Quantity Allocation Research Articles

Freshwater management in Aotearoa-New Zealand: is trading a viable option for water quantity allocation?

ABSTRACT Water trading has been proposed as an effective mechanism for delivering economic benefits within environmental limits. It represents one possibility for applying a dynamic allocation framework within which environmental river flows can be monitored and improved to support ecological, cultural, aesthetical and recreational values. Based on international experience, requirements for the successful implementation of water trading are assessed in the Aotearoa-New Zealand context. It is concluded that, if the environment is recognised as a water user, water trading has potential to promote sustainable water use but is associated with significant set-up and ongoing costs. A co-governance policy framework for water trading has not been agreed by iwi/hapū and the Crown based on Te Tiriti o Waitangi (1840). The institutional capacity required for development and implementation of trading is currently lacking, and the roles that government agencies and iwi/hapū would play are unclear. Market operational requirements relating to collection, collation and communication of hydrological data are only partially in place. Finally, New Zealand’s flashy hydrology is not conducive to seasonal water trading and smaller catchments may contain insufficient users to constitute a functioning market. Water trading should therefore not be viewed as an easily implementable solution for improved freshwater management.

Production planning method for seru production systems under demand uncertainty

The seru production system is a new-type manufacturing system arising from Japanese production practices that can simultaneously achieve high efficiency, flexibility, and responsiveness. In this paper, a worker and production quantity allocation optimization method for multi-product seru production systems under demand uncertainty is proposed. To handle such demand uncertainty, the Monte Carlo technique is employed and the optimization problem is formulated as a bi-level mixed-integer linear programming problem. At the first step, a worker allocation problem is solved before demand realization is observed. As the second optimization step, the production quantity allocation is then conducted based on the observed demand realization. The proposed method is applied to several numerical problems to illustrate its effectiveness.

Research on optimization of assembly line based on product scheduling and just-in-time feeding of parts

PurposeReducing production auxiliary time is the key to improve the efficiency of the existing mixed-flow assembly line. This paper proposes a method combining improved genetic algorithm (GA) and Flexsim software. It also investigates mixed-flow assembly line scheduling and just-in-time (JIT) parts feeding scheme to reduce waste in production while taking the existing hill-drop mixed-flow assembly line as an example to verify the effectiveness of the method.Design/methodology/approachIn this research, a method is presented to optimize the efficiency of the present assembly line. The multi-objective mathematical model is established based on the objective function of the minimum production cycle and part consumption balance, and the solution model is developed using multi-objective GA to obtain the mixed flow scheduling scheme of the hill-drop planter. Furthermore, modeling and simulation with Flexsim software are investigated along with the contents of line inventory, parts transportation means, daily feeding times and time points.FindingsTheoretical analysis and simulation experiments are carried out in this paper while taking an example of a hill-drop planter mixed-flow assembly line. The results indicate that the method can effectively reduce the idle and overload of the assembly line, use the transportation resources rationally and decrease the accumulation of the line inventory.Originality/valueThe method of combining improved GA and Flexsim software was used here for the first time intuitively and efficiently to study the balance of existing production lines and JIT feeding of parts. Investigating the production scheduling scheme provides a reference for the enterprise production line accompanied by the quantity allocation of transportation tools, the inventory consumption of the spare parts along the line and the utilization rate of each station to reduce the auxiliary time and apply practically.

Diagnostic test decision for suppliers in cruise-building supply chain with information asymmetry

To solve the potential supply disruption in cruise-building supply chain, this study explores a supply system consisting of a monopoly supplier, a new entrant supplier, and a cruise-building manufacturer. A two-step option contract including diagnostic test and process improvement is designed to solve the order quantity allocation and to avoid supply disruption. The analysis combines the decision tree with the principal-agent model under information asymmetry. The result shows that the cruise-building manufacturer would like to allocate more orders to the unreliable supplier who implements process improvement. In the end, the detailed numerical analysis is implemented to justify the proposal.

Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area.

