Journal of Industrial and Systems Engineering
Volume:11 Issue: 3, Summer 2018

Mixed-integer polynomial programming (MIPP) problems are one class of mixed-integer nonlinear programming (MINLP) problems where objective function and constraints are restricted to the polynomial functions. Although the MINLP problem is NP-hard, in special cases such as MIPP problems, an efficient algorithm can be extended to solve it. In this research, we propose an algorithm for global optimization of the MIPP problems, in which, first, the MIPP is reformulated as a multi-parametric programming by considering integer variables as parameters. Then, the optimality conditions of resulting parametric programming give a parametric polynomial equations system (PES) that is solved analytically by Grobner Bases (GB) theory. After solving PES, the parametric optimal solution as a function of the relaxed integer variables is obtained. A simple discrete optimization problem is resulted for any non-imaginary parametric solution of PES, which the global optimum solution of MIPP is determined by comparing their optimal value. Some numerical examples are provided to clarify proposed algorithm and extend it for solving the MINLP problems. Finally, a performance analysis is conducted to demonstrate the practical efficiency of the proposed method.

Health service management plays a crucial role in human life. Blood related operations are considered as one of the important components of the health services. This paper presents a bi-objective mixed integer linear programming model for dynamic location-allocation of blood facilities that integrates strategic and tactical decisions. Due to the epistemic uncertain nature of strategic decisions, in order to cope with the inherent uncertainties, a robust possibilistic programming approach is applied to the proposed model. Finally, to test the applicability of the proposed model, sensitivity analysis and some numerical examples are being proposed.

In humanitarian relief operations (HRO), due to the excessive number of relief organizations, multiple organizational coordination is a demanding and complicated task. Considering such a problem, this paper proposes a two-phase mechanism to coordinate multiple heterogeneous relief organizations in a decentralized HRO logistics network. To address such a problem, first a bi-level mixed integer linear model under the demand and supply uncertainties is developed, and then a capacity sharing-based-coordination mechanism is proposed. To solve the model for large-scale instances in an acceptable computation time, a fuzzy Kth-Best algorithm is developed. Finally, to validate the proposed mathematical model, we compare it to a centralized relief logistics model considering a computational experiment on the earthquake in Tehran, Iran. Results show that the proposed coordinated model reduced the amount of shortage and wastage in Tehran compared to the traditional centralized model employed previously by Tehran Disaster Mitigation and Management Organization.

Nowadays, cross docking plays an important role in the supply chain networks especially in transportation systems. According to the cross dock system advantages such as reducing transportation costs, lead times, and inventories, scheduling in a cross-dock center would be more complicated by increasing the number of suppliers, customers and product types. Considering the cross dock limited capacities (equipment, storage space, work force, and etc.), sometimes it is not possible to deliver the supplier's products to customers in the right times. Thus, suppliers pay more tardiness penalties for scheduling problems in the cross dock centers. The current paper aims to propose an integer programming mathematical model that enables the suppliers to choose appropriate transportation paths according to amount of products delivered and moreover considering cross docks scheduling time constraints. In fact, cross dock centers present the list of outbound trucks departure times and suppliers reserve certain capacity based on their tardiness, transportation and inventory holding costs. Moreover, in this paper, a Lightening search algorithm (LSA) is developed to solve the proposed model. Additionally, to develop the solving procedure, a heuristic algorithm is proposed and compared with the LSA.

Portfolio optimization is one of the most important issues for effective and economic investment. There is plenty of research in the literature addressing this issue. Most of these pieces of research attempt to make the Markowitz’s primary portfolio selection model more realistic or seek to solve the model for obtaining fairly optimum portfolios. An efficient frontier in the typical portfolio selection problem provides an illustrative way to express the tradeoffs between return and risk. With regard to the modern portfolio theory as introduced by Markowitz, returns are usually extracted from past data. Therefore our purpose in this paper is to incorporate future returns scenarios in the investment decision process. In order to representative points on the efficient frontier, the minimax regret portfolio is calculated, on the basis of the aforementioned scenarios. In this way, the areas of the efficient frontier that are more robust than others are identified. The main contribution in this paper is related to the extension of the conventional minimax regret criterion formulation, in multi-objective programming problems. The validity of the proposed approach is verified through an empirical testing application on the top 75 companies of Tehran Stock Exchange Market in 2017.

This paper proposes the interval network data envelopment analysis (INDEA) approach under constant return to scale (CRS) and variable return to scale (VRS) assumptions which can assess the performance of investment companies (ICs) by considering uncertainty and internal structure. The presented approach of the paper is capable to model two-stage efficiency with intermediate measures in a single implementation. Finally, a real-life case study from Tehran stock exchange (TSE) is implemented to demonstrate applicability and exhibit the efficiency and effectiveness of the presented INDEA approach for performance measurement, ranking and classification of ICs in the presence of uncertain data.

Wildfires are of the forest-related disasters caused by inhumane factors. Spreading of these fires is due to the increase of the density of flammable plants. Two important approaches to prevent this occurrence are fuel treatment and fire suppression resources preparedness. In this paper, a mixed integer programming model is proposed based on the covering location and assignment problems which seeks fuel reduction over a multi period of time in a forest area, along with fire suppression resources preparedness and dispatch of firefighters in the last period. One of the forest areas in northern Iran was considered to fuel treatment and fire suppression resources preparedness and assuming the growth of vegetation species varies in different parts, the region is separated into distinct and discrete network points. Obtained results of the model solving show an increase in the vegetation cover volume and reduction of the risk of fire.

