International Journal of Industrial Engineering & Production Research

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  The advent of e-commerce has prompted many manufacturers to redesign their traditional channel structure by engaging in direct sales. In this paper, we present a dual channel inventory model based on queuing theory in a manufacturer-retailer supply chain, consisting of a traditional retail channel and a direct channel which stocks are kept in both upper and lower echelon. The system receives stochastic demand from the both channel which each channel has an independent demand arrival rate. A lost-sales model which no backorder is allowed is supposed. The replenishment lead times are assumed independent exponential random variables for both warehouse and the retail store. Under the replenishment inventory policy, the inventory position is kept constant at a base-stock level. To analyze the chain performance, an objective function included holding and lost sales costs is defined. At the end, a proposed algorithm named, Best Neighborhood (BN) is used to find a good solution for inventory and the results are compared with Simulated Annealing (SA) solutions.

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  Although control charts are very common to monitoring process changes, they usually do not indicate the real time of the changes. Identifying the real time of the process changes is known as change-point estimation problem. There are a number of change point models in the literature however most of the existing approaches are dedicated to normal processes. In this paper we propose a novel approach based on clustering techniques to estimate Shewhart control chart change-point when a sustained shift is occurrs in the process mean. For this purpose we devise a new clustering mechanism, a new similarity measure and a new objective function. The proposed approach is not only capable of detecting process change-points, but also automatically estimates the true values of the out-of-control parameters of the process. We also compare the performance of the proposed approach with existing methods.

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  In this paper, a single machine sequencing problem is considered in order to find the sequence of jobs minimizing the sum of the maximum earliness and tardiness with idle times (n/1/I/ET_max). Due to the time complexity function, this sequencing problem belongs to a class of NP-hard ones. Thus, a special design of a simulated annealing (SA) method is applied to solve such a hard problem. To compare the associated results, a branch-and-bound (B&B) method is designed and the upper/lower limits are also introduced in this method. To show the effectiveness of these methods, a number of different types of problems are generated and then solved. Based on the results of the test problems, the proposed SA has a small error, and computational time for achieving the best result is very small.

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  In this paper Semi-Markov models are used to forecast the triple dimensions of next earthquake occurrences. Each earthquake can be investigated in three dimensions including temporal, spatial and magnitude. Semi-Markov models can be used for earthquake forecasting in each arbitrary area and each area can be divided into several zones. In Semi-Markov models each zone can be considered as a state of proposed Semi-Markov model. At first proposed Semi-Markov model is explained to forecast the three mentioned dimensions of next earthquake occurrences. Next, a zoning method is introduced and several algorithms for the validation of the proposed method are also described to obtain the errors of this method.

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Cutoff grade is a grade used to assign a destination label to a parcel of material. The optimal cutoff grades depend on all the salient technological features of mining, such as the capacity of extraction and of milling, the geometry and geology of the orebody, and the optimal grade of concentrate to send to the smelter. The main objective of each optimization of mining operation is to maximize the net present value of the whole mining project, but this approach without consideration of environmental issues during planning is not really an optimum design. Lane formulation among the all presented algorithms is the most commonly used method for optimization of cutoff grades. All presented models for optimum cutoff grades are ore-oriented and in none of them the costs related to waste materials which must to be minimized during the mine life are considered. In this paper, after comparison of traditional and modern approaches for cutoff grade optimization in open pit mines, a real case study is presented and discussed to ensure optimality of the cutoff grades optimization process.

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The Outburst can be defined as a sudden release of coal and rock accompanied by large quantities of gas into the underground coal mine workings which represents a major hazard in underground coal mines. Gas drainage has been proven to be successful in reducing outburst hazards by decreasing the in-situ gas pressure. One of aspect of gas drainage from coal seams is coal matrix volume changes. Current study is primarily concerned with experimental studies related to coal volume change (coal shrinkage) under various gas types and pressures. Two types of tests were conducted on each sample, the adsorption test for coal swelling and the desorption test for coal shrinkage. The gases used in the study were CH₄, CO₂, CH₄/CO₂ (50-50% volume), and N₂. In this research, tests were conducted with respect to volumetric change behavior in different gases and their corresponding comparative results were presented.

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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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It was suggested in 2004 by some researchers that it might be possible to improve production systems performance by applying the first and second laws of thermodynamics to reduce system entropy. Then these laws were used to modify the economic order quantity (EOQ) model to derive an equivalent entropic order quantity (EnOQ). Moreover the political instability or uncertainty of a country (as well as the whole world) leads to a much more unstable situation in the present world economy. Thus, changes in inflation take place, and it is needed to consider uncertain inflation rate. In this paper we extend the EnoQ model by considering deteriorating items with imperfect quality and price dependent demand. We also assume fuzzy inflation and discount rates.‌ A mathematical model is developed to determine the number of cycles that maximizes the present value of total revenue in a finite planning horizon. The fuzzified model for inflation and discount rate is formulated and solved by two methods: signed distance and fuzzy numbers ranking. Numerical examples are presented and results are discussed. Results show that the number of cycles decreases in fuzzy inflationary conditions. They also illustrate that defuzzification method results in more cycles than fuzzy method.

