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Presents a mathematical model for determining Economic Production Quantity (EPQ) in a multistage flow‐shop production system for the case where the demand for items per unit time is deterministic and the planning horizon is finite. Solves an example problem to illustrate the model.

This paper sets out to test the claim made in the lean production literature that the lean production system is universally applicable. The proponents of the lean production model insist that it will supersede both mass production and specialised batch production; but this paper argues that, whilst lean production practices have been adopted and adapted by many producers, batch production has an enduring value from both a work design and a manufacturing process design perspective. The paper reviews the evidence for the persistence of batch production and of craft work methods in both luxury vehicle manufacture and specialty component manufacture and highlights the conditions under which batch production remains viable.

The aim of this paper is to present a method for finding the optimum balance between sequence-dependent setup costs, holding costs, delivery costs and delay penalties in an integrated production–distribution system with lot sizing decisions.

Two mixed integer linear programming models and an optimality property are proposed for the problem. Since the problem is NP-hard, a genetic algorithm reinforced with a heuristic is developed for solving the model in large-scale settings. The algorithm parameters are tuned using the Taguchi method.

The results obtained on randomly generated instances reveal a performance advantage for the proposed algorithm; it is shown that lot sizing can reduce the average cost of the supply chain up to 11.8%. Furthermore, the effects of different parameters and factors of the proposed model on supply chain costs are examined through a sensitivity analysis.

Although integrated production and distribution scheduling in make-to-order industries has received a great deal of attention from researchers, most researchers in this area have treated each order as a job processed in an uninterrupted time interval, and no temporary holding costs are assumed. Even among the few studies where temporary holding costs are taken into consideration, none has examined the effect of splitting an order at the production stage (lot sizing) and the possibility of reducing costs through splitting. The present study is the first to take holding costs into consideration while incorporating lot sizing decisions in the operational production and distribution problem.

This paper integrates condition-based maintenance (CBM) with production planning in a single-stage production system that deteriorates with usage during a specified finite planning horizon. The purpose of this paper is to develop an integrated production and maintenance model to minimize the expected total cost over the horizon.

A joint production planning and CBM model is proposed. In the model, a set of products must be produced in lots. The system degradation is a stationary gamma process and the degradation level is detected by inspection between production lots. Maintenance actions including imperfect preventive maintenance (PM) should be taken when the failure risk exceeds the maintenance threshold. A fix-iterative heuristic algorithm is proposed to address the joint model.

The proactive policy expressed as a prognosis maintenance threshold is introduced to integrate CBM with batch production perfectly. Experiments are carried out to conduct sensitivity analysis, which provides some insights to facilitate industrial manufacturing. The superiority of the proposed joint model compared with a separate decision method is demonstrated. The results show an advantage in cost saving.

Few studies have been made to integrate production planning and CBM decisions, especially for a multi-product system. Their maintenance decisions are usually based on a periodic review policy, which is not appropriate for batch production system. A prognosis maintenance threshold based on system condition and production quantity is suitable for the integrated decisions. Moreover, the imperfect PM is taken into consideration in this paper. A fix-iterative algorithm is developed to solve the joint model. This work forms a proactive maintenance for batch production.

The learning effect is well established in the repetitive performance of industrial tasks and it is essential that production management be aware of the potential for improved performance (measured by dynamic gain) and the rate at which this may be achieved/measured by the time constraint. Performance and contingency logging and trend modelling should be part of the management information system so that the appropriate learning curve can be established and stored in the databank for future planning purposes. The application of the learning curve to batch production shows that performance slippage caused by elapsed time between batch completions is a significant factor in manufacturing time increase, and, hence, labour costs. This should be allowed for when optimising batch production policy, forecasting deliveries, and

Addresses the problem of implementing just‐in‐time (JIT) principles in a processing shop organized in a batch manufacturing environment. The focus is on a processing shop separated from an assembly shop by a parts store. Presents a multi‐criteria variant of the capacitated lot‐sizing model. The approach involves a pre‐emptive priority for JIT processing schedules, and a secondary priority for desirable load profiles. Solution properties are derived and used to facilitate the solution process for both the primary and secondary criterion models. Load profiles are assessed in the context of capacity requirements planning as well as from the alternative viewpoint of providing feedback to capacity planning through a consideration of capacity cushions. At both planning levels the approach is shown to allow optimum resource utilization without violating JIT principles. Presents optimization procedures, including a heuristic with an analytic performance bound.

