Optimization and predictive control of a vapor compression cycle under transient pulse heat load

There are well established control methods to stabilize the vapor compression cycle (VCC) about a given operating point. However, it is challenging to design such local controllers to handle large transient heat flux disturbances, due to complex coupling and constraints, and potential violation of critical heat flux (CHF) which could lead to the damaging dryout condition. Since VCCs are locally stable with relatively slow dynamics, model predictive control (MPC) is ideally suited to address these challenges. MPC solves a constrained receding horizon minimization problem under known transient heat disturbance. The objective function is a combination of the exit evaporator wall temperature, which provides an indication for the onset of partial dryout, energy consumption, and control input effort. This paper presents results from the application of MPC to a VCC testbed in our laboratory. We show that MPC can significantly increase the robustness with respect to transient disturbances by moving the system to an advantageous operating point in anticipation of known disturbances.     

An exploratory study of risk aversion in supply chain dynamics via human experiment and agent-based simulation

The literature on the impact of risk aversion on supply chains (SCs) is relatively limited and, in particular, there is a dearth of theory and a lack of empirical evidence concerning: (1) the impact of individual risk aversion on the generation and dynamics of the order policy (e.g. order patterns and inventory holding costs); (2) the impact of several combinations of risk-averse members in each stage of a multi-echelon SC. We explore these gaps through a multi-method approach (i.e. human experiments and agent-based simulation), thus using both empirical and simulated data. Specifically, based on results from a human experiment, we develop the conjecture that risk aversion is positively correlated to the desired stock level and consequently to the safety stock factor of inventory order policies. Building on this finding, we perform a simulation study to infer the impact of individual risk aversion in a multi-echelon SC. Results show that alternative compositions of the SC in terms of risk aversion levels of the echelons significantly influence inventory holdings and SC dynamics. The study implies that a company facing problems of high inventory days-on-hand should favour low-risk aversion managers, as instrumental to lowering stock and improving net working capital.     

Joint hybrid repair and remanufacturing systems and supply control

The control of a stochastic manufacturing system that executes capital asset repairs and remanufacturing in an integrated system is examined. The remanufacturing resources respond to planned returns of worn-out equipment at the end of their expected life and unplanned returns triggered by major equipment failures. Remanufacturing operations for planned demand can be executed at different rates and costs corresponding to different replacement and repair modes. The replacement components inventory is provided by an upstream supply with random lead times. The objective is to determine a control policy for both the supply and remanufacturing activities that minimises the average repair/replacement, acquisition and inventory/shortage total cost over an infinite horizon. We propose a suboptimal joint remanufacturing and supply control policy, composed of a multi-hedging point policy (MHPP) for the remanufacturing stage and an (s, Q) policy for the replacement parts supply. The MHPP is based on two inventory thresholds that trigger the use of predefined remanufacturing modes. Control policy parameters are obtained combining analytical modelling, simulation experiments and response surface methodology. The effects of the distribution, mean and variability of the lead time are tested and a sensitivity analysis of cost parameters is conducted to validate the proposed control policy. We also show that our policy leads to a significant cost reduction as compared to a combination of a hedging point policy (HPP) and an (s, Q) policy.     

Capacity planning with sequential two-level time constraints in the back-end process of wafer fabrication

A time constraint is a queue-time boundary that is set between particular sequential operations to ensure final product yield. These time boundaries, called ‘sequential time constraints’, can be found in a series of operations on the back-end of wafer fabrication. Wafers exceeding the time constraints are traced through the fabrication process, but generally pass through the remaining processes. Nonetheless, it is a waste of capacity to continue processing wafers with unacceptable yield. Unfortunately, these unacceptable wafers cannot be identified before the wafer acceptance test using the current control policy. This work proposes a control rule for two-level time constraints with capacity planning methodology under this rule. Wafers exceeding the lower time constraints will be treated as normal wafers; however, once wafers exceed the upper time constraint, they will be scrapped immediately. In the capacity planning model, a GI/G/m queuing network is applied to determine the required number of machines. By pre-setting target yields, the rates of wafers being marked or scrapped can be controlled. Furthermore, a novel scheme–regarding machine failures as irregular customers–is introduced to describe the effect of service interruptions. The results show that the proposed control rule and capacity planning model can more effectively resolve the issues of sequential time constraints. Moreover, the results of the analysis indicate that the current capacity expansion policy of the semiconductor industry should be re-examined.     

