International Journal of Industrial Engineering & Production Research

  In the classical versions of “Best Choice Problem”, the sequence of offers is a random sample from a single known distribution. We present an extension of this problem in which the sequential offers are random variables but from multiple independent distributions. Each distribution function represents a class of investment or offers. Offers appear without any specified order. The objective is to accept the best offer. After observing each offer, the decision maker has to accept or reject it. The rejected offers cannot be recalled again. In this paper, we consider both cases of known and unknown parameters of the distribution function of the class of next offer. Two optimality criteria are considered, maximizing the expected value of the accepted offer or the probability of obtaining the best offer. We develop stochastic dynamic programming models for several possible problems, depending on the assumptions. A monotone case optimal policy for both criteria is proved. We also show that the optimal policy of a mixed sequence is similar to the one in which offers are from a single density .

  Managing products’ end-of-life and recovery of used products is gaining significant importance during last years. Therefore, managing the reverse flow of products can be an important potential for winning consumers in future competitive markets. In this context, establishing reverse logistics networks is becoming a main problem in reverse supply chains. Genetic Algorithm (GA) is utilized to solve the proposed NP-hard problem and find the best possible design for different facilities. In order to test the applicability of proposed GA, we suppose a tire reverse logistic case and solve the problem. The results show that the least cost will be achieved by using the free space of distribution centers and integrating collection and inspection centers within them. In addition, we suggest using hybrid algorithm in future allocation problems to obtain best solutions .

This paper offers an approach that could be useful for diverse types of layout problems or even area allocation problems. By this approach there is no need to large number of discrete variables and only by few continues variables large-scale layout problems could be solved in polynomial time. This is resulted from dividing area into discrete and continuous dimensions. Also defining decision variables as starting and finishing point of departments in area makes it possible to model layout problem so. This paper also provides new technique that models basic constraints of layout problems.

This paper considers a bi-objective mathematical model for locations of landfills, transfer stations and material recovery facilities (MRFs) in order to serve the entire regions and simultaneously identify the capacities of landfills. This is a mixed-integer programming (MIP) model, whose objectives are to minimize the total cost and pollution simultaneously. To validate the model, a numerical example is solved an augmented ε-constraint method and the associated computational results are presented to show the number of solid waste facilities and location of sites for solid waste facilities.

Currently, due to increased competition in the services and manufacturing, many companies are trying to lower price and good quality products offer to the market. In this paper, the multi-criteria decision-making techniques to evaluate and select the best supplier from among the existing suppliers. The first, hierarchical structure for selecting suppliers of raw materials used and the analytic hierarchy process to obtain the relative importance of quantitative and qualitative criteria related to green supply chain is applied.  Then, a fuzzy TOPSIS technique any raw material suppliers is ranked according to the relevant criteria. Finally, with regard to the weight of suppliers and demand of raw material and resource constraints by a multi-objective mathematical model, optimum order is determined. The objectives are to minimize the total cost, maximize amount of purchases of desirable suppliers and minimize of raw materials required are not provide. The proposed method in a case study used Food Company and the relevant results are expressed.

Hubs are critical elements of transportation networks. Location of hubs and allocation of demands to them are of high importance in the network design. The most important purpose of these models is to minimize the cost, but path reliability is also another important factor which can influence the location of hubs. In this paper, we propose a P-center hub location model with full interconnection among hubs while there are different paths between origins and destinations. The purpose of the model is to determine the reliable path with lower cost. Unlike the prior studies, the number of hubs in the path is not limited to two hubs. The presented model in this paper is bi-objective and includes cost and reliability to determine the best locations for hubs, allocation of the demands to hubs and the best path. In order to illustrate our model, a numerical example is presented and solved using the Cuckoo Optimization Algorithm.

The eternal need for humans' blood as a critical commodity makes the healthcare systems attempt to provide efficient blood supply chains (BSCs) by which the requirements are satisfied at the maximum level. To have an efficient supply of blood, an appropriate planning for blood supply chain is a challenge which requires more attention. In this paper, we address a mixed integer linear programming model for blood supply chain network design (BSCND) with the need for making both strategic and tactical decisions throughout a multiple planning periods. A robust programming approach is devised to deal with inherent randomness in parameters data of the model. To illustrate the usefulness of the model as well as its solution approach, it is tested into a set of numerical examples, and the sensitivity analyses are conducted. Finally, we employ two criteria: the mean and standard deviation of constraint violations under a number of random realizations to evaluate the performance of both the proposed robust and deterministic models. The results imply the domination of robust approach over the deterministic one.

Recently, sustainable supply chain management (SSCM) has become one of the important subjects in the industry and academia. Supplier selection, as a strategic decision, plays a significant role in SSCM. Researchers use different multi-criteria decision making (MCDM) methods to evaluate and select sustainable suppliers. In the previous studies, evaluation is solely based on the desirable features of suppliers and their risks are neglected. Therefore, current research uses failure mode and effects analysis (FMEA) as a risk analysis technique to consider supplier's risk in combination with the MCDM method. Practically, this study operated in two main stages. In the first stage, the score of the suppliers obtains by integration Fuzzy MOORA and FMEA. In the second stage, the output of the previous stage used as input parameters in developed mix-integer linear programming to select suppliers and order optimum quantity. Finally, to demonstrate the effectiveness of the proposed approach, a case study in a chemical industry and sensitivity analysis is presented.  

In this article, a multi objective model is presented to select and allocate the order to suppliers in uncertainty condition and in a multi source, multi customer and multiproduct case in a multi period state at two levels of supply chain. Objective functions considered in this study as the measures to evaluate suppliers are cost including purchase, transportation and ordering costs, timely delivering, shipment quality or wastages which are amongst major quality aspects, partial and general coverage of suppliers in respect of distance and finally suppliers weights making the products orders amount more realistic. The major limitations are price discount for products by suppliers which are calculated using signal function. In addition, suppliers weights in the fifth objective function is calculated using fuzzy Topsis technique. Lateness and wastes parameters in this model are considered as uncertain and random triangular fuzzy number. Finally the multi objective model is solved using two multi objective algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Particle Swarm Optimization (PSO) and the results are analyzed using quantitative criteria Taguchi technique was used to regulate the parameters of two algorithms. 

This paper addresses supplier selection and order allocation problem while considering the losses arising from the risk of sanction in Iran’s Oil & Gas Drilling Industry. In the proposed study, two general classes of items and two different classes of suppliers are considered. AHP is first used to rank the potential suppliers. Then, a multi-objective linear programming model is proposed to determine the best suppliers and their allocated orders. A numerical example is presented to demonstrate the applicability of the proposed model.

In the new strategies of purchasing and production, suppliers play a key role in achieving of competitive capability for big companies. The selection of suitable suppliers is a critical component of this strategy. The problem of allocation order to supplier is a multi-objective problem that includes fuzzy parameters and also suppliers usually consider discount due to different levels of purchase amount. Since there is not a multi-objective fuzzy model for allocating orders in the literature that consider discount and shortfall simultaneously in the planning horizon of multi-products, this research proposes a new model includes minimization of costs, delays and the percentage of defective parts as objective functions. Jimenez method is used to defuzzy price, demand, delay and percentage of defective parts. Since the model is NP-hard, the two metaheuristic algorithms, NSGAII and MOPSO that their parameters were set by the Taguchi method has been developed. According to the results of numerical problems, the proposed algorithms can provide a good approximation of the optimal solutions for intended objectives.