Production Planning with Inventory-Based Financing

Optimal electric vehicle production strategy under subsidy and battery recycling

The aim of the paper is to develop an optimization model to support the analysis of decision-making on Macedonian family farms. For this purpose, normative linear programming paradigm is applied, utilising its optimization potential. The model is tested and presented on vegetable farm level. Hence, the model provides optimal vegetable productions plan, taking into consideration relevant technological and resource limitations faced by the farm. The results are given in base scenario format and are compared with the optimum production plans within the alternative model scenarios. The optimal production structure in all scenarios indicates that the optimal solution is given on production of tomato, pepper, cabbage, carrot and bean, thus corresponding to the most frequent types of vegetables in the Macedonian agriculture. The optimal production plan also confirms the diversification as a characteristic of the Macedonian agriculture. The base scenario reveals a total net return over the variable costs of 17,924 € which is highest compared to other scenarios. The working capital available is a binding constraint in second and third scenario, where the optimal solution reveals that the land resource is not exhausted i.e. an arable land of about 3 ha remains. Furthermore, the analysis of the crop rotation reflects the seasonal character of the vegetable production. Gross margin sensitivity was examined using the working capital parameterisation. The model is quite flexible thus enabling different crop enterprises to be added additionally.

A mathematical programming approach to optimize process plant performance subject to equipment failure is presented. The optimal production planning and scheduling in multipurpose process plants involves the efficient utilization of assets and resources to produce a number of products so as to satisfy market demands while optimizing a performance criterion. However, the degree of utilization of process plant components, within the time horizon of operation, critically depends on the level of equipment availability. The interactions between production and maintenance planning as well as the necessary links to quantify the strong interactions between them are studied. The preventive maintenance planning and crew allocation problem are used to demonstrate the effectiveness of the proposed approach. The overall problem is first formulated as an optimal control problem by integrating an aggregate production planning model with a continuous time Markov chain maintenance model. The resulting problem is then transformed into a mixed-integer linear programming model by using an Euler discretization scheme and appropriate linearizations of bilinear terms. Finally, extensions to include design aspects are also discussed. The applicability of the proposed approach is demonstrated by a number of illustrative examples.

Demand forecasting is essential for businesses, particularly in the fashion industry, because it predicts future demand for clothing and accessories, thereby aiding in informed production decisions. However, the continuous increase in inventory stock outages, inaccurate forecasting, and inefficient supply chain coordination in the Nigerian fashion industry offers a valid opportunity for empirical investigation. This study examines the effect of the weighted moving average (WMA) demand forecasting on production planning and resource optimization in the fashion industry. A cross-sectional survey of 105 fashion industry professionals was conducted. Data collected through the structured questionnaire were analyzed using partial least squares structural equation modeling (PLS-SEM). The study found that accurate demand forecasting using the WMA positively impacts resource optimization and production planning. Effective resource optimization measures significantly enhance production outcomes. Emphasizing adaptive forecasting methods and dynamic resource management capabilities strengthens the relationship between demand forecasting and production planning. The findings provide valuable insights for fashion industry operation managers to improve demand forecasting practices and resource management, leading to better production planning and enhanced operational efficiency.

The processes of optimization of the production plan according to certain criteria by modeling were investigated. Achieving effective results directly depends on the optimal production plan. The most important thing in determining the optimal production plan is the choice of modeling criteria. For the most part, the quality of decisions is characterized not by one but by many incomparable criteria. Therefore, it is necessary to make decisions based not on one but on many criteria. This so-called multi-objective optimization problem. For solving such problems is widely used mathematical methods. Mathematical approach can be used to solve problems in any particular activity as mathematics abstracted from specific features characteristic of a particular solution. Therefore, from the point of view of mathematics, the optimal result can be obtained with various established criteria, but from the economic point of view it is important to choose the ones that are of decisive importance. That is, their weight is important for the consumer when making a purchase decision, and for the manufacturer – in terms of production capabilities of certain types and results (production efficiency). The basis of the operation of any enterprise is a production program (production and sales plan). The main task of the production plan is to meet the needs of consumers in high-quality products, which are produced with the best use of resources, on the one hand, and the enterprise to get the maximum profit, on the other. With this in mind, a two-criteria optimization model that allows to make a production plan was proposed. The plan ensures that products are produced with the best use of available resources and at the same time ensures maximum quality of manufactured products and maximum profit from sales of these products. The solution of the problem with two objective functions and linear constraints is achieved by step-by-step solution of the proposed mathematical model of optimization of the production plan using the method of sequential restrictions. The simplex method was also used. An example shows an algorithm for solving the optimization problem.

