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As one of the most popular techniques for performance evaluation, Data Envelopment Analysis (DEA) has been widely applied in many areas. However, the self-evaluation used in DEA leaves it open to much criticism. Moreover, most researchers have ignored the fact that reality abounds with uncertainty and have assumed that the data used for evaluation is deterministic and accurate. Both assumptions make it difficult to evaluate the efficiency of real-world production processes correctly and reasonably. In this paper, we propose a series of robust cross-efficiency (RCE) models based on robust optimization theory and cross-efficiency to deal with these problems. First of all, the proposed RCE models allow the conservatism level to be adjusted easily to suit the attitude of the decision-maker towards uncertainty. In addition, the RCE models have better discrimination power than the existing robust CCR models. We present two applications to demonstrate the effectiveness and stability of our models. [ABSTRACT FROM AUTHOR]

Energy cost for keeping fresh seafood in cold condition is a main feature of a fresh seafood delivery routing problem. In the delivery routing problem, energy cost varies during the transportation process and the service process. In addition, there are many fresh seafood product factories whose seafood products should be delivered to a set of customers. Therefore, this paper models the fresh seafood delivery problem as a multi-depot vehicle routing problem, which aims to find the routes with the least cost. Due to the complexity of the problem, a method is used to reduce the complexity by changing the multi-depot vehicle routing problem into a vehicle routing problem with a dummy depot in this paper. Then, ant colony optimization (ACO) is used to solve this problem. Scanning strategy and crossover operation are also adopted to improve the performance of ACO. At last, the computational results of the benchmark problems of the multi-depot vehicle routing problem indicate the effectiveness of the algorithm. Furthermore, the real-life fresh seafood delivery routing problem from Dalian city suggests the proposed model is feasible. [ABSTRACT FROM AUTHOR]

The pharmaceutical supply chain (PSC) is a highly important strategic issue in the healthcare domain because of its significant effect on human wellbeing and healthcare costs. This paper provides a mathematical optimization model to design a PSC by considering the drug's corruption. Given the importance of the sustainable development goals, three objectives are considered: economic, social, and environmental. The cost of fixed installation, transportation, holding, collection, distribution, recycling and disposal is reduced by the economic objective. A reduction in greenhouse gas emissions, water use, and nonrenewable energy consumption is achieved by the environmental objective. Based on the social objective, the number of employment opportunities, economic growth, job satisfaction, and national self-sufficiency are all increased, while the number of days lost due to work-related accidents decreases. To achieve the weight of elements in environmental and social objectives, the fuzzy best–worst method is used. Due to the inherent uncertainty in the PSC, its main parameters are referred to as unpredictable, which calls for a robust optimization approach to eliminate the model parameters' uncertainty. A fuzzy programming approach is used to solve the developed model. Numerical evidence drawn from a practical case shows that the suggested model is efficacious and valid. We suggest that the priorities and challenges of sustainable growth contribute to the determination of the PSC's managerial and operational characteristics. [ABSTRACT FROM AUTHOR]

The paper investigates hyperparameter optimization problem. Hyperparameters are the parameters of model parameter distribution. The adequate choice of hyperparameter values prevents model overfit and allows it to obtain higher predictive performance. Neural network models with large amount of hyperparameters are analyzed. The hyperparameter optimization for models is computationally expensive. The paper proposes modifications of various gradient-based methods to simultaneously optimize many hyperparameters. The paper compares the experiment results with the random search. The main impact of the paper is hyperparameter optimization algorithms analysis for the models with high amount of parameters. To select precise and stable models the authors suggest to use two model selection criteria: cross-validation and evidence lower bound. The experiments show that the models optimized using the evidence lower bound give higher error rate than the models obtained using cross-validation. These models also show greater stability when data is noisy. The evidence lower bound usage is preferable when the model tends to overfit or when the cross-validation is computationally expensive. The algorithms are evaluated on regression and classification datasets. [ABSTRACT FROM AUTHOR]

A preface to the journal "Annals of Operations Research" is presented.

This paper introduces the serial batching scheduling problems with position-based learning effect, where the actual job processing time is a function of its position. Two scheduling problems respectively for single-machine and parallel-machine are studied, and in each problem the objectives of minimizing maximum earliness and total number of tardy jobs are both considered respectively. In the proposed scheduling models, all jobs are first partitioned into serial batches, and then all batches are processed on the serial-batching machine. We take some practical production features into consideration, i.e., setup time before processing each batch increases with the time, regarded as time-dependent setup time, and we formalize it as a linear function of its starting time. Under the single-machine scheduling setting, structural properties are derived for the problems with the objectives of minimizing maximum earliness and number of tardy jobs respectively, based on which optimization algorithms are developed to solve them. Under the parallel-machine scheduling setting, a hybrid VNS-GSA algorithm combining variable neighborhood search (VNS) and gravitational search algorithm (GSA) is proposed to solve the problems with these two objectives respectively, and the effectiveness and efficiency of the proposed VNS-GSA are demonstrated and compared with the algorithms of GSA, VNS, and simulated annealing (SA). This paper demonstrates that the consideration of different objectives leads to various optimal decisions on jobs assignment, jobs batching, and batches sequencing, which generates a new insight to investigate batching scheduling problems with learning effect under single-machine and parallel-machine settings. [ABSTRACT FROM AUTHOR]

