Production Allocation Research Articles

“You Get What You Need”: Early Results of BloodTrack System on Reducing Inefficiencies in Blood Product Allocation

Abstract Introduction/Objective The preparation of blood products allocated for massive transfusion protocol (MTP) activation and surgical cases consumes much time and energy of blood bank technologists. Often, these blood products are returned to the blood bank unused. Clinicians may activate the MTP preemptively to ensure the availability of blood products in case they are needed. Most blood products ordered for surgeries are placed on hold and not transfused. These returned units require additional inspections before they can be made available again. To reduce these inefficiencies, we implemented the BloodTrack [Haemonetics, Boston, MA] system, wherein blood products are stored in remote refrigerators and made available to clinicians near the patients’ bedside. Besides safeguarding units placed on hold for surgeries, we hypothesized that BloodTrack would provide greater visibility of available products in the emergency department (ED), thus reducing the rate of preemptive MTP activations. Methods/Case Report From March 2022 onwards, BloodTrack refrigerators with OnDemand software were active in the ED, operating room (OR), and two intensive care (ICU) units. We educated ED, OR, and ICU nurses on the appropriate use of BloodTrack. MTP activations were recorded from August 2021 to June 2022. Data on units allocated to OR patients by BloodTrack were collected from software-generated logs. Data on units allocated to OR patients by BloodTrack were collected from software-generated logs. Results (if a Case Study enter NA) Since its introduction in March, BloodTrack has dispensed 92 red blood cell units, which represents 12% of the total 766 units assigned. The remainder of 674 units were unused, and represent substantial savings in time otherwise spent re-instating them. Moreover, the rates of preemptive MTP activations trended downwards, from 28% (32/113) in the 7 months prior to implementation, to 13% (3/24) after implementation. Conclusion In conclusion, our implementation of BloodTrack has led to reduced MTP activations, improved efficiency of our blood product allocation process, and developed interdisciplinary care team integration.

Evolution of household carbon emissions and their drivers from both income and consumption perspectives in China during 2010–2017

Household inputs and consumption play important roles in driving carbon emissions in China. However, existing studies have mainly studied consumption-based household carbon emissions in specific years to highlight consumption guidance and management, and little attention has been given to income-based accounting and policy-making focused on primary input behaviors and product allocation behaviors. In the quest for more coordinated and efficient mitigation strategies, we applied input–output analysis (IOA) combined with the biproportional scaling method (RAS) to obtain both income- and consumption-based annual accounting of rural and urban household carbon emissions from 2010 to 2017 and then used structural decomposition analysis (SDA) to determine key driving factors and sectors. Our results revealed that the proportions of income-based household emissions in gross emissions were higher than that of consumption-based household emissions. In terms of driving factors, per capita income/consumption contributed the largest increase in household emissions for most of the period, and population changes also showed a weak positive effect. However, intermediate input/output structure and carbon emission intensity were the main offsetting factors for household emissions. Compared with the consumption-based results, the income-based results can identify some new critical sectors that lead to household emission changes. Furthermore, the discrepant results for rural and urban household carbon emissions from both income and consumption perspectives suggest that differentiated measures of rural and urban households in key sectors are necessary. Finally, we propose industrial chain adjustment strategies and household input and consumption behavior recommendations in the context of urbanization.

An original deconvolution approach for oil production allocation based on geochemical fingerprinting

