This paper models the process of ordered product design decision making. The problem is formulated by introducing the notion of Quality Loss to quantify the loss of design freedom incurred by the decision makers in the later stages of a cross-functional decision process. In this context, the optimal order is the decision order with the lowest quality loss, whose characterization is one of the contributions of this paper. In this paper, we present a novel decision support system for order in a Make-to-Stock or Make-to-Order production environment. The proposed decision support system is comprised of six steps. The customers are prioritized based on a new method. In this paper, the idea of the algorithm “Knapsack” is used to prioritize customers. Finally, numerical experiments are conducted to show the tractability of the applied mathematical programming model.
This paper presents an alternate technique based on fuzzy goal programming (FGP) approach to solve multi-objective programming problem with fuzzy relational equations (FREs) as constraints. The proposed technique is more efficient and requires less computational work than that of algorithm suggested by Jain and Lachhwani (2009) [Jain, & Lachhwani (2009). Multiobjective programming problem with fuzzy relational equations. International Journal of Operations Research, 6(2), 55?63.]. In FGP formulation, objectives are transformed into the fuzzy goals using maximum and minimal solutions elements of FREs feasible solution set. A pseudo code computer algorithm is developed for computation of maximum solution of FREs. Suitable linear membership function is defined for each objective function. Then achievement of the highest membership value of each of the fuzzy goals is formulated by minimizing the sum of negative deviational variables. The aim of this paper is to present a simple and efficient solution procedure to obtain compromise optimal solution of multiobjective optimization problem with FREs as constraints. A comparative analysis is also carried out between two methodologies based on numerical examples.
Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Traditional DEA models deal with measurements of relative efficiency of DMUs regarding multiple-inputs vs. multiple-outputs. One of the drawbacks of these models is the neglect of intermediate products or linking activities. Recently, DEA has been extended to examine the efficiency of network structures, where there are lots of sub-processes that are linked with intermediate parameters. These intermediate parameters can be considered as the outputs of the first stage and simultaneously as the inputs for the second stage. In contrast to the traditional DEA analysis, network DEA analysis aims to measure different sub-processes’ efficiencies in addition to the total efficiency. Lots of network DEA technique has been used recently, but none of them uses Analytic Hierarchy Process (AHP) in network DEA for assessing a network’s efficiency. In this paper, AHP methodology is used for considering the importance of each sub-process and network DEA is used for measuring total and partial efficiencies based on the importance of each department measured from AHP methodology. In this regard, the case of Iranian Handmade Carpet Industry (IHCI) is used.
From a wide variety of queuing models, the finite-capacity queuing models are the most commonly used, where arrival and service rates follow an exponential distribution. Based on two criteria of system cost and expected degree of customer satisfaction, the present study defines a new productivity rate index and evaluates the optimization of a queuing model with finite capacity. In queuing models, obviously, as the number of servers increases, the length of waiting lines decreases, the expected degree of customer satisfaction enhances, and obviously the system cost increases. This study deals with the mathematical relationships involved in the computations of these two criteria, and proposes a novel approach to determine an optimal number of servers by considering a decision-maker & apos; s priority and establishing a trade-off between criteria.
Timely identification of newly emerging trends is needed in business process. Data mining techniques like clustering, association rule mining, classification, etc. are very important for business support and decision making. This paper presents a method for redesigning the ordering policy by including cross-selling effect. Initially, association rules are mined on the transactional database and EOQ is estimated with revenue earned. Then, transactions are clustered to obtain homogeneous clusters and association rules are mined in each cluster to estimate EOQ with revenue earned for each cluster. Further, this paper compares ordering policy for imperfect quality items which is developed by applying rules derived from apriori algorithm viz. a) without clustering the transactions, and b) after clustering the transactions. A numerical example is illustrated to validate the results.
There is an ever increasing need of providing quick, yet improved solution to dynamic scheduling by better responsiveness following simple coordination mechanism to better adapt to the changing environments. In this endeavor, a cognitive agent based approach is proposed to deal with machine failure. A Multi Agent based Holonic Adaptive Scheduling (MAHoAS) architecture is developed to frame the schedule by explicit communication between the product holons and the resource holons in association with the integrated process planning and scheduling (IPPS) holon under normal situation. In the event of breakdown of a resource, the cooperation is sought by implicit communication. Inspired by the cognitive behavior of human being, a cognitive decision making scheme is proposed that reallocates the incomplete task to another resource in the most optimized manner and tries to expedite the processing in view of machine failure. A metamorphic algorithm is developed and implemented in Oracle 9i to identify the best candidate resource for task re-allocation. Integrated approach to process planning and scheduling realized under Multi Agent System (MAS) framework facilitates dynamic scheduling with improved performance under such situations. The responsiveness of the resources having cognitive capabilities helps to overcome the adverse consequences of resource failure in a better way.
The aim of this work is to present a reliability and profit analysis of a two-dissimilar parallel unit system under the assumption that operative unit cannot fail after post repair inspection and replacement and there is only one repair facility. Failure and repair times of each unit are assumed to be uncorrelated. Using regenerative point technique various reliability characteristics are obtained which are useful to system designers and industrial managers. Graphical behaviors of mean time to system failure (MTSF) and profit function have also been studied. In this paper, some important measures of reliability characteristics of a two non-identical unit standby system model with repair, inspection and post repair are obtained using regenerative point technique.
Differential Search (DS) algorithm is a new meta-heuristic for solving real-valued numerical optimization. This paper introduces a new method based on DS for solving Resource Constrained Project Scheduling Problem (RCPSP). The RCPSP is aimed to schedule a set of activities at minimal duration subject to precedence constraints and the limited availability of resources. The proposed method is applied to PSPLIB case studies and its performance is evaluated in comparison with some of state of art methods. Experimental results show that the proposed method is effective. Also, it is among the best algorithms for solving RCPSP.
In the context of supply chain management, supplier selection plays a key role in reaching desirable production planning. In today & apos; s competitive world, many enterprises have focused on selecting the appropriate suppliers in an attempt to reduce purchasing costs and improve quality products and services. Supplier selection is a multi-criteria decision problem, which includes different qualitative and quantitative criteria such as purchase cost, on time delivery, quality of service, etc. In this study, a fuzzy multi-objective mathematical programming model is presented to select appropriate supplier and assign desirable order to different supplies. The proposed model was implemented for an organization by considering 16 different scenarios and the results are compared with two other existing methods.
This paper considers a multi-objective version of the Multiple Traveling Salesman Problem (MOmTSP). In particular, two objectives are considered: the minimization of the total traveled distance and the balance of the working times of the traveling salesmen. The problem is formulated as an integer multi-objective optimization model. A non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve the MOmTSP. The solution scheme allows one to find a set of ordered solutions in Pareto fronts by considering the concept of dominance. Tests on real world instances and instances adapted from the literature show the effectiveness of the proposed algorithm.