The magnitude of the recent forest fires, the time required to extinguish them, and the damage they caused have attracted the attention of all humanity. If the current trend continues, it will cause great irreversible losses. There is a great need for scientific studies to prevent or reduce the damages of these fires. In this context, this paper proposes algorithms and mathematical models for generating the routes of unmanned aerial vehicles to detect forest fires that may occur especially in regions far from residential areas. A novel heuristic dispatching rule and a simulation-based optimization algorithm are proposed. The striking features of the proposed algorithms are that the routes are created with a focus on minimizing the fire probabilities. The uncertainties and dynamics of real-life are also considered. Various scenarios have experimented on a realistic case. Experimental results and findings are promising. [ABSTRACT FROM AUTHOR]

While theoretical studies on parametric multiobjective programs (mpMOPs) have been steadily progressing, the algorithmic development has been comparatively limited despite the fact that parametric optimization can provide a complete parametric description of the efficient set. This paper puts forward the premise that parametrization of the efficient set of nonparametric MOPs can be combined with solving parametric MOPs because the algorithms performing the former can also be used to achieve the latter.This strategy is realized through (i) development of a generalized scalarization, (ii) a computational study of selected parametric optimization algorithms, and (iii) applications in a real-life context. Several variants of a generalized weighted-sum scalarization allow one to scalarize mpMOPs to match the capabilities of algorithms. Parametric multiobjective quadratic programs are scalarized into parametric quadratic programs (mpQPs) with linear and/or quadratic constraints. In the computational study, three algorithms capable of solving mpQPs are examined on synthetic instances and two of the algorithms are applied to decision-making problems in statistics and portfolio optimization. The real-life context reveals the interplay between the scalarizations and provides additional insight into the obtained parametric solution sets. [ABSTRACT FROM AUTHOR]

Forests are among the most sensitive systems in nature. This is attributed to the fact that, forests are directly affected by fluctuations in price of fossil fuels. Wood products and especially forest fuel products are accessible by anyone, without any prior processing. As forest fuel is a subsidy for fossil fuels (oil) for heating purposes, households turn to forest fuel especially in countries that are heavily impacted by economic recession. The over-exploitation of this natural resource leads the forest to abnormal situation and eventually to deforestation. The exhaust of the natural resource capital has negative impact not only on the local economy, where fuelwood market contributes especially in mountainous regions, but also on the environmental stability of ecosystems. In this paper, two multi-period Linear Programming models are proposed for management of coppice forests. The aim of these models is to maximize the Net Present Value, which is constructed as a function of the revenue from trading fuelwood (price times the logged quantities) minus the transportation cost from the forest to merchants. Two aspects have been investigated in this paper; sustainability and maximum yield. The sustainability aspect is guaranteed by imposing constraints for equalization of non-logged areas at the end of the planning horizon. With maximum yield aspect, the maximization of the logged quantities (and therefore the maximization of the objective function) is guaranteed. The model is solved for various scenarios regarding transportation cost. The applicability of the model is demonstrated through a real-world case study of an even coppice forest in Achladochori–Aggistro–Sidirokastro. The proposed model is easy to be implemented, since it uses only the initial conditions of the forest (area) and can be applied to even and uneven aged forests. [ABSTRACT FROM AUTHOR]

This paper proposes an investor-friendly random fuzzy portfolio selection model considering both robustness and adjustment of future asset returns derived from investor’s forecasts for financial tendency using a fuzzy inference method. It is important to predict the price or the return of each asset appropriately considering current market trends in portfolio optimization. In this paper, a standard multi-factor model, namely Arbitrage Pricing Theory (APT), is introduced as an asset pricing model. In addition, in order to extend standard APT by integrating important rules of current markets trends derived from technical analysis and fundamental analysis, each factor of APT is assumed to be a random fuzzy variable whose mean is adjusted by the fuzzy reasoning method, particularly product-sum-gravity method. Furthermore, it is also important for the investor to reduce the worst case of the total loss in terms of risk-averse. Therefore, worst-case conditional Value-at-Risk, which is a robust programming approach without assuming some specific random distribution, is considered. Since the proposed model is formulated as a biobjective programming problem both minimizing the value of worst-case conditional Value-at-Risk and maximizing the total expected return, it is equivalently transformed into the deterministic nonlinear programming problem using the satisficing trade-off method, and the efficient algorithm to obtain the optimal portfolio is developed. By solving our proposed model, the investor can obtain the risk-averse optimal portfolio with the large total return complying with current market trends. [ABSTRACT FROM AUTHOR]

