Aggregate production planning (APP) is an important technique in Operations Management. It is a mid-term capacity planning that determines a principle of workforce, production, inventory, subcontract, backlog, etc., over a specific time horizon which ranges from 2 to 12 months, to satisfy fluctuated demand requirements(Al-E-Hashem, Aryanezhad, & Sadjadi, 2012; Graves, 2002; Stevenson, 2009). In recent decades, as the advance in heuristic technique and modeling approach, the APP problems discussed have become quite complex and large scaled. Paiva and Morabito (2009) propose an optimization model to support decisions in the APP problem of the sugar and ethanol milling factory. The model is a mixed integer programming formulation based on the industrial process
Although the issue of APP was introduced in the 1950s, it is still extensively researched by many researchers. Over the past few decades, they have constructed various models, each with their own pros and cons, to effectively solve the aggregate production planning problem. They also classified each method as being capable of either generating an optimal or near-optimal solution. Some researchers used linear programming approaches with different application cases to solve APP problem. Hsieh and Wu  created a deterministic linear programming model for APP with an imprecise nature. This research examines how the imprecise nature of the Computer- Integrated Production Management System (CIPMS) affects the outcomes of the planning. Wang and Fang  suggested Fuzzy Linear Programming (FLP) technique for solving the issue of APP with different objectives where the item price, the unit cost to subcontract, the workforce level, the manufacturing capability, and the market requirements are inherently fuzzy. However, the limitation of this model is that it applied the conventional mathematical programming technique to medium-term production planning. Wang and Liang  proposed an interactive multiple fuzzy objective linear programming model for solving the aggregate production decision problem in fuzzy environment. They considered the time value of money to construct constraints of this model. Gulsun et al.  outlined the LP model for aggregate production planning to determine the most appropriate approach while minimizing general production costs and minimizing the impact of hiring or layoff decisions on the level of motivation of the workers. An integrated model combining with linear programming, simulation, and interactive approach was proposed by Nowak  in which the linear programming models were used to generate initial solutions, simulation experiments were performed to check the fluctuation in demands and interactive procedure was used for identifying the final solution of the problem. Chakrabortty et al.  developed multi-period and multi-product APP which was formulated as an integer linear programming model using a triangular possibility distribution .
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The aggregate production planning problem is an important part of the production planning process. APP greatly reduces the amount of data used during the planning process and therefore enables plans to be updated more frequently. Numerous APP models with varying degrees of sophistication have been introduced in the last four decades. The study conducted by Nam and Logendran (1992) categorized the literature on APP since early 1950 to 1990, and there has not been any systematic survey in the literature. In order to provide readers with a background for understanding current knowledge on a topic and illuminate the significance for new study, a well structured literature review was needed. In this paper a literature review that is characterized by a logical flow of ideas; current and relevant references with consistent, appropriate referencing style; proper use of terminology; and an unbiased and comprehensive view of the previous research on the APP models has been presented. The purpose of this review was to provide a systematic structure for classifying APP models and to demonstrate the gaps existing in the literature in order to extract future trends and directions of this research area.
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In hierarchical production planning system, Aggregate Production Planning (APP) falls between the broad decisions of long-range planning and the highly specific and detailed short-range planning decisions. This study develops an interactive Multi-Objective Genetic Algorithm (MOGA) approach for solving the multi-product, multi-period aggregate production planning (APP) with forecasted demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. Here several genetic algorithm parameters are considered for solving NP-hard problem (APP problem) and their relative comparisons are focused to choose the most auspicious combination for solving multiple objective problems. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed MOGA approach yields an efficient APP compromise solution for large-scale problems.
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Aggregate Production Planning (APP) is an intermediate range production problem (the planning horizon is generally three months to two years), in which one attempts to achieve a (cost– effective) balance between productive capacity, on one hand, and forecasts of fluctuating demand, on the other. The production manager typically has at his disposal a set of production options with which to achieve the balance. Despite the strong interest that has been shown in APP models reported examples of successful implementation have been rare . Akinc and Roodman [1,2], first suggest several reasons for this failure, and then introduce a mixed integer-programming model for aggregate
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Leung et al. (2003) addressed the problem of aggregate production planning (APP) for a multinational lingerie company in Hong Kong. The multi-site production planning problem considered the production loading plans among manufacturing factories subject to certain restrictions, such as production capacity, workforce level, storage space and resource conditions of the factories. Leung et al. (2003) developed a multi-objective model to solve the associated production planning problem, in which the profit was maximized but production penalties resulting from going over/under quotas and the change in workforce level were minimized. Sha and Che, (2006) proposed a novel multi-phase mathematical approach for the design of a complex supply chain network. The proposed approach was based on the genetic algorithm (GA), the analytical hierarchy process (AHP), and the multi-attribute utility theory (MAUT). Kogan and Portugal, (2006) focused on the control decisions in the area of multi- period, aggregate production planning. The goal was to minimize the expected total costs including productivity, overtime as well as over- and under- production costs.