The purposes are to use water resources efficiently and ensure the sustainable development of social water resources. The edge computing technology and GIS (Geographic Information Science) image data are combined from the perspective of sustainable development. A prediction model for the water resources in the irrigation area is constructed. With the goal of maximizing comprehensive benefits, the optimal allocation of water quality and quantity of water resources is determined. Finally, the actual effect of the model is verified through specific instance data in a province. Results demonstrate that the proposed irrigation area ecological prediction model based on edge computing and GIS images can provide better performance than other state of the art models on water resources prediction. Specifically, the accuracy can remain above 90%. The proposed model for ecological water demand prediction in the irrigation area and optimal allocation of water resources is based on the principle of quality water supply. The optimal allocation of water resources reveals the sustainable development ideas and the requirements of the optimal allocation model, which is very reasonable. The improvement of the system is effective and feasible, and the optimal allocation results are reasonable. This allocation model aims at the water quality and quantity conditions, water conservancy project conditions, and specific water demand requirements in the study area. The calculation results have great practicability and a strong guiding significance for the sustainable utilization and management of the irrigation area.

Accounting of Transboundary Ecocompensation Standards Based on Water Quantity Allocation and Water Quality Control Targets

Accounting of Transboundary Ecocompensation Standards Based on Water Quantity Allocation and Water Quality Control Targets

An interactive fuzzy programming approach for a sustainable supplier selection under textile supply chain management

Textile dyestuff supplier evaluation and quantity allocation is a multi-period multi-objective optimization problem of a business triad comprising of cost, quality, and time. To date, no such sufficing textile supply chain management model is reported that optimizes all three components of the business triad along with customer complaints and external quality audit. This research work initiates an advanced integrated multi-dimensional evaluation approach to support small and medium-sized textile industries in effective supplier selection and quantity allocation. Many small and medium-sized enterprises in the textile sector have little or no facilities to evaluate the quality of incoming dyes in their premises. An intervention of government laboratories is recommended in this study, which play a compelling role to regulate and help evaluate the dyestuff suppliers according to the acceptable quality limit. A multi-period multi-objective textile supply chain management (MPMOTSCM) model is developed for simultaneous achievement of supplier evaluation and order allocation. Based on the recommended business triad and expert committee input, an interactive weighted fuzzy goal programming model is developed. A numerical assessment using metaheuristic approach is carried out to elaborate the practical applicability of the model. The experimental outcomes show that the proposed model has achieved 45.77% satisfaction level for the cost, 80.73% for the quality, and 95.04% for the time. Analysis of the model indicates that this model provides a robust decision support system to the managers for dyestuff supplier evaluation, selection, and quantity allocation.

Lessons Relearned? H1N1, COVID-19, and Vaccination Planning.

Lessons Relearned? H1N1, COVID-19, and Vaccination Planning.

Optimizing Allocation of Sea Area Utilization Based on Genetic Algorithm

Optimal allocation of sea area utilization solves the problem of quantity allocation and spatial distribution of sea area used by various departments. It is a necessary method to coordinate the conflicts between different departments and realize the dual goals of development and ecological protection. This paper presents an optimal allocation method of sea area utilization based on genetic algorithm. The basic principle and solving process of optimal allocation of sea area utilization based on genetic algorithm are described. The fitness evaluation model, genetic operator and constraint conditions are designed.

Supplier Selection and Order Allocation in the Presence of Suppliers with Exact Annual Capacity

In this paper, we focus on the supplier selection and order quantity allocation for a single retailer. The retailer orders a product from multiple suppliers with capacities, adds value to the produ...

A coordination mechanism for supplier selection and order quantity allocation with price-sensitive demand and finite production rates

We study a supplier selection and order quantity allocation problem in a two-stage supply chain, which is composed of a set of potential suppliers and a single buyer/retailer trading one product. The demand at the buyer's stage is price-sensitive and suppliers have finite production rates. Within this framework, the optimization of the supply chain is evaluated by considering the performance of both stages and guaranteeing their profitability through a coordination mechanism based on profit-sharing. Two mixed integer nonlinear programming models are presented under different lot-sizing policies to determine the optimal set of selected suppliers, retail price, and order quantity and number of orders per order cycle for each selected supplier. The structure of the models' solutions is examined by deriving properties of the optimal supplier selection and order quantity allocation scheme. Numerical examples are also provided to illustrate the applicability of the proposed models.

