Kamran Shahanaghi

A New Method for Location Selection: A Hybrid Analysis

Site selection is a part of strategic management activities. Location selection decisions involved many factors that may be conflicting in nature. Considering the tangible along with intangible factors in location selection problem, this paper propose a hybrid method of multi criteria decision making (MCDM) that make it possible to select the optimal location that satisfies the decision maker. With the aid of fuzzy AHP, our proposed model considers objective, critical, and subjective factors as the three main common factors in location analysis. The last two factors, critical and subjective factors, are defined by decision makers judgments for more adoption with the real world problems. Besides, analysis of a numerical example and the sensitivity analysis are discussed to clarify the practicability and effectiveness of the proposed model in the facility location problem.

An interactive robust data envelopment analysis model for determining alternative targets in Iranian electricity distribution companies

One of the primary concerns on target setting the electricity distribution companies is the uncertainty on input/output data. In this paper, an interactive robust data envelopment analysis (IRDEA) model is proposed to determine the input and output target values of electricity distribution companies with considering the existence perturbation in data. Target setting is implemented with the uncertain data and the decision maker (DM) can search the envelop frontier and find the targets based on his preference. In order to search the envelop frontier, the paper combine the DEA and multi-objective linear programming method such as STEM. The proposed method of this paper is capable of handling uncertainty in data and finding the target values according to the DMs preferences. To illustrate ability the proposed model, a numerical example is solved. Also, the input and output target values for some of the electricity distribution companies in Iran are reported. The results indicate that the IRDEA model is suitable for target setting based on DMs preferences and with considering uncertain data.

Joint optimisation of price, warranty and recovery planning in remanufacturing of used products under linear and non-linear demand, return and cost functions

We investigate joint optimisation of remanufacturing, pricing and warranty decision-making for end-of-life products. A novel mathematical–statistical model is proposed where decisions involve pricing of returned used products (cores), degree of their remanufacturing, selling price and the warranty period for the final remanufactured products. The virtual age reliability improvement approach is chosen to model the upgrading of the cores to higher quality levels. We consider price- and warranty-dependent demand, price- and age-dependent return, and age-dependent remanufacturing cost in the model development. Both linear and non-linear forms of these functions are investigated. First, under some restrictive conditions of upgrade level and age distribution of received cores, special cases of the problem, which can be solved using a recently developed non-linear optimisation solver, are presented. We also implement a particle swarm optimisation algorithm for the solution of the original problem when all the restrictive assumptions are dropped. Finally, numerical experiments and sensitivity analysis are presented to address different aspects of the model and the solution approaches.

Optimal pricing, lot-sizing and marketing planning in a capacitated and imperfect production system

In the classical economic production quantity (EPQ) problem demand is considered to be known in advance. However, in the real-world, demand of a product is a function of factors such as products price, its quality, and marketing expenditures for promoting the product. Quality level of the product and specifications of the adopted manufacturing process also affect the unit products cost. Therefore, in this paper we consider a profit maximizing firm who wants to jointly determine the optimal lot-sizing, pricing, and marketing decisions along with manufacturing requirements in terms of flexibility and reliability of the process. Geometric programming (GP) technique is proposed to address the resulting nonlinear optimization problem. Using recent advances in optimization techniques we are able to optimally solve the developed, highly nonlinear, mathematical model. Finally, using numerical examples, we illustrate the solution approach and analyze the solution under different conditions.

A new method for plant location selection problem: A fuzzy-AHP approach

This paper presents a multi-criteria decision making (MCDM) methodology for the location problem. In fact, a new mathematical model is proposed with the aid of the fuzzy analytic hierarchy process (FAHP) to make the plant location decision. This model makes possible to select the optimal plant location that is the most preferable for both investors and managers. To illustrate the application of our proposed mathematical model, a numerical example is provided and solved. Finally, the associated results are compared with the results reported in the literature and the informational efficacy of the proposed model is also discussed. The approach of this study is applied to those organizations seeking a site for the new facility or a relocation of existing facilities; furthermore, with the consideration of the relevant factors it could apply for the power plants location selection.

