Decision Support System For Scheduling Research Articles

Integrating Irrigation Decision Support Systems for Efficient Water Use: A Case Study on Mediterranean Agriculture

Irrigation plays a pivotal role in Southern Europe, and its importance is expected to further increase due to rising climatic variability. Concurrently, the European Water Framework Directive (WFD) mandates the safeguarding of water bodies and the implementation of incentive pricing strategies to promote efficient water management. Within this context, Irrigation Scheduling Decision Support Systems (IS-DSS) could contribute to the achievement of these objectives, although there are still obstacles to their adoption due to uncertainties regarding their potential benefits. This paper aims to derive a pricing model that reflects actual water use through the adoption of an IS-DSS. The innovation of this study lies in showing that adopting an IS-DSS to reduce irrigation volumes can potentially lower concession fees in collective irrigation systems. Thus, it contributes to the fulfilment of the WFD’s objectives regarding incentive water pricing. Notably, the tool is evaluated using the case study of a farm located in the Mediterranean region. The results demonstrate the dual benefits of IS-DSS adoption: on the one hand, it helps preserve water resources with a 24% reduction in irrigation volumes; on the other, it decreases irrigation costs by 20% at the farm level and by 9.4% at the irrigation district level. Therefore, the presented study provides insights into the potential of IS-DSS to enhance water pricing policies to promote efficient water management in Southern European agriculture in alignment with the WFD requirements.

An adaptive decision support system for outpatient appointment scheduling with heterogeneous service times

Appointment scheduling (AS) plays a crucial role in outpatient clinic management. Traditional methods involve patient grouping using pre-defined rules and scheduling based on these groups. However, pre-defined rules may not adequately capture the heterogeneity in patients’ service times (i.e., consultation duration). Advanced machine learning (ML) methods can address individual-level heterogeneity but pose challenges for practical scheduling. To strike a balance, we propose a data-driven AS decision support system, Cluster-Predict-Schedule (CPS), integrating both supervised and unsupervised ML for efficient patient grouping and scheduling. The novelty of CPS lies in its adaptability to service time heterogeneity through a data-driven approach, determining patient groups based on data rather than pre-defined rules. Additionally, CPS includes a generic and efficient algorithm for generating appointment templates adaptable to any number of patient groups. Our system’s efficacy is demonstrated using a real-world dataset. Evaluated by the weighted sum of patient wait times, physician idle time, and overtime, CPS achieves up to 15.0% cost reduction compared to the FCFA (first-call, first-appointment) scheme and over 4.7% savings against the common New/Return classification with traditional sequencing candidate (TSC) rules. In addition, CPS enhances outpatient operational efficiency without compromising fairness.

Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines

Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines

Decision support system for appointment scheduling and overbooking under patient no-show behavior

Decision support system for appointment scheduling and overbooking under patient no-show behavior

Assessing Suitability of Irrigation Scheduling Decision Support Systems for Lowland Rice Farmers in Sub-Saharan Africa—A Review

Assessing Suitability of Irrigation Scheduling Decision Support Systems for Lowland Rice Farmers in Sub-Saharan Africa—A Review

Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2

Irrigated cotton (Gossypium hirsutum L.) is produced mainly in Northwest China, where groundwater is heavily used. To alleviate water scarcity and increase regional economic benefits, a four-year (2016–2019) field experiment was conducted in Qira Oasis, Xingjiang Province, to evaluate irrigation water use efficiency (IWUE) in cotton production using the Root Zone Water Quality Model (RZWQM2), that was calibrated and validated using volumetric soil water content (θ), soil temperature (Tsoil°) and plant transpiration (T), along with cotton growth and yield data collected from full and deficit irrigation experimental plots managed with a newly developed Decision Support System for Irrigation Scheduling (DSSIS). In the validation phase, RZWQM2 adequately simulated (S) topsoil θ and Tsoil°, as well as cotton growth (average index of agreement (IOA) > 0.76). Relative root mean squared error (RRMSE) and percent bias (PBIAS) of cotton seed yield were 8% and 2.5%, respectively, during calibration, and 20% and −10.3% during validation. The cotton crop’s (M) T was well S (−18% < PBIAS < 14% and IOA > 0.95) for both full and deficit irrigation fields. The validated RZWQM2 model was subsequently run with seven irrigation scenarios with 850 to 350 mm water (Irr850, Irr750, Irr700, Irr650, Irr550, Irr450, and Irr350) and long-term (1990–2019) weather data to determine the best IWUE. Simulation results showed that the Irr650 treatment generated the greatest cotton seed yield (4.09 Mg ha−1) and net income (US $3165 ha−1), while the Irr550 treatment achieved the greatest IWUE (6.53 kg ha−1 mm−1) and net water production (0.94 $ m−3). These results provided farmers guidelines to adopt deficit irrigation strategies.

