In this study, a new integrated multi-criteria group decision-making (MCGDM) model under a hesitant fuzzy context is proposed to select reliable suppliers for the company. Different mutually conflicting criteria, human vagueness, and uncertainties are involved in this process. The selection process can be biased if the decision is made by a single expert instead of multiple experts. Classical fuzzy set theory (FST) is used to handle these issues. However, classical FST cannot completely handle uncertainty to some extent. On the contrary, the hesitant fuzzy set (HFS) is considered as one of the efficient and superior tools that can handle uncertainty completely. It can process and aggregate hesitant fuzzy information differently and capture their interrelationship. It can also handle uncertainty in the group decision-making process while experts have hesitation about several membership values for an element within a set. Due to its unique advantage, a new integrated MCGDM model is proposed which is consisted of the vague set, HFS, and weighted generalized hesitant fuzzy power geometric (WGHFPG) operator. The vague set is used to estimate the subjective weights of both the criteria and decision-makers; the HFS is used to consider multiple membership values in the decision-making process to handle uncertainty; and finally, the WGHFPG operator is used to aggregate the decision-matrices to get the final scores of the alternatives. The efficiency and practicability of the proposed model are illustrated by setting a numerical example. It is also benchmarked by other models including complex proportional assessment (COPRAS), combinative distance-based assessment (CODAS), and measurement alternatives and ranking according to compromise solution (MARCOS) by estimating Spearman’s ranking correlation coefficient. Finally, a sensitivity analysis is performed to test the robustness and stability of the introduced methodology. The result shows that the proposed model is more robust compared to other approaches.

This paper proposes an adaptive heterogeneous particle swarm optimization with a comprehensive learning strategy for solving single-objective constrained optimization problems. In this algorithm, particles can use an exploration strategy and an exploitation strategy to update their positions. The historical success rates of the two strategies are used to adaptively control the adoption rates of strategies in the next iteration. The search strategy in the canonical particle swarm optimization algorithm is based on elite solutions. As a result, when no particles can discover better solutions for several generations, this algorithm is likely to fall into stagnation. To respond to this challenge, a new strategy is proposed to explore the neighbors of the elite solutions in this study. Finally, a constraint handling method is equipped to the proposed algorithm to make it be able to solve constrained optimization problems. The proposed algorithm is compared with the canonical particle swarm optimization, differential evolution, and several recently proposed algorithms on the benchmark test suite. The Wilcoxon signed-rank test results show that the proposed algorithm is significantly better on most of the benchmark problems compared with the competitors. The proposed algorithm is also applied to solve two real-world mechanical engineering problems. The experimental results show that the proposed algorithm performs consistently well on these problems.

We formulate the problem of the relocation of books in a university library as a mixed-integer programming problem and solve it by an optimization software. The problem is essentially to divide bookshelves into sections so that each section has an enough space for the current and future buying books in its assigned book group, and its solution allows us to avoid the frequent relocation of books. We addressed the case of Nanzan University Library. At present, the library staff spends much time to generate a book relocation plan manually. Our method reduces the time but also the burden of the staff for rearranging books.

Mass customization is an important strategy to improve production systems to satisfy customers’ preferences while maintaining production efficiency for mass production. Module production is one of the ways to achieve mass customization, and products are produced by combining modules. In the module production, it becomes much more important for manufacturing companies to reflect customers’ preferences for selling products. The manufacturer can increase its total profit by providing customized products that satisfy customers’ preferences by increasing customers’ satisfaction. In conventional production planning, there are some cases where module production is conducted by the demands from customers’ preferences. However, the customer decision-making model has not been employed in the production planning model. In this paper, a production planning model incorporating customers’ preferences is developed. The customers’ purchasing behavior is generated by using a machine learning model. Customer segmentation is conducted by clustering data that uses the purchase data of multiple customers. The resulting production planning model is a bilevel production planning problem consisting of a single company and multiple customers. Each company can sell products that combine modules that customers require in each segment. We show that the proposed model can obtain higher customers’ satisfaction with greater profits than the model that does not employ the customers’ purchasing model.

Most real-life optimization problems are subjected to uncertainty, and the robust optimization approach is one of the efficient techniques to deal with uncertain optimization problems. Supply chain optimization problems are highly sensitive to data perturbations mostly due to inappropriate estimation of the problems’ parameters and the highly dynamic environment. In this study, we propose an adaptable robust optimization model for the dual-channel closed-loop supply chain (CLSC) and present two counterpart models; the first model is a mixed integer linear programming (MILP) model based on the adjustable box uncertainty set, while the second robust model is a mixed integer nonlinear programming (MINLP) model based on the adjustable ellipsoidal uncertainty set. We provide a novel approach for considering multiple uncertainty sets in the objective function, that provide flexibility and control risk based on the preferences of the decision-makers. This study aims at minimizing the total cost of the dual-channel CLSC network considering uncertain purchasing, transportation, fixed, and processes costs, in addition to uncertain customer demand. Intensive computational experiments are conducted on the two robust models using GAMS software. Robust solutions are obtained and sensitivity analysis is conducted on both models considering 10% perturbation of the uncertain parameters around their nominal values as well as probability guarantee for not violating the constraints.

