Scientia Iranica
Volume:24 Issue: 4, 2017

This paper studies a simultaneous weekly assignment and scheduling decisionmaking problem in operating theaters with elective patients. Because of limited recourses in hospitals, considering assignment and scheduling decisions simultaneously can help mangers exploit the available resources more eciently and make the work-load uniformly distributed during the planning horizon. This procedure can signi cantly reduce hospital costs and increase satisfaction of patients and personnel. This paper formulates the mentioned problem as a Mixed Integer Linear Program (MILP) considering applicable assumptions like nite recovery beds and limitation of equipment. Since the problem is NP-hard, in order to solve large-scale instances and deal with the complexity, two e ffective and ecient algorithms are designed. Finally, as a practical case of study, a real data set of a surgery department of a big hospital in Iran (Aalinasab-e Tabriz) is used to solve the
studied problem by the proposed algorithms.

Decisions, about product acceptance or rejection, based on technical measurement report in ultra-precise and high-tech manufacturing environment is highly challenging as product reaches nal stage after high value-added processes. Moreover, the role of technical personnel in decision making process for inventory models with focus on group-technology manufacturing setup has been considered relatively less. Most of the literature assumes that decisions are perfect and error free. However, in reality, human errors exist in making such decisions based on measurement reports. This paper incorporates human errors into the decision making process focusing on group-technology inventory model, where high value-added machining processes are involved. Therefore, a mathematical model is developed for the optimal lot size considering human errors in the decision making process and the imperfect production process with focus on work-inprocess inventory. Lot size is optimized based on average cost minimization by incorporating human error Type I and human error Type II. Numerical examples are used to illustrate and compare the proposed model with the previously developed models for group-technology high-tech manufacturing setups. The proposed model is considered more flexible as it incorporates imperfection in process with human errors in decision making process.

Classi cation is an important machine learning technique used to predict group membership for data instances. In this paper, we propose an ecient prototypebased classi cation approach in the data classi cation literature by a novel soft-computing approach based on extended imperialist competitive algorithm. The novel classi er is
called EICA. The goal is to determine the best places of the prototypes. EICA is evaluated under three di erent tness functions on twelve typical test datasets from the UCI Machine Learning Repository. The performance of the proposed EICA is compared with well-developed algorithms in classi cation including original Imperialist Competitive Algorithm (ICA), the Arti cial Bee Colony (ABC), the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), the Gravitational Search Algorithm (GSA), the Grouping Gravitational Search Algorithm (GGSA), and nine well-known classi cation techniques in the literature. The analysis results show that EICA provides encouraging results in contrast to other algorithms and classi cation techniques.

We consider minimizing the maximum earliness in the single-machine scheduling problem with flexible maintenance. In this problem, preemptive operations are not allowed, the machine should be shut down to perform maintenance, tool changing or resetting takes a constant time, and the time window inside which maintenance should be performed is prede ned. We show that the problem is NP-hard. Afterward, we propose some dominance properties and an ecient heuristic method to solve the problem. Also, we propose a branch-and-bound algorithm, in which our heuristic method, the lower bound, and the dominance properties are incorporated. The algorithm is computationally examined using 3,840 instances up to 14,000 jobs. The results impressively show that the proposed heuristic algorithm obtains the optimal solution in about 99.5% of the cases using an ordinary processor in a matter of seconds at most.

This paper proposes two models to formulate a Supplier Selection Problem (SSP) in a single-buyer, multi-supplier two-echelon supply chain network. The model coordinates order allocation and supplier selection problems under all-unit quantity discount policy. In this way, bi-level programming is employed to obtain two models: 1) The model with buyer as a leader; 2) The model with vendor as a leader. The resulted nonlinear bi-level programming problems are hard to solve. Therefore, Particle Swarm Optimization (PSO) algorithm is used to deal with the complexity of the model and makes it solvable. Numerical results show that the proposed model is ecient for SSP in compliance with order allocation decision making.