• A novel deconvolution algorithm for production allocation is introduced. • An improved experimental procedure for GC targeting production allocation is proposed. • Extensions to traditional production allocation algorithms are formulated. We tackle oil commingling scenarios and develop an original deconvolution approach for geochemical production allocation. This yields robust assessment of the proportions of oils forming a mixture originating from commingling oils associated with diverse reservoirs or, wells. Our study starts from considering that production allocation performed by means of geochemical fingerprinting is relevant in the context of modern and sustainable use of georesources, with the added benefit of favoring shared facilities and production equipment. A geochemical production allocation workflow is typically structured according to two steps: (i) determination of the chromatograms associated with the mixture (and eventually with each of the End Members, EMs, constituting the fluids in the mixture), and (ii) the use of a deconvolution algorithm to estimate the mass fraction of each EM. Concerning the latter step, we introduce an original approach and the ensuing deconvolution algorithm (hereafter termed PGM) that does not require additional laboratory efforts in comparison with traditional approaches. We also present extensions of widely used deconvolution algorithms, which we frame in a (stochastic) Monte Carlo context to improve their robustness and reliability. The new PGM approach is assessed jointly with a suite of typically used approaches and algorithms against new laboratory-based commingling scenarios. The latter are based on the design and introduction of a novel and low-cost experimental method. The results of the study (i) constitute a unique and rigorous comparison of the traditionally employed production allocation deconvolution algorithms, (ii) document the critical importance of the number of features of the chromatograms used during a quantitative deconvolution, and (iii) suggest that our new PGM approach is very robust and accurate compared to existing approaches.

A bi-objective integrated transportation and inventory management under a supply chain network considering multiple distribution networks

In order to respond to the customer’s needs effectively and efficiently, logistics is characterized as a part of the supply chain that executes and handles forward and reverse movement and storage of products, services, and related data. An efficient logistic network is needed for the supply chain that executes forward and reverses products’ movement. This study resolves the supply chain network’s logistic problem to determine the appropriate order allocation of products from multiple plants, warehouses, and distributors to minimize total transportation and inventory costs by simultaneously determining optimal locations, flows, shipment composition, and shipment cycle times. The multi-objective logistic cost minimizes through the value function approach for obtaining the optimal order allocation. An actual data-based case study has been applied to examine the effectiveness of the multi-objective supply chain network. These results are very relevant for the manufacturing sectors, particularly those facing the logistics issue in the supply chain network. The findings indicate the optimal logistic costs. The results enable managers to cope with various types of logistics risks.

Optimizing Agricultural Input and Production for Different Types of at-Risk Peasant Households: An Empirical Study of Typical Counties in the Yimeng Mountain Area of Northern China.

Using typical counties in the Yimeng Mountain area of northern China as an example, this paper analyzed the household and agricultural input characteristics of different types of peasant households using survey data from 262 farm households. The target minimization of the total absolute deviations (MOTAD) model was applied to determine the optimal combinations in the allocation of agricultural input factors and production for different types of at-risk peasant households to obtain the ideal agricultural income. The relevant results are twofold. (1) The agricultural input behaviors of different types of peasant households vary significantly. The highest levels of agricultural land, labor, and yield-increasing and labor-saving inputs included I part-time peasant households (I PTPH), followed by full-time peasant households (FTPH), while the input levels of II part-time peasant households (II PTPH) and non-agricultural peasant households (NAPH) with higher levels of non-agricultural employment gradually decreased. In general, an increase in peasant households' part-time employment revealed an inverted U-shaped trend in the agricultural input level, with a trajectory of I PTPH > FTPH > II PTPH > NAPH. (2) The current agricultural inputs and production combinations of different types of peasant households have room for improvement. It is necessary to adjust agricultural inputs and optimize production combinations to obtain target incomes. Overall, all types of peasant households must streamline labor inputs and increase capital inputs, except for I PTPH, for which capital inputs should be reduced. Following optimization, economic crops gradually replace grain crops, and the optimal agricultural incomes of peasant households will be improved. The study results provide practical policy insights for reducing agricultural production risks and improving agricultural production incomes.

