Assignment Model Research Articles

PID-based attitude control of quadrotor using robust pole assignment and LPV modeling

PID-based attitude control of quadrotor using robust pole assignment and LPV modeling

Common due-date assignment problems with fixed-plus-linear earliness and tardiness costs

We study single machine scheduling and common due-date assignment problems. The cost considered is fixed plus linear, i.e., jobs are penalized by two cost components: (i) a variable cost which is proportional to their earliness/tardiness values, (ii) a fixed cost which is independent of their actual earliness/tardiness values. For the general version of this problem, three heuristics are introduced and tested. We also study a number of modifications and special cases. We focus first on the special case of identical job processing times. Both versions of minsum and minmax are shown to have a polynomial time solution, even with the additional option of job rejection. Another special case of variable earliness cost and fixed tardiness cost is also studied. For this NP-hard problem, a pseudo-polynomial dynamic programming algorithm is introduced and tested. An extension to the case of bounded rejection cost, and a modified version with an additional bound on the total tardiness cost, are also studied. The solution algorithms for these problems are tested numerically as well. The last studied problem is that of job-independent cost components. This version, which generalizes the first and most cited due-date assignment model, is shown to have a polynomial time solution.

Exploring the performance impact of unit load selection in order picking: evidence from a cold retail supply chain

PurposePrior literature has widely established that the design of storage locations impacts order picking task performance. The purpose of this study is to investigate the performance impact of unit loads, e.g. pallets or rolling cages, utilized by pickers to pack products after picking them from storage locations.Design/methodology/approachAn empirical analysis of archival data on a manual order picking system for deep-freeze products was performed in cooperation with a German brick-and-mortar retailer. The dataset comprises N = 343,259 storage location visits from 17 order pickers. The analysis was also supported by the development and the results of a batch assignment model that takes unit load selection into account.FindingsThe analysis reveals that unit load selection affects order picking task performance. Standardized rolling cages can decrease processing time by up to 8.42% compared to standardized isolated rolling boxes used in cold retail supply chains. Potential cost savings originating from optimal batch assignment range from 1.03% to 39.29%, depending on batch characteristics.Originality/valueThis study contributes to the literature on factors impacting order picking task performance, considering the characteristics of unit loads where products are packed on after they have been picked from the storage locations. In addition, it provides potential task performance improvements in cold retail supply chains.

A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System

Logistics is significantly impacted by quality/quantity issues associated with data collection and data sharing restrictions. Nonetheless, public data from national entities and internet-of-things (IoT) solutions enable the development of integrated tools for performance analysis and real-time optimization of logistics networks. This study proposes a three-module data-driven system architecture that covers (a) logistics data collection tools, (b) logistics services performance evaluation, and (c) the transition to synchromodal systems. Module 1 integrates multisource data from national logistics platforms and embedded devices placed within intermodal containers. A multigraph representation of the problem is conceived. Environmental, economic, and operational data are generated and injected into a digital twin. Thus, key performance indicators (KPIs) are computed by simulation or direct transformation of the collected data. Module 2 uses Multi-directional Efficiency Analysis, an optimization algorithm that benchmarks multimodal transportation routes of containers using prior KPIs. Outputs are a technical performance index relevant to logistics clients and improvement measures for logistics service providers. A real case study application of the solution proposed for Module 2 is presented. Module 3 provides real-time scheduling and assignment models using CP-sat solvers, accommodating varying system dynamics and resource availability, minimizing makespan and operational costs.

Modeling and control of drug assignment for pharmaceutical cabinets using discrete event systems approaches

AbstractThe public health sector, considered to be vital, especially in this pandemic crisis of COVID‐19, requires automation and control of drug distribution in pharmacies commonly called: “Dispensing.” In this paper, we address the medication assignment problem for automated dispensing cabinets (ADCs). First, we use a network of conflicting timed event graphs (CTEGs), a class of timed Petri nets with shared resources, to model pharmaceutical cabinets. Second, we develop a new method for controlling CTEGs under mutual exclusion constraints (MECs) to solve the problem of drug assignment, using a control approach based on Min‐Plus algebra. Finally, a case study of assigning drugs is given to illustrate the proposed methodology and show the efficiency of the developed control laws.