Reducing Outpatient Waiting Time: A Simulation Modeling Approach

Objectives The objective of this study was to provide a model for reducing outpatient waiting time by using simulation. Materials and Methods A simulation model was constructed by using the data of arrival time, service time and flow of 357 patients referred to orthopedic clinic of a general teaching hospital in Tehran. The simulation model was validated before constructing different scenarios. Results In this study 10 scenarios were presented for reducing outpatient waiting time. Patients waiting time was divided into three levels regarding their physicians. These waiting times for all scenarios were computed by simulation model. According to the final scores the 9th scenario was selected as the best way for reducing outpatients waiting time. Conclusions Using the simulation as a decision making tool helps us to decide how we can reduce outpatients waiting time. Comparison of outputs of this scenario and the based- case scenario in simulation model shows that combining physicians work time changing with patients admission time changing (scenario 9) would reduce patient waiting time about 73.09%. Due to dynamic and complex nature of healthcare systems, the application of simulation for the planning, modeling and analysis of these systems has lagged behind traditional manufacturing practices. Rapid growth in health care system expenditures, technology and competition has increased the complexity of health care systems. Simulation is a useful tool for decision making in complex and probable systems.

Scheduling and balancing assembly lines with the task deterioration effect

Abstract Assembly lines are special flowline production systems that are of great importance in industrial mass production. This paper introduces task deterioration into the traditional simple assembly line balancing problem (SALBP). Task deterioration means that a task processed later consumes more time than the same task processed earlier. The aim is to find an optimal assignment and schedule of tasks at workstations so as to minimize the cycle time for a given number of stations, which in the literature is known as SALBP-2. For this purpose, a mathematical model is proposed. As SALBP-2 is classified as NP hard, and as the considered problem becomes more complicated with the additional deterioration parameter, a genetic algorithm is proposed. Finally, several well-known examples are solved to illustrate the approach proposed.

Multi-floor dynamic facility layout: a simulated annealing-based solution

Today’s consumer market demands that manufacturing companies be competitive. Every manufacturing company wants to prevail over the uncertainties of the demand. Being able to meet the demand determines the success of a manufacturing company. A company can look for improvements in planning, manufacturing, distributing, marketing as well as other areas. Within a manufacturing system, the facility layout is one of the most appropriate criteria to reduce costs. Dynamic facility layout, as a problem that considers the flow changes between departments with time, can be extended and used for this problem. In this paper, with consideration of changes in material flow data over time, a mathematical model to formulate the multi-floor dynamic facility layout problem is developed and to solve this model, a simulated annealing-based solution method is proposed. Consideration of dynamic concept and capacity constraint for elevators leads to more compatible model with real world.

Extended VIKOR as a new method for solving Multiple Objective Large-Scale Nonlinear Programming problems

The VIKOR method was introduced as a Multi-Attribute Decision Making (MADM) method to solve discrete decision-making problems with incommensurable and conflicting criteria. This method focuses on ranking and selecting from a set of alternatives based on the particular measure of “closeness” to the “ideal” solution. The multi-criteria measure for compromise ranking is developed from the l – p metric used as an aggregating function in a compromise programming method. In this paper, the VIKOR method is extended to solve Multi-Objective Large-Scale Non-Linear Programming (MOLSNLP) problems with block angular structure. In the proposed approach, the Y-dimensional objective space is reduced into a one-dimensional space by applying the Dantzig-Wolfe decomposition algorithm as well as extending the concepts of VIKOR method for decision-making in continues environment. Finally, a numerical example is given to illustrate and clarify the main results developed in this paper.

Analysis of nonhomogeneous input data using likelihood ratio test

Performing an accurate input analysis in simulation experimentation basically involves selecting the exact probability distributions of random input variables. Frequently in practice these inputs are not constant over time i.e., the underlying distribution may be affected by their time-dependent parameters. In this paper, we propose an approach that can identify whether or not a set of observations follow an identical distribution in a specific period. The model is formulated in a base of likelihood ratio test in the case that input observations come from nonhomogeneous exponentially random variable. Finally, performance comparisons are explored through simulation studies.