Assessment and Comparison of Agricultural Technology Development under Different Farmland Management Modes: A Case Study of Grain Production, China

Agricultural technological change plays a crucial role in food security and agricultural development. In the case of considering economic risks and technical risk tolerance, farmers will use different technologies to match production factors to achieve the optimal production state. Therefore, under different farmland management modes, farms show different characteristics of technological progress. This paper attempts to compare and analyze agricultural technology development under different farmland management modes: the unified management mode of collective organizations (UMCO) and the decentralized management mode of contracted families (DMCF). The Stochastic Frontier Analysis (SFA) of the translog average production function was applied to the 24 farms of the Hulunbuir Agricultural Reclamation Group, of which 11 farms in the western part of the Greater Khingan Mountains (Western Farms) were managed by the DMCF, and the other 13 farms in the eastern part of the Greater Khingan Mountains (Eastern Farms) were managed by the UMCO. The results are as follows: (1) without considering the resource allocation efficiency, from 2000 to 2019, the generalized technological progress rate (TFPG) of the 13 Eastern Farms (7.65%) was higher than that of the Western Farms (2.25%). (2) The returns to scale (SRC) of the Western Farms was higher than that of the Eastern Farms. (3) The technological efficiency change rate (TEC) and the technical progress (TP) of the Eastern Farms is higher than that of the Western Farms. It is recommended that farms strengthen the construction of their infrastructure and service systems, resist natural disasters, reduce the disaster’s impact on technological progress, give full play to the overall planning advantages of the collective organizations, improve the product allocation efficiency factors, and create connotative profit points.

A Combined Dynamic Programming and Simulation Approach to the Sizing of the Low-Level Order-Picking Area

In order to increase the efficiency of the order-picking process, warehouses are forced to find ways to adopt to constantly intensifying changes in the assortment and quantities of stored products. Accordingly, we present a methodology that deals with such a problem at a tactical level by defining the optimal size and an allocation of products within the order-picking area of the most typical order-picking setting. The methodology combined two methods, dynamic programming and simulation modelling, with the aim of taking advantages of their positive features. In that sense, the optimal allocation of products for different sizes of the order-picking zone were obtained by the dynamic programming approach. Afterwards, the influence of a demand’s seasonality and variations were treated by the simulation model, so that the more realistic performances of the system were captured for the optimal allocation of products. The methodology was tested on the retailer data with significant week seasonality. The obtained results confirmed the practical applicability of the methodology in real systems, while the sensitivity analysis of results showed that special attention and effort should be given to the determination of costs related to the engagement of order-pikers, storage equipment and unit replenishment during a planning period.

The Research of Declining Factor and a New Model of Decline Rate for Gas Well Production in Low Permeability Reservoir

The decline rate of gas wells varies with production time and is related to reservoir and working system. A typical single well numerical model of low permeability gas wells is established. The variation law of decline rate and its main influencing factors are studied by single factor analysis method. The results show that there are two stages of rapid and slow decline for the decline rate of gas wells, and the decline rate tends to be stable after the gas wells entering the decline period of 2–3 years; the decline rate increases with the increase of permeability, gas saturation and wellhead pressure, and decreases with the increase of porosity and well-controlled reserves; the decline rate in late production does not change with the change of reservoir thickness, initial production allocation and formation pressure. Then the decline rate model is and Then the decline rate model and the non-linear model of each factor are established by using response surface method are used to predict the decline rate quickly and accurately. According to the prediction model, the larger the well-controlled reserves, the smaller the permeability and the smaller the decline rate. In order to reduce production decline rate in gas well production process, it is suggested that the influence of various parameters on production decline rate should be considered comprehensively, and the well-controlled reserves of gas well should be increased as far as possible.

Research on the Main Controlling Factors for Injection and Production Allocation of Polymer Flooding

Abstract A clear understanding of the main controlling factors for injection and production allocation of polymer flooding is the key to successful differential adjustment for well management in high water cut reservoirs. Generally, sensitivity analysis or design of experiment is used to study the main controlling factors, but the number of adjustment parameters is limited and the optimal results are hard to obtain. Therefore, the paper regards the problem as an inverse problem and studies the controlling factors by combining intelligent optimization and correlation analysis. In general, the correlation between the optimal results of injection and production allocation and each controlling factor is analyzed, and the main controlling factors with the strongest correlation are selected. Results show that injection rate allocation is mainly controlled by pore volume, polymer concentration allocation is mainly controlled by pore volume and formation coefficient, and production rate allocation is mainly controlled by remaining reserves and oil saturation. The case study indicates injection and production adjustment based on the main controlling factors obtains satisfactory development performance while using much less computation cost than that of the intelligent optimization method. The research results provide a good reference for well redistribution adjustment of polymer flooding in large-scale oilfields.