Faster Convergence of Integrated Activity-Based Models in Dynamic Multimodal Transit Assignment Using Macroscopic Road Congestion Estimation

This paper proposes an integrated framework of an activity-based behavior model and a multimodal transit assignment-simulation tool that captures road network congestion dynamics. The framework has two levels: the upper level is the demand-side activity-based model that decides individual travelers’ behavioral choices based on up-to-date information from the lower level; the lower level consists of both transit and road network estimation models on the supply side, whose inputs are trips from the upper level. The objective of this framework is to assess impacts of transit service policies, so the transit network is simulated with an agent-based multimodal hyperpath assignment model in each iteration, while the road network is mainly estimated by a macroscopic model of congestion (metamodel) instead of a simulation-based assignment model to accelerate execution time toward an equilibrated solution. Convergence under this framework is also defined from two aspects: individual choice behaviors and transit hyperpath assignment. One contribution of this paper is to incorporate the exogenous effects of road network dynamics into the integrated demand and transit assignment model, and to reduce the time to reach convergence with macroscopic modeling. This paper uses mode choice behavior as an example to demonstrate mathematical formulations and implementation procedures to reach two-level convergence. The framework is tested with the large-scale regional network of the Greater Chicago metropolitan area. The results suggest that the major advantage of the macroscopic road model is to accelerate convergence toward equilibrium when it is used to capture the traffic network congestion effects in this integrated mode choice-transit assignment framework.

Online and Offline Model for Housekeeping Order Assignment Based on Dynamic Programming Algorithm

With the booming development of door-to-door housekeeping service, the platform faces the problem of order assignment. Improving the matching mechanism between orders and housekeepers based on a dynamic programming (DP) algorithm can not only achieve flexible order allocation but can also improve the service efficiency and service quality. In this paper, a single objective nonlinear programming model is established, which takes the maximum total weight value as the objective function to study the order allocation problem under offline and online conditions. Under the offline condition, the number of housekeepers is taken as the decision variable. The status of order and housekeeper, order time, and action trajectory are taken as constraints. For online assignment, the order backlog status is treated as the decision variable. The reliability of the model was verified using real data from 20 groups of housekeepers and 50 groups of orders. Finally, the effect of order backlog on online allocation is discussed and the optimal threshold and maximum weight are found. The online order assignment model is compared with the nearest distance assignment model. The results show that the online assignment model with a total weighted score of 1045.14 is better than the nearest distance assignment model with a score of 810.25.

Model of Educational Leadership Management in Boarding Schools

Islamic boarding schools have a wealth of knowledge that can be researched and analyzed from various scientific aspects, so research on Islamic boarding schools will be available. This study will focus on the management pattern of pesantren leadership education by choosing the largest pesantren in the country, namely the Darussalam Gontor Modern Islamic Boarding School. A qualitative method with a case study approach. Data were obtained from key informants, Islamic boarding school documents and books, journals and articles as support related to the management of leadership education in the era of globalization. Data analysis in this study used content analysis techniques. The results of the study found that the management of leadership education in the form of regeneration in the style of the Gontor Islamic Boarding School was classified into six: briefing, training, assignment, habituation, control and role modelling. The revival of leadership values at the Gontor Islamic Boarding School includes education of ibrah, habituation, mauidzah, discipline, independence and exemplary. The pattern of leadership in Darussalam Gontor Islamic Boarding School is collegial (Trimurti). The administration of the Trimurti pesantren Darussalam Gontor became the central figure of the pesantren in the leadership of three kiai. With this collective leadership model, the Gontor Islamic Boarding School will eventually become a modern institution whose survival does not depend on a single kiai as the supreme leader. Thus, Islamic boarding schools are expected to be able to foster and produce Islamic human resources.

Resource Assignment and Performance Optimisation in Intelligent Supply chain

The article gives the problem of resource assignment and performance optimisation in intelligent supply chain (ISC). The task of the resource assignment for intelligent supply chain is multi-product and multi-criterion task. Scale of the ISC determines the allocation of the suppliers and consumers of product. The customer demands for certain kind of product in ISC can be deemed as stochastic process. As goal of performance optimization task we should to evaluate parameters of ISC through assignment of production resources. The article presents the resource assignment model of ISC and its performance measures. On the base of simulation the verification and validation of presented model is carried out. The problem statement, solution method, numerical example and validation of model via simulation are represented for resource assignment and performance optimization model.

