International Journal of Industrial Engineering & Production Research

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Planning and design procedure of an open pit mining project just can be started after ultimate pit determination. In the carried out study in this paper it was shown that the most important factor in ultimate pit determination and in consequence in the whole planning and design procedure of an open pit mine is the metal price. Metal price fluctuations in recent years were exaggerated and imposed a high degree of uncertainty to the mine planning procedure while none of the existent algorithms of the pit limit determination consider the metal price uncertainty. Real Option Approach (ROA) is an efficient method of decision making in the condition of uncertainty. This approach usually used for evaluation of defined natural resources projects up to now. This study considering the metal price uncertainty used real option approach to prepare a methodology for determining the Ultimate Pit Limits (UPL). The study was carried out on a non-ferrous metallic cylindrical ore deposit but the achieved methodology can be adjusted for all kinds of the deposits. The achieved methodology was comprehensively described through the examples in a way that can be used by the mine planners.

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Improvement in supply chain performance is one of the major issues in the current world. Lack of coordination in the supply chain is the main drawback of supply chain that many researchers have proposed different methodologies to overcome it. VMI (Vendor-managed inventory) is one of these methodologies that implementing it has some obstacles. This paper proposes new model that is agent-managed SC. This paper is trying to use intelligent agent technology in the supply chain. In this paper supply chain assessment performance measure indicators have been divided into three categories cost, flexibility and customer responsiveness indicators. In the first category we use holding and backordered inventory costs, for second category, bullwhip effect are used and for the last one customer responsiveness indicator has been applied. Bullwhip effect is one of the main phenomena’s that has been tried to reduce it with the agent-based systems.

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The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristic and metaheuristic algorithms have a great influence on their effectiveness and efficiency, parameter tuning and calibration has gained importance. In this paper a new approach for robust parameter tuning of heuristics and metaheuristics is proposed, which is based on a combination of Design of Experiments (DOE), Signal to Noise (S/N) ratio, Shannon entropy, and VIKOR methods, which not only considers the solution quality or the number of fitness function evaluations, but also aims to minimize the running time. In order to evaluate the performance of the suggested approach, a computational analysis has been performed on the Simulated Annealing (SA) and Genetic Algorithms (GA) methods, which have been successfully applied in solving respectively the n-queens and the Uncapacitated Single Allocation Hub Location combinatorial problems. Extensive experimental results showed that by using the presented approach the average number of iterations and the average running time of the SA were respectively improved 12 and 10.2 times compared to the un-tuned SA. Also, the quality of certain solutions was improved in the tuned GA, while the average running time was 2.5 times faster compared to the un-tuned GA.

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Having a comprehensive evaluation model with reliable data is useful to improve performance of supply chain. In this paper, according to the nature of supply chain, a model is presented that able to evaluate the performance of the supply chain by a network data envelopment analysis model and by using the financial, intellectual capital (knowledge base), collaboration and responsiveness factors of the supply chain. At the first step, indicators were determined and explained by explanatory Factor Analysis. Then, Network Data Envelopment Analysis (NDEA) model was used. This paper is the result of research related to supply chain of pharmaceutical companies in Tehran Stock Exchange and 115 experts and senior executives have been questioned as sample. The results showed that responsiveness latent variable had the highest correlation with supply chain performance and collaborative, financial and intellectual capital (knowledge base) latent variables were respectively after that. Four of the twenty eight supply chains which were studied obtained 1 as the highest performance rate and the lowest observed performance was 0.43.

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Flexible and dynamic supply chain network design problem has been studied by many researchers during past years. Since integration of short-term and long-term decisions in strategic planning leads to more reliable plans, in this paper a multi-objective model for a sustainable closed-loop supply chain network design problem is proposed. The planning horizon of this model contains multiple strategic periods so that the structure of supply chain can be changed dynamically during the planning horizon. Furthermore, in order to have an integrated design, several short-term decisions are considered besides strategic network design decision. One of these short-term decisions is determining selling price and buying price in the forward and reverse logistics of supply chain, respectively. Finally, an augmented e-constraint method is used to transform the problem to a single-objective model and an imperialist competitive algorithm is presented to solve large scale problems. The results’ analysis indicates the efficiency of the proposed model for the integrated and dynamic supply chain network design problem. 

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The aim of this study is to present a new method to predict project time and cost under uncertainty. Assuming that what happens in projects implementation which is expressed in the form of Earned Value Management (EVM) indicators is primarily related to the nature of randomness or unreliability, in this study, by using Monte Carlo simulation, and assuming a specific distribution for the time and cost of project activities, a significant number of predicting scenarios will be simulated. According to the data, an artificial neural network is used as efficient data mining methods to estimate the project time and cost at completion.

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In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy numbers, a similarity criterion based on the intersection region of the fuzzy numbers is used.  The performance of the suggested clustering method has been experimented on both benchmark and artificial datasets. These datasets are used in the fuzzy form. The experiential results represent that the suggested clustering method with fuzzy cluster centers can cluster triangular fuzzy datasets like other standard uncertain data clustering methods. Experimental results demonstrate that, in almost all datasets, the proposed clustering method provides better results in accuracy when compared to Uncertain K-Means and Uncertain K-medoids algorithms.

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This paper presents a model for minimizing the makespan in the flow shop scheduling problem. Due to the impact of increased workloads, flow shops are becoming more popular and widely used in industries. To solve the challenge of minimizing makespan, a Hybrid-Heuristic-Metaheuristic-Genetic-Algorithm (HHMGA) is proposed. The proposed HHMGA algorithm is tested using the simulation software and demonstrated with steel industry data. The results are compared with those of the best available flow shop problem algorithms such as Palmer’s slope index, Campbell-Dudek-Smith (CDS), Nawaz-Enscore-Ham (NEH), genetic algorithm (GA) and particle swarm optimization (PSO). According to empirical results and relative differences from the lower bound, the proposed technique outperforms the three heuristics and two metaheuristics algorithms in three of six cases, while the remaining three produce the same results as the NEH heuristic. In comparison to the steel industry's regular job scheduling technique, the simulation model based on HHMGA can save 4642 hours. It was discovered that the suggested model enhanced the job sequence based on the makespan requirements.

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