Responses of cotton photosynthesis and growth to a new irrigation control method under deficit irrigation

Derived from an agricultural system model, a new cost-effective and water-saving decision support system for irrigation scheduling (DSSIS) was developed to control field irrigation of cotton ( Gossypium hirsutum L .) at the Qira oasis, Xinjiang, China. DSSIS used the water stress factor and soil water content simulated by the Root Zone Water Quality Model (RZWQM2) to trigger irrigation and to calculate the irrigation amount. In this study, its potential effects on cotton photosynthesis and development were evaluated in field experiments (2017, 2018). Arranged in a randomized complete design with four replicates, the experiment contained three irrigation control methods [DSSIS–based (DSS), soil moisture sensor–based (SMS), and conventional experience–based (CE)] and two irrigation rates [full and deficit irrigation (75% of full irrigation)]. Based on the two–year results, the DSS significantly improved both leaf photosynthesis and root biomass. Compared to the SMS, the DSS and CE significantly enhanced net leaf photosynthetic rate ( Pn ) by 20.1% and 19.5%, respectively, stomatal conductance ( Gs ) by 38.9% and 36.5%, respectively, and transpiration rate ( Tr ) by 19.5% and 17.1%, respectively. The DSS and CE outperformed the SMS by producing higher aboveground and root biomass, plant height, leaf area, and weight and number of marketable bolls per plant ( p ≤ 0.05). Compared to full irrigation, deficit irrigation caused 11.2%, 18.7%, and 10% decline in Pn, Gs, and Tr , respectively, but did not negatively affect fruiting branches or individual boll weight. The DSS demonstrated its capacity to maintain leaf photosynthesis and growth of cotton under arid conditions with better water use efficiency. • Cotton photosynthesis was evaluated under a new irrigation control method. • Leaf photosynthesis and root biomass significantly improved under DSS. • Deficit irrigation significantly decreased leaf photosynthesis. • The DSS didn’t affected cotton growth and development under arid conditions.

Container Transportation Scheduling Between Port Yards and the Hinterland in Yunfeng

This paper introduces a container transportation scheduling problem in a large logistic company (Yunfeng) in Shanghai City, PR China. This problem considers two types of container transportation orders (i.e., door-to-door orders and warehouse orders) between port yards and the hinterland. Unlike the pickup and delivery problem with time windows, this problem needs to consider not only container pickups and deliveries, but also the loading or unloading of cargo inside containers. Moreover, this problem takes account of some constraints on container transportation regulations in China. We formulate this problem as a mixed-integer programming model and design an improved backtracking search optimization algorithm. Based on the proposed model and algorithm, a vehicle scheduling decision support system is also developed to integrate data collection, preprocessing, Amap (a digital map, navigation, and location service provider in China) interfacing, and optimization. The improvement of the operational efficiency is validated by the usage of the proposed system in practice.

Intelligent Service Scheduling Decision Technology under Artificial Intelligence Technology

The intelligent production line is a new intelligent production line resource formed by highly integrating modern sensor technology, communication technology, information technology, computer technology and control technology on the large physical real basis of the existing production line. It can finish data acquisition, information processing, dynamic virtual reconfiguration of variable batch production line, embedded algorithm, algorithm optimize the management of the production line manufacturing field, change the dynamic virtual reorganization of the mass production line, and become the choice of the development direction of the next generation of new production lines for mankind. Scheduling is the nerve center of the production line virtual reorganization system. The traditional main auxiliary decision-making tool management system can no longer fully meet the requirements of production line operation monitoring in the new environment, so the construction of an intelligent scheduling system is also one of the important contents of the construction of the production line virtual reorganization. Based on this, this article explores and researches intelligent service scheduling decision-making technology under artificial intelligence technology. The research topic of this article is intelligent scheduling of production lines, so this article first analyzes the current research status of intelligent service scheduling and decision support systems; then conducts research based on Agent decision support systems, and analyzes the framework and characteristics of decision support systems. realizes the intelligent production line scheduling DSS system; then this paper designs the system’s scheduling model with stochastic disturbance, system database and system fault diagnosis function, and realizes an intelligent service scheduling decision support system based on multi-agent technology. Finally, this article tests the data transmission speed and fault diagnosis function of the system. The test results show that the system is relatively fast in data transmission speed and the fault diagnosis function is also very reliable.

