Journal of Operational Research in its Applications
Year:2022 | Volume:19 | Issue:4|Papers Count:8

The lack of renewable primary resources is one of the most serious challenges in recent decades. So under this circumstance, managers are looking for appropriate solutions for the optimal use of these resources to improve the efficiency of the under management units. One of the most widely used techniques in finding the optimal resource is data envelopment analysis (DEA). So in this paper, in order to resource re-allocation in parallel systems, a new DEA model is introduced. Then by using of bi-level programing, a new method is applied to solve the proposed model. Finally, for the validity of the proposed model, the new model is applied to a real case study and analyzed the obtained results.

Scheduling problems have been considered by researchers from different aspects, and it has been widely addressed in the literature. Most scheduling problems, and almost all that arise in real-world applications, are NP-hard, therefore finding a proper and feasible solution, a solution that is performed in a reasonable time (polynomial time), is difficult. Approximation scheme, among different methods, is one approach that can overcome difficulties. Polynomial-time approximation and differential approximation schemes are categorized as approximation approaches based on comparing the proposed algorithm with optimum and worst-case solutions. As the first attempt, the paper investigates differential approximation schemes for the minimization of maximum delivery time. In this paper, the main methods and tools for proving the existence of an approximation scheme are presented. To do so, a differential approximation scheme is proposed by employing a polynomial-time approximation scheme (PTAS). The paper also reviews efficient approximation schemes on single machine problems with objective functions minimization of maximum delivery time, and minimizing the total weighted completion time. Also, in this paper, using the polynomial time approximation scheme (PTAS), a differential approximation scheme has been studied for the problem of minimizing the maximum delivery time of works, which has not been investigated so far.

In today's distribution environment, cross-docking has attracted the attention of many researchers due to its vital role in reducing costs in supply chains. The cross-docking system reduces the cost of distribution by eliminating storage and sorting. In this article, we look at the issue of truck scheduling in a cross-docking system. According to the research literature, the issue of cross-docking scheduling is one of the Np-hard problems, so in this paper, a new hybrid approach to solve this problem is presented. The Imperial Competition Algorithm (ICA) is a socio-political algorithm inspired by imperialist competition. However, when searching in a complex environment, its efficiency is significantly reduced, and this limitation confines the algorithm for achieving a good solution. This paper introduces a new search mechanism to solve this problem. This algorithm is based on the principal component analysis (PCA) method in which PCA actually extracts a low-dimensional set of features from a high-dimensional set to help record more information with fewer variables. This is why the method is called PCICA. The results from the new algorithm were compared with ICA, SA, and GA methods. Results show that PCICA performs significantly better than others and can find reasonable solutions.

In recent years, the problem of selecting the right supplier in the supply chain has become a strategic and important issue. Due to the existence of multiple and contradictory criteria, the problem of selecting a supplier is a multi-objective problem and sometimes it is very difficult to find the optimal solution. Choosing the right supplier on the one hand, significantly reduces the cost of purchasing raw materials and the waiting time until the ordered shipment arrives, on the other hand, it increases the level of competitiveness of organizations. The purpose of this study is to provide a hybrid method for solving multi-objective problems that has been used in evaluating and selecting the appropriate supplier. The proposed FOPSIS hybrid method is a combination of the TOPSIS method and the cuckoo optimization algorithm. The proposed method is first considered for experimental problems and then the implementation supplier is selected for multi-purpose application problem. The speed and accuracy of the results obtained from the implementation of the proposed FOPSIS method show the efficiency of the algorithm in solving multi-objective problems and this method can well identify Pareto solutions of the problem in comparison with similar methods. Also, due to the use of the hybrid method, the advantages of the cuckoo meta-heuristic algorithm can be used simultaneously in solving large-scale problems and the TOPSIS method can be used to evaluate and select a more efficient option.

The problems caused by the mono-product economy and the reliance on oil revenues have led to the creation of an economy affected by external factors, especially unstable oil revenues. With this feature of Iran's economy, we can understand the importance and role of non-oil exports in the country. Today, small and medium-sized industries play a role in various aspects of society, industrial production and service delivery in most countries of the world. These industries with their significant exports play an effective role in the economic development of countries. In addition, intensifying global competition, increasing uncertainty and increasing demand for a variety of products has made these industries more popular. Small and medium enterprises (SME) make up a large part of the country's production and industrial units. Considering the importance of export promotion as the main factor driving the economy and the small share of exports of SME in the country, the purpose of this study is to identify, prioritize and analyze the interaction of effective components on the export performance of SME exporting food and beverage products of Iran. For this purpose, important effective components on export performance were identified using grounded theory and obtaining the opinions of 35 professors, elites and experts in the field of export of SME in Iran. Then, the interaction between the components as well as its prioritization is analyzed based on the combination of fuzzy MADM methods including fuzzy DMATEL method and fuzzy ANP method. The results showed that the components of company capabilities, company strategies, programs and macro government, customer and export performance promotion are the most important factors affecting export performance. Also, the highest effect of one component on another component had the component of "macro government programs and policies" on the component of "corporate capabilities". Among the identified components, the component "macro government programs and policies" has had the highest priority.

In this paper, a new modified of well-known Variational Iteration Method (VIM) has been used to solve a system of integro-differential equations of predator-prey derived from the problem of two biological species ‎living together. The error function has been obtained for each equation and consequently the maximum error on the supposed interval successively for iterations. We have also dissected the interval into n numbers of equal sub-interval and have chosen a special iteration of VIM by a suitable choice based on minimizing maximum error on the first sub-interval. Then, by using the selected iteration of the VIM, a new Multistep Variational Iteration Method has been applied to some steps. In the proposed method, VIM has been applied only on the first special sub-interval, and it has been used the value of the solution at the end of each step as the initial condition of the next step. In the end, we transmit the solution on each sub-interval to its real place. We have indicated that the error of the solution by the proposed method decreases significantly.

Multicriteria decision making (MCDM) in intuitionistic Fuzzy environments is a very important research topic. When available information is precise, many methods exist to solve this problem. But the uncertainty and fuzziness inherent in the structure of information make rigorous mathematical models inappropriate for solving this type of problems. Intuitionistic fuzzy set (IFS) is very useful in providing a flexible model to elaborate uncertainty and vagueness involved in decision making. This paper investigates multiple criteria decision-making problems with intuitionistic fuzzy information. A modified TOPSIS method is proposed. First, the proposed method calculates the relative positive ideal solution and the relative negative ideal solution for each alternative. Then, the weighted Hamming distances between every alternative and positive ideal solution and negative ideal solution are calculated. Then, according to the weighted Hamming distances, the relative closeness coefficient to the positive ideal solution is calculated to rank all alternatives. Finally, two illustrative examples are given.

In this paper, we have proposed a family of the two-step with-memory method for solving nonlinear equations. They have two parameters self-accelerator. In the following, by approximating the self-accelerator parameters, a class of new methods with memory is proposed that most effective of which has a convergence order of 6. 37, i. e., and improvement in the R-order of convergence is 59. 37%. Another advantage of the new methods is that they do not require derivative computation. We have used Steffensen-like methods and have solved nonlinear equations with simple roots with the appropriate initial approximation of roots to show the correctness of the theorems with numerical examples.