COMPUTER AIDED ASSEMBLY PLANNING USING MS EXCEL SOFTWARE – A CASE STUDY

The issue of planning assembly operations remains crucial decision-making area for many of manufacturing companies. It becomes particularly significant in case of small and medium enterprises that perform unit or small-scale production, where the option of applying specialized software is often very limited – both due to high purchase price, but also due to its applicability to single unit manufacturing, that is executed based on individual customer orders. The present article describes the possibility of applying the MS Excel spreadsheet in the planning of machine assembly processes. It emphasises, in particular, the method for using the spreadsheet in subsequent stages of the process, and the identification of possible causes that have impact on problems with the planning process. We performed our analysis on the basis of actual data from one of the machine industry enterprises that manufactures in central Poland

Revenue management in an assemble-to-order production system

In this paper, we consider demand management decisions for an assemble-to-order production system in which both the availability of intermediate material and assembly capacity are limited. For each incoming order, the manufacturer must decide whether to accept it and what due date to quote for an accepted order. The actual assembly dates are still subject to change after these decisions, and a production schedule must be maintained to guarantee that the quoted due dates are met. Therefore, the decisions on accepting orders and quoting due dates must be made with incomplete knowledge of the actual resources used to fulfill the orders. To address these factors, we model this situation and develop a novel revenue management approach using bid prices. An extensive numerical study demonstrates the good performance of the proposed approach in comparison with benchmark algorithms and an ex-post optimal solution applied over a wide range of different supply and demand scenarios. Our results suggest that the consideration of assembly capacity constraints is more vital than the consideration of intermediate material constraints in our test cases

A Continuous Review inventory Control Model within Batch Arrival Queuing Framework: A Parameter-Tuned Imperialist Competitive Algorithm

In this paper, a multi-product continues review inventory control problem within batch arrival queuing approach (MQr/M/1) is modeled to find the optimal quantities of maximum inventory. The objective function is to minimize summation of ordering, holding and shortage costs under warehouse space, service level, and expected lost-sales shortage cost constraints from retailer and warehouse viewpoints. Since the proposed model is Np-Hard, an efficient imperialist competitive algorithm (ICA) is proposed to solve the model. To justify proposed ICA, a simulated annealing algorithm has been utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure is executed. Finally, the performance of the proposed ICA is analyzed using some numerical illustrations

Optimal Policies for the Management of a Plug-In Hybrid Electric Vehicle Swap Station

Optimizing operations at plug-in hybrid electric vehicle (PHEV) battery swap stations is internally motivated by the movement to make transportation cleaner and more efficient. A PHEV swap station allows PHEV owners to quickly exchange their depleted PHEV battery for a fully charged battery. The PHEV-Swap Station Management Problem (PHEV-SSMP) is introduced, which models battery charging and discharging operations at a PHEV swap station facing nonstationary, stochastic demand for battery swaps, nonstationary prices for charging depleted batteries, and nonstationary prices for discharging fully charged batteries. Discharging through vehicle-to-grid is beneficial for aiding power load balancing. The objective of the PHEV-SSMP is to determine the optimal policy for charging and discharging batteries that maximizes expected total profit over a fixed time horizon. The PHEV-SSMP is formulated as a finite-horizon, discrete-time Markov decision problem and an optimal policy is found using dynamic programming. Structural properties are derived, to include sufficiency conditions that ensure the existence of a monotone optimal policy. A computational experiment is developed using realistic demand and electricity pricing data. The optimal policy is compared to two benchmark policies which are easily implementable by PHEV swap station managers. Two designed experiments are conducted to obtain policy insights regarding the management of PHEV swap stations. These insights include the minimum battery level in relationship to PHEVs in a local area, the incentive necessary to discharge, and the viability of PHEV swap stations under many conditions

Inventory consideration and management in two supply chain problems

Ph.DDOCTOR OF PHILOSOPH

Bid price-based revenue management approaches in manufacturing industries

Whenever demand exceeds capacity, the available resources must be allocated to the incoming demand. A simple first-come-first-served allocation can lead to rather poor results if the demand is heterogeneous, i.e., if orders differ in their strategic importance for a company, their willingness to pay, their lead time requirements, etc. In addition, the decisions must be made under uncertainty because demand is often stochastic. If too many low value orders are rejected, capacity might remain unused. On the other hand, if too many low value orders are accepted, there might be not enough capacity to accept all high value orders. Apart from the order acceptance decision, it might also be preferable to delay the order release of already accepted orders, even if capacity is available, to reserve capacity for future high value orders. This thesis considers the demand management decisions in different order-driven production environments. Orders are accepted or rejected immediately upon arrival, and at the beginning of each planning period, the set of orders to be released is selected. As exact solution methods are computationally intractable, bid price-based revenue management approaches are applied to maximize the contribution margin gained by accepted orders minus holding and backlog costs. The first essay considers a deterministic single-stage assembly process, where both capacity and intermediate materials are scarce resources. In this essay, for each accepted order a due date is quoted and the accepted orders are scheduled such that the quoted due dates are kept. The second essay considers a deterministic multi-stage make-to-order production system. In this study, intermediate materials are assumed to be unlimited available. The third essay considers a multi-stage make-to-order production system with stochastic influences. To take into account that in these production systems, lead times depend non-linearly on the load of the system, clearing functions are used to model the production system

Coordinated planning in revenue management

Revenue management has been applied in service industries for more than thirty years. Since then, revenue management has been transferred to other industries like manufacturing or e- fulfillment. Short-term revenue management decisions are taken based on other, longer-term decisions such as decisions about actual capacity, segment-based prices or the price fences in place. While optimization approaches have been developed for each of these planning tasks in isolation, existing approaches typically do not consider interactions between planning tasks. This thesis considers coordinated planning in revenue management, that is the interaction of revenue management decisions with other planning tasks. First, we provide an overview of both the literature on coordinated decision making in the context of revenue management in different industries, and the literature on existing frameworks, which aim to structure the planning tasks around revenue management. We find that the planning tasks relevant to revenue management differ across the industries considered. Moreover, planning tasks are relevant on different hierarchical levels in different industries. We discuss an approach for an industry-independent framework. Based on the relevant planning tasks identified, we investigate the long-term performance of revenue management and therefore the integration of revenue management and customer relationship management. We present a stochastic dynamic programming approach, where the firm’s allocation decision impacts future customer demands by influencing the repurchase probabilities of customers, depending on whether their request has been accepted or rejected. We show that a protection level policy is not necessarily optimal in a two-period setting. In a numerical study, we find that the value of looking ahead in time is low on average but may be substantial in some scenarios. However, the benefit from regular demand updates is considerably higher than the additional value of looking ahead in time on average. Lastly, we investigate the interaction of revenue management and fencing. We account for the trade-off between price-driven demand leakage on the one hand and costs for fencing on the other hand. We show that fencing decisions have an impact on the optimal capacity allocation, but that this is not the case vice versa as the fencing decision does not depend on the allocation decision. Taking both decisions sequentially is therefore optimal. We extend our approach in order to account for additional stock-out-based demand substitution. Then, both decisions depend on each other and firms should take both decisions simultaneously