Scientia Iranica
Volume:20 Issue: 6, 2013

This article presents a new model for pricing a new product considering skimming pricing strategy in the presence of competition. We consider two periods for price setting including skimming and economy period. The problem is deciding on skimming price as well as economy price in order to maximize the total profit. The derived model is a non-linear programming model and we analyzed the structure and properties of optimal solution to develop a solution method. Analytical results as well as managerial insights are presented by mathematical and numerical analysis.

In this paper, we study the intuitionistic fuzzy information aggregation operators based on Einstein operation laws with the condition of the aggregated arguments are independent. The Einstein based intuitionistic fuzzy Choquet averaging (EIFCA) operator is proposed. Furthermore, the relationship between the EIFCA operator and the IFCA operator is investigated. The desirable properties of the EIFCA operator, such as boundeness, monotonicity, shift-invariance and homogeneity are discussed. A multi-criteria decision making approach based on the EIFCA operator is proposed under intuitionistic fuzzy environment. A comparative example is given for demonstrating the applicability of the proposed decision procedure and for finding links with other operators based decision approach.

This work presents an efficient hybrid method based on Particle Swarm Optimization (PSO) and Termite Colony Optimization (TCO) for solving Resource Constrained Project Scheduling Problem (RCPSP). The search process of this hybrid method employs PSO iterations for global search and TCO iterations for local search. The proposed method works by interleaving the PSO and TCO search processes. The PSO method update schedules by considering the best solution found by the TCO approach. Next the TCO approach picks the solutions found by PSO search and perform local search around each solution. Each individual in TCO approach moves randomly but it is biased towards locally best observed solutions. Apart from hybridization, a new constraint handling approach is proposed to convert the infeasible solutions to the feasible ones. The standard benchmark problems of size j30, j60, j90, and j120 from PSPLIB are used to show the efficiency of the proposed method. The results showed that although PSO and TCO methods independently gives good solutions, the hybrid of PSO and TCO gives better solution compared to PSO and TCO methods.

Hub location problems deal with locating hub facilities in one level of services or one type of facility, but some systems are performed by several types of facilities. So this paper attempts to study the single allocation hierarchical hub covering facility location problem over complete network linking in the first level, which is consisted of hub facilities known as central hubs. In addition, the study proposes a mixed integer programming formulation and finds the location of the hubs in the second level and central hubs in the first level so that the non-hub and hub nodes allocate to the opening hub and central hub nodes. Therefore, the travel time between any origin destination pair is within a given time bound. The current study presents an innovative method for computing the values of radiuses in order to improve computational time of the model and to test the performance of the mentioned heuristic method on the CAB data set and on the Turkish network. The helpful results were obtained including: the severe reduction in the time of solution, the rational distribution of the centers for presenting results, equality (Justice) and appropriate accessibility consistent with the different levels of servicing. A computational experience was applied to Iranian hub airports location.

New challenges in the business environment such as increasing competition and influence of Internet as a main distribution channel lead to fundamental changes in traditional revenue management models. Within these conditions, modeling individual’s decisions more accurately is becoming a key factor. Nearly all research studies about the choice-based revenue management models used the well-known multinomial logit model. This model has one important restriction that is called independence of irrelevant alternatives, a property which states that the ratio of choice probabilities for two distinct alternatives is independent of the attributes of any other alternatives.In this paper a nested logit model is proposed for removing this limitation and incorporating correlation between alternatives in each nest. The new subproblem of column generation is introduced and a combination of heuristic and metaheuristic algorithms for solving this problem is provided. Interesting outcomes obtained during analyzing the results of experimental computations such as offer sets and iterations trend with respect to the correlation measure inside each nest. Simulation results show although changing choice model might lead to significant improvement in revenue in some conditions, during all scenarios, observing correlation should not cause to change choice model immediately.

This paper, introduces a Single-Item Lot-sizing and Scheduling Problem with Multiple Warehouses (SILSP-MW). In this problem, the inventory deteriorates over time, depending on the warehouse conditions, so multiple warehouses with different technologies are considered in this study. Each warehouse has a specified deterioration rate and holding cost. The purpose of the SILSP-MW is to determine production periods and quantities and to select the appropriate warehouse to hold the inventory in each period, such that specified demand in each period is being satisfied while the total cost is minimized. We shall present a Mixed-Integer Linear Programming (MILP) formulation to model the problem. Moreover, a Simulated Annealing (SA) algorithm will be presented to solve this problem. We will evaluate the performance of the algorithm by computational experiments with small- and medium-sized examples. In addition, a full factorial design is developed to investigate the effect of the model parameters on the proposed SA algorithm.

Nowadays supply chain management (SCM) is an interesting problem that has attracted the attention of many researchers. Transportation network design is one of the most important fields of SCM. In this paper, an integrated multi-stage and multi-product logistic network design including forward and reverse logistic is considered. At first, a mixed integer nonlinear programming model (MINLP) is formulated in such a way as to minimize purchasing and transportation costs. Then, a hybrid priority-based genetic algorithm (pb-GA) and simulated annealing algorithm (SA) is developed in two phases to find the proper solutions. The solution is represented by a matrix and a vector. Response surface methodology (RSM) is used in order to tune the significant parameters of the algorithm. Several test problems are generated in order to examine the proposed meta-heuristic algorithm performance.