Decision Support System Applications for Scheduling in Professional Team Sport. The Team's Perspective.

Background: Periodization implies the systematic planning of training and competition with the goal of reaching the best possible performance in the most important competition. In team sports, this consists of finding a flight-and-practice schedule that maximizes the opportunities to perform the periodized contents (e.g., trips, practices, games, and days off). This process is conducted whilst considering known constraints (e.g., competitive schedule, roster availability, weather, especial events, holidays, or emotional effect of days away). The way a scheduling decision support system (DSS) leads users to make a decision should allow for flexibility, whilst minimizing users' confusion and facilitating the understanding of the recommendation given by the scheduling decision support system. Traditional approaches to solving scheduling problems use either simulation models, analytical models, heuristic approaches or a combination of these methods. When it comes to evaluate how the scheduling DSS is performing, three overarching aspects need to be reviewed: context satisfaction, process efficiency, and output quality. Appropriate training periodization and scheduling of trips and training sessions are critical for teams to optimize training and recovery processes in order to maximize health and performance. This article presents a methodological framework for designing decision-support systems for scheduling in professional team sports.

Decision support system for truck scheduling in logistic network through cross-docking strategy

In recent years, companies around the world need to find new ways to reduce costs, increase productivity, improve product quality, and meet various customer demands. Each Distribution Center (DC) or warehouse is specifically designed to minimize costs in the company’s supply chain. Cross-docking is a logistics technique that eliminates storing and picking up items at warehouses. Cross-docking has several advantages compared to other product distribution strategies from both an economic and an environmental point of view. Cross-docking decisions are influenced by many factors such as the level of product demand, the cost of stock-outs, and the distance from suppliers to customers. This research builds a Decision Support System (DSS) that can help companies to ensure sustainability in the supply chain. This study assumes the demand is deterministic which is indicated by Economic Order Quantity (EOQ). This system can detect the time and quantity of certain items that experience cross-docks accurately. If the customers’demand cannot be fulfilled by the warehouse, then the goods are categorized as out of stock. By knowing the time and quantity of goods at the time of the cross-dock, the warehouse manager can make operational decisions quickly and accurately related to the resources such asoperators and forklifts.

Digital twin-driven decision support system for opportunistic preventive maintenance scheduling in manufacturing

Preventive maintenance interventions are scheduled in industrial systems to prevent machine failures and breakdowns, which are associated with the incurrence of repair, unavailability, and quality-related costs. The execution of such interventions, however, generally represents a penalty to a manufacturing system’s production throughput due to machine interruption requirements. By the use of a digital twin architecture, we develop a decision support system to schedule preventive maintenance interventions with the aim of minimizing production throughout penalties via the exploitation of real-time opportunities such as supply shortages, momentary machine idleness or machine breakdowns. The decision support system has its application demonstrated by a case in a furniture manufacturer in the State of Santa Catarina – Brazil.

Designing a scheduling decision support system for the skin pass line: A case study of the steel finishing line

In this paper, we investigate the scheduling policies in the iron and steel industry, and in particular, we formulate and propose a solution to a complicated problem called skin pass production scheduling in this industry. The solution is to generate multiple production turns for the skin pass coils and, at the same time, determine the sequence of these turns so that productivity and product quality are maximized, while the total production scheduling cost, including the costs of tardiness, flow of material, and the changeover cost between adjacent and non-adjacent coils, is minimized. This study has been prompted by a practical problem in an international steel company in Iran. In this study, we present a new mixed integer programming model and develop a heuristic algorithm, as the commercial solvers would have difficulty in solving the problem. In our heuristic algorithm, initial solutions are obtained by a greedy constraint satisfaction algorithm, and then a local search method is developed to improve the initial solution. The experimental results tested on the data collected from the steel company show the efficiency of the proposed heuristic algorithm by solving a large-sized instance in a reasonable computation time. The average deviation between the manual method and the heuristic algorithm is 30%. Also, in all the components of the objective function, the algorithm performs better compared to the manual method. The improved values are greater than 15. In addition, we develop a commercial decision support system for the implementation of the proposed algorithm in the steel company.