How to fairly and efficiently assign tasks in individually rational agents’ coalitions? Models and fairness measures

An individually rational agent will participate in a multi-agent coalition if the participation, given available information and knowledge, brings a payoff that is at least as high as the one achieved by not participating. Since agents? performance and skills may vary from task to task, the decisions about individual agent-task assignment will determine the overall performance of the coalition. Maximising the efficiency of the one-on-one assignment of tasks to agents corresponds to the conventional linear sum assignment problem, which considers efficiency as the sum of the costs or benefits of individual agent-task assignments obtained by the coalition as a whole. This approach may be unfair since it does not explicitly consider fairness and, thus, is unsuitable for individually rational agents? coalitions. In this paper, we propose two new assignment models that balance efficiency and fairness in task assignment and study the utilitarian, egalitarian, and Nash social welfare for task assignment in individually rational agents? coalitions. Since fairness is a relatively abstract term that can be difficult to quantify, we propose three new fairness measures based on equity and equality and use them to compare the newly proposed models. Through functional examples, we show that a reasonable trade-off between efficiency and fairness in task assignment is possible through the use of the proposed models.

Model for controller assignment and placement to minimize migration blackout time with load-balancing platform in software-defined network

Model for controller assignment and placement to minimize migration blackout time with load-balancing platform in software-defined network

A new model for physician assignment based on fuzzy rules extraction from climatic factors

A new model for physician assignment based on fuzzy rules extraction from climatic factors

Automated prediction of ground state spin for transition metal complexes.

Exploiting crystallographic data repositories for large-scale quantum chemical computations requires the rapid and accurate extraction of the molecular structure, charge and spin from the crystallographic information file. Here, we develop a general approach to assign the ground state spin of transition metal complexes, in complement to our previous efforts on determining metal oxidation states and bond order within the cell2mol software. Starting from a database of 31k transition metal complexes extracted from the Cambridge Structural Database with cell2mol, we construct the TM-GSspin dataset, which contains 2063 mononuclear first row transition metal complexes and their computed ground state spins. TM-GSspin is highly diverse in terms of metals, metal oxidation states, coordination geometries, and coordination sphere compositions. Based on TM-GSspin, we identify correlations between structural and electronic features of the complexes and their ground state spins to develop a rule-based spin state assignment model. Leveraging this knowledge, we construct interpretable descriptors and build a statistical model achieving 98% cross-validated accuracy in predicting the ground state spin across the board. Our approach provides a practical way to determine the ground state spin of transition metal complexes directly from crystal structures without additional computations, thus enabling the automated use of crystallographic data for large-scale computations involving transition metal complexes.

Optimization of assignment model based on tugs service performance using the hungarian method in the waters of the kupp tanjung santan area

Optimization of assignment model based on tugs service performance using the hungarian method in the waters of the kupp tanjung santan area

Pascal's triangle graded mean defuzzification approach for solving fuzzy assignment models by using pentagonal fuzzy numbers

Pascal's triangle graded mean defuzzification approach for solving fuzzy assignment models by using pentagonal fuzzy numbers

Generalization of Beckmann’s Transformation for Traffic Assignment Models with Asymmetric Cost Functions

An optimization model is developed to solve the deterministic traffic assignment problem under congested transport networks with cost functions that have an asymmetric Jacobian. The proposed formulation is a generalization of Beckmann’s transformation that can incorporate network links with multivariate vector cost functions to capture the asymmetric interactions between the flows and costs of the different links. The objective function is built around a line integral that generalizes the simple definite integral in Beckmann’s transformation and is parameterised to ensure the solution of the new problem satisfies Wardrop’s first principle of network equilibrium. It is shown that this method is equivalent to the variational inequality approach. Our new approach could be extended to supply‐demand equilibria models in other markets than transportation, with complementary or substitute goods/services in which there are asymmetric interactions between prices.