This paper studies the performance evaluation for a manufacturing system considering reworking actions from the industrial engineering perspective. Due to failure, partial failure, and maintenance, the capacity of each machine in a manufacturing system is stochastic. Therefore, a manufacturing system can be constructed as a stochastic-flow network, namelystochastic-flow manufacturing network (SFMN) herein. To evaluate the capability of an SFMN with reworking actions, we measure the probability that the SFMN satisfiesdemand and such a probability is referred to as the system reliability. First, a decomposition method is proposed to decompose the SFMN into one general processing path and several reworking paths. Subsequently, two algorithms are designed for different network models to generate the lower boundary vector of machine’s capacity for guaranteeing that the SFMN producessufficient products. The system reliability of SFMN is derived in terms of such a vector afterwards.According to the system reliability, the production manager may plan and adjust the production capacity in a flexible competing environment as customers’ demand change.

This paper considers two-machine Flow shop scheduling problem while there is ineligible transportation lags in production procedure. There is one transporter to convey semi-finished jobs between machines, and another transporter to deliver finished jobs to the warehouse (customers). The problem is formulated as a mixed integer linear programming (MILP) model to minimize the makespan as an objective function. To solve the problem in an efficient way, two heuristic algorithms are also developed. Furthermore, five lower bounds are proposed and computational experiments are carried out to verify the effectiveness of the proposed lower bounds and heuristic algorithms. The results show the performance of the heuristics to deal with medium and large size problems.

This paper presents a Markovian model for flexible manufacturing systems (FMSs). The model considers two features of automated flexible manufacturing systems equipped with automated guided vehicle (AGV) namely, the reliability of machines and the reliability of AGVs in a multiple AGV jobshop manufacturing system. Performance measure is a critical factor used to judge the effectiveness of a manufacturing system. The studies in the literature did notcompare Markovian and neural networks especially in the reliability modeling of an advanced manufacturing system considering AGVs. The current methods for modeling reliability of a system involve determination of system state probabilities and transition states. Since, the failure of the machines and AGVs could be considered in different states, therefore a Markovian model is proposed for reliability assessment. Also, a neural network model is developed to point out the difference in the accuracy of the Markovian model in comparison with the neural network. The optimization objectives in the proposed model are maximizing the total reliability of machines in shops in the whole jobshop system and maximizing the total reliability of the AGVs. The multi-objective mathematical model is optimized using an analytic hierarchy process.

In many situations, the quality of a process can be characterized better by a relationship, known as a profile, between a response variable and one or more predictors. Almost all research efforts assume that response variable is continuous and follows a Normal distribution while there are instances in which the response is a binary variable and methods such as logistic regression are commonly used. In this paper four control schemes namely Hotelling, MEWMA, likelihood ratio test (LRT) and LRT/EWMAare proposed to monitor binary response profiles in phase II. The performance of the proposed control charts are evaluated and compared by simulation experiments for different shift values in the parameters of the profile in terms of the average run length (ARL) criterion. The results show that all methods work well in the sense that they can effectively detect shifts in the process parameters. Based on the results, MEWMA and LRT/EWMA methods display a better performance for small to moderate and large shift values, respectively.

This study investigates optimal strategies for price, warranty length, and production rate of a new product, in which both static markets for non-durable and dynamic markets for durable products are involved. The mathematical model incorporates both the demand and the cost functions including production, warranty length, and inventory costs. Using the maximum principle approach, the optimal strategies and interactions among price, warranty length, and production rate in both markets are analyzed using some propositions. The analysis shows that to maximize profits in all cases, the price, the warranty length, and the production rate all must go up simultaneously or one of them must increase and the other two must decrease concurrently.

Decision-making analysis methods are employed to find the best option among feasible alternatives where an amount of alternatives versus criteria is introduced as only one value level with stationary numerical value. In real-world decision situations, the condition of multi-segment problems may exist in practice. In this paper, a new method is proposed to rank the alternatives in multiple criteria decision-making (MCDM) problems where the amount of alternatives to the criteria can be represented by several segments. Hence, a multi-segment decision matrix can be obtained. Moreover, the proposed method based on the simple additive weight (SAW)can be employed to solve the decision problems where the amount of alternatives versus the assessment criteria at each level is introduced as a function of some parameters. These functions can be regarded as linear, exponential, and trigonometric. Finally, three real case studies are given to demonstrate the solution procedure of the proposed method, and then a sensitivity analysis for each case is reported.

This paper addresses the no-wait two-stage assembly flow shop scheduling problem (NWTSAFSP) with the objective of makespan minimization. The problem is a generalization of previously proposed general problem in the two-stage assembly flow shop scheduling problem (TSAFSP). The TSAFSP is NP-hard, thus the NWTSAFSP is NP-hard too and three meta-heuristic algorithms namely genetic algorithm (GA), differential evolution algorithm (DEA) and population-based variable neighborhood search (PVNS) are proposed in this article to solve this problem. Computational results reveal that PVNS outperforms other algorithms in terms of average error and average coefficient of variation (CV). Nevertheless, GA has the least run time among the proposed algorithms.