A Decision Support System for Elective Surgery Scheduling under Uncertain Durations

The operation room (OR) is one of the most expensive material resources in hospitals. Additionally, the demand for surgical service is increasing due to the aging population, while the number of surgical interventions performed is stagnated because of budget reasons. In this context, the importance of improving the efficiency of the surgical service is accentuated. The main objective of this work is to propose and to evaluate a Decision Support System (DSS) for helping medical staff in the automatic scheduling of elective patients, improving the efficiency of medical teams’ work. First, the scheduling criteria are fixed and then the scheduling problem of elective patients is approached by a mathematical programming model. A heuristic algorithm is proposed and included in the DSS. Moreover, other different features are implemented in a software tool with a friendly user interface, called CIPLAN. Considering realistic data, a simulation comparison of the scheduling obtained using the approach presented in this paper and other similar approaches in the bibliography is shown and analyzed. On the other hand, a case study considering real data provided by the Orthopedic Surgical Department (OSD) of the “Lozano Blesa” hospital in Zaragoza (HCU) is proposed. The simulation results show that the approach presented here obtains similar occupation rates and similar confidence levels of not exceeding the available time than approaches in the bibliography. However, from the point of view of respecting the order of the patients in the waiting list, the approach in this paper obtains scheduling much more ordered. In the case of the Orthopedic Surgical Department of the “Lozano Blesa” hospital in Zaragoza, the occupation rate may be increased by 2.83%, which represents a saving of 110,000 euros per year. Moreover, medical doctors (who use this tool) consider CIPLAN as an intuitive, rapid and efficient software solution that can make easier the corresponding task.

The Volkswagen Pre-Production Center Applies Operations Research to Optimize Capacity Scheduling

The Volkswagen Pre-Production Center (VPC) is responsible for the entire prototype assembly of the Volkswagen brand. In their assembly sites, however, they often cannot employ their manufacturing staff to the full utilization level, and therefore, the manufacturing volume is low. Maximizing the manufacturing volume for a given available capacity is a frequently pursued objective in industrial manufacturing. The planning task of capacity scheduling contributes to this objective by deciding on resource allocation and the selection and scheduling of orders. We were asked to evaluate possible operations research/management science (OR/MS) solutions for their capacity scheduling problem. To this end, we developed a prototype for capacity scheduling based on binary integer programming. After the prototype had revealed high improvement potential, we developed a spreadsheet-based decision support system (DSS) for daily capacity scheduling. The schedules generated by the DSS were substantially better than the solutions generated by the current manual procedure, in terms of both accomplished manufacturing volume and planning effort. After successful test implementation and rollout, the VPC estimated the annual cost savings to lie in the six-digit Euro range. Meanwhile, we continue spreading OR/MS methods to neighboring departments of the VPC.

Irrigator Pro: Progression of a Peanut Irrigation Scheduling Decision Support System

HighlightsThe logic used in developing a decision support system for irrigating peanut based on max/min soil temperature is describedLogic to transform decision support system from peanut to irrigate corn and cotton with and without soil sensors.Progression of a decision support system from a desktop program to a web/mobile applicationAbstract. Irrigator Pro is a decision support tool for scheduling irrigation events in peanut. It was deployed in 1995 as a rule-based system using crop history, yield potential, soil type, in-season irrigation/rainfall and maximum/minimum soil temperature. As computing platforms have progressed from desktop personal computers to mobile web-based platforms, Irrigator Pro has been updated and is now deployed as a web-based program and an application for mobile devices. Irrigator Pro not only works for peanuts but has been modified to irrigate both corn and cotton. The irrigation decisions are now based on in-field soil water potential measurements in addition to the traditional checkbook with max/min soil temperatures. Users are individual growers, extension agents, and agronomic consultants. The objective of this manuscript is to document the initial development of Irrigator Pro as an expert system combining data and experiential knowledge and the progression from a checkbook-based decision support system to a hybrid system using observed weather data and soil moisture measurement. The background knowledge, equations, and thresholds for triggering irrigation recommendations are included. Keywords: Decision support system, Irrigation scheduling, Irrigator Pro, Mobile app, Peanut, Soil water potential.