Production planning is a tactical scheme in the supply chain management of product that aims to provide optimal solutions based on the inventory stock of the company in terms of fulfilling the production demand. The variable of demand, supply, and production amount is used in this research as an input to determine the production by the implementation of Fuzzy-Mamdani Logic (FML). This research aims to obtain the optimal production and supply planning for several periods, which later comparing the total cost through the Fuzzy-Mamdani Logic implementation with the method used by the company. The objects for this research are the 7’ and 10’ big-corrugation types of zinc sheets manufactured by PT Y, which is the highest demand product of the company. The result shows that the implementation of FML outlined as an appropriate solution for the decision making to determine the production amount. The amount production itself for the 7’ and 10’ big-corrugation type of zinc sheets during June 2017 - May 2018 were 663,700 and 640,400 quantities for each model, while production in June 2017 was only 18,100 and 32,800 quantities.

Develops a linear programming model for integrated production planning based on the practice of a major Canadian steel making company. Considers the entire planning activity in the company as an integrated process involving a number of closely related sub‐functions, such as raw material purchasing, semi‐finished product purchasing and production, and capacity allocation, as well as finished product production and distribution. The mathematical programming model takes into account production costs, product throughput rates, customer demands, sales prices and facility capacities for optimal production planning. Presents a numerical example based on realistic system structure and practical planning data to illustrate the model. Computation results and analysis show that the integrated methodology is a feasible and practical approach for steel production planning.

Increasing productivity of palm oil company could be done by solving problems in the company such as non-optimal production quantity, over range of production cost, stagnation of production activity, and unplanned of production condition. Those problems can be solved by managing production planning. The purpose of this research is to focus on production planning by determining the optimal production planning, determining the best production condition, and determining criticality of crude palm oil components process. Determination of optimal production planning using goal programming shows five goals formulation: minimize the production cost of crude palm oil (CPO) and fresh fruit bunch (FFB), minimize the deficiency of FFB, maximize production to fulfil demand capacity and maximize the number of FFB processed into CPO. The result of scenario simulation shows that by increasing the productivity of palm oil plantation (11-12%), increasing the yield of CPO (0.21-0.23%), decreasing the effect of maturity rate of FFB (2.5%), and fulfill the requirement of number of truck could decrease free fatty acids by 2.43% and increase number of production by 8408247 in January 2020. The result of the analysis of the assessment of critical components of the palm oil production process results in four classifications. The four classifications of equipment criticality rating (ECR) respectively, ECR 1 at station of boiler, thrasher, and digester, ECR 2 at station of sterilizer, ECR 3 at station of clarification and engine room, and ECR 4 at receiving station, fat pit, and water treatment.

This paper deals with a model of the firm engaged in production to order. Models of this situation frequently conclude that the size of the order and production rate each influence the costs of production. Despite this fact, empirical work on production costs focus almost exclusively on either production rate or the size of the order as explainers of variations in costs. In this paper, a model is developed which is consistent with both these facts. The model permits production rate to be a decision variable in the manufacturer's production planning. The model is solved for an optimal production plan which requires production rate to change throughout the life of the program. The planning situation, where alternative order sizes are considered, is then contrasted with the production situation, where unit costs are related to cumulative output given a production plan. This approach provides guidance for the use of data on past programs in the estimation of cost functions and the evaluation of future production plans.

A mathematical model is developed and presented to solve the optimal control problem in matching multi-stages production plan at any point in time. The main purpose of this paper is to investigate the optimal choice between production rate which may affect the inventory level and the optimal production plan at each time through different strategies so as to achieve due date. This method provides a way for decision makers to attend to the best production starting point for any given time point while operating within the best production function. Specifically in deciding whether a manufacturer needs to produce extra quantities for the next batch. Dynamic programming is also shown to be a practical method for the multi-stage optimization involved.

