Scientia Iranica
Volume:21 Issue: 3, 2014

This paper addresses the Simple Assembly Line Balancing Problem of type II (SALBP-II), with Simultaneous e ects of deterioration and learning in which there are sequence-independent setup times relating to each task. In many real industrial environments, although the actual task processing times are de ned as a function of their starting times due to deterioration e ects, workstations improve continuously as a result of repeating the same activities by worker(s) or machine(s). In this paper, a mathematical model is developed for this novel problem, attempting to minimize the cycle time for a given number of workstations. In addition to the balancing of the assembly line, the developed model presents the execution scheduling of tasks assigned to each workstation. Moreover, a hybrid meta-heuristic method is proposed to solve such an NP-hard problem. This robust and simply structured solution approach uses the tabu search within the Variable Neighbourhood Search (VNS/TS). The computational experiments and comparison with a Di erential Evolution Algorithm (DEA) re ect the high eciency of our proposed algorithm for a number of well-known instances.

The feasible dierential problem is solved using a tension rectication algorithm. In this paper, we present a scaling implementation of a tension rectication algorithm. Let n; m;U denote the number of nodes, number of arcs, and maximum arc capacity value of an arc, respectively. Our implementation runs in O(mnlog U), which is O(mnlog n) under the similarity assumption. The tension rectication algorithm runs in O(m2) time, so, our implementation is an improvement if n log n < m. Another merit of our algorithm is that, in cases where the feasible dierential problem does not have a solution, it presents some information that is useful to the modeler in estimating the maximum cost of adjusting the network.

This paper develops a model for illustrating how a manufacturer can use his initiative to organize the retailers when they take decisions as independent actors. The candidate retailers are able to distribute product over geographical dispersed markets with stochastic demands. Each manufacturer’s decision about selecting a set of retailers results in a unique distribution network design. Taking transportation and inventory costs into account, each candidate retailer determines order quantity to satisfy markets’ demands, while the manufacturer specifies wholesale price pursuing uniform or retailer-specific pricing polices depending on trade legislations. In this single period problem and under mild assumptions on demands distributions, we show a non-cooperative equilibrium exists for each distribution network design. We also propose distinctive coordination mechanisms corresponding to the pricing policies. Using these mechanisms in each design of the distribution network, profits of the manufacturer and retailers are better off compared to the non-cooperative situation. Lastly, numerical examples presented in the paper, comprising the sensitivity analysis of some key parameters, seek to compare the results of different distribution network designs under various pricing policies and yield some applicable managerial insights.

In previous investigations in the field of flexible flow shop scheduling problem, rework probability for operations was ignored. As these kinds of problems are NP-hard, so we presented an enhanced invasive weed optimization (EIWO) meta-heuristic algorithm in order to solve the addressed problem with probable rework times, transportation times with a conveyor between two subsequent stages, different ready times and anticipatory sequence dependent setup times. The optimization criterion is to minimize makespan. Although invasive weed optimization (IWO) is an efficient algorithm and has been attracted by many researchers recently, but to increase the capability of IWO, we added mutation operation to enhance the exploration in order to prevent sticking in local optimum. In addition, affinity function is embedded to obstruct premature convergence. With these changes, we balance exploration and exploitation of IWO. Since, the performance of our proposed algorithm depends on parameters values, hence, we applied a popular design of experimental methodology called response surface method (RSM). To evaluate the proposed algorithm, first some random test problems were generated and compared with three benchmark algorithms. The related results were analyzed by statistical tools. The experimental results and statistical analyses demonstrated that the proposed EIWO was effective for the problem.

 This paper considers a flexible assembly job shop scheduling problem with the sequence dependent setup times, and its objective is minimization of makespan which integrates the process planning and scheduling activities. This is a combinatorial optimization problem with substantially large solution space, suggesting that it is highly difficult to find the best solution with the exact search method. In this paper a particle swarm optimization based algorithm is proposed which applies a novel solution representation method to fit the continuous nature of algorithm in the discrete modeled problem. The numerical experiments also have been performed to demonstrate the effectiveness of the proposed algorithm.

Nowadays Sourcing problemhas become more challenging for supply chain members. Different types of sourcing for different market conditions are presented in the literature. In this paper an option contract as an efficient tool for sourcing is developed in a multi-period setting in which the price and demand follow two stochastic processes. Sourcing decision is analyzed from risk neutral and risk averse decision maker’s point of view. This paper applies the stochastic programming approach to model the presented option contract based on price and demand uncertainties. Next, using CVaR as a coherent risk measure, the effects of risk on sourcing problem are studied. By numerical example, using the presented efficient frontier, the simulation results of our developed models show that the decision maker can make a trade-off between risk and cost associated with the sourcing problem. The paper also performs sensitivity analysis in order to demonstrate the effects of change in cost parameter on results of our option model.

A pro le is a relation between one response variable and one or more explanatory variables that represent the quality of a product or the performance of a process. Process Capability Indices (PCI) are measured to evaluate processes in producing conforming products. All existing methods that measure process capability indices in a simple linear pro le consider response variables at some levels of explanatory variable and ignore all ranges of x-values. In this paper, a functional approach is proposed to measure the process capability index of simple linear pro les in all ranges of explanatory variable. This new approach follows the traditional de nition of process capability indices and leads to their accurate values for a simple linear pro le. The functional approach uses a reference pro le, functional speci cation limits and functional natural tolerance limits to present a functional form of process capability indices. This functional form results in measuring the process capability at each level of the explanatory variable in a simple linear pro le, as well as the unique value of a process capability index for a simple linear pro le. A comparison study using a non-conforming proportion method shows the better performance of functional process capability indices in measuring the process capability in a simple linear pro le.