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Generally ordering policies are done by two methods, including fix order quantity (FOQ) and fix order period (FOP). These methods are static and either the quantity of ordering or the procedure of ordering is fixing in throughout time horizon. In real environments, demand is varying in any period and may be considered as uncertainty. When demand is variable in any period, the traditional and static ordering policies with fix re-order points cannot be efficient. On the other hand, sometimes in real environments some costs may not be well-known or precise. Some costs such as holding cost, ordering cost and so on. Therefore, using the cost based inventory models may not be helpful. In this paper, a model is developed which can be used in the cases of stochastic and irregular demand, and also unknown costs. Also some attributes consisting of expected positive inventory level, expected negative inventory level and inventory confidence level are considered as objective functions instead the objective function of total inventory cost. A numerical example is also presented for more explanation.

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In this paper, a new distribution that is highly applicable in the fields of reliability and economics is introduced. Also the parameters of this distribution is estimated using two methods of Maximum Likelihood and Bayes with two prior distributions Weibull and Uniform, and these two methods are compared using Monte-Carlo simulation. Finally, this new model is fit on the real data(with the failure time of 84 aircraft) and some of comparative criteria are calculated to confirm superiority of the proposed model compared to other models.

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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.
 

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We study the problem of order acceptance, scheduling and pricing (OASP) in parallel machine environment. Each order is characterized by due date, release date, deadline, controllable processing time, sequence dependent set up time and price in MTO system. We present a MILP formulation to maximize the net profit. Then under joint optimization approach, the pricing decisions set for unrelated parallel machine environment. The results show that the basic developed problem can solve the scheduling decisions based on different levels of products’ priced. Thus the problem solves these two categories of decisions simultaneously. Moreover, the changes of accepted orders in pricing levels can be analyzed regarding its dependency to price elasticity of items for future research.

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Customization is increasing so build-to-order systems are given more attention to researchers and practitioners. This paper presents a new build-to-order supply chain model with multiple objectives that minimize the total cost and lead time, and maximize the quality level.  The model is first formulated in a deterministic condition, and then investigated the uncertainty of the cost and quality by the stochastic programming based on the scenario. The return policy and outsourcing are the new issues in a build-to-order supply chain by considering the cost and inventory. A Benders decomposition algorithm is used to solve and validate the model. Finally, the related results are analyzed and compared with the results obtained by CPLEX for deterministic and stochastic models.

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In many real scheduling situations, it is necessary to deal with the worker assignment and job scheduling together. However, in traditional scheduling problems, only the machine is assumed to be a constraint and there isn’t any constraint about workers. This assumption could be due to the lower cost of workers compared to machines or the complexity of workers' assignment problems. This research proposes a flexible flow shop scheduling problem with two simultaneous issues: finding the best worker assignment, and solving the corresponding scheduling problem. We present a mathematical model that extends flexible flow shop scheduling problem to admit the worker assignment. Due to the NP-hardness of the research problem, two approximation approaches based on particle swarm optimization, named PSO and SPSO, are applied to minimize the makespan. The experimental results show that the proposed algorithms can efficiently minimize the makespan but the SPSO generates better solutions especially for large-size problems.

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The CPM (critical path method) technique is to search out the longest path to try and do activities, so as to compress and cut back the time it takes for a project, which finally ends up inside the creation of an identical and intensive network of activities inside the targeted work. This formal random simulation study has been recognized as a remedy for the shortcomings that are inherent to the classic critical path technique (CPM) project analysis. Considering the importance of time, the cost of activities within the network, and rising the calculation of the critical path during this study, Critical Path technique has been applied to improve critical routing intelligence. This study, by simulating and analyzing dragonfly's splotched and regular patterns, has obtained the precise algorithm of attainable paths with the smallest amount cost and time for every activity. This has been done to put down the restrictions and enhance the computing potency of classic CPM analysis. The simulation results of using Dragonfly Algorithm (DA) in CPM, show the longest path in shortest time with the lowest cost. This new answer to CPM network analysis can provide project management with a convenient tool.
 

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Inventory management plays a vital role in attaining the desired service level and prevents excess capital from being tied up in the form of dead stock. This paper presents a framework to effectively determine the items subject to obsolescence in an automotive spare parts warehouse. The inventory management techniques are applied to minimize the costs and a framework is proposed based on ABC-XYZ and FSN analysis to prioritize the spare parts based on their criticality. Further, the importance of items in the warehouse is carried out to eliminate the dead stock. The ABC classification findings reveal that A-class items accounted for 10.39% and hold the highest inventory value grouping. XYZ classification concludes that much priority should be given to the management of 52.7% of items under the Z category as the demand trend of these items is highly fluctuating. The N category items have no demand in recent times and need immediate attention, thereby preventing further unnecessary procurement. Thus, based on the ABC-XYZ and FSN analysis, the non-critical items, i.e., the non-moving items having fluctuating demand, are sorted out.

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