scholarly journals Multiple Precast Component Orders Acceptance and Scheduling

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Wen Jiang ◽  
Lanjun Wu ◽  
Yunzhong Cao
Keyword(s):  
Production Capacity ◽  
Order Acceptance ◽  
Numerical Examples ◽  
Scheduling Model ◽  
Proposed Model ◽  
Multiple Orders ◽  
Order Acceptance And Scheduling ◽  
Total Profit ◽  
Component Order

Precast components manufacturer generally operates under limited production capacity and produces products of one order which may delay another. This paper develops a precast component order acceptance and scheduling model that aims to maximize the total profit in a stochastic multiple orders environment. In that model, the increasing of the overall profit of the precast component manufacturer is achieved by using a heuristic algorithm and a dynamic order acceptance heuristic. Results of numerical examples indicate the proposed model realizes the increasing total profit in most cases comparing to accept all of the orders. Besides, this study tested three order acceptance criteria and found that the profit-based criterion is to be more stable in terms of maximum total profit. This approach is anticipated to provide support to precast component manufacturers when faced with multiple orders in long-term production.

scholarly journals Long-Term Generation Scheduling for Cascade Hydropower Plants Considering Price Correlation between Multiple Markets

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2239
Author(s):  
Bin Luo ◽  
Shumin Miao ◽  
Chuntian Cheng ◽  
Yi Lei ◽  
Gang Chen ◽  
...  
Keyword(s):  
Electricity Market ◽  
Large Scale ◽  
Copula Function ◽  
Market Environment ◽  
Scheduling Model ◽  
Multiple Markets ◽  
Generation Scheduling ◽  
Hydropower Plants ◽  
Proposed Model

The large-scale cascade hydropower plants in southwestern China now challenge a multi-market environment in the new round of electricity market reform. They not only have to supply the load for the local provincial market, but also need to deliver electricity to the central and eastern load centers in external markets, which makes the generation scheduling much more complicated, with a correlated uncertain market environment. Considering the uncertainty of prices and correlation between multiple markets, this paper has proposed a novel optimization model of long-term generation scheduling for cascade hydropower plants in multiple markets to seek for the maximization of overall benefits. The Copula function is introduced to describe the correlation of stochastic prices between multiple markets. The price scenarios that obey the Copula fitting function are then generated and further reduced by using a scenario reduction strategy that combines hierarchical clustering and inconsistent values. The proposed model is applied to perform the long-term generation scheduling for the Wu River cascade hydropower plants and achieves an increase of 106.93 million yuan of annual income compared with the conventional scheduling model, without considering price scenarios, showing better performance in effectiveness and robustness in multiple markets.

Customer's order acceptance and scheduling to maximise total profit

2019 ◽  
Vol 34 (2) ◽  
pp. 301
Author(s):  
Mohammad Reisi Nafchi ◽  
Naser Mollaverdi ◽  
Mehdi Fazeli Kebria ◽  
Ghasem Moslehi
Keyword(s):  
Order Acceptance ◽  
Order Acceptance And Scheduling ◽  
Total Profit

scholarly journals An Inventory Model for Deteriorating Items with Stock Dependent Demand and Partial Backlogging

2021 ◽  
Vol 9 (12) ◽  
pp. 296-302
Author(s):  
Vikas Kumar
Keyword(s):  
Waiting Time ◽  
Simple Algorithm ◽  
Inventory Model ◽  
Deteriorating Items ◽  
Partial Backlogging ◽  
Numerical Examples ◽  
Proposed Model ◽  
Stock Dependent Demand ◽  
Total Profit ◽  
Dependent Demand

Abstract: In this paper, we formulate a deteriorating inventory model with stock-dependent demand Moreover, it is assumed that the shortages are allowed and partially backlogged, depending on the length of the waiting time for the next replenishment. The objective is to find the optimal replenishment to maximizing the total profit per unit time. We then provide a simple algorithm to find the optimal replenishment schedule for the proposed model. Finally, we use some numerical examples to illustrate the model. Keywords- Inventory, Deteriorating items, Stock dependent demand, Partial backlogging

