Supply chain design considering cellular structure and alternative processing routings

Nowadays, in highly competitive global markets and constant pressure to reduce total costs, enterprises consider group technology and Supply Chain Management (SCM) accordingly and usually separately as the key elements for intra and inter facilities improvement. Simultaneous consideration of the elements of these two disciplines in an integrated design can result in higher efficiency and effectiveness. A three-echelon supply chain that has several markets, production sites, and suppliers is designed again in this paper as a Cellular Manufacturing System (CMS). Every product can be manufactured in the CMS through alternative process routings, in which machines are likely to fail. A linear integer programming model is presented here that seeks to minimize the intercellular movement, procurement, production, and machine breakdown costs. We present a number of illustrative examples to demonstrate the effectiveness of the integrated design. The proposed examples reveal that although the procurement and logistics costs increase slightly in the integrated design, the total cost is dropped considerably

Incorporating dynamic cellular manufacturing into strategic supply chain design

For increasing the efficiency of the supply chain (SC), it is necessary to take into account the interactions and relationships between the stages of procurement of raw materials, manufacturing the products, and distributing them. An integrated framework is proposed in this paper for companies interested in meeting the demand for different products in the customer zones by establishing a number of plants and distributors at the candidate sites and in having SC design with reconfiguration capability based on changes in demand and more proper economic opportunities. For this purpose, a geographically distributed cell design is proposed for the selection of the proper location for each of the facilities and the production process of the products. A mixed integer linear programming model is presented here for the integration of the sectors for procurement, production, and distribution of the products in the SC. In light of the NP-hard class of the cell formation problem, a new algorithm titled hybrid genetic ant lion optimization (HGALO) algorithm is presented for finding the optimal or near-optimal solutions. A comparison is also made here between the proposed algorithm and the genetic algorithm (GA) for demonstration of the efficiency of the proposed algorithm. The quality of the solutions generated based on the HGALO algorithm demonstrates the capability and effectiveness of the algorithm in finding high quality solutions

Optimum target value for multivariate processes with unequal non-conforming costs

In quality control charts, the problem of determining the optimum process mean arises when the deviation of a quality characteristic in one direction is more harmful than in the opposite direction. The failure mode in these two directions is usually different. A great majority of researches in this area have considered asymmetric cost function for processes with single quality characteristics. In this paper, we consider processes in which there are more than one quality characteristics to monitor. The quality characteristics themselves may or may not be independent. Based upon the specification limits and the costs associated with the deviations we derive a formula to determine the optimum process mean. To illustrate the proposed formula and to estimate the costs associated with the optimum process mean we present four numerical examples by simulation. The results of the simulation studies show that considerable amount of savings can be obtained by applying the proposed process means

Bi-objective resource-constrained project scheduling with robustness and makespan criteria

Resource-constrained project scheduling problem (RCPSP) is one of the most important problems in the context of project scheduling. Considering a single objective, such as makespan minimization, net present value minimization or cost minimization has been the cornerstone of most studies done so far. At the other hand, taking this problem into account as a multi objective one has not been well studied. In this paper, a bi-objective model of RCPSP is presented. The first objective is makespan to be minimized, and the second one, a recently developed measure, is robustness maximization aimed at floating time maximization to make scheduling more reliable. The problem formed in this way is an NP-hard one forcing us to use simulated annealing algorithm to obtain a global optimum solution or at least a satisfying one. Finally, we have illustrated our new algorithm using a numerical example with different weights for robustness. (As an example, when robustness weight is zero, the problem will be solved as a makespan objective.)

Applying simulated annealing to cellular manufacturing system design

Cell formation and cellular layout design are the two main steps in designing a cellular manufacturing system (CMS). In this paper, we will present an integrated methodology based on a new concept of similarity coefficients and the use of simulated annealing (SA) as an optimization tool. In comparison with the previous works, the proposed methodology takes into account relevant production data, such as alternative process routings and the production volumes of parts. The SA-based optimization tool is parallel in nature and, hence, can reduce the computation time significantly, so it is capable of handling large-scale problems. Finally, the SA-based procedure is compared with a genetic algorithm (GA) based procedure and it will be shown that the SA-based algorithm can be as effective as a GA-based algorithm, but with less computational time and effort

Artificial neural networks in applying MCUSUM residuals charts for AR(1) processes

The usual key assumptions in designing quality control charts are the normality and independency of serial samples. While the normality assumption holds in most cases, in many continuous-flow processes such as the chemical processes, serial samples have some degrees of autocorrelation associated with them. Ignoring the autocorrelation structure in constructing control charts, results in decreasing the in-control run length, and so increasing the false alarms. Moreover, when the object is to detect small shifts in the mean vector of a process, the performance of Cumulative Sum (CUSUM) control charts is dramatically better than Schewhart control charts. One of the methods, which have been developed to deal with autocorrelation, is to use the residuals charts, the residuals being the difference between the real and the predicted values of the mean vector of the process variables. In this paper we design a neural network-based model to forecast and construct residuals CUSUM chart for multivariate Auto-Regressive of order one, AR(1), processes. We compare the performance of the proposed method with the time series-based residuals chart and the auto-correlated MCUSUM chart and report the results