Optimal Allocation of Future Oil Production under Uncertainty: Lessons from Nigeria for Emerging Oil Producing Countries

Under the uncertainties presented by future upstream oil production, increasing domestic demand for petroleum products, volatile energy prices, a build-out of domestic refining capacity, and a global energy transition underway, this paper seeks an optimal allocation of Nigeria's projected oil production under scenarios representing possible versions of the future. Using the Reference Energy System developed by Gbakon et al. (2021) for crude oil utilization through a network of possible end-uses, the framework is tested under different scenarios. Furthermore, the framework is coupled to a Monte Carlo formulation of the problem, which allows greater flexibility in addressing questions of the likelihood of attaining desirable policy outcomes such as petroleum product self-sufficiency. Within this solution structure, a family of curves is generated, which represents the spread of outcomes. Scenario analysis, for example, shows that under 'Energy Transition' scenario, oil export ratio declines from 43% in 2025 to 6% in 2040. Whereas, under the 'Business-as-Usual' scenario, the oil export ratio declines from 37% in 2025 to 0% in 2034, with clear consequences for foreign exchange earnings. While under the ‘Stated Policy' scenario oil export ratio declines from 60% in 2025 to 54% in 2040. Implications for net system benefits and the respective drivers are further interrogated.

Factors driving current account performance of South Asian economies: A comparative empirical analysis

Considering annual data from 1980 to 2020, this study attempts to investigate major factors determining Current Account Deficits (CADs) for five South Asian economies (Bangladesh, India, Pakistan, Nepal and Sri Lanka) in the South Asian Region. Using VAR estimation framework on a basic model of CAD, it is observed that exchange rate depreciation majorly helps to improve the Current Account Balances (CABs), whereas increased per capita income, trade openness, net foreign capital inflows don’t pose major threats to deterioration in their CABs of countries. While fiscal deficit doesn’t have substantial effect on CAB except for Bangladesh and Nepal, increased savings have an unfavourable effect on CAB, which is a major policy concern for almost all the countries in the region. Given the trends of liberalisation which is intensifying over time along with sustained growth of per capita incomes across economies, we conclude that unless some sectoral import restrictions are imposed along with ensuring the stability of exchange rates and productive allocation of external credits, the economies are likely to experience deterioration in their CABs and macro policy environment.

How much aboveground net primary production can be used for human activities in the alpine grasslands in the Three Rivers Source Region (TRSR), China?

Sustainable management of grasslands has always been an urgent issue for policy-makers. The three rivers source region (TRSR) contains widely distributed natural grasslands and is sensitive to climate warming. To enable the sustainable development of the human-nature system in the TRSR, we propose a novel indicator based on the allocation of aboveground net primary production (ANPP). The indicator we proposed is the ANPP that can be used for human activities (UANPP). In the study, we simulated the spatial and temporal patterns of the UANPP in the alpine grasslands in the TRSR during 1979–2016 and explored the main driving factors of the UANPP. The results revealed that (a) the annual total UANPP in the TRSR was 13.22 TgC, approximately accounting for 47% of total ANPP. (b) The areas with negative UANPP values accounted for 16% of the entire TRSR, and they were primarily located within the Nature Reserve of the Yangtze and Yellow river source regions, while three-quarters of the area exhibited improvement trends. (c) The regional mean UANPP significantly increased during 1979–2016, at a rate of 0.28 gC m−2 yr−1 (p < 0.01). In the entire TRSR, 87% of the area exhibited increasing trends. (d) The UANPP in most areas of the TRSR was strongly correlated with precipitation, and the effect of human activities on the UANNP increased slightly during the 38 year study period. The UANPP represents the upper limit of human use of nature. These findings provide a reference for policy-makers to make decisions toward human-nature system sustainability while meeting human needs for grassland resources. ANPP allocation between nature and human system is a potentially important tool from the standpoint of sustainable development.