A Bilevel EV Charging Station and DC Fast Charger Planning Model for Highway Network Considering Dynamic Traffic Demand and User Equilibrium

The rapid electric vehicle (EV) adoption and relatively short EV driving range urge city planners to expand charging infrastructure (i.e., EV charging stations (EVCSs) and direct current fast chargers (DCFCs)) in the highway network for supporting long-distance intercity EV travels. To properly address distinct roles as well as interactions of the EVCS planner, traffic network, power distribution networks (PDNs), and drivers, this paper explores a bilevel planning model in which the upper level determines EVCS and DCFC construction plans and the two lower-level subproblems describe the user equilibriumbased traffic assignment model of the traffic network and the operation model of PDNs. Moreover, the statistical charging time is modeled in the traffic network subproblem as a function of statistical charging demand and the number of DCFCs, leveraging the macro traffic pattern and micro charging behavior of individual drivers with balanced accuracy and computational complexity. The proposed model also captures the impacts of social factors, charging infrastructure, and driver behaviors on dynamic EV traffic demands. Reformulation and linearization techniques are applied to convert the proposed bilevel problem into a tractable single-level mixed-integer linear programming model for effective computation. The effectiveness of the proposed approach is tested on an intercity highway network with multiple PDNs. The findings from numerical case studies highlight the important interplay among the construction plans of EVCSs and DCFCs, the PDN constraints, the user equilibrium traffic network, and the EV drivers. Case studies illustrate the feasibility and necessity of considering the number of DCFCs to calculate the macro-perspective charging time in the user equilibrium traffic network model. The dynamic traffic demand, induced by social factors and traffic demand elasticity, was also found to be a crucial factor in reshaping EV traffic. Thus, properly considering these important factors would lead to a more accurate quantification of traffic demands and ultimately result in a more reasonable charging facility construction plan.

Optimizing Perishable and Non-Perishable Product Assignment to Packaging Lines in a Sustainable Manufacturing System: An AUGMECON2VIKOR Algorithm

Identifying appropriate manufacturing systems for products can be considered a pivotal manufacturing task contributing to the optimization of operational and planning activities. It has gained importance in the food industry due to the distinct constraints and considerations posed by perishable and non-perishable items in this problem. Hence, this study proposes a new mathematical model - according to knowledge discovery as well as an assignment model to optimize manufacturing systems for perishable, non-perishable, and hybrid products tailored to meet their unique characteristics. In the presented model, three objective functions are taken into account: (1) minimizing production costs by assigning the products to the right set of manufacturing systems, (2) maximizing the product quality by assigning the products to the systems, and (3) minimizing total CO2 emissions of the machines. A numerical example is utilized to evaluate the performance of AUGMECON2VIKOR compared to AUGMECON2. The results show that AUGMECON2VIKOR obtains superior Pareto solutions across all objective functions. Furthermore, the sensitivity analysis explores the positive green impacts, influencing both cost and quality.

Buzzard to Cardinal: Improved Mock Catalogs for Large Galaxy Surveys

We present the Cardinal mock galaxy catalogs, a new version of the Buzzard simulation that has been updated to support ongoing and future cosmological surveys, including the Dark Energy Survey (DES), DESI, and LSST. These catalogs are based on a one-quarter sky simulation populated with galaxies out to a redshift of z = 2.35 to a depth of m r = 27. Compared to the Buzzard mocks, the Cardinal mocks include an updated subhalo abundance matching model that considers orphan galaxies and includes mass-dependent scatter between galaxy luminosity and halo properties. This model can simultaneously fit galaxy clustering and group–galaxy cross-correlations measured in three different luminosity threshold samples. The Cardinal mocks also feature a new color assignment model that can simultaneously fit color-dependent galaxy clustering in three different luminosity bins. We have developed an algorithm that uses photometric data to further improve the color assignment model and have also developed a novel method to improve small-scale lensing below the ray-tracing resolution. These improvements enable the Cardinal mocks to accurately reproduce the abundance of galaxy clusters and the properties of lens galaxies in the DES data. As such, these simulations will be a valuable tool for future cosmological analyses based on large sky surveys.

Assignment model for employing skilled IT professionals for appropriate job roles under uncertainty with different ranking approaches using membership functions

Assignment model for employing skilled IT professionals for appropriate job roles under uncertainty with different ranking approaches using membership functions