A spread-sheet model for efficient production and scheduling of a manufacturing line/cell

In this paper we develop an efficient spread-sheet production planning/scheduling model for a resource-constraint production line or a manufacturing cell that produces several products but one at a time with significant changeover time and changeover cost. There are also management and physical constraints related to the operating hours, production capacity and amount of inventory allowed. The production line/cell supplies several products to customers who pull the products according to their own operating policy (working hours) that may be different from manufacture's operating hours. We also show several real-world applications and highlight the benefits and merits of the model.     

Comparisons between drum–buffer–rope and material requirements planning: a case study

Drum–Buffer–Rope (DBR) is an alternative approach to manufacturing planning and control that is not as formally tested as Material Requirements Planning (MRP) systems which have traditionally been around for years. Yet, some reports indicate very good performance for DBR and the associated use of synchronous manufacturing principles. But how do these systems compare and relate to one another? Based on our experiences of studying a Bearing Manufacturing Company that actually made the transition from an MRP system to a DBR system, we conduct simulation-based experiments in this paper with the objective of providing a more formal comparison between these two systems than what has been offered in prior literature. To our knowledge, this is the only study of its kind that uses a real-world setting to evaluate key differences and convergence points between comprehensive MRP and DBR systems. Our results show that even though the MRP and DBR systems position inventory differently and provide different dynamic responses to customer demand, there are several operating policies that can be implemented in either system. While the DBR performance in our simulation model was clearly superior to a nominal MRP implementation, we show that even within the constraints of the structural design of MRP system, policy modification based on DBR principles can significantly reduce these performance differences. This finding has an important implication for practising managers who need not necessarily switch from a MRP system to a DBR type of a system (as was done by our case-study firm) in order to take advantage of attractive features of the DBR system. Future researchers can use our study to understand more fully how these Structural Design and Operating Policy differences can be further exploited to implement unique systems that combine the best features of both DBR and MRP systems.     

Decentralised decision-making in a multi-party supply chain

In this paper, we consider multi-party coordination in a supply chain (SC) that consists of a set of independent producers and a set of resource managers. A decentralised decision-making approach is proposed for a coal SC, with three independent parties – multiple mines, a rail operator and a terminal. The rail operator and the terminal act as common resource managers and connects the independent mines via a rail network. The objective of this SC is to efficiently use an independent rail operator to transport coal from different mines to meet the shipping demand at the terminal. The underlying coordination problem can be seen as a multi-resource constrained scheduling problem. A major part of this paper addresses the key challenges in a decentralised approach based on column generation (CG), which are to compute the value of a column, better upper bounds and to update the multipliers using decentralised methods. We have also discussed the mathematical models for different decision units, the CG algorithm and different strengthening methods. A comprehensive computational experiment based on randomly generated instances highlights the effect of decentralisation and the value of information-sharing. The proposed solution approaches can be extended to a multi-party case with any number of common resources.     

An integrated robust replenishment/production/distribution policy under inventory inaccuracy

In this paper, a centralised production/distribution system is studied, in which the manufacturer controls the processes of raw material ordering, production and final goods distribution. The inventory records of raw materials, work-in-process, final goods in the manufacturer and the retailers are inaccurate. The objective of this research is to develop an integrated policy that can hedge against the negative impact of inventory inaccuracy at all stages of a supply chain. To achieve this aim, a backward recursive integrated robust policy is constructed. First, the time, quantity and vehicle route of the next distribution are forecasted by a distribution sub-policy; then the forecasted distribution time and quantity are used as the objective of the production process control, which is implemented by a production control sub-policy. Raw materials are ordered by a replenishment sub-policy according to the forecasted raw materials consumption in the production process. Numerical experiments are conducted to verify the feasibility and robustness of the proposed policy, which shows that besides of deploying radio frequency identification (RFID) devices in inventory management systems for raw materials, WIPs, final goods of the manufacturer and the retailers, integrated robust policies also can be employed to hedge against the impact of inventory inaccuracy.     

Manufacturing planning and control approaches: market alignment and performance

The design of manufacturing planning and control (MPC) systems is a strategic decision for manufacturing operations. In this paper we analyze the interrelationships between the choice of MPC approaches at different hierarchical levels with market requirements and operational performance. These relationships are explored through an extensive survey comprising responses from 128 manufacturing firms. The results show that the choice of MPC approaches, primarily at the sales and operations planning and master scheduling levels, has a significant mediating role in improving performance. The alignment between market requirements and the choice of MPC approaches is significant and has a significant impact on performance. In a dynamic environment, the choice of MPC approaches is shown to have a positive mediating effect on operational performance.     

