Order Scheduling Research Articles

Development of an Excel Dashboard-Based Warehousing Administration System Using the Design Thinking, EOQ, and POQ Approaches

Warehousing is an important part of a manufacturing process where ideal warehouse management will improve the company's logistics in times of crisis. This research seeks to gain an in-depth understanding of the problems faced by warehousing and create user-focused solutions. Furthermore, EOQ and POQ methods are used to optimize stock and order management. EOQ helps determine the economical number of bookings to be made, while POQ considers demand variability in determining order schedules. The results of this study show that with Design Thinking, it was found that the needs of users are the use of an easier application system and stock filling standards so that the accuracy of stock is better maintained. It can be concluded that the EOQ and POQ methods can overcome understock and overstock. However, the EOQ method has less risk than POQ, this is because the company does not spend a lot of funds and takes with little frequency so it is more effective and the warehouse movement space can be more minimal.

Implementation of CPOE, ERBI, and ePPID in Critical Care Operating Rooms at a Tertiary Care Academic Center

Abstract Introduction/Objective Blood transfusion in operating rooms (ORs) is a critical and complex process, often complicated by issues such as paper-based orders, lack of guidelines, and delays in blood delivery. In response, a multidisciplinary task force developed a quality improvement plan. This study assesses the impact of implementing an electronic positive patient identification (ePPID) system, standardized computer provider order entry (CPOE), and electronic remote blood issue (ERBI) process in a critical care OR setting at our institution. Methods/Case Report The team initially implemented ePPID in cardiac surgery ORs before expanding hospital-wide. CPOE was developed in collaboration with the laboratory information systems (LIS) and hospital information technology (IT). ERBI required cross-departmental coordination to establish a bi-directional interface between LIS and ERBI software. Staff received training through a train-the-trainer approach, interactive sessions, videos, and surveys. CPOE focused on order set development and creating the Maximum Surgical Blood Order Schedule (MSBOS). ePPID began with a pilot program and expanded with training sessions. Data collection tracked blood ordering, ERBI workflow, and transfusion data for compliance and utilization rates. Results (if a Case Study enter NA) The implementation of ePPID saw a rise in compliance rates from 71% to 98%, with 60 completed cases and 233 blood products transfused by 24 clinicians. ePPID’s utilization for blood products increased from 60% to 98% within 90 days. ERBI lowered RBC return rates from 30% to 15% and plasma return rates from 45% to 35% over a 2.5 year span. ERBI utilization rates reached 81% at 24 months post-implementation. Conclusion The implementation of ePPID, CPOE, and ERBI in critical care ORs was well-received among clinicians. Significant improvements in compliance and utilization rates for blood products following ePPID implementation were evident. ERBI implementation also led to substantial reductions in return rates of blood products allocated to ORs. The steady increase in ERBI utilization indicates successful integration into OR workflows.

Minimizing the earliness–tardiness for the customer order scheduling problem in a dedicated machine environment

AbstractThe customer order scheduling problem has garnered considerable attention in the recent scheduling literature. It is assumed that each of several customer orders consists of several jobs, and each customer order is completed only if each job of the order is completed. In this paper, we consider the customer order scheduling problem in a machine environment where each customer places exactly one job on each machine. The objective is to minimize the earliness–tardiness, where tardiness is defined as the time an order is finished past its due date, and earliness is the time a job is finished before its due date or the completion time of the corresponding order, whichever is later. Even though the earliness–tardiness criterion is an important objective for just-in-time production, this problem has not been studied in the context of the customer order scheduling problem. We provide a mixed-integer linear programming (MILP) formulation for this problem and derive multiple problem properties. Furthermore, we develop six different heuristics for this problem configuration. They follow the structure of the iterated greedy algorithm and additionally use a refinement function in which they differ. In a computational experiment, the algorithms were compared with each other and outperformed a solver solution of the MILP, which proves their ability to efficiently solve the problem configuration.

