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The purpose of this paper is to consider various possible constraints of the problem of production and maintenance planning control for a multi-machine under subcontracting constraint, in order to bring the manufacturer industry closer to real mode. In this paper, we present an efficient and feasible optimal solution, by comparing optimization procedures.

Our manufacturing system is composed of parallel machines producing a single product, to satisfy a random demand under a given service level. In fact, the demand is greater than the total capacity of the set of machines; hence there rises a necessity of subcontracting to complete the missing demand. In addition, we consider that the unit cost of subcontracting is a variable depending on the quantity subcontracted. As a result, we have developed a stochastic optimal control model. Then, to solve the problem we compared three optimization methods: (exact/approximate), the genetic algorithm (GA), the Pattern Search (PS) and finally fmincon. Thus, we validate our approach via a numerical example and a sensitivity analysis.

This paper defines an internal production plan, a subcontracting plan and an optimal maintenance strategy. The optimal solution presented in this paper significantly improves the ability of the decision maker to consider larger instances of the integrated model. In addition, the decision maker can answer the following question: Which is the most optimal subcontractor to choose?

The approach developed deals with the case of the real-mode manufacturing industry, taking into consideration different constraints and determining decision variables which allow it to expand the profits of the manufacturing industry in different domains such as automotive, aeronautics, textile and pharmacies.

This paper is one of the few documents dealing with the integrated maintenance in subcontracting constraint production which considers the complex aspect of the multi-machine manufacturing industry. We also dealt with the stochastic aspect of demand and failures. Then, we covered the impact of the unit cost variation of subcontracting on the total cost. Finally, we shed light on a comparison between three optimization methods in order to arrive at the most optimal solution.

The purpose of this paper is to examine whether Korean multinational corporations (MNCs) in the electronics and steel industries do shift their production across their foreign subsidiaries, located in different countries, as exchange rates fluctuate in foreign countries.

A case study was taken as a qualitative methodology to examine whether MNCs actually shift their production as multinational operational flexibility perspective predicts.

From a case study of two Korean MNCs (LG Electronics and POSCO), it was found that even facing heightened production costs associated with host country currency appreciation, Korean MNCs do not shift their production to less costly locations due to industrial characteristics, limited capacity, and high tariff barriers. It was also found that they reduce the production costs internally and they also negotiate the costs with employees and suppliers to adjust the production costs associated with appreciated currency.

Our findings imply that certain industrial and environmental constraints make it difficult for MNCs to take flexible actions as multinational operational flexibility perspective predicts. The findings also shed additional light on the less‐explored argument over operational flexibility and vertical integration associated with cross‐country shifts of value chain activities, including production or sales.

Almost all literature taking the multinational operation flexibility view argues that MNCs are able to shift their productions for their own benefits. However, the authors of this paper find from their case studies that firms take advantage of other methods than production shifts in their responses to exchange rate fluctuations in their host countries. Thus this study gives an insight into when and how firms behave as the theory predicts.

Although research attention has been given to the modelling process for simultaneous demand‐output management in manufacturing systems, little interest has been demonstrated in service organisations despite the fact that such organisations face unique conditions that further complicate the demand‐output management issue. In response to this lack of emphasis, we review the relevant research from both marketing and operations management and present a cost‐effectiveness model for balancing demand and service output.

Due to considerable contributions of the construction industry to the global carbon emissions, a great deal of attention is placed on possible incorporation of carbon footprint minimization as an important objective in the planning of construction operations. The purpose of this paper is to present a framework to estimate and minimize the carbon emissions of the concrete placing operation through identifying the optimal number of pumps and the inter-arrival time of truck mixers.

The proposed framework integrates discrete event simulation and multi-objective optimization to estimate and minimize the carbon emission, costs and production rate of the concrete placing operation. An actual construction project is used to demonstrate the application of the proposed framework. Furthermore, a sensitivity analysis is performed to investigate the sensitivity of the results to variations in modeling parameters including the ratio of idle to non-idle emission rates of equipment and the activity duration distributions.

The results of the case study highlight that variations in the number of pumps and inter-arrival time of truck mixers significantly affect the carbon emissions, cost and production rate of the concrete placing operation. Furthermore, the results of the sensitivity analysis show that variations in the ratio of idle to non-idle emission rates for pumps and truck mixers have little effects on the selected setting for the project. This is contrary to the effect of uncertainty in the activity duration distributions, which was found to be significant.

Results of this study provide an insight into the trade-off between carbon emissions, cost and production rate of the concrete placing operation.

This research has addressed a quantitative approach for improving energy management through applying statistical techniques aimed at identifying and controlling factors linked to energy consumption rates at manufacturing plants. The paper presents analysis and results of multiple linear regression models used to establish the significance of a number of energy related management factors in controlling energy usage. Regression models constructed for this purpose proved the existence of statistically valid relationships between electrical energy consumption and maintenance and production management factors, namely, failure rate and production rate, where R² values of the magnitude of 65 per cent were obtained. Furthermore, an economical treatment based on the derived regression models was formulated and demonstrated that effective management practices associated with proper maintenance, cost accounting and reporting systems can result in highly significant savings in energy usage.

The purpose of this paper is to present a practical proposal for integrating production control (PC) and quality control (QC) at the shop floor level.

The proposed method is based on three principles which relate PC and QC at the shop floor level. The proposal is applied successfully at the world's largest pencil factory.

