Original Production Plan Research Articles

Research on Blast Furnace Ingredient Optimization Based on Improved Grey Wolf Optimization Algorithm

Blast furnace ironmaking plays an important role in modern industry and the development of the economy. A reasonable ingredient scheme is crucial for energy efficiency and emission reduction in blast furnace production. Determining the right blast furnace ingredients is a complicated process; therefore, this study examines the optimization of the ingredient ratio. In this paper a model of the blast furnace ingredients is established by considering cost of per ton iron, CO2 emissions, and the theoretical coke ratio as the objective functions; ingredient parameters, process parameters, main and by-product parameters as variables; and the blast furnace smelting theory and equilibrium equation as constraints. Then, the model is solved by using an improved grey wolf optimization algorithm and an improved multi-objective grey wolf optimization algorithm. Using the data collected from the steel mill, the conclusion is that multi-objective optimization can consider the indexes of each target, so that the values of all the targets are excellent; we also compared the multi-objective solution results with the original production scheme of the steel mill, and we found that using the blast furnace ingredient scheme optimized in this study can reduce the cost of iron per ton, CO2 emissions per ton, and the theoretical coke ratio in blast furnace production by 350 CNY/t, 1000 kg/t, and 20 kg/t, respectively, compared with the original production plan. Thus, steel mill decision makers can choose the blast furnace ingredients according to different business strategies and the actual needs of steel mills can be better met.

Delay time model for determining the effectiveness of a system under adverse conditions of production and inspection

The effectiveness of production is influenced by different events that may disrupt the original production plan, such as degradation in the production process, additional demands and inspection errors. To analyze the impact of these issues on production planning, we propose a delay time model for a single-component system that can experience four different states: good, minor defective, major defective and failed. The model uses cost-rate as a measure of production effectiveness. We conducted an investigation into the consequences of the degradation process, specifically focusing on its impact on the generation of nonconforming products. Additionally, we examined the effects of additional demands on the rate of rework products and analyzed the impact of inspection errors, including both the induction and non-detection of defects. These issues represent adverse conditions that many production systems run into their operation and maintenance processes. The results of our study advise the best strategies to deal with these adverse conditions in order to minimize their impacts on effectiveness of a production system. The proposed model serves as a guide and illustrative example of how these issues can be incorporated in the process of optimizing the effectiveness of production. From this perspective, this paper’s main contribution is a series of insights that widens the manager’s view about the production process in a real system, in which perfect and suitable conditions are not commonly present.

A dynamic scheduling method with Conv-Dueling and generalized representation based on reinforcement learning

In modern industrial manufacturing, there are uncertain dynamic disturbances between processing machines and jobs which will disrupt the original production plan. This research focuses on dynamic multi-objective flexible scheduling problems such as the multi-constraint relationship among machines, jobs, and uncertain disturbance events. The possible disturbance events include job insertion, machine breakdown, and processing time change. The paper proposes a conv-dueling network model, a multidimensional state representation of the job processing information, and multiple scheduling objectives for minimizing makespan and delay time, while maximizing the completion punctuality rate. We design a multidimensional state space that includes job and machine processing information, an efficient and complete intelligent agent scheduling action space, and a compound scheduling reward function that combines the main task and the branch task. The unsupervised training of the network model utilizes the dueling-double-deep Q-network (D3QN) algorithm. Finally, based on the multi-constraint and multi-disturbance production environment information, the multidimensional state representation matrix of the job is used as input and the optimal scheduling rules are output after the feature extraction of the conv-dueling network model and decision making. This study carries out simulation experiments on 50 test cases. The results show the proposed conv-dueling network model can quickly converge for DQN, DDQN, and D3QN algorithms, and has good stability and universality. The experimental results indicate that the scheduling algorithm proposed in this paper outperforms DQN, DDQN, and single scheduling algorithms in all three scheduling objectives. It also demonstrates high robustness and excellent comprehensive scheduling performance.