The bounded degree sum-of-squares (BSOS) hierarchy of Lasserre et al. (EURO J Comput Optim 1-31, 2015) constructs lower bounds for a general polynomial optimization problem with compact feasible set, by solving a sequence of semi-definite programming (SDP) problems. Lasserre, Toh, and Yang prove that these lower bounds converge to the optimal value of the original problem, under some assumptions. In this paper, we analyze the BSOS hierarchy and study its numerical performance on a specific class of bilinear programming problems, called pooling problems, that arise in the refinery and chemical process industries. [ABSTRACT FROM AUTHOR]

Supply contracts are designed to minimize inventory costs or to hedge against undesirable events (e.g., shortages) in the face of demand or supply uncertainty. In particular, replenishment terms stipulated by supply contracts need to be optimized with respect to overall costs, profits, service levels, etc. In this paper, we shall be primarily interested in minimizing an inventory cost function with respect to a constant replenishment rate. Consider a single-product inventory system under continuous review with constant replenishment and compound Poisson demands subject to lost-sales. The system incurs inventory carrying costs and lost-sales penalties, where the carrying cost is a linear function of on-hand inventory and a lost-sales penalty is incurred per lost sale occurrence as a function of lost-sale size. We first derive an integro-differential equation for the expected cumulative cost until and including the first lost-sale occurrence. From this equation, we obtain a closed form expression for the time-average inventory cost, and provide an algorithm for a numerical computation of the optimal replenishment rate that minimizes the aforementioned time-average cost function. In particular, we consider two special cases of lost-sales penalty functions: constant penalty and loss-proportional penalty. We further consider special demand size distributions, such as constant, uniform and Gamma, and take advantage of their functional form to further simplify the optimization algorithm. In particular, for the special case of exponential demand sizes, we exhibit a closed form expression for the optimal replenishment rate and its corresponding cost. Finally, a numerical study is carried out to illustrate the results. [ABSTRACT FROM AUTHOR]

A conference-cum-workshop on Game Theory and Optimization was held at the Indian Institute of Technology Madras, during June 6–10, 2016. One of the primary objectives of the event was to commemorate the 75th birthday of Professor T. Parthasarathy, fondly referred to as TP. A special session was devoted to his life and mathematical career. This provided a platform for many of TP's students and collaborators to share their experiences with the audience. The conference was made possible through funding from the National Mathematics Initiative. Additional funding was received from the Office of Alumni and International Relations, the Departments of Mathematics, and Management Studies, Indian Institute of Technology Madras (IIT Madras). Following is a brief overview of the contributions to this special issue, a biography of Professor TP, and a list of his publications. The Guest Editors would like to thank all the authors, Annals of Operations Research Editor-in-Chief, Endre Boros, and all referees who provided peer review. This special issue would not have been possible without their valuable time and input. [ABSTRACT FROM AUTHOR]

In many service systems, the service rates of the servers or the machines degrade over time due to a variety of reasons like fatigue, deficiency (due to manufacturing defect or energy loss, deformation, or excessive heat), and lack of proper maintenance. To make a service system beneficial for both customers and management points of view, it is imperative that models are developed to understand the impact of degradation. In this paper, we study M A P / P H / 1 - type queueing models incorporating degradation, failures/breakdowns and repairs. The degradation and to restore to normalcy in the service rate we look at two scenarios. In the first one, the service is restored to normalcy immediately after the server becomes idle or a fixed number of services is offered. In the second one, the restoration takes a random time that is modeled using a phase type distribution. Also, there are a variety of reasons that lead to failures/breakdowns of the server. We model these using (possibly) different phase type distributions. Both the models are studied analytically using matrix-analytic methods and illustrative numerical examples bringing out the qualitative behavior of the impact of degrading services are discussed. Two cost optimization problems whose solutions are obtained using particle swarm optimization technique along with a few illustrative examples are presented. [ABSTRACT FROM AUTHOR]

Inevitable simplifications of models of real world activities lead to some inadequacies. In the DEA literature several methods were proposed to overcome such difficulties. Some authors proposed to use specific production units in the primal space of inputs and outputs as a starting point in order to improve the frontier of the DEA models. In our previous papers, we introduced the notion of terminal units. It was proved that only terminal units form necessary and sufficient set of units for improving the frontier. In this paper, the relationship between all sets of units proposed for improving the frontier is established. Our theoretical results are confirmed by extensive and instructive graphical examples and also verified by computational experiments using real-life data sets. [ABSTRACT FROM AUTHOR]