A decision support system for home dialysis visit scheduling and nurse routing

Over the last years home dialysis has become the preferred treatment option for some patients with kidney failure. However, determining efficient and effective daily home-dialysis service plans imposes multiple challenges to hospital administrators, as it is a complex task with a number of interrelated decisions. These decisions are about the number of nurses required for daily visits and their travel itineraries, and involve multiple (often conflicting) objectives.Working together with physicians and administrators from The Ottawa Hospital (TOH) in Canada, we have developed a system intended to support administrators of nephrology departments in creating daily visit schedules and routes for nurses assisting with dialysis treatment in patients' homes. The decision support system, called Home Dialysis Scheduler System (HDSS), employs a mixed-integer linear programming model to create daily nurse itineraries that minimize the cost of providing home dialysis for a pre-specified group of patients. In developing this model, we also considered nurses' workload balance, overtime work, need for mealtime breaks (lunch or dinner, depending on shift times), restrictions and preferences associated with the time of the visits, and different types of services provided to patients. The model was validated using data provided by the Division of Nephrology at TOH. The interface of the HDSS was developed following the principles of user-centred design and validated with a group of end-users. In the validation stage, daily visit schedules and nurse routes generated by the HDSS were compared to nurse itineraries created manually by hospital administrators. The use of the HDSS resulted in improved workload distribution among nurses, simpler routes, and reduced total distance travelled — which translates into lower costs for the home dialysis program. In this paper, we provide details about the mixed-integer linear programming model, describe the HDSS, and discuss its implementation results and managerial implications.

An Agent-Based Model Driven Decision Support System for Reactive Aggregate Production Scheduling in the Green Coffee Supply Chain

The aim of this paper is to contribute to the thread of research regarding the need for logistic systems for planning and scheduling/rescheduling within the agro-industry. To this end, an agent-based model driven decision support system for the agri-food supply chain is presented. Inputs in this research are taken from a case example of a Mexican green coffee supply chain. In this context, the decision support agent serves the purposes of deriving useful knowledge to accomplish (i) the decision regarding the estimation of Cherry coffee yield obtained at the coffee plantation, and the Parchment coffee sample verification decision, using fuzzy logic involving an inference engine with IF-THEN type rules; (ii) the production plan establishment decision, using a decision-making rule approach based upon the coupling of IF-THEN fuzzy inference rules and equation-based representation by means of mixed integer programming with the aim to maximize customer service level; and (iii) the production plan update decision using mathematical equations once the customer service level falls below the expected level. Three scenarios of demand patterns were considered to conduct the experiments: increasing, unimodal and decreasing. We found that the input inventory and output inventory vary similar over time for the unimodal demand pattern, not the case for both the increasing and decreasing demand patterns. For the decreasing demand pattern, ten tardy orders for the initial production schedule, an 88% service level, and nineteen tardy orders from the estimated production results, a 77% service level. This value falls below the expected level. Consequently, the updated aggregate production schedule resulted in ten tardy orders and an 88% service level.

A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity

A precisely timed irrigation schedule to match crop water demand is vital to improving water use efficiency in arid farmland. In this study, a real-time irrigation-scheduling infrastructure, Decision Support System for Irrigation Scheduling (DSSIS), based on water stresses predicted by an agro-hydrological model, was constructed and evaluated. The DSSIS employed the Root Zone Water Quality Model (RZWQM2) to predict crop water stresses and soil water content, which were used to trigger irrigation and calculate irrigation amount, respectively, along with forecasted rainfall. The new DSSIS was evaluated through a cotton field experiment in Xinjiang, China in 2016 and 2017. Three irrigation scheduling methods (DSSIS-based (D), soil moisture sensor-based (S), and conventional experience-based (E)), factorially combined with two irrigation rates (full irrigation (FI), and deficit irrigation (DI, 75% of FI)) were compared. The DSSIS significantly increased water productivity (WP) by 26% and 65.7%, compared to sensor-based and experience-based irrigation scheduling methods (p < 0.05), respectively. No significant difference was observed in WP between full and deficit irrigation treatments. In addition, the DSSIS showed economic advantage over sensor- and experience-based methods. Our results suggested that DSSIS is a promising tool for irrigation scheduling.

Development and field validation of a decision support system for irrigation scheduling of open field vegetable crops based on determining soil evaporation and plant transpiration

Development and field validation of a decision support system for irrigation scheduling of open field vegetable crops based on determining soil evaporation and plant transpiration