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in decision making in the aggregate production planning of the pump manufacturing company. The MILP formulation is based on industrial production. The aim is to help the production managers in selecting the methods used to produce pumps and the ﬁnal inventory strategy. A case study about a pump manufacturing company is presented here. Under level strategy, the objective is to maintain a stable workforce, avoiding frequent hiring/firing/layoffs. Here production is not synchronized with demand and inventories are built up during low demand periods for use during high demand periods. We use Python program to optimize the problem.
Aggregate production planning is capacity planning from 6 to 18 months ahead. It is concerned to meet requirements and to meet changing demand over the planning period. Aggregate Production Planning (APP) is deﬁned as the same time determination of production, the inventory and the workforce levels of a company on a ﬁnite time horizon. The aim is to reduce the total overall expenses to meet a no constant demand assuming ﬁxed sale and production capacity. This problem is particularly complex in production systems producing several types products with demands requiring the maintenance of a large inventory.
Aggregate production planning (APP) determines the optimal production plan for the medium term planning horizon. The purpose of the APP is effective utilization of existing capacities through facing the fluctuations in demand. Recently, fuzzy approaches have been applied for APP focusing on vague nature of cost parameters. Considering the importance of coping with customer demand in different periods at different and variable rates, in this research, demand is considered fuzzy and the APP decisions modeled through a bi-objective LP model optimizing production and workforce level costs. The APP decisions are taken in two rounds, First The fuzzy model is transformed to a crisp goal programming counterpart and in the second round as the principal contribution of this paper, the APP decisions for rest of the horizon are updated based on actual demand occurred during starting periods. By generating several sample problems and using the Lingo, the validity of the proposed model is shown.
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Carlos Gomes da Silvaa et al (9), have presented an aggregate production planning (APP) model applied to a Portuguese firm that produces construction materials. A multiple criteria mixed integer linear programming (MCMILP) model is developed with the following performance criteria: (1) maximize profit, (2) minimize late orders, and (3) minimize work force level changes. It includes certain operational features such as partial inflexibilityof the work force, legal restrictions on workload, work force size (workers to be hired and downsized), workers in training, and production and inventory capacity . The purpose is to determine the number of workers for each worker type, the number of overtime hours, the inventory level for each product category, and the level of subcontracting in order to meet the forecasted demand for a planning period of 12 months.
This study was motivated by the poor inventory management performance in a Brazilian food company with a high seasonal demand. It was clearly recognized that the best inventory management would depend on improvements in demand forecasting and in the production planning process itself. In order to deal with the identified problems, an aggregate production planning model based on linear programming has been developed. The model determines the monthly production rates and inventory levels of finished products as well as the work-force requirements to accomplish productions plans. A simple disaggregating method, which searches for equal run out times, translates the aggregate plans into a detailed master production schedule for a shorter horizon of three months. With the effective usage of this model, and improvements in the demand forecasting processes, a global reduction of inventory levels of both raw materials and final products can be achieved.
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was, in fact, possible to assume a top-down approach to establish the main links between each phase along the supply chain and then detail each component, avoiding possible distortions of interactions at the upper levels. After being saw in one of the 4 sewing line, when the right quantity of items is reached, they are loaded into trucks and then moved on to the next phase in the operative chain. The transport times through the network are set according to the average values recorded in the actual system. The assignment of each job to the proper sewing line in the tailoring, accessorising and finishing stages is performed by choosing the least loaded factory. To maintain productivity aligned with the observed values, breakdowns have been introduced into every shop. As regards materials, in the actual system material purchase orders are set mainly on the basis of forecasts and spaced out along the campaign to meet requirements. Thus are to enhance the capacity of assigning products to the factories. Concerning the link between colour mix of jobs and client orders, analysed the two situations where this relation is maintained or partially removed. It allowed the heuristic to generate colour-incomplete production orders when needed to saturate factories, providing the simulation model with an area where jobs could wait until rejoining the lacking coloured units before leaving the system.