In this paper, a method is developed for evaluating the locations of facilities in a network in which users visit exactly one facility on their way from origin to destination, such as the daily commute to work. The focus is on a continuous network in which the origins and destinations of trips are distributed uniformly and independently along edges of the network, and an analytical method is proposed for deriving the distance distributions. It is assumed that every traveler selects a route that minimizes the sum of the distance from origin to facility and the distance from facility to destination. Compared to a single summary index such as average distance, distance distributions contain rich information about the overall accessibility of facilities and so are useful for analyzing actual facility configurations and evaluating several planning alternatives. The proposed method can also be used to evaluate solutions obtained from facility location models that assume demands represented as flows traveling over a network. For the case of only one facility, a method is presented that uses an extended shortest-path tree rooted at the facility node. For the case of two or more facilities, an existing method is extended to obtain the shortest travel-length distribution in a continuous network in the case in which travelers visit exactly one facility on their way from origin to destination. By applying the proposed framework to an actual road network, it is found that the shapes of distributions differ greatly depending on the location of facilities and hence they are much more useful compared to using a single index such as the average distance.

Sports scheduling is the research region that concerns making a reasonable game schedule, especially a round-robin tournament, for sports competitions. One of the important factors in game scheduling is fairness. Based on the fairness of the schedule, the two elements of the break and the carry-over effect are considered in this study. First, three types of home-away tables that are limited to a small number of breaks, i.e., the number of two consecutive home/away games are evaluated, and these tables are characterized by introducing a number sequence called space-sequence. The space-sequences also clarify the property of feasibility, which helps to enumerate feasible home-away tables. Next, the carry-over effect value minimization problem with a small number of breaks was solved by using an integer programming problem. Adding some valid inequalities and converting the quadratic objective function into linearization slightly improve the calculation of the minimum carry-over effect values, but it still cannot obtain the solution for a large number of teams. Since it is difficult to calculate the entire integer programming model, isomorphic home-away tables obtained by rotation of rounds are defined and the candidates home-away tables are reduced. By solving the problem for each of the nonisomorphic home-away tables enumerated, better carry-over effect values can be found for the small number of teams.

Given a directed graph with n vertices, m edges and costs on the edges, the linear ordering problem (LOP) consists of finding a permutation of the vertices so that the total cost of the reverse edges is minimized, where an edge is called a reverse edge if its head vertex is at a position before the tail in the permutation. Known as an NP-hard problem, the LOP has a number of real-world applications such as the analysis of data called industry transaction table or input-output matrix (I/O table) in the field of economy. In this paper, we propose an algorithm based on tabu search for the LOP. We generalize an algorithm called TREE, which was proposed to search for a best solution in the neighborhood efficiently, so that the new algorithm can be incorporated in the tabu search algorithm and can search for a non-tabu best solution in the neighborhood efficiently. Our algorithm also features an adaptive control mechanism of the tabu tenure by using a cycling-detection mechanism based on a probabilistic analysis. Finally, we compare the experimental results of the proposed algorithm with those of an existing iterated local search algorithm (ILS). The proposed algorithm obtained better results than the ILS for many instances.

Round­robin tournaments are widely used in sports leagues such as football and baseball leagues. The round­robin tournament scheduling problem is one of the most well­known problems in sports scheduling. When creating a schedule for a round­robin tournament, various factors are considered such as travel distance of teams and the number of breaks. Among such factors, the carry­over effect is often considered to evaluate a schedule from the viewpoint of fairness. In this paper, we propose a metaheuristic algorithm for the problem of minimizing the carry-over effect value. For a complete graph whose edges describe all possible matches in a round­robin tournament, we consider a rainbow perfect matching, which we call a base matching, to describe a round of a schedule. By using the circle method, a round­robin tournament schedule can be generated from any base matching. Then we design a metaheuristic algorithm based on iterated local search to search for a “good” base matching, that is, one that generates a round­robin tournament schedule with a low carry­over effect value. We confirmed through computational experiments that our proposed algorithm obtained solutions with carry­over effect values equal to or lower than the best­known values for 15 out of the 17 tested instances with up to 40 teams. In addition, we updated the best­known records for 2 instances.

Preventive maintenance is a critical element of maintenance policies in a wide range of industries, including the power sector. To achieve reasonable and effective maintenance of nuclear power plants (NPPs), proper aging management is critical and should be optimized from both safety and economic perspectives. Thus, in this paper, we propose a maintenance-scheduling model based on an adaptive parallel particle swarm optimization (PSO) to minimize the total number of maintenance activities over the lifetime of an NPP while ensuring the reliability of safety-critical functions. The proposed model recognizes that effective maintenance activities differ depending on the cause of the latent failure. In addition, the applied PSO algorithm, which is based on the dynamic exchange of hyperparameters between adjacent swarms, allows us to optimize inertia factor and learning factors adaptively during the solution search process. The proposed model is verified by applying it to a representative case in which the best maintenance schedules for the components constituting a water injection function are produced.

In this paper, we propose a point-to-point airline network design model where a new company plans to enter a market to maximize its revenue under a regulation. The presented model prevents the entrant company from developing a route that may drastically reduce the existing company’s revenue. Under this restriction, the entrant company considers cooperation and competition with the existing companies when entering the market. We use a hub connection cost, which is the required cost for passengers when they transfer to other routes, to represent various levels of cooperation. Small hub connection cost means cooperative relations between the companies. We also incorporate the path’s attractiveness that shows how attractive it is for passengers, depending on the hub connection cost and how it detours compared to a non-stop path between the origin and destination of the path. If a path is not sufficiently attractive, passengers do not use it, which causes revenue reduction. Hence the entrant company requires to find an optimal strategy, i.e., cooperative or competitive relations to be developed with the existing company, to maximize its revenue. We formulate the model as a 0-1 integer programming problem and obtain optimal solutions using optimization software. From computational results using the standard CAB hub location data set, we observe that the results greatly depend on the existing company’s network; however, cooperative relations and a certain de-regulation achieve the total revenue increase by developing an attractive network.