Personnel scheduling problems have attracted research interests for several decades. They have been considerably changed over time, accommodating a variety of constraints related to legal and organisation requirements, part-time staff, flexible hours of staff, staff preferences, etc. This led to a myriad of approaches developed for solving personnel scheduling problems including optimisation, meta-heuristics, artificial intelligence, decision-support, and also hybrids of these approaches. However, this still does not imply that this research has a large impact on practice and that state-of-the art models and algorithms are widely in use in organisations. One can find a reasonably large number of software packages that aim to assist in personnel scheduling. A classification of this software based on its purpose will be proposed, accompanied with a discussion about the level of support that this software offers to schedulers. A general conclusion is that the available software, with some exceptions, does not benefit from the wealth of developed models and methods. The remaining of the paper will provide insights into some characteristics of real-world scheduling problems that, in the author's opinion, have not been given a due attention in the personnel scheduling research community yet and which could contribute to the enhancement of the implementation of research results in practice. Concluding remarks are that in order to bridge the gap that still exists between research into personnel scheduling and practice, we need to engage more with schedulers in practice and also with software developers; one may say we need to get wet if we want to learn how to swim. [ABSTRACT FROM AUTHOR]

In this paper, we propose a branch-and-price approach for solving the problem of maximizing the expected number of transplants in Kidney Exchange Programs (KEPs). In these programs, the decision on which transplants will be conducted is usually made with the support of optimization models with the assumption that all operations will take place. However, after a plan of transplants is defined, a pair may leave the KEP or a more accurate compatibility evaluation exam may invalidate a transplant. To model these possible events we consider probabilities of failure of vertices and of arcs and the objective of maximizing the expected number of transplants. The proposed approach is based on the so-called cycle formulation, where decision variables are associated with cycles. Built on the concept of type of cycle a branch-and-price algorithm is conceived. One subproblem is defined for each type of cycle. We present computational results of the proposed branch-and-price algorithm and compare them with solving directly the cycle formulation (with a general purpose mixed integer programming solver—CPLEX) showing that the proposed approach is the only one suitable for larger instances. [ABSTRACT FROM AUTHOR]

We consider the one-dimensional skiving stock problem which is strongly related to the dual bin packing problem: find the maximum number of objects, each having a length of at least L, that can be constructed by connecting a given supply of m∈N smaller item lengths l1,…,lm with availabilities b1,…,bm. For this NP-hard discrete optimization problem, the (additive integrality) gap, i.e., the difference between the optimal objective values of the continuous relaxation and the skiving stock problem itself, is known to be strictly less than 3 / 2 if li∣L is assumed for all items, hereinafter referred to as the divisible case. Within this framework, we derive sufficient conditions that ensure the integer round-down property, i.e., a gap smaller than one, of a given instance. As a main contribution, we present improved upper bounds for the gap (of special subclasses) of the divisible case by means of combinatorial and algorithmic approaches. In a final step, possible generalizations of the introduced concepts are discussed. Altogether, the results presented in this paper give strong evidence that 22 / 21 represents the best possible upper bound for the gap of divisible case instances. [ABSTRACT FROM AUTHOR]

This paper addresses the crew scheduling problem for a mass rapid transit (MRT) system. The problem is to find a minimum number of duties to cover all tasks while satisfying all the hard and soft scheduling rules. Such rules are complicated in real-world operations and difficult to follow through optimization methods alone. In this paper, we propose a constraint programming (CP)-based approach to solve the problem. The approach involves a CP model for duty generation, a set covering problem model for duty optimization, and alternative ways to identify the final solution in different situations. We applied the proposed CP-based approach to solve a case problem for the Taipei MRT. Case application results using real-world data showed that our approach is capable of reducing the number of daily duties from 58 to 55 and achieving a 5.2 % savings in labor costs. We also incorporated the soft rule considerations into the CP model in order to generate alternative optimum solutions that would improve the workload balance. The coefficient of variation of the work time distribution improves significantly, falling from 21 % to approximately 5 %. Given the CP model's comprehensive coverage of various scheduling rules, our proposed approach and models would also be applicable to other MRT systems. [ABSTRACT FROM AUTHOR]

In this paper, we consider a variational-type inequality and study its weak sharp solutions in terms of a dual gap function, under certain assumptions and convex notion of a functional. Moreover, we aim to constitute the relationship between the minimum principle sufficiency property and weak sharp solutions of the considered variational-type inequality. A numerical result is also constructed to give a better insight for the main derived result. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new class of higher-order cone-convex, (K1,K2)-pseudoconvex and quasiconvex functions which encapsulates several already known functions. Higher-order sufficient optimality conditions have been established for a vector optimization problem over cones by using these functions, under weaker conditions on multipliers as compared to other papers in this domain. Wolfe type and Mond-Weir type higher-order duals are formulated and corresponding duality results are established. A number of previously studied problems appear as special cases of our primal-dual models. In case of nonlinear programming problem, our higher-order duals reduce to the corresponding higher-order duals given by Mangasarian (J Math Anal Appl 51:607-620, 1975) and Mond and Zhang (Generalized convexity, generalized monotonicity: recent results. Kluwer, Dordrecht, pp 357-372, 1998). [ABSTRACT FROM AUTHOR]