The quality of the road pavement has always been one of the major concerns for governments around the world. Cracks in the asphalt are one of the most common road tensions that generally threaten the safety of roads and highways. In recent years, automated inspection methods such as image and video processing have been considered due to the high cost and error of manual methods. For this purpose, different image processing techniques and classification methods have been developed by many researchers. In this study, we propose an integrated model includes a heuristic image segmentation technique for crack detection. Furthermore, the accuracy of various classification models such as KNN, decision tree and SVM will be compared. Finally, 5-fold cross validation shows that Subspace KNN method will be more accurate than other classification models which are used in this study. On the other hand, we also simulate the depth and density of different segment of crack by utilizing density matrix values.

The efficient management of nursing personnel is of vital importance in a hospital’s environment comprising a vast share of the hospital’s operational costs. In the nurse scheduling problem (NSP), the target is to allocate shifts to the nurses in order to satisfy the hospital’s demand during the planning horizon by considering different objective functions. This paper presents a multi-objective mathematical model with the aims of reducing the costs that the hospital is supposed to pay, maximizing nurses’ satisfaction, and balancing the workload of nurses. Nurses’ preferences for working in particular shifts and weekend off are considered in this model. In order to solve the model, a two-step procedure is used. In the first step, to show the applicability of the proposed model, a real case study is provided and is solved using augmented ε-constraint method. Then, the best solution is selected among Pareto solutions using data analysis envelopment (DEA). Finally, several analyses are performed to develop managerial implications.

In this paper, a bi-objective model is proposed for designing a pre-positioning network of pharmaceutical supplies considering their limited shelf-life. The presented model aims to consider both cost efficiency and responsiveness by minimization of total cost and the maximum amount of unmet demand under uncertainties in demand and supply sides’ data. Moreover, for effective distribution of relief supplies in the post-disaster phase, multiple coverage levels are incorporated. The bi-objective model is solved by the well-known ε-constraint method and some numerical experiments are developed to explore the applicability of the presented model. The results indicate the impact of considering perishability of the pharmaceutical supplies as well as considering multiple coverage levels for satisfying demand on the total cost and responsiveness of the relief network.

This paper proposes a bi-objective reliable supply chain network design that immunizes the network against different sources of uncertainties. In this regard, scenario based stochastic programming method is applied to model different disruption scenarios affecting accurate performance of network stages. Also, reliable and unreliable facilities are suggested for lessening vulnerability of network against disruptions. To maximize responsiveness of the network, maximal covering concept is applied aside with a new facility reliability measuring method. To achieve to the noted aims, total expected costs of network design is minimized as well as maximizing responsiveness of facilities. Also, a possibilistic flexible programming method is suggested to cope with uncertainty of parameters and flexibility of constraints. The proposed method is capable of controlling risk-aversion of output decisions based on opinion company decision makers. Finally, the model is solved based on the derived from real case study of tire manufacturing and output results are analysed that show applicability and effectiveness of the extended network design model.

This research considers a fresh-product supply chain consisting of a single-buyer, a single-supplier for deteriorating products where the market demand is dependent on the retail price, fresh rate, and remaining rate. Firstly, in a competitive model, the primary decision variables (i.e., the supplier's wholesale price and preservation-technology investment and also buyer's order quantity and retail price) are determined. Afterward, a centralized model is developed to optimize the whole system so that all the players of supply chain reach equilibrium. Then, a combined incentive mechanism based on revenue and preservation-technology investment sharingis designed to motivate the members to participate in the centralized model. Finally, the proposed models are accreditedwith the data set of a real-life case study. The results indicate that the designed contract is capable of coordinating the fresh-product supply chain under a wide variety of sharing rate. Moreover, the transactions in the centralized mode will have less Lost-of-Profit than the decentralized ones while it also has a higher whole channel's profit.

This paper presents a bi-objective MIP model for the flexible flow shop scheduling problem (FFSP) in which the total weighted tardiness and the energy consumption are minimized simultaneously. In addition to considering unrelated machines at each stage, the set-up times are supposed to be sequence- and machine-dependent, and it is assumed that jobs have different release times. Two Taguchi-based-tuned algorithms: (i) non-dominated sorting genetic algorithm II (NSGA-II), and (ii) non-dominated ranked genetic algorithm (NRGA) are applied to solve themodel. Six numerical examples with different sizes (small, medium, and large) are used to demonstrate the applicability and to exhibit the efficacy of the algorithms. The results show that the NRGA outperforms significantly the NSGA-II in the performance metrics for all six numerical examples.

Lung cancer is one of the most dangerous diseases that cause a large number of deaths. Early detection and analysis can be very helpful for successful treatment. Image segmentation plays a key role in the early detection and diagnosis of lung cancer. K-means algorithm and classic PSO clustering are the most common methods for segmentation that have poor outputs. In this article, we propose a new that of K-means and classic PSO clustering. The obtained results show that the new PSO clustering has better results as compared to the other methods. Comparison between the proposed method and classic PSO, in terms of fitness function and convergence of fitness function indicate that the proposed method is more effective in detecting lung cancer.

The supply chain network design has a crucial role in decreasing total transportation cost. On the other hand, the value of some effective parameters, such as established facilities cost and demand, often is uncertain. In this regard, a multi-objective multi-commodity scenario-based supply chain model in the presence of disaster is proposed. Minimizing the probability of travel time exceeded at a pre-specific threshold value in different scenarios is defined as the objective function. In addition, failure probability and budget constraint can be considered as other innovations of this paper. A multi-objective vibration damping optimization (MOVDO) algorithm is developed to solve large-scale instances of the presented problem. The obtained results show that a 75-node network can be solved.