This paper aims to create a new decision-making procedure that uses “Lot-by-Lot Acceptance Sampling Plan by Attributes” methodology in the production processes when any production interruption is observed in tobacco industry, which is a significant example of batch production.

Based on the fish bone diagram, the reasons of the production interruptions are categorized, then Lot-by-Lot Acceptance Sampling Plan by Attributes is studied to overcome the reasons of the production interruptions. Furthermore, managerial aspects of decision making are not ignored and hence, acceptance sampling models are determined by an Analytical Hierarchy Process (AHP) among the alternative acceptance sampling models.

A three-phased acceptance sampling model is generated for determination of the reasons of production interruptions. Hence, the necessary actions are provided according to the results of the proposed acceptance sampling model. Initially, 729 alternative acceptance sampling models are found and 38 of them are chosen by relaxation. Then, five acceptance sampling models are determined by AHP.

The current experience dependent decision mechanism is suggested to be replaced by the proposed acceptance sampling model which is based on both statistical and managerial decision-making procedure.

Acceptance sampling plans are considered as a decision-making procedure for various cases in production processes. However, to the best of our knowledge Lot-by-Lot Acceptance Sampling Plan by Attributes has not been considered as a decision-making procedure for batch production when any production interruption is investigated.

The Profit Impact of Marketing Strategy (PIMS) database was used to test the relationship between production process type (small batch, large batch/assembly, and continuous) and eight organizational policy decisions (new products, new plant and equipment, finished goods inventory, raw materials/work‐in‐process inventory, capacity utilization, fixed capital assets, manufacturing costs, gross margin). In addition, the effect of six broad industry types on the proposed relationships was also investigated. Overall industries, raw materials/work‐in‐progress, capacity utilization, manufacturing costs, fixed assets, and gross margin varied with production process type while new products, new plant and equipment, and finished goods inventory did not vary. Within each industry, the findings showed less support for the relationships between production process type and the eight organizational policy decisions. Further analysis showed that most of the industries are dominated by a production process type. Suggests a movement away from the traditional differentiation of production process technologies and a shift of research emphasis to the differing uses of a particular production process technology within an industry.

The purpose of this paper is to understand the effect of batching on bullwhip effect in a model of multi‐echelon supply chain with information sharing.

The model uses the system dynamics and control theoretic concepts of variables, flows and feedback processes and is implemented using iThink^® software.

It has been seen that the relationship between batch size and demand amplification is non‐monotonic. Large batch sizes, that when combined in integer multiples can produce order rates that are close to the actual demand, produce little demand amplification, i.e. it is the size of the remainder of the quotient that is the determinant. It is further noted that the value of information sharing is greatest for smaller batch sizes, for which there is a much greater improvement in the amplification ratio.

Batching is associated with the inventory holding and backlog cost. Therefore, future work should investigate the cost implications of order batching in multi‐echelon supply chains.

This is a contribution to the continuing research into the bullwhip effect, giving supply chain operations managers and designers a practical way into controlling the bullwhip produced by batching across multi‐echelon supply chains.

Previous similar studies have used control theoretic techniques and it has been pointed out that control theorists are unable to solve the lot sizing problem. Therefore, system dynamic simulation has been applied to investigate the impact of various batch sizes on bullwhip effect.

The purpose of this paper is to analyze the effect of batching on bullwhip effect in a model of multi‐echelon supply chain with information sharing.

The model uses the system dynamics and control theoretic concepts of variables, flows, and feedback processes and is implemented using iThink^® software.

It has been seen that the relationship between batch size and demand amplification is non‐monotonic. Large batch sizes, when combined in integer multiples, can produce order rates that are close to the actual demand and produce little demand amplification, i.e. it is the size of the remainder of the quotient that is the determinant. It is further noted that the value of information sharing is greatest for smaller batch sizes, for which there is a much greater improvement in the amplification ratio.

Batching is associated with the inventory holding and backlog cost. Therefore, future work should investigate the cost implications of order batching in multi‐echelon supply chains.

This is a contribution to the continuing research into the bullwhip effect, giving supply chain operations managers and designers a practical way into controlling the bullwhip produced by batching across multi‐echelon supply chains. Economies of scale processes usually favor large batch sizes. Reducing batch size in order to reduce the demand amplification is not a good solution.

Previous similar studies have used control theoretic techniques and it has been pointed out that control theorists are unable to solve the lot sizing problem. Therefore, system dynamic simulation is then applied to investigate the impact of various batch sizes on bullwhip effect.