The authors in a previous paper devised certain subcones of the semidefinite plus nonnegative cone and showed that satisfaction of the requirements for membership of those subcones can be detected by solving linear optimization problems (LPs) with O(n) variables and O(n2) constraints. They also devised LP-based algorithms for testing copositivity using the subcones. In this paper, they investigate the properties of the subcones in more detail and explore larger subcones of the positive semidefinite plus nonnegative cone whose satisfaction of the requirements for membership can be detected by solving LPs. They introduce a semidefinite basis (SD basis) that is a basis of the space of n×n symmetric matrices consisting of n(n+1)/2 symmetric semidefinite matrices. Using the SD basis, they devise two new subcones for which detection can be done by solving LPs with O(n2) variables and O(n2) constraints. The new subcones are larger than the ones in the previous paper and inherit their nice properties. The authors also examine the efficiency of those subcones in numerical experiments. The results show that the subcones are promising for testing copositivity as a useful application. [ABSTRACT FROM AUTHOR]

The article offers information on the 11th International Conference on the Practice and Theory of Automated Timetabling (PATAT) in Udine, Italy on August 23-26, 2016.

In this paper, we consider a bank asset allocation problem with uncertain migration risk of credit ratings and capital adequacy ratio (CAR) regulations. In the practical scenarios, the future market values of each risky asset are largely affected by outer complex environments. We only observe the information about their first-moment and marginal second-moment of year-ahead market value of each loan asset. Based on these scenarios, we propose a new distributionally robust optimization model with the chance constraint characterized by uncertain CAR. Following the duality theory in infinite-dimensional optimization problem and the theory of conic linear optimization model, we can reformulate the original problem into a tractable linear deterministic semi-definite programming (SDP) model. By using this tractable linear SDP model, we can provide a robust asset allocation policy to bank managers. Further, we conduct a simulation study to illustrate the application of our method under two different economic conditions, a downward condition and an upward condition. Then a series of sensitivity tests is applied to examine the impacts of various factors, including safety probability, target CAR and recovery rate, on the optimal asset allocations. We also compare the performance of our model and the CVaR model. We demonstrate our model provides an efficient way to deal with the trade-off between expected return and CAR. [ABSTRACT FROM AUTHOR]

As the electrification of vehicles keeps being widespread, and facing the impossibility of storing big amounts of electrical energy, the challenge of controlling and adapting the electricity supply and demand has become a necessity. Therefore, electric vehicles could be an optimal solution for the storage and the retrieval of energy depending on the supply and demand of electricity. Thus, this study proposes an energetic strategy based on a multi-objective and multi-criteria optimization algorithm related to the control of the energy flows between the electric vehicles and the grid, home or building depending on the electricity supply and demand. The main focus at this stage involves the optimization of the vehicles' charging mode. Hence, in this paper, the multi-objective optimization proposed is first implemented through the presentation of its algorithm and the description and modeling of its objective functions and their corresponding constraints. Then, the genetic algorithm optimization approach is adopted for the charging process of the study to find the optimal solution for each objective function. Besides, the weighted sum approach is applied after the normalization of all the fitness functions and several case studies are carried out to find the optimal solutions based on the priority of specific objectives over the others. Once all calculations are done, a simulation via Matlab software is performed and the results are discussed and compared. The simulation of the results has verified the theoretical calculations proving the effectiveness of the proposed methodology. However, as some of the calculated optimal solutions of the system seem to be conflicting, the prioritization of some objectives over others had to be operated in order to figure out a global solution for the multi-objective system. Thus, as a compromise had to be applied for the calculation of quasi-optimal solutions that would converge towards the Pareto-front, the decision maker's preference would set the final solution using the weighted sum approach. [ABSTRACT FROM AUTHOR]

In this paper, we present a set of (exact and approximate) mathematical models and algorithms for determining the set of (globally) optimal distributed antenna deployments and the supported user demand in cellular code division multiple access (CDMA) systems. We focus on the uplink (user-to-base station) formulation and assume that the base station combines all the received signals at each of the antennas using path-gain based weights. In CDMA systems, as all users occupy the system bandwidth at the same time thereby interfering with each other, this results in a complicated 0-1 mixed-integer multilinear programming problem, where the objective function maximizes the total system capacity, while ensuring that the minimum signal-to-interference-plus-noise ratio (SINR) constraints and maximum transmit power constraints for each user are satisfied. This highly nonlinear, nonconvex problem is reformulated to yield a tight mixed-integer 0-1 linear programming representation via the addition of several auxiliary variables and constraints, and a specialized algorithm is designed to determine globally optimal solutions. The computational results obtained, corresponding to different user distributions, demonstrate the efficacy of the proposed models and algorithms, and clearly exhibit the viability of both exact and approximate models. [ABSTRACT FROM AUTHOR]