This paper considers the problem of planning the optimal production company (industry, ministry) volume, which structurally has a certain coordinating (managing) Center and a network of enterprises (branches). Enterprises can be located in different regions, and are not connected with each other. Resource stocks and the technological matrix of resource costs for output are known for each of the enterprises. The profit from the sale of each type of product unit is also known. An iterative algorithm for finding production plans for each of the enterprises is proposed due to this algorithm the total profit of the company can be increased. The Center finds the optimal production plan for each of the enterprises according to this algorithm and using the classical formula of the optimal production planning problem. The Center solves the problem of resolving production bottlenecks for each of the enterprise. Further, for each of them, the Center determines the supply volumes of resources scarce. New technical capabilities due to the volume and speed of data transmission and processing, allow us to solve these problems. The Center supplies scarce resources to enterprises and forms a new adjusted output plan for each of the enterprises. We consider several scenarios for the company’s operation that allow it to increase its total profit, even if the company is limited in its resources and capabilities. If it is impossible to supply scarce resources to meet the needs of all enterprises in the company, the options for the most promising planning models are offered. Owing to the offered options, the total profit of all enterprises in the company will be the greatest.

In this paper, we present an algorithm for optimizing reservoir production using smart well technology. The term smart well is used to indicate an unconventional well equipped with down hole inflow control valves (ICVs) and instrumentation. This additional instrumentation extends the degree of freedom in the field production planning, since production can be efficiently distributed on the different well segments available. By proper utilization of the ICVs through optimal production planning, an increased oil recovery for the reservoir can be expected. We propose a method for optimal closed-loop production known from control theory as model predictive control (MPC). A commercial reservoir simulator, ECLIPSE, is used for modeling and predictions. MPC is chosen for its ability to provide an optimal solution for the constrained multivariable control problem. To compute the optimal ICV settings, we propose using a nonlinear MPC (NMPC) application, which can handle the severe nonlinearities found in reservoir models. The NMPC uses a single shooting multi-step quasi-Newton (SSMQN) method to solve the optimization problem. As the term multistep suggests, this is an iterative method which solves a sequence of quadratic problems (QPs) in each time step. We apply our method to a benchmark reservoir model with multiple geostatistical realizations. This model has already proven potential for increased oil recovery by using optimization techniques. We show an even additional increase over the former approach in production totals, using the SSMQN method, with as much as 68% increase in one case, and 30% on average compared to a reference case.

Agricultural production and farm management are inextricable, since managerial aspects for safe and of high-quality food products have led to the development of successful production plans but multifaceted controversies as well. These controversies arise from the focus of policymakers, especially in the EU, to the environmental aspects of agricultural production, creating conflicting objectives for farmers. Energy from biomass derivatives could play a significant role in the dispute for economic and environmental sustainability in agriculture, along with the formulation of agro-energy districts. In this context, an MCDM model was developed integrating LCA data for the assessment of economic, environmental and energy sustainability regarding thirteen major crops in the Region of Central Macedonia in Greece. The model's objectives consist of maximization of farmers' gross income, minimization of emissions coming from farming practices and maximization of energy potentially coming from biomass. Furthermore, three different scenario-based directions allocate different weights to the respective objectives, creating different managerial strategies. The optimal production plan was the scenario in which the weights were allocated by goal programming. The optimal plan proposes the cultivation expansion of energy crops, tree crops, alfalfa and hard wheat to a higher degree. Moreover, a significant reduction to the cultivated areas of tobacco, rice, barley and soft wheat could lead to a potentially viable production plan.

The paper deals with a single-item production planning problem with uncertain demands modeled by fuzzy intervals whose membership functions are possibility distributions for the values of the uncertain demands. Optimization criteria, in the setting of possibility theory, that lead to choose robust production plans under fuzzy demands are given. Algorithms for determining optimal robust production plans with respect to the proposed criteria are provided and some computational experiments are presented.

New integration of preventive maintenance and production planning with cell formation and group scheduling for dynamic cellular manufacturing systems