The demand for Red Tomato’s gardening tools from consumers is highly seasonal, peaking in the spring as people plant their gardens. This seasonal demand ripples up the supply chain from the retailer to Red Tomato, the manufacturer. Red Tomato has decided to use aggregate planning to overcome the obstacle of seasonal demand and maximize profits. The options Red Tomato has for handing the seasonality are adding workers during the peak season, subcontracting out some of the work, building up inventory during the slow months, or building up a backlog of orders that will be delivered late to the customers. To determine how to best use these options through an aggregate plan, Red Tomato’s vice president of supply chain starts with the first task – building a demand forecast. Although Red Tomato could attempt to forecast this demand itself, a much more accurate forecast comes from a collaborative process used by both Red Tomato and its retailers to produce the forecast shown in Table-1.
Using the technology of AR (Augmented Reality) to design an App that allow users to have an overview before they actually buy the decor and start decorating the site. The App will work as a virtual fitting room. Users can take whatever they want, and placing them wherever they want. It allows users to check if their expectation can be meet beforehand. Users can always change the objects they don’t like before finalizing their purchase. This App saves not only a lot of time and money, but also help designers to modify their plans when necessary.
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Full length CD74 with an N-terminal FLAG tag were trans- fected to HeLa cells. As shown in Figure 1B, the immuno- precipitation of CD74 from the total lysates indicated that predominantly mature APP (mAPP) interacted with CD74. There is little binding to the two APP C-terminal fragments (APP CTFs, indicated as C99 and C83). No con- sistent change was observed for the amount of APP CTFs. This CD74-APP interaction was further confirmed by the reciprocal immunoprecipitation. The total lysates were prepared from the HeLa cells cotransfected with FLAG- CD74 and APP, and the CD74 fragments bound to the precipitated APP were analyzed. The Westerm blot shows that CD74 indeed bound to APP. Interestingly, the 33 kDa fragment (CD74 NTF33), but not the 14 kDa fragment (CD74 NTF14) of CD74 was associated with APP, even though NTF14 was clearly the major CD74 NTF in the total lysates (Figure 1B). The preferred binding of NTF33 over NTF14 indicates that NTF 14 is not sufficient for the CD74-APP interaction, and hints that the trimeric region of CD74 is required for the interaction.
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Real world production management, planning, and control problems are usually imprecise, complex, and critically depend on human activities. However managers are to interact in an intelligent way with this environment. Thus, they have to reach out for new kind of reasoning based on such situation. (Turksen and FazelZarandi, 1999). A SC system usually contains several sub-systems with unlimited relations and interfaces. Each subsystem and its interfaces with others in the context of Material Flow, Information Flow, and Suppler-Buyer relations naturally contain a lot of uncertainties. It is a challenge to model a SC with an integrated approach and to capture relations between different elements of such a chain. Petrovic et al. (1999) demonstrate the uncertainties in SC systems as follows:
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When a customer wants to place an order, they have to notify the sales department at least three months in advance. The customer has to state the number of products they want to buy and in which month they want the products to be finished. At this moment, the number of products the customer wants to buy is deduced from its preliminary forecast. This confirmed forecast is also called the ‘Ready to Produce’ (RTP) quantity. This RTP quantity is placed in SAP, so the planner knows all production quantities that have to be made and the month that these production quantities need to be finished. Once an RTP order is placed, the customer can, generally speaking, not change the RTP quantity. However, the sales department still often accepts a proposed RTP change by the customer. The main reason for this is that the sales department is assessed based on their customer satisfaction and the number of sales they have made instead of the overall functioning of the company. An increase of RTP quantities leads to a new production plan that can lead to orders being shifted and therefore being delivered late. However, when Taman always declines a change of the RTP amount, the customer can decide to go to another garden furniture manufacturer that is more flexible.
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polysaccharides on APP processing were tested in primary cortical cells derived from Tg2576 mice. The results showed that the effect of GAGs on APP processing was both size- and sulfation-dependent. Mucosal heparins (MHs) with small sizes (5 kDa and 3kDa) were less potent in reducing A than high molecular weight MHs (6 kDa
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MOHAMMAD HASMATH et. al. (2015): -The present study is to investigate use of waste plastic as coarse aggregate in concrete with the addition of plastics. From research, it is found compressive as well as tensile strength got reduced and thermal conductivity also reduced. The main aim of the experimental program is to compare the properties of concrete made with and without plastics used as coarse aggregate to check strength.
They indicate that the output elasticity of labor for the economy is greater than one and higher than capital elasticity, indicating that the real GDP is elastic with respect to LF. The elasticity of real GDP with respect to capital is less than one (inelastic). In other words, during the past two decades, the Bahrain economy relied more heavily on labor than capital in production processes. The important thing is that the coefficient of the inputs Labor force and Gross Capital Formation are mostly highly significant. Moreover, the adjusted R 2 is high in all models. Durbin-Watson
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