基于供需一体化协同控制的供应链库存模型

根据供需一体化协同库存控制策略，构建了一个一体化库存模型。在模型里，假定提前期需求是随机的且服从正态分布，允许在提前期内发生缺货，并产生缺货成本；假定提前期是可控的，能够通过增加成本缩短提前期。该模型通过同时计算最优订货量、再订货点、提前期以及运送批次．使供需一体化库存总成本最小。实例分析证明该模型能够达到节约成本的目的。     

Analyzing the effects of inventory cost setting rules in a disassembly and recovery environment

In this study we consider a disassembly and recovery facility receiving end-of-life products and facing demand for a specific part that is disassembled from the product and then recovered. The disassembly and recovery operations can be either performed before hand, or upon customer arrival. In the latter case, a discount on the selling price is applied to compensate the customer for waiting for the completion of the disassembly and recovery operations. One of the difficulties faced in planning for such a system is the determination of the opportunity cost associated with carrying recovered parts inventory. The difficulty arises in seeking the value added to the part given the costs incurred for maintaining the product return, disassembly and recovery costs and revenue earned from the hulk, that is the remaining product after the disassembly of the part. The main objective of the study is to investigate the effect of different rules to determine this opportunity cost on the performance of the system. Six rules are considered in the study. The performance of the rules is assessed by a computational study under an approximate inventory control policy.     

Inventory performance of some supply chain inventory policies under impulse demands

This paper attempts to study the impact of impulsive demand disturbances on the inventory-based performance of some inventory control policies. The supply chain is modelled as a network of autonomous supply chain nodes. The customer places a constant demand except for a brief period of sudden and steep change in demand (called demand impulse). Under this setting, the behaviour of each inventory policy is analysed for inventory performance of each node. It is found that the independent decision-making by each node leads to a bullwhip effect in the supply chain whereby demand information is amplified and distorted. However, under a scenario where the retailer places a constant order irrespective of the end customer demand, the inventory variance was actually found to decrease along the supply chain. The variance of the inventory remained constant along the chain when only the actual demands are transmitted by each node. The results also showed that the inventory policy which is best for one supply chain node is generally less efficient from a supply chain perspective. Moreover, the policy which performs poorly for one node can be most efficient for the supply chain. In a way, our results also provide a case for coordinated inventory management in the supply chain where all members prepare a joint inventory management policy that is beneficial for all the supply chain nodes. The results have significant industrial implications.     

Dynamic programming algorithms for multi-stage safety stock optimization

The task of multi-stage safety stock optimization is very complex. Therefore, simplifying models with specific assumptions are considered. In this paper, the inventory system is controlled by a base-stock policy where each stockpoint of the inventory system follows a periodically reviewed order-up-to policy. End item demands are assumed to be normally distributed. To reduce the occurrences or size and duration of internal and external stockouts, appropriate service level constraints are specified for all items. Applying such a control policy within systems of serial, convergent or divergent structure, solution properties hold which reduce the solution set to a limited number of cut-levels. Dynamic programming allows to evaluate the relevant alternatives with little computational effort. For the serial system, both a forward and a backward recursion with different types of service levels are presented and extended to a backward algorithm for divergent and a forward algorithm for convergent systems. Bounds for the complexity of the algorithms are discussed and numerical examples are presented to demonstrate differences in size and allocation of safety stocks according to the prespecified type of service level.     

Linking manufacturing strategy decisions on process choice with manufacturing planning and control systems

For any manufacturing firm, theory suggests that the firm is better off if the manufacturing planning and control (MPC) system supports the market strategy as well as the manufacturing strategy. Typically, the strongest link between market requirements and manufacturing strategy concerns the process choice, i.e. choosing a manufacturing process that supports a firm's competitive priorities. The general aim of this paper is to examine the role of the MPC system in a manufacturing strategy. More specifically, the purpose is to link market requirements, product characteristics, and the process choice to the MPC system. A special focus will be placed on the link between the process choice and the design of the MPC system. Two key factors are identified as major processspecific elements influencing the MPC system design: the number of planning points, and set-up times at individual resources. The process choice affects the lower planning levels of the MPC systems, where the physical reality of the plant becomes apparent. This is especially true for production activity control, but also for requirements planning (material and capacity). Concerning the MPC system design for longer-term planning, such as sales and operations planning and master scheduling, the impact from market requirements and product characteristics dominates.     

Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments

Firms currently operate in highly competitive scenarios, where the environmental conditions evolve over time. Many factors intervene simultaneously and their hard-to-interpret interactions throughout the supply chain greatly complicate decision-making. The complexity clearly manifests itself in the field of inventory management, in which determining the optimal replenishment rule often becomes an intractable problem. This paper applies machine learning to help managers understand these complex scenarios and better manage the inventory flow. Building on a dynamic framework, we employ an inductive learning algorithm for setting the most appropriate replenishment policy over time by reacting to the environmental changes. This approach proves to be effective in a three-echelon supply chain where the scenario is defined by seven variables (cost structure, demand variability, three lead times, and two partners’ inventory policy). Considering four alternatives, the algorithm determines the best replenishment rule around 88% of the time. This leads to a noticeable reduction of operating costs against static alternatives. Interestingly, we observe that the nodes are much more sensitive to inventory decisions in the lower echelons than in the upper echelons of the supply chain.     

Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain

Recently, the applications of Blockchain technology have begun to revolutionise different aspects of supply chain (SC) management. Among others, Blockchain is a platform to execute the smart contracts in the SC as transactions. We develop and test a new model for smart contract design in the SC with multiple logistics service providers and show that this problem can be presented as a multi-processor flexible flow shop scheduling. A distinctive feature of our approach is that the execution of physical operations is modelled inside the start and completion of cyber information services. We name this modelling concept ‘virtual operation’. The constructed model and the developed experimental environment constitute an event-driven dynamic approach to task and service composition when designing the smart contract. Our approach is also of value when considering the contract execution stage. The use of state control variables in our model allows for operations status updates in the Blockchain that in turn, feeds automated information feedbacks, disruption detection and control of contract execution. The latter launches the re-scheduling procedure, comprehensively combining planning and adaptation decisions within a unified methodological framework of dynamic control theory. The modelling complex developed can be used to design and control smart contracts in the SC.     

Robust production control policy for a multiple machines and multiple product-types manufacturing system with inventory inaccuracy

Inventory inaccuracy often exists in manufacturing systems, which has great negative impact on the performance of production control, e.g. very high work-in-process holding cost or backlog penalty. To hedge against inventory inaccuracy, the robust production control problems will be investigated for a multiple machines and multiple product-types manufacturing system with uncertain production capacity. The objective of our problem is to minimise the average production cost. To solve this problem, a robust production control policy is developed, which is insensitive to the inventory record errors, and whose robustness is better than the traditional hedging point policy for optimal production control. Finally, numerical experiments are conducted to examine the performance of the proposed robust production control policy against inventory inaccuracy. Based on the experimental results, the conditions of applying the proposed policy are also obtained.     

A combined model predictive control and time series forecasting framework for production-inventory systems

Model Predictive Control (MPC) has been previously applied to supply chain problems with promising results; however most systems that have been proposed so far possess no information on future demand. The incorporation of a forecasting methodology in an MPC framework can promote the efficiency of control actions by providing insight in the future. In this paper this possibility is explored, by proposing a complete management framework for production-inventory systems that is based on MPC and on a neural network time series forecasting model. The proposed framework is tested on industrial data in order to assess the efficiency of the method and the impact of forecast accuracy on the overall control performance. To this end, the proposed method is compared with several alternative forecasting approaches that are implemented on the same industrial dataset. The results show that the proposed scheme can improve significantly the performance of the production-inventory system, due to the fact that more accurate predictions are provided to the formulation of the MPC optimization problem that is solved in real time.     

Production control in a complex production system using approximate dynamic programming

Development of an efficient production and inventory control policy for a production system with multiple working stations, intermediate components and end products is difficult. In particular, uncertain demand and large changeover times at the work stations cause significant problems. In this paper, we consider an assembly line for dishwashers which require multiple types of wire racks that must be fabricated and coated at different work centres before supplying the assembly lines. An approximate dynamic programming (ADP) method is proposed to address the complexities associated with such a system. In addition, an Artificial Neural Network model is designed to approximate state values of the system, thus helping the system to make decisions at particular states. A near optimal production and inventory control policy is developed through an ADP algorithm. The proposed method can be extended to any similar system.     

Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain†

The ripple effect refers to structural dynamics and describes a downstream propagation of the downscaling in demand fulfilment in the supply chain (SC) as a result of a severe disruption. The bullwhip effect refers to operational dynamics and amplifies in the upstream direction as ordering oscillations. Being interested in uncovering if the ripple effect can be a driver of the bullwhip effect, we performed a simulation-based study to investigate the interrelations of the structural and operational dynamics in the SC. The results advance our knowledge about both ripple and bullwhip effects and reveal, for the first time, that the ripple effect can be a bullwhip-effect driver, while the latter can be launched by a severe disruption even in the downstream direction. The findings show that the ripple effect influences the bullwhip effect through backlog accumulation over the disruption time as a consequence of non-coordinated ordering and production planning policies. To cope with this effect, a contingent production-inventory control policy is proposed that provides results in favour of information coordination in SC disruption management to mitigate both ripple and bullwhip effects. The SC managers need to take into account the risk of bullwhip effect during the capacity disruption and recovery periods.