Photosynthetic product allocations of Pinus massoniana seedlings inoculated with ectomycorrhizal fungi along a nitrogen addition gradient.

Quantifying the allocation of photosynthetic products among different carbon (C) pools is critical for understanding and predicting plant C turnover response to climate change. A field experiment with ectomycorrhizal fungi (EMF) and nitrogen (N) was established to investigate the effects on allocation of photosynthetic products in Pinus massoniana (Lamb.) seedlings given increased N deposition. Seedlings were subjected to N addition and symbiosis with EMF, and the short-term allocation of a 13C photosynthetic pulse into leaves, branches, stems, roots, and soil was traced. Photosynthetic rate and root respiration were measured. It was found that N addition changed the allocation pattern of photosynthetic products in various organs of P. massoniana. Furthermore, N addition, mycorrhizal symbiosis, and interaction of N and EMF, all increased the amount of C produced by photosynthesis. N application less than 60 kg N hm–1 a–1 could promote the transfer and allocation of photosynthetic products in P. massoniana organs, which peaks at 60 kg N hm–1 a–1, and the highest N treatment began to decrease at 90 kg N hm–1 a–1. EMF inoculation could expand the absorption area of plant roots to obtain more nutrients and synthesize more C and N compounds for promoting the growth of itself and the host plant, improving the net photosynthetic rate and the distribution of C produced by photosynthesis in various organs. This forms a benign C and N cycle, thereby reducing the effect of high N addition on plants. The optimal N addition concentration was 60 kg N hm–1 a–1, and the optimal EMF was Pt, which provides a theoretical basis for inoculating EMF during increasing N deposition in the future climate change scenario. This enables plants to distribute more photosynthetic products to their roots, thus affecting their own C distribution for promoting growth.

Short-term N and P additions differentially alter the multiple functional traits and trait associations of a desert ephemeral plant in China

Nitrogen (N) and phosphorus (P) are the main restrictive elements in terrestrial ecosystems. In a N-deficient desert, low exogenous N input is conducive to plant growth; however. However, the influence of P and N additions on the growth and physiology of desert plants remains controversial. In this study, Alyssum linifolium , an ephemeral plant widely distributed in the Gurbantunggut Desert in China, was administered with different amounts of N (CK, N1, N2, and N3: 0, 3, 6, and 18 g N m –2 yr –1 , respectively), P (CK, P1, P2, and P3: 0, 0.6, 1.2, and 3.6 g N m –2 yr –1 , respectively), and N + P (CK, N1P1, N2P2, and N3P3) to systematically analyze the effect of these additions on soil nutrients and plant multiple traits. Certain N–P interaction effects were observed on soil properties (15 out of 18) and plant traits (13 out of 33). N addition markedly improved soil N availability and plant N content and reduced plant P content; meanwhile, soil P availability remained steady. P addition enhanced soil P availability and plant P concentration but severely decreased soil N availability. N + P addition improved the effectiveness of soil N and P, thereby promoting plant N content but largely reducing plant P content. N and N + P additions remarkably enhanced the photosynthetic capacity, soluble protein content, allocation proportion of leaf biomass, and even individual growth (including plant height, root length, leaf length, and organ biomass) and chlorophyll content at low concentrations. Leaf N was negatively scaled to P under each nutrient treatment, and this trend was completely different from that in natural ecosystems. The effect of N and P under N + P addition differed from those under individual N and P additions. In addition, plant trait networks differed with nutrient treatment, and the network complexity and trait association were weakened markedly under P addition. A degree of modularity was observed in the plant trait network in response to N and P additions, and biomass accumulation was identified as the hub trait. Multivariate analysis showed that available soil N and N:P levels were the primary factors influencing the variation in plant traits. Structural equation modeling revealed that soil N availability regulated the allocation of photosynthetic products by affecting plant stoichiometry, enzyme activity, and photosynthetic capacity. In conclusion, P addition alone markedly reduced soil N availability and plant performance in N-deficient habitats, and low-concentration N or N + P addition was generally conducive to soil nutrient availability and plant growth. • P addition reduced available soil N, and N and N + P additions enhanced leaf N and reduced leaf P. • Leaf N was negatively allometrically related to leaf P under N, P, and N + P treatments. • Soil N availability regulated biomass allocation by influencing physiological indicators. • N, P, and N + P treatments altered plant traits and trait associations. • N, P, and N + P additions had different effects on soil nutrients and plant growth.