A heuristic method for the supplier selection and order quantity allocation problem

This paper addresses the so-called supplier-selection and order-quantity allocation problem. Because of the complexity of this of problem (e.g., non-linear, discontinuous cost function), a new heuristic method is proposed and tested. This heuristic method explores the search space in a short period of time to find cheaper solutions. In order to test the efficiency of the proposed heuristic, two metaheuristic algorithms are applied: particle swarm optimization (PSO) and differential evolution (DE). Additionally, two numerical examples were solved. In the first one, it is shown that the proposed heuristic performed best compared to other solutions previously published in the literature, both in terms of computational time and total cost. The complexity analysis found that in the worst case, the proposed heuristic executes on average 99.9% less numerical operations than PSO and DE. In the second numerical example, larger instances were studied. Our findings show that the proposed heuristic was able to find a feasible solution in 15 out 15 instances, while the PSO and the DE algorithms were unable to find a solution in 9 out of 15 instances. Therefore, the proposed heuristic does not just lead to lower total cost solutions, but it also performs a more exhaustive search in shorter computational times for larger instances of the problem. Finally, Wilcoxon and Kruskal-Wallis statistical tests demonstrate significant difference between the proposed heuristic and the PSO and DE; the proposed heuristic presented a lower median in most cases.

An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem

Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.

Integrated allocation model of water quantity and quality and its application in the Yellow River

Abstract Taking a typical reach of the Yellow River as the research object, using decomposition, coordination, coupling and control technology and through real-time data transmission and feedback, the simultaneous coupling of water quantity and quality is realized. Based on the identification parameters of water intake, section discharge and water quality index in the water function areas, the on-line identification and process control of the allocation target are realized, and an integrated model of water quantity and quality with the functions of cycle iteration, on-line feedback and rolling correction is established. Taking the natural runoff from 1956 to 2000 as input and the water demand and sewage discharge from the upper reaches of the Yellow River in 2015 as an example and through optimization, an integrated water quantity and quality allocation scheme for the typical reach of the Yellow River is put forward. It is shown that the model can realize the integrated allocation of water quantity and quality in the Yellow River.

Appraisal of kappa-based metrics and disagreement indices of accuracy assessment for parametric and nonparametric techniques used in LULC classification and change detection

In the face of rapid urbanization, monitoring urban expansion has gained importance to sustainably manage the land resources and minimize the impact on the environment. Monitoring urban growth using satellite data involves computing the state of land use–land cover and their change over time. A number of computing methods have been developed to process and interpret the satellite results for an urban environment. However, due to a large number of parametric and nonparametric algorithms used for land-use–land-cover classification, there is uncertainty regarding choosing the best algorithm to measure the urban processes. In this study, several parametric (maximum likelihood) and nonparametric (support vector machine, spectral angle mapper, artificial neural network and decision tree) algorithms were used. The study was aimed at finding out the best available classification technique for land-use–land-cover classification and change detection. Landsat 8, the latest in Landsat series, and Landsat 7 and 5 freely available satellite data were used. Due to the redundancy reported for the traditional kappa-based indices, we applied modern disagreement indices to assess the accuracy of the classification process. Artificial neural network for Landsat 8 image had the highest kappa coefficient, while spectral angle mapper had the highest overall agreement (97%) and least quantity allocation error (1%). Spectral Angle Mapper gave the highest accuracy, while maximum likelihood classification gave the least for allocation and spatial disagreement indices. We found that spectral angle mapper gave the best results for land-use–land-cover change analysis in terms of least omission and commission errors (2.5% each) and highest overall agreement, whereas artificial neural network performed better in land-use–land-cover classification studies.

CAR: heuristics for the inventory routing problem

Vendor managed inventory (VMI) is a streamlined approach to inventory and order fulfillment and is a system in which vendors continuously and automatically replenish a trading partner’s inventory. Vendors must ensure appropriate quantities of storages at the point of demand and must ensure optimal distribution plans, including routing of the distribution vehicles. This problem is known as the inventory routing problem (IRP) and seeks to integrate the routing of vehicles used for collection and distribution, with conventional inventory management. This paper addresses an IRP with a manufacturer that supplies a product using a fleet of vehicles to a set of warehouses over a defined time horizon. We develop a mixed integer linear program to determine the optimal allocation and the routing schedule for warehouses over the defined time horizon. To solve this problem, we propose a three-phase heuristic approach, called CAR: clustering of receiver nodes, allocation of quantities to these nodes, and routing of delivery vehicles through clusters of nodes. Computational studies are carried out and experimental trials conducted over a large number of data sets provide encouraging results and show usefulness of the solution approaches. The proposed ILP would provide optimal solution to the problem but it demands huge computational effort. However, CAR2, a proposed heuristic, is able to get solutions 7.41% better than the upper bound solutions obtained from CPLEX. These approaches can easily be implemented into existing VMI systems.