A sustainability program is about implementing essential sustainability practices at various intensities that will not harm people because of firm actions. This paper formulates an optimization model to identify an optimal intensity of implementation of selected sustainability practices referred as sustainability program for maximising manufacturing industry sustainability performance for a known set of budgetary and minimum thresholds constraints with respect to economic, social and socio-economic criteria. Besides modelling the paper proposes a random search procedure embedding the NN to determine the optimal sustainability program and explains the usefulness with a sample problem. Sensitivity analysis is carried out to understand the behaviour of the model. It is observed that the sustainability performances depend on the constraints such as budget limitation and threshold values of performance criterion. [ABSTRACT FROM AUTHOR]

In this paper, we study a scheduling model in which the features of deteriorating jobs, serial batches, multiple job types, and setup times are considered simultaneously. In this proposed model, the jobs of each type are first partitioned into serial batches, and then all batches of different job types are processed on a single serial-batching machine. The actual job processing time is an increasing function of its starting time, and the setup time of the batches is sequence-dependent, i.e., setup time is required only when a new batch is processed first on the machine or immediately after a batch belonging to another job type. We develop optimization algorithms to solve the makespan minimization problem, the maximum tardiness minimization problem, the maximum lateness minimization problem, and the maximum earliness minimization problem, respectively. We also propose optimization algorithms to solve the problem of minimizing the number of tardy jobs under a certain agreeable condition. Finally, we discuss two special cases of the total completion time minimization problem and develop optimization algorithms to solve them. [ABSTRACT FROM AUTHOR]

Network routing problems are often modeled with the assumption that the network structure is deterministic, though they are often subject to uncertainty in many real-life scenarios. In this paper, we study the traveling salesman and the shortest path problems with uncertain topologies modeled by arc failures. We present the formulations that incorporate chance constraints to ensure reliability of the selected route considering all arc failure scenarios. Due to the computational complexity and large scales of these stochastic network optimization problems, we consider two cutting plane methods and a Benders decomposition algorithm to respectively solve them. We also consider to solve the reformulations of the problems obtained by taking the logarithm transformation of the chance constraints. Numerical experiments are performed to obtain results for comparisons among these proposed methods. [ABSTRACT FROM AUTHOR]

Guarding the perimeter of an area in order to detect potential intruders is an important task in a variety of security-related applications. This task can in many circumstances be performed by a set of camera-equipped unmanned aerial vehicles (UAVs). Such UAVs will occasionally require refueling or recharging, in which case they must temporarily be replaced by other UAVs in order to maintain complete surveillance of the perimeter. In this paper we consider the problem of scheduling such replacements. We present optimal replacement strategies and justify their optimality. [ABSTRACT FROM AUTHOR]

Sustainability is the major issue of small and medium sized enterprises (SMEs) all across the globe. Although SMEs contribute to GDP of any country their negative contribution to environment is also significant. Prior studies on SMEs' sustainability mainly classified into three categories—the correlation between environmental and social practices with economic performance, sustainable supply chain performance measurement, and empirical research on sustainability practices. There is no study that objectively derives the sustainable structure of SMEs through optimal combination of sustainability practices (inputs) and performance (outputs). Therefore, the main objective of this paper is to generate optimal structure of sustainable SMEs by combining neural network and particle swarm algorithm while considering Multi-Objective framework. The study uses data from 54 SMEs of Normandy in France and 30 SMEs of Midlands in the UK. The data was gathered through questionnaire survey. As we do not have the explicit expression of our objective functions, we train a neural network on our databases in order to enable the generation of value of the different objectives for any profile. We design and run a multi-objective version of particle swarm optimization (MPSO) to generate efficient companies' structures. The weighted sum method is then used for different weights. The comparison of observed data and the results of the PSO analysis facilitates to derive improvement measures for each individual SME. [ABSTRACT FROM AUTHOR]

The multiperiod vehicle routing problem with profit (mVRPP) is a selective vehicle routing problem where the planning horizon of each vehicle is divided into several periods. The aim of solving mVRPP is to design service itineraries so that the total amount of collected profit is maximized and the travel time limit of each period is respected. This problem arises in many real life applications, as the one encountered in cash-in-transit industry. In this paper, we present a metaheuristic approach based on the particle swarm optimization algorithm (PSO) to solve the mVRPP. Our approach incorporates an efficient optimal split procedure and dedicated local search operators proposed to guarantee high search intensification. Experiments conducted on an mVRPP benchmark show that our algorithm outperforms the state of the art metaheuristic approaches in terms of performance and robustness. Our PSO algorithm determines all the already known optimal solutions within a negligible computational time and finds 88 strict improvements among the 177 instances of the benchmark. [ABSTRACT FROM AUTHOR]

This paper addresses the duality theory of a nonlinear optimization model whose objective function and constraints are interval valued functions. Sufficient optimality conditions are obtained for the existence of an efficient solution. Three type dual problems are introduced. Relations between the primal and different dual problems are derived. These theoretical developments are illustrated through numerical example. [ABSTRACT FROM AUTHOR]