Product allocation and network configuration in global production networks

Driven by an increasing demand for individualized products and shorter product life-cycles, companies continuously extend their product portfolio. Simultaneously, companies expand into new markets to reach customers and to exploit varying location factors to reduce costs. Global production networks (GPNs) have to be adapted constantly to react to new circumstances and changes in the demand of products. To remain competitive, product allocation and production network configuration are essential. At the same time, companies face an increasing complexity while handling these tasks. This poses a challenge particularly for small and medium sized companies, which have limited planning capacities and management resources. Current literature describes optimization-based approaches for the integrated product allocation and network configuration of production networks. Yet, multi-objective models lack transparency of results and user friendliness. Therefore, this paper presents a multi-objective optimization model that incorporates flexibility and reconfiguration aspects to determine an optimal product allocation and network configuration of a GPN over a given planning horizon. The preemptive goal programming approach is used to identify Pareto-optimal solutions and to increase user friendliness. The subsequent verification, validation and post-optimality analysis combined in a structured process enables a wide range of companies to apply the approach. The model is successfully applied in the GPN of a special machine manufacturer, which produces high precision metrology machines. Due to its transparent approach for complex planning problems, the developed method provides a solid base for well-founded, objective decisions. Hence, the risk of costly errors in the planning phase is reduced.

GAS-CONDENSATE FLUID PVT MODEL QUALITY CHECK BASED ON THE CONCEPT OF A SINGLE-CELL SIMULATION MODEL

The problems of gas-condensate PVT-models (Pressure Volume Temperature, PVT) creation under limited input information were analyzed. Traditional fluid phase behavior characterization approach relies on creation of the equation of state (EOS) based on initial composition of reservoir fluid and its future regression for critical parameters (pressure and temperature), binary interaction coefficients, acentric factors of residual “plus” fraction or pseudo-components. The adjustment is done until the moment when EOS is reproducing the results of laboratory experiments. Classic PVT experiments performed on gas-condensates and volatile oils are constant composition expansion (CCE), constant volume depletion (CVD) and separator tests. However, in the case of most Ukrainian fields, discovered and explored in the last century, not only the reliable detailed initial fluid composition is not available, but phase behavior was studied with non-equilibrium method of so-called differential condensation, that does not allow their direct application for PVT models creation. Previously, the authors [1, 2] presented an alternative method for fluid characterization based on the fractional distillation test. At the same time, due to significant uncertainty in input data, particularly a) condensate production allocation; b) commingled production from multiple reservoirs with different C5+ yield; c) non-recorded change of separator conditions that affects liquid extraction and its density; d) technological production losses, issues of reproducing the condensate production during history matching of several models of Dniper-Donetsk Basin were faced. There was proposed and explained in detail an example of single-cell reservoir simulation model application concept for quality check of created PVT model for one of the fields with potential yield of 86 g/m3. The idea of the concept is based on the reproduction of material balance of gas-condensate reservoir through one conditional well controlled on a primary (gas) phase, that allows quick identification of changes into calculated gas-condensate yield curve, necessary for matching of condensate production. Implementation of these changes allows quick and precise full-field model calibration.