Supply Chain Network Design of Ethanol in Thailand

2015 ◽  
Vol 781 ◽  
pp. 647-650
Author(s):  
Rojanee Homchalee ◽  
Weerapat Sessomboon
Keyword(s):  
Supply Chain ◽  
Production Capacity ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Allocation Model ◽  
Total Cost ◽  
Location Allocation ◽  
Proposed Model ◽  
Direct Distribution

The proposed model is location-allocation model developed to design and manage the plants-to-customers ethanol supply chain in Thailand with the objective to minimize the total cost. The results showed that Thailand should have only one ethanol export depot and central depot located along wharfs in Samut Prakan province and along the highway in Non Sung district, Nakhon Ratchasima province, respectively. This model also provided the solutions on opening and expanding of production capacity of ethanol plants over time and appropriate ethanol allocation both of direct distribution and through the central depot for long term (2012-2021).

scholarly journals Optimal Transfer-Ordering Strategy for a Deteriorating Inventory in Declining Market

2009 ◽  
Vol 2009 ◽  
pp. 1-15 ◽  
Author(s):  
Nita H. Shah ◽  
Kunal T. Shukla
Keyword(s):  
Optimal Strategy ◽  
Transportation Cost ◽  
Numerical Examples ◽  
Optimal Transfer ◽  
Display Area ◽  
Proposed Model ◽  
Constant Rate ◽  
Total Profit

The retailer's optimal procurement quantity and the number of transfers from the warehouse to the display area are determined when demand is decreasing due to recession and items in inventory are subject to deterioration at a constant rate. The objective is to maximize the retailer's total profit per unit time. The algorithms are derived to find the optimal strategy by retailer. Numerical examples are given to illustrate the proposed model. It is observed that during recession when demand is decreasing, retailer should keep a check on transportation cost and ordering cost. The display units in the show room may attract the customer.

Customer's order acceptance and scheduling to maximise total profit

2019 ◽  
Vol 34 (2) ◽  
pp. 301
Author(s):  
Mehdi Fazeli Kebria ◽  
Ghasem Moslehi ◽  
Naser Mollaverdi ◽  
Mohammad Reisi Nafchi
Keyword(s):  
Order Acceptance ◽  
Order Acceptance And Scheduling ◽  
Total Profit

scholarly journals Integrated inventory model involving quality improvement investment and advance-cash-credit payments

2021 ◽  
Author(s):  
Chih-Te Yang ◽  
Chien-Hsiu Huang ◽  
Liang-Yuh Ouyang
Keyword(s):  
Optimal Solution ◽  
Inventory Model ◽  
Decision Makers ◽  
Optimal Solutions ◽  
Order Quantity ◽  
Defective Items ◽  
Numerical Examples ◽  
Proposed Model ◽  
Production Inventory ◽  
Total Profit

This paper investigates the effects of investment and inspection policies on an integrated production–inventory model involving defective items and upstream advance-cash-credit payment provided by the supplier. In this model, retailers offer customers a downstream credit period. Furthermore, the defective rate of the item can be improved through capital co-investment by the supplier and retailer. The objective of this study was to determine the optimal shipping quantity, order quantity, and investment alternatives for maximizing the supply chain's joint total profit per unit time. An algorithm was developed to obtain the optimal solution for the proposed problem. Several numerical examples are used to demonstrate the proposed model and analyze the effects of parameters changes on the optimal solutions. Finally, management implications for relevant decision makers are obtained from the numerical examples.

Modeling of unforced demand response programs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hassan Jalili ◽  
Pierluigi Siano
Keyword(s):  
Demand Response ◽  
Load Shifting ◽  
Response Models ◽  
Temperature Changes ◽  
Proposed Model ◽  
Equipment Failures ◽  
Demand Response Programs ◽  
Simultaneous Implementation ◽  
Long Term Studies

Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

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