CROATIAN NATURAL GAS BALANCING MARKET ANALYSIS

The Network Code on Gas Balancing of Transmission Networks stimulates the development of the wholesale gas market by encouraging balance responsible parties to use standardized balancing mechanisms. To balance their portfolios, balance responsible parties can use renominations of quantities at entry and exit points, trade on a virtual trading point or trade on a trading platform. In the event of a system imbalance, Plinacro, as the operator of the gas transmission system in the Republic of Croatia, activates the balancing energy to return the system within acceptable limits. In accordance with the Rules on the Organization of the Gas Market, the Croatian Energy Market Operator performs a monthly calculation of the daily imbalance charge, trades conducted on the trading platform for balancing activities, a neutrality charge and a charge for deviation from the nominated quantities which have been analysed in this paper based on the case study of a chosen balancing group. The analyses conducted in the paper have shown that the balance responsible party may be entitled to compensation or be liable to pay compensation based on the monthly calculation of the Croatian Energy Market Operator, HROTE, depending on the value of each charge. Plinacro as the forecasting party is preparing a new model for the allocation of gas quantities that will affect the operations of gas suppliers, DSOs and especially BRPs. Based on this analysis, it could be concluded that more accurate estimated consumption for a balancing group leads to cost optimization and a more transparent gas market.

Order quantity allocation for total inventory cost optimisation to selected suppliers through VIKOR technique

In modern era, no. of potential suppliers for the industries are considerably increased and minimisation of total inventory cost becomes complicated. In response to the concern, we present and address the problem in two stages which efficiently assists to figure out the best suppliers among many and allocates order quantity to each supplier so as to mitigate overall inventory cost. Firstly, potential suppliers are listed and quantitative, qualitative data are collected based on opinion and judgemental survey. The normalised data is represented in square matrix and ranking is awarded to suppliers using utility measures and regret measures. Secondly, the fitness function is formulated and necessary constraints that primarily influences objective function is framed as nonlinear programming model. Further, the model is solved using LINDO software for its optimal solutions. The results show that the model considerably reduces total cost function as no. of orders from customer's increases.

Order Quantity allocation for Total inventory cost optimization to selected suppliers through VIKOR Technique

In modern era, no. of potential suppliers for the industries are considerably increased and minimisation of total inventory cost becomes complicated. In response to the concern, we present and address the problem in two stages which efficiently assists to figure out the best suppliers among many and allocates order quantity to each supplier so as to mitigate overall inventory cost. Firstly, potential suppliers are listed and quantitative, qualitative data are collected based on opinion and judgemental survey. The normalised data is represented in square matrix and ranking is awarded to suppliers using utility measures and regret measures. Secondly, the fitness function is formulated and necessary constraints that primarily influences objective function is framed as nonlinear programming model. Further, the model is solved using LINDO software for its optimal solutions. The results show that the model considerably reduces total cost function as no. of orders from customer's increases.

Inventory replenishment decision model for the supplier selection problem using metaheuristic algorithms.

In supply chain management, fast and accurate decisions in supplier selection and order quantity allocation have a strong influence on the company's profitability and the total cost of finished products. In this paper, a novel and non-linear model is proposed for solving the supplier selection and order quantity allocation problem. The model is introduced for minimizing the total cost per time unit, considering ordering, purchasing, inventory, and transportation cost with freight rate discounts. Perfect rate and capacity constraints are also considered in the model. Since metaheuristic algorithms have been successfully applied in supplier selection, and due to the non-linearity of the proposed model, particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), are implemented as optimizing solvers instead of analytical methods. The model is tested by solving a reference model using PSO, GA, and DE. The performance is evaluated by comparing the solution to the problem against other solutions reported in the literature. Experimental results prove the effectiveness of the proposed model, and demonstrate that metaheuristic algorithms can find lower-cost solutions in less time than analytical methods.