Formulations for automatic optimization of decommissioning timing in offshore oil and gas field development planning

A mathematical programming formulation has been developed to optimize the drilling program, the production allocation, and the decommissioning time in early-stage field development planning. Various abandonment constraints are considered in the optimization model when searching for the best decommissioning time for the long-term field development strategy. The model is tested on a real field development planning consisting of two reservoirs produced with subsea wells to an offshore facility. The novelty of this work is the utilization of mathematical programming to formulate and solve early-stage field development planning considering the variable decommissioning time. The results show that considering a variable decommissioning time in early-stage field development planning yields improved economic margins. Further, it provides operators a decision-support tool with enhanced capabilities of forecasting the best decommissioning time, improving the forecasts for processing and production planning, expected revenue and lifetime of facilities, and anticipating and preparing for field abandonment in advance. The optimization model is capable of determining the drilling and production schedules, and the optimal decommissioning time that yielded the maximum net present value for the project.

Bayesian Model-Based Offline Reinforcement Learning for Product Allocation

Product allocation in retail is the process of placing products throughout a store to connect consumers with relevant products. Discovering a good allocation strategy is challenging due to the scarcity of data and the high cost of experimentation in the physical world. Some work explores Reinforcement learning (RL) as a solution, but these approaches are often limited because of the sim2real problem. Learning policies from logged trajectories of a system is a key step forward for RL in physical systems. Recent work has shown that model-based offline RL can improve the effectiveness of offline policy estimation through uncertainty-penalized exploration. However, existing work assumes a continuous state space and access to a covariance matrix of the environment dynamics, which is not possible in the discrete case. To solve this problem, we propose a Bayesian model-based technique that naturally produces probabilistic estimates of the environment dynamics via the posterior predictive distribution, which we use for uncertainty-penalized exploration. We call our approach Posterior Penalized Offline Policy Optimization (PPOPO). We show that our world model better fits historical data due to informative priors, and that PPOPO outperforms other offline techniques in simulation and against real-world data.

An E‐commerce prediction system for product allocation to bridge the gap between cultural analytics and data science

AbstractWith the emerging era of E‐commerce and online shopping, people are also in a habit to receive default product recommendations on the web pages that they access. Google is already providing such suggestions. Till now recommendations were made only based on previous sentiments or feedback or ratings, but this research has improved the product recommendation method by including one more parameter for the same. This article represents two parameters for making predictions of product allocation to a new customer. These parameters are ratings given by the existing users for that particular product and the region to which the new customer belongs. Following these parameters, a prediction model and an algorithm, Improved_Collab_Similarity, have been implemented. The dataset has been developed where India as a country along with all its States has been considered for products which are popular for their creation based on regional and ancient skills of the people belonging to that area. Results for the mentioned prediction model have been discussed in this article where generally precision increases with the increase in a number of products but at some points, it does not increase when a smaller number of that product was purchased by the customers.

Facility Layout Design of New Warehouse and Implementation of Multi-Criteria Decision Making for Product Allocation at PT XYZ

As the company's business grows, there product distribution network will increase. This will cause distribution costs to be increase. Therefore, the company decided to build a new warehouse in order to increase the efficiency of distribution costs. This new warehouse will use two storage systems, those are racking system and floor storage, so it need to design a good layout so that the space provided can be used effectively and efficiently. A dedicated storage policy is used to make workers easier to find products so that the time of product retrieval is faster. Another thing to consider is the proper allocation of products because this can affect the total distance of product movement which can also affect the time of product retrieval. In this study, multi-criteria decision making (MCDM) will be used to determine which product will be the main priority to be placed near the inbound/outbound (I/O). Furthermore, we will consider supporting facilities so that warehousing activities can run well where the preparation of each facility will use the activity relationship chart (ARC).