In this paper we deal with the use of local searches within global optimization algorithms. We discuss different issues, such as the generation of new starting points, the strategies to decide whether to start a local search from a given point, and those to decide whether to keep the point or discard it from further consideration. We present how these topics have been faced in the existing literature and express our opinion on the relative merits of different choices. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a duality theory for nonsmooth optimization problems with vanishing constraints (MPVC) defined by locally Lipschitz functions. In order to do this, we first formulate a new mixed-type dual problem for the considered MPVC, which is a generalization of Wolfe and Mond–Weir dual problems. Since this dual problem depends on the feasible points of the primal problem, we introduce another mixed-type dual problem that does not have this dependence. Then, we present the weak, strong, converse, restricted converse, and the strict converse duality results for these parametric dual problems. Finally, we compae the results of the article written by Mishra et al. (Ann Oper Res 243(1):249–272, 2016) with our results and state the correct version of some of its incorrect theorems. [ABSTRACT FROM AUTHOR]

The training phase is the most crucial stage during the machine learning process. In the case of labeled data and supervised learning, machine learning entails minimizing the loss function under various constraints. We provide an innovative model for learning with numerous data sets, resulting from the application of multicriteria optimization techniques to existing deep learning algorithms. Data fitting is formulated as a multicriteria model in which each criterion measures the data fitting error on a specific data set. This is an optimization model involving a vector-valued function, and it has to be analyzed using the notion of Pareto efficiency. We present stability results for efficient solutions in the presence of input and output data perturbations. The multiple data set environment comes into play to eliminate the bias caused by the selection of a specific training set. To apply this concept, we present a scalarization strategy as well as numerical experiments in digit classification using MNIST data. [ABSTRACT FROM AUTHOR]

In this paper we present a Generalized Nash Equilibrium model of supply chain network competition among blood service organizations which compete not only for blood donors but also for business from hospitals and medical centers. The model incorporates not only link capacities and associated arc multipliers to capture perishability, but also bounds on the number of donors in regions as well as lower and upper bounds on the demands at the demand points in order to ensure needed amounts for surgeries, treatments, etc., while reducing wastage. The concept of a variational equilibrium is utilized to transform the problem into a variational inequality problem, and alternative formulations are given. A Lagrange analysis yields economic insights. The proposed algorithmic procedure is then applied to a series of numerical examples in order to illustrate the impacts of disruptions in the form of a reduction on the number of donors as well as that of decreases in capacities of critical links such as testing and processing on RBC prices, demands, net revenues of the blood service organizations, and their overall utilities. [ABSTRACT FROM AUTHOR]

Shift scheduling problem (SSP) is a complex NP-hard integer programming problem, especially when many shifts and large number of workers of various ranks and multi-skills are involved. Shift scheduling also needs to comply with certain labour regulations and organisation's rules and policies. Various models for SSP have been developed and solved. However, studies on SSP for workers of power stations or utility providers are still lacking. This paper presents the study on shift scheduling of workers at the largest power station in Malaysia. The objectives of this study are to identify main criteria and conditions of SSP at the power station, to formulate a Binary Integer Goal Programming (BGP) model for SSP that optimises workers' day-off preference and to determine the optimal schedule for workers based on the model. The scheduling focuses on three processes which are demand modelling, shift scheduling and day-off scheduling. The study involves scheduling 43 workers in a selected department of the power station for 28 days where workers work in three shifts (morning, evening and night shifts) and have standby and rest days. The SSP-BGP model considers workers' day-off preference as its main feature and introduces objective function that maximises the overachievement and minimises under-achievement of the day-off preference. It addresses conflicting multi-objectives in determining the optimal schedule using the goal programming approach. The model also put forward new hard and soft constraints, which reflect the nature of shifts' requirements for these workers when determining the optimal schedule. The required data have been obtained from the power station and validation and verification of the model has been acquired with the cooperation from its personnel. The SSP-BGP model was solved using MATLAB in reasonable time. It significantly reduces the time to obtain a monthly schedule as compared to current manually done scheduling. Based on the 28-day schedule obtained, the day-off preference's satisfaction of workers has increased by 37.21% that is from 43.02% based on the existing prepared schedule to 80.23% according to the schedule produced based on the SSP-BGP model. The model has shown good performance by not only generating optimal schedule based on the required number and composition of workers for shifts but also providing positive impacts on workers through day-off preferences' satisfaction and the possibility of working with different groups. [ABSTRACT FROM AUTHOR]

This paper examines the incorporation of useful information extracted from the evolutionary process, in order to improve algorithm performance. In order to achieve this objective, we introduce an efficient method of extracting and utilizing valuable information from the evolutionary process. Finally, this information is utilized for optimizing the search process. The proposed algorithm is compared with the NSGAII for solving some real-world instances of the fuzzy portfolio optimization problem. The proposed algorithm outperforms the NSGAII for all examined test instances. [ABSTRACT FROM AUTHOR]