Choice modelling is valuable for understanding and predicting customer behaviour. This study introduces the Group Method of Data Handling (GMDH) into choice modelling and applies this new technique to model consumer choice in the longdistance communication market. When we compare the GMDH with the Arti cial Neural Network (ANN) and logit models, the results show that the new model provides better predictions of customer choice than the ANN and logit models. In addition, the new model can identify the important explanatory variables that a ect customer choice, and reveal how the variables a ect this choice, which cannot be directly accomplished using the ANN model. This advantage will help rms to better analyse the behaviour of their customers and, thereby, develop suitable marketing strategies.

In this study a two-echelon single-vendor supply chain is selected to do a cost-based comparison between short-term performances of vendor managed inventory (VMI) and retailer managed inventory (RMI) while the inventory cost includes ordering and storing expenses; rate of consumption and price of good are constant; rate of production and pace of transportation are infinite and shortage is not allowed. The paper after a comprehensive literature review is followed by three cases of single retailer, -retailer and two-retailer chains. Unlike the second case, in the first case, VMI shows an absolute superiority to RMI and this is the reason of devising the third case in which a deeper analysis, including a typical performance assessment system for two-retailer chains, is done. The third case reveals that although VMI is not always the better choice but in most of conditions it can be chosen as the better approach

In general, reliability is the ability of a system to perform and maintain its functions in routine, as well as hostile or unexpected, circumstances. The Redundancy Allocation Problem (RAP) is a combinatorial problem which maximizes system reliability by discrete simultaneous selection from available components. The main purpose of this study is to develop an e ective approach to solve RAP, expeditiously. In this study, the basic assumption is considering Erlang distribution density for component failure rates. Another assumption is that each subsystem can have one of coldstandby or active redundancy strategies. The RAP is a NP-Hard problem which cannot be solved in reasonable time using exact optimization techniques. Therefore, an approach that combines an Ant Colony Optimization (ACO) algorithm as a meta-heuristic phase, and three other heuristics, is used to develop a solving methodology for RAP. Finally, to prove the eciency of the proposed approach, some well-known benchmarks in the literature are solved and discussed in detail.

This paper considers a multi-skilled project scheduling problem that is a newly developed extension of the resource-constrained project scheduling problem (RCPSP). The main difference in such problems compared with the classic scheduling problems is associated with the given resources, which are just depended on the human type. Additionally, the net present value of a given project is considered by the cash in and outflows to guarantee the project success. To solve the given problem, an enhanced two-phase method is proposed using the genetic and path relinking algorithms, whose parameters are tuned by the Taguchi method to provide robust comparisons. Furthermore, the potential changes in the project execution method are considered for some of the mostly used payment methods. Finally, some different-sized instances are tested to check the performance and efficiency of the proposed method.

The cost parameters in economic-statistical models of control charts are usually assumed to be deterministic in the literature. Considering uncertainty in the cost parameters of control charts is very common in application. So, several researchers used scenario-based approach for robust economic-statistical design of control charts. In this paper, we specifically concentrate on the multivariate exponentially weighted moving average (MEWMA) control chart and consider interval uncertainty in the cost parameters of the MEWMA control chart and develop a robust economic-statistical design of the MEWMA control chart by using interval robust optimization technique. Meanwhile, the Lorenzen and Vance cost function is used and to calculate the average run length criterion, the Markov chain approach is applied. Then, genetic algorithm for obtaining optimal solution of the proposed robust model is used and effectiveness ofthis model is illustratedthrough a numerical example. Also, a comparison with certain situation of the cost parameters is performed. Finally, a sensitivity analysis is done to investigate the effect of changing the intervals of cost parameters of the Lorenzen and Vance model on the optimal solutions. Furthermore, a sensitivity analysis on the other certain cost parameters of the Lorenzen and Vance model is done.

With respect to the stochastic hybrid multi-attribute decision making problems with interval probability and unknown attribute weight, a multi-attribute decision making method based on prospect theory is proposed. To begin with, the hybrid attribute vales, including real numbers, interval numbers, triangular fuzzy numbers, linguistic variables, uncertain linguistic variables and intuitionistic fuzzy values, are converted to trapezoidal fuzzy numbers, and intervalprobability is expressed by trapezoidal fuzzyprobability; the prospectvalue function of the trapezoidal fuzzy numbers for every alternative under every attribute and everynatural state based on the decision-making reference point of each attribute, and the weight function of trapezoidal fuzzyprobability,can be constructed; then the prospect value of attribute for every alternative is calculated through prospectvalue function and the weight function. Then, a maximizing deviation method is used to determine the attribute weightsand the weighted prospect value of alternative is get by weighting the prospect values, and all the alternatives are ranked according to the expected values of the weighted prospect values. Finally, an illustrate example is given to show the decision-making steps, the influence on decision making for different parameter values in value function and weight function and different decision-making reference points. ing

Reducing product delivery time is a key factor for enterprises in increasing their core competitiveness. In order to resolve problems, including lack of a warning and monitoring system for Master Production Schedules (MPS) in cement equipment manufacturing enterprises, we propose a warning echanism based on theoretical nish percentage and actual nish percentage. First, an MPS model, based on the product Manufacturing Bill Of the Material (MBOM), is proposed. Second, we present an approach for generating planned time, actual time, actual nish percentage and theoretical nish percentage, and yellow and red warnings are introduced to evaluate whether the production plan is normal or not. Finally, we use an example to illustrate the proposed algorithm process. Experimental results have shown that the proposed approach is able to support MPS warning.