-size problems. Practical uses of the proposed algorithm are illustrated and analyzed by different-size problems. Finally, a cell site assignment problem, as a real world application of the presented fuzzy graph vertex-coloring, is formulated and solved. [ABSTRACT FROM AUTHOR]

Based on the D-matrix model, the dependency theory is widely used in the field of fault diagnosis to model the fault flows in complex electronic systems. However, the traditional dependency model can only handle a single fault; it fails to recognize and diagnose multiple faults. In addition, it is not tolerant with system structural or functional changes. These inherent weaknesses of the traditional dependency theory may lead to unsatisfactory acquisition of the diagnosis results. To solve the problem, an improved dependency model is invented as novel analytic diagnosis model to better describe the relationships between faults and tests. The system fault diagnosis based on the improved dependency model is formulated as an optimization problem with binary logic operations where all the fault hypotheses are tested. The calculation process consists of three steps: establishment of the objective function, determination of the nominal states, and determination of the expected states. Finally, the proposed method is demonstrated via an avionic processor case using the improved dependency model. The optimization-based fault diagnosis problem is formulated and the optimal solution is obtained. The diagnosis result demonstrates that the proposed method is successful on performance assessment and fault diagnosis. [ABSTRACT FROM AUTHOR]

This paper studies the optimal charging problem for future plug-in electric taxi (PET) with time-varying profits, i.e., time-varying service incomes and charging costs. Aiming at maximizing the average profit of a PET in long term under the constraint of state of charge (SoC) dynamics of PET battery, this problem is formulated as a constrained binary programming problem in infinite time horizon. The main contribution of this paper consists of three parts. First, because the original infinite binary programming problem cannot be directly solved, it is divided into a series of periodic subproblems. Each of the subproblems is in finite time horizon and much easier to solve, which is also proven rigorously to be equivalent to the original one. Second, an efficient and optimal algorithm is proposed to solve the finite time constrained binary programming subproblem, which also yields the optimal initial SoC of PET. Third, a transient control strategy is proposed to transfer the any initial SoC to the optimal one, if they are not identical. The performance of the proposed method is verified by numerical results. [ABSTRACT FROM PUBLISHER]

This paper discusses the problem of assigning $$N$$ streams of requests (clients) to $$M$$ related server machines with the objective to minimize the sum of worst-case processing times. The completion time of a batch of requests is measured as a sum of weights of the subset of clients which share a single machine. Such problem can be seen as minimizing the sum of total weights of blocks of $$M$$-partition, each multiplied by the cardinality of a block. We prove that this problem can be solved in polynomial time for any fixed $$M$$ and present an efficient backward induction algorithm. [ABSTRACT FROM AUTHOR]

Highlights: [•] A novel hybrid method based on decomposition of SCUC into QP and BP problems is proposed. [•] An adapted binary programming and an enhanced dual neural network model are applied. [•] The proposed EDNN is exactly convergent to the global optimal solution of QP. [•] An AC power flow procedure is developed for including contingency/security issues. [•] It is suited for large-scale systems, providing both accurate and fast solutions. [Copyright &y& Elsevier]