In this paper we propose a new variant of the Variable Neighborhood Decomposition Search (VNDS) heuristic for solving global optimization problems. We call it Ascent-Descent VNDS since it performs "boundary effect", or local search step, even if the improvement in solving the subproblem has not been obtained. We apply it in detecting communities in large networks by modularity maximization, the criterion which is, despite of some recent criticism, most widely used. Computational analysis is performed on 22 instances from the 10th DIMACS Implementation Challenge. On 13 instances where optimal solutions were not known, we got the improved best known solutions on 9 instances and on 4 instances the solution was equal to the best known. Thus, the proposed new heuristic outperforms the current state-of-the-art algorithms from the literature. [ABSTRACT FROM AUTHOR]

A queueing-inventory system, with the item given with probability γ to a customer at his service completion epoch, is considered in this paper. Two control policies, ( s, Q) and ( s, S) are discussed. In both cases we obtain the joint distribution of the number of customers and the number of items in the inventory as the product of their marginals under the assumption that customers do not join when inventory level is zero. Optimization problems associated with both models are investigated and the optimal pairs ( s, S) and ( s, Q) and the corresponding expected minimum costs are obtained. Further we investigate numerically an expression for per unit time cost as a function of γ. This function exhibit convexity property. A comparison with Schwarz et al. (Queueing Syst. 54:55-78, ) is provided. The case of arbitrarily distributed service time is briefly indicated. [ABSTRACT FROM AUTHOR]

In a typical project portfolio optimization problem, the decision makers would choose the optimal project portfolio from the candidates under the constraints of certain resources. However, in lots of real world examples, traditional approaches cannot perfectly solve the project portfolio optimization problems with unknown information about the importance of attributes. An innovative feature of this paper is to propose a stochastic multiattribute acceptability analysis (SMAA) to solve the multiattribute project portfolio optimization problems. SMAA includes a series of methods, which provide full ranks for a set of alternatives by exploring the weight space to make each alternative the most preferred one. Our proposed method provides feasible procedures to deal with the project portfolio optimization problems and, as an extension of traditional SMAA methods, expands the SMAA applications from the comparison of single alternatives to the comparison of sets of alternatives. [ABSTRACT FROM AUTHOR]

High levels of availability and reliability are essential in many industries where production is subject to high costs due to downtime. Examples include the mechanical drive in natural gas pipelines and power generation on oil platforms, where gas turbines are commonly used as a power source. To mitigate the effects of service outages and increase overall reliability, it is also possible to use one or more redundant units serving as cold standby backup units. In this paper, we consider preventive maintenance optimization for parallel k-out-of- n multi-unit systems, where production at a reduced level is possible when some of the units are still operational. In such systems, there are both positive and negative effects of grouping activities together. The positive effects come from parallel execution of maintenance activities and shared setup costs, while the negative effects come from the limited number of units which can be maintained at the same time. To show the possible economic effects, we evaluate the approach on models of two production environments under a no-fault assumption. We conclude that savings were substantial in our experiments on preventive maintenance, compared to a traditional preventive maintenance plan. For single-unit systems, costs were on average 39 % lower when using optimization. For multi-unit systems, average savings were 19 %. We also used the optimization models to evaluate the effects of re-planning at breakdown and effects due to modeling of inclusion relations. Breakdown re-planning saved between 0 and 11 % of the maintenance costs, depending on which component failed, while inclusion relation modeling resulted in an 7 % average cost reduction. [ABSTRACT FROM AUTHOR]

This paper reviews the state-of-the-art in the theory of deterministic and uncertain linear semi-infinite optimization, presents some numerical approaches to this type of problems, and describes a selection of recent applications in a variety of fields. Extensions to related optimization areas, as convex semi-infinite optimization, linear infinite optimization, and multi-objective linear semi-infinite optimization, are also commented. [ABSTRACT FROM AUTHOR]

Due to intrinsic complexity and sophistication of decision problems in tourism and recreation, respective decision making processes can not be implemented without making use of modern computer technologies and operations research approaches. In this paper, we review research works on modeling recreational systems. [ABSTRACT FROM AUTHOR]

The aim of this paper is to characterize some of the pointwise and global well-posedness notions available in literature for a set optimization problem completely by compactness or upper continuity of an appropriate minimal solution set maps. The characterizations of compactness of set-valued maps, lead directly to many characterizations for well-posedness. Sufficient conditions are also given for global well-posedness. [ABSTRACT FROM AUTHOR]

In this paper, the NP-complete multiple level warehouse layout problem is addressed. The problem consists of assigning items to cells and levels with the objective of minimizing transportation costs. A worm optimization algorithm (WO) is introduced, based on the foraging behaviors of Caenorhabditis elegans (Worms), and its performance was assessed by comparing with a genetic algorithm (GA), ant colony optimization (ACO), and an exact solution (B&B) for small problems. The computational results reflected the superiority of WO in large problems, with a marginally better performance than ACO and GA in smaller ones, while solving the tested problems within a reasonable computational time. Furthermore, WO was able to attain most of the known optimal solutions. [ABSTRACT FROM AUTHOR]