Hyper-heuristic for steelmaking casting rescheduling based on strong disturbance

The stochastic disturbance in steelmaking casting often leads to failure of the original production plan. Rescheduling is necessary for the steel manufacturing system. In the background of the increasing scale of scheduling and the numerous kinds of disturbance, the different algorithms are designed for different rescheduling problems, which will greatly increase the workload of the scheduling. In this paper, the disturbances are classified as strong disturbance and weak disturbance, and a rescheduling driven rule of strong disturbance is designed. Especially, the various disturbances impact on the production plan could be reflected in a time change, so a rescheduling model under time strong disturbance is established. In this paper, a hyper-heuristic genetic algorithm (HHGA) is designed based on the heuristic rules and genetic algorithms. This combination is intended to reduce the generation time of rescheduling plan under the strong disturbance. In the end, the simulation results indicate the success with the HHGA.

Hyper-heuristic for steelmaking casting rescheduling based on strong disturbance

The stochastic disturbance in steelmaking casting often leads to failure of the original production plan. Rescheduling is necessary for the steel manufacturing system. In the background of the increasing scale of scheduling and the numerous kinds of disturbance, the different algorithms are designed for different rescheduling problems, which will greatly increase the workload of the scheduling. In this paper, the disturbances are classified as strong disturbance and weak disturbance, and a rescheduling driven rule of strong disturbance is designed. Especially, the various disturbances impact on the production plan could be reflected in a time change, so a rescheduling model under time strong disturbance is established. In this paper, a hyper-heuristic genetic algorithm (HHGA) is designed based on the heuristic rules and genetic algorithms. This combination is intended to reduce the generation time of rescheduling plan under the strong disturbance. In the end, the simulation results indicate the success with the HHGA.

Optimal production and inventory decisions under demand and production disruptions

Unanticipated events may take place and disrupt demand and/or production in a supply chain. Conditional on the type, magnitude and duration of disruptions, changes may be called to revise the original production plan. We analyse different disruption scenarios and propose optimal production–inventory models for products facing demand and production disruptions. To lower the cost, we optimise the production run time, purchasing times and order quantity for the manufacturer. Numerical experiments are conducted to examine the influences of disruption time and magnitude on optimal production and purchasing decisions.

Advertising and Pricing Decisions in a Manufacturer-Retailer Channel with Demand and Cost Disruptions

This paper investigates advertising level, pricing and production quantity decision problems in a supply chain when demand and production cost disruptions occur simultaneously. The supply chain consists of one manufacturer and one retailer where customer demand depends on the retail price and advertisement expenditure. The authors examine this problem in two game-theoretic models: a cooperative game and a non-cooperative Stackelberg-manufacturer game, respectively. Propositions and insights are developed from the analysis of these models. The original production plan has some robustness in face of the disruptions of demand and production cost in both cooperative and non-cooperative models. Only when the disruptions exceed some thresholds will the channel members take an overall adjustment. In addition, the cooperative channel achieves higher channel profits and higher social surplus than the non-cooperative model by using a lower retail price and a higher level of advertising efforts to promote demand.

Disruptions management of a supply chain under strategic subsidy policy for the demand-stimulating inventory

This paper introduces a game model of one manufacturer and one retailer with the demand depending on the amount of inventory displayed on the retailer’s shelf. We study coordination mechanisms of the supply chain with the two kinds of disruptions. To coordinate the channel as well as make a profit, the manufacturer needs to augment the wholesale price lever by another, i.e., an inventory-holding cost subsidy to the retailer. We show that the inventory-subsidy contract for disruption(s) situation has its rationality and limitation: from the perspective of feasibility analysis, we find that when the disrupted amount of inventory-holding cost is larger than a certain threshold value, both players can achieve win–win by using inventory-subsidy contract. Otherwise, it may be ineffective. For two-factor disruptions, there are some mutual restraints between the disrupted inventory-holding cost and the disrupted demand when the coordination mechanism is used. We also find that both disruption situations have their own robust scales, in which the manufacturer should not change the original production plan but at the expense of providing a more attractive subsidy scheme to the retailer. Interestingly, some counter-intuitive managerial insights can be observed in robust scales. For example, the market demand increases with the displayed inventory level in the setting of the demand-stimulating inventory. However, the higher the demand, the less displayed inventory level will be in the robust scale.