The results show that the proposed method contributes to improve four performance indicators related to PC and QC at the company studied: increase the average throughput in about 28.9 per cent; reduce the average value of work in process (WIP) in about 35.6 per cent, reduce the average lead time by about 45.4 per cent, and reduce the average defect rate by about 71.4 per cent.

The proposal (mainly because of principle III) is developed to be applied in repetitive production (RP) systems, i.e. environments characterized by low production volume and low product variety.

Some practical implications for industrial managers arises from this study: managers must consider the importance of integrated PC and QC functions in order to get better results concerning performance indicators such as throughput, WIP, lead time, and rejection rate; the materials flow simplification is a prerequisite for a lot of improvement initiatives at the shop floor level; the adequate choice of the production control system (PCS) is vital in order to get positive results regarding the performance indicators related to PC; the determination of the production pace (or rate) for a RP system must take account capacity restrictions and the influence of defect rate on production rate.

The paper is original in that it shows that the performance of the shop floor level can be improved by means of integrating PC and QC, by discussing and implementing a method which simplifies the material flow in the shop floor level, chooses the most adequate PCS and shows how the production rate influence on the rejection rate. Therefore, the paper is important for those which practice industrial management, more specifically on PC and QC functions.

The purpose of this paper is to deal with the problem of integration of production and maintenance policies. In this context, the authors consider production systems made of parallel machines producing a single product over a finite horizon made of equal periods for which a forecasted demand is known. The authors investigate the impact of switching production in case of failure of any given machine.

A mathematical model is first developed to find an optimal production plan which minimizes the average total storage, shortage and production costs. Then, using this optimal production plan and taking into account the influence of the production rate on the degradation of each machine, optimal preventive maintenance (PM) policies are proposed for the situations with and without switching.

Optimal production rates are determined for each production period and for each machine. Optimal PM periods are also computed for each machine.

Usually, in manufacturing systems, the production rate of a machine influences its failure rate. In case a machine fails, it takes a random time to repair it during which production is lost. The paper attempts to propose a switching policy (SP) according to which the lost production is compensated by all the other machines. The effects of the SP coupled with the PM strategy are shown through a numerical example.

Contrarily to previous works, the authors consider more realistic settings with a non-negligible random time for repairing failed machines. In order to compensate the lost production during the repair of a failed machine, a SP is proposed to transfer the load uniformly to all the other machines. As a result, those machines will produce at a higher production rate and will consequently have their failure rate increased. It will therefore be essential to determine an optimal PM schedule knowing that durations of these activities are not negligible. It is shown that the simultaneous implementation of periodic PM and load transfer in case of failure is the most economical integrated strategy.

In any organization, the information relevant to the problem area is essential in making decisions. The top management makes decisions like expansion of the plant, automation, recruitment of senior personnel and diversification of products, etc. There are many kinds of tools or methods available to analyse these problem areas. Among these, turnover rate is useful for many purposes, especially in evaluating the performance of an organization. Here, the turnover rate is considered in order to identify the effect of the number of types of products or product mix on the performance of a manufacturing system. It can be defined as the ratio of average demand to average inventory level. The model presented discusses the effect of the number of products/product mix on the turnover rate of a production system when the appropriate market value of the products is considered. The turnover rate discussed is based on the economic production quantity (EPQ) and the manufacturing cycle time of each product. An example is presented to explain the application of the model.

The purpose of this paper is to present detailed algorithms for simulation of individual and group control of production wells in hydrocarbon reservoirs which are implemented in a finite volume-based reservoir simulator.

The algorithm for individual control is described for the multi-lateral multi-connection ones based on the multi-segment model considering cross-flow. Moreover, a general group control algorithm is proposed which can be coupled with any well model that can handle a constraint and returns the flow rates. The performance of oil production process based on the group control criteria is investigated and compared for various cases.

The proposed algorithm for group control of production wells is a non-optimization iterative scheme converging within a few number of iterations. The numerical results of many computer runs indicate that the nominal power of the production wells, in general, is the best group control criterion for the proposed algorithm. The production well group control with a proper criterion can generally improve the oil recovery process at negligible computational costs when compared with individual control of production wells.

Although the group control algorithm is implemented for both production and injection wells in the developed simulator, the numerical algorithm is here described only for production wells to provide more details.

The proposed algorithm can be coupled with any well model providing the fluid flow rates and can be efficiently used for group control of production wells. In addition, the calculated flow rates of the production wells based on the group control algorithm can be used as candidate solutions for the optimizer in the simulation-optimization models. It may reduce the total number of iterations and consequently the computational cost of the simulation-optimization models for the well control problem.

A complete and detailed description of ingredients of an efficient well group control algorithm for the hydrocarbon reservoir is presented. Five group control criteria are extracted from the physical, geometrical and operating conditions of the wells/reservoir. These are the target rate, weighted potential, ultimate rate and introduced nominal power of the production wells. The performance of the group control of production wells with different group control criteria is compared in three different oil production scenarios from a black-oil and highly heterogeneous reservoir.

The demand variability in case of assembly line operations can be absorbed either by multi‐skilling of operators on the line, empowering them to handle a wider mix of work‐elements or by holding finished goods inventory. This paper examines trade‐offs between these two groups of policies by developing a simulation‐based model. Four policies are evaluated and their cost implications examined to enable decision makers to choose the best policy depending upon the situation specific parameters. A case study to illustrate the proposed model is presented and results are found to be insightful. A methodology to identify training needs in case of multi‐skilling is also developed.