This study is concerned with how the quality of perishable products can be improved by shortening the time interval between production and distribution. Since special types of food, such as dairy products, decay fast, the Integration of Production and Distribution Scheduling (IPDS), is investigated. This article deals with a variation of IPDS that contains a short shelf life product; hence, there is no inventory of the product in the process. Once a speci c amount of the product is produced, it must be transported with the least transportation time directly to various customer positions within its limited lifespans to minimize the delivery and tardy costs required to complete producing and distributing of the product to satisfy the demand of customers within the limited deadline. After developing a mixed-integer nonlinear programming model of the problem, because it is NP-hard, an Improved Particle Swarm Optimization (IPSO) is proposed. IPSO performance is compared with commercial optimization software for small-size and moderate-size problems. For large-size ones, it is compared with the genetic algorithm existing in the literature. Computational experiments show the eciency and e ectiveness of the proposed IPSO in terms of both the quality of the solution and the time of achieving
the best solution .

The concept of sustainability in supply chain management refers to a logical balance between economic development, environmental considerations, and social responsibilities. In this paper, a sustainable model has been proposed to design a supply chain network in textile industries considering the key environmental and social factors. Regarding the type of industry and characteristics of the area under study (Zanjan, northwest Iran), minimizing the negative e ects of wasteful extraction of ground waters and the environmental pollution resulting from industrial wastewaters and maximizing justicebased employment were considered. The supply chain consists of the following elements: suppliers, plants, distribution centers, water re nery centers, and customer zones. One of the important features of the proposed model is that it considers the lost opportunity cost of facilities and focuses on wastewater recycling in water re neries. To solve the model, the Multi-Objective Vibration Damping Optimization (MOVDO) algorithm has been used. In addition, to evaluate the proposed model, as a case study, the supply chain network design problem was solved in textile industry. In addition, to evaluate the solution performance of the used algorithm in comparison with that of the NSGA-II algorithm, ten random problems with di erent sizes were solved, and the results were analyzed using di erent indexes. All in all, the results show that the proposed method has the necessary performance.

This paper deals with an Economic Production Quantity (EPQ) model to determine production-inventory policies for perishable products. Shortage is permitted and fully backordered. The demand rate is stochastic- and stock-dependent. Since the problem is mathematically challenging and intractable via analytical approaches, this paper designs a simulation-based optimization algorithm by combining a grid search and a simulation model to solve the problem. The grid search plays the role of optimizer to determine the model variables, and the simulation model is utilized to evaluate the quality of solutions obtained by the optimizer through an iterative procedure. Eventually, a numerical example is discussed to illustrate how the solution procedure works, and a comparison study is carried out to demonstrate the superiority of suggested approach.
Moreover, a comprehensive sensitivity analysis with respect to the problem parameters is performed.

Shewhart-Cucconi and Shewhart-Lepage are two nonparametric control charts used for monitoring joint shifts in the process location and scale parameters. This study investigates impact of the light and heavy-tailed distributions on the performances of these charts. The e ect of reference and test samples is also a part of this study.

With respect to the interval neutrosophic Multi-Attribute Decision-Making (MADM) problems, the MADM method is developed based on some interval neutrosophic aggregation operators. Firstly, the Induced Generalized Interval Neutrosophic Hybrid Arithmetic Averaging (IGINHAA) operator and the Induced Generalized Interval Neutrosophic Hybrid Geometric Mean (IGINHGM) operator are proposed, which can weight all the input arguments and their ordered positions. Further, regarding the situation where the input elements are interdependent, the Induced Generalized Interval Neutrosophic Shapley Hybrid Arithmetic Averaging (IGINSHAA) operator and the Induced Generalized Interval Neutrosophic Shapley Hybrid Geometric Mean (IGINSHGM) operator are proposed, which are extensions of IGINHAA and IGINHGM operators, respectively, and some properties of these given operators are investigated. Furthermore, the interval neutrosophic cross entropy, which is an extension of single-valued neutrosophic cross entropy, is de ned, and the models based on the interval neutrosophic cross entropy and generalized Shapley function are respectively constructed to determine the optimal fuzzy measures on the attribute and ordered sets. Finally, an approach to interval neutrosophic MADM with interactive conditions and incomplete known weight information is proposed based on these given operators, and a practical example is shown to verify the practicality and feasibility of the new approach.