Pricing and production decisions in a dual-channel supply chain when production costs are disrupted

This paper studies a pricing and production problem in a dual-channel supply chain when production costs are disrupted. When a production cost disruption occurs, the original production plan which is designed based on the initially estimated production cost needs to be revised. It is necessary to explicitly consider possible related deviation costs caused by changes of the original production plan. We consider this problem in the centralized and decentralized dual-channel supply chain, respectively. The optimal prices and production quantity under production cost disruptions are derived. We find that the original production plan has some robustness with production cost disruptions. Only when the production cost disruption exceeds some thresholds will the decision-maker change the production quantity. The production cost disruption robustness region in the centralized dual-channel supply chain is the same as that in the decentralized dual-channel supply chain. In the centralized dual-channel supply chain, it is always beneficial for the central decision-maker to take timely response to the disruptions and utilize the revised strategy under production cost disruptions. In the decentralized dual-channel supply chain we characterize a threshold. If customers' preference for the direct channel is below the threshold, the optimal direct sale price equals the wholesale price; otherwise, the optimal direct sale price and wholesale price equals the optimal direct sale price and retail price in the centralized dual-channel supply chain. The manufacturer always gets better off if he takes timely response to the production cost disruptions, while the retailer gets better off only if the production cost disruption is negative and gets worse off otherwise.

An analysis of supply chain decisions with asymmetrical retailers : effects of disruptions and static service cost on coordination mechanism

The risk of demand or production cost disruption is one of the challenging problems in the supply chain management. This paper explores a generalized supply chain game model incorporating the possible disruptions. We find that a nonlinear Grove wholesale price scheme can fully coordinate such a supply chain even when both market demand and production cost are disrupted. The nonlinear Grove wholesale price scheme has three sides to coordinate the decision behavior of the players. One is that the mechanism can induce the retail pricing decided by the dominant retailer to be equal with that of the channel; the second is that the mechanism can induce fringe retailers to be not priced out of the market; the third is that the mechanism can ensure that the manufacturer uses minimum incentives to induce the dominant retailer to sell its product as well as providing the demand-stimulating service. Disruptions from both demand side and production cost side may also affect the wholesale price, order quantity as well as retail price, and the share of the dominant retailer and the subsidy rate provided by the manufacturer are unity of opposites. We also find that it is optimal for the manufacturer to keep the original production plan when the joint-disruption amount is sufficiently small.

Pricing and production decisions in dual-channel supply chains with demand disruptions

This paper develops a two-period pricing and production decision model in a one- manufacturer-one-retailer dual-channel supply chain that experiences a disruption in demand during the planning horizon. While disruption management has long been a key research issue in supply chain management, little attention has been given to disruption management in a dual-channel supply chain once the original production plan has been made. Generally, changes to the original production plan induced by a disruption may impose considerable deviation costs throughout the supply chain system. In this paper, we examine how to adjust the prices and the production plan so that the potential maximal profit is obtained under a disruption scenario. We first study the scenario where the manufacturer and the retailer are vertically integrated with demand disruptions. Then we further assume that the manufacturer bears the deviation costs and obtain the manufacturer’s and the retailer’s individual optimal pricing decision, as well as the manufacturer’s optimal production quantity in a decentralized decision-making setting. We derive conditions under which the maximum profit can be achieved. The results indicate that the optimal production quantity has some robustness under a demand disruption, in both centralized and decentralized dual-channel supply chains. We also find that the optimal pricing decisions are affected by customers’ preference for the direct channel and the market scale change, in both centralized and decentralized dual-channel supply chains.

THE VALUE OF INFORMATION IN SALMON FARMING. HARVESTING THE RIGHT FISH AT THE RIGHT TIME

Research on management practices in fish farming has traditionally focused on two topics: production planning and forecasting of prices. This article combines these two areas of research, and illustrates how information on price patterns can change production plans, and hence increase the value of the farm enterprise. It will present a model farm and illustrate, with different levels of price information, how information on future prices alters the original production plan and hence creates extra value for the farmer. Although the specifics and empirical application of the paper are on salmon and salmon farming, the ideas and general results could be applied to all farmed species.

Coordinating dyadic supply chains when production costs are disrupted

This paper studies a supply chain coordination problem under production cost disruptions. When a production cost disruption occurs, the coordination scheme designed for the initially estimated production cost needs to be revised. To resolve this issue, it is necessary to explicitly consider the possible deviation costs caused by any changes in the original production plan. How to model the production cost disruptions and their impacts, and how to design coordination schemes under disruptions is discussed. Results obtained under various scenarios including the cases of a single retailer and multiple retailers, the cases of linear and nonlinear demand-price functions, and different wholesale policies are presented.