Optimal EV Charging and PV Siting in Prosumers towards Loss Reduction and Voltage Profile Improvement in Distribution Networks

In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction.

Customer order scheduling on a serial-batch machine in precast bridge construction

Prefabricated construction is widely adopted in the current bridge construction project, especially offshore bridges. The realistic requirements of large quantities of prefabricated parts and tight delivery schedules make it extremely challenging to develop optimal scheduling. We address a new customer order scheduling on a serial-batch machine (COS-SBM) to reduce the sum of inventory holding costs of finished jobs and tardiness costs of orders in precast bridge construction. In the COS-SBM problem, all jobs with incompatibility in orders need to be divided into batches, which are then scheduled for processing on a serial-batch machine. We develop a mixed-integer linear programming model to formulate this new problem. Since the COS-SBM problem is NP-hard, we propose a genetic algorithm based on a novel batch sequencing and forming encoding method (GA-BSFE), which makes the scheduling and batching decisions simultaneously to enhance its exploration. Moreover, we design an efficient three-stage heuristic based on the order weighted modified due date rule and batch weighted longest processing time rule. The three-stage heuristic is introduced into the initiation of GA-BSFE to enhance its exploitation. Finally, a set of instances generated based on the realistic production of precast girders is tested to validate the effectiveness of GA-BSFE. The performance analysis suggests that GA-BSFE is the most appropriate for the COS-SBM problem.

Challenges in reliable preoperative blood ordering: A qualitative interview study.

Presurgical blood orders are important for patient safety during surgery, but excess orders can be costly to patients and the healthcare system. We aimed to assess clinician perceptions on the presurgical blood ordering process and perceived barriers to reliable decision-making. This descriptive qualitative study was conducted at a single large academic medical center. Semi-structured interviews were conducted with surgeons, anesthesiologists, nurse anesthetists, nurse practitioners working in preoperative assessment clinics, and transfusion medicine physicians to assess perceptions of current blood ordering processes. Interview responses were analyzed using an inductive open coding approach followed by thematic analysis. Twenty-three clinicians were interviewed. Clinicians felt that the current blood ordering process was frequently inconsistent. One contributor was a lack of information on surgical transfusion risk, related to lack of experience in ordering clinicians, insufficient communication between stakeholders, high turnover in academic settings, and lack of awareness of the maximum surgical blood ordering schedule. Other contributors included differing opinions about the benefits and harms of over- and under-preparing blood products, leading to variation in transfusion risk thresholds between clinicians, and disagreement about the safety of emergency-release blood. Several barriers to reliable decision-making for presurgical blood orders exist. Future efforts to improve ordering consistency may benefit from improved information sharing between stakeholders and education on safe transfusion practices.

An improved intelligent optimization algorithm for small-batch order production scheduling

Although the critical path method (CPM) is effective for the integrated scheduling of small-batch orders, its overemphasis on vertical process relationships and neglect of horizontal parallel relationships have imposed limitations on scheduling, often leading to suboptimal outcomes in terms of the total product completion time. This study introduces an innovative algorithm designed to overcome these limitations and further optimize the total processing time of products. We propose a strategy of "exchanging adjacent processes on the same device", which operates based on the scheduling results of the CPM. By swapping adjacent and interchangeable processes within the constraints of the problem, this algorithm generates multiple new scheduling schemes, effectively expanding the solution space. This expansion enables the discovery of optimized solutions that leverage "horizontal parallel relationships", which is crucial for reducing the "total processing time of products". Finally, the effectiveness of the proposed algorithm is verified through experiments.

Adaptive Ant Colony Optimization Algorithm Based on Real-Time Logistics Features for Instant Delivery.

Ant colony optimization (ACO) algorithm is widely used in the instant delivery order scheduling because of its distributed computing capability. However, the order delivery efficiency decreases when different logistics statuses are faced. In order to improve the performance of ACO, an adaptive ACO algorithm based on real-time logistics features (AACO-RTLFs) is proposed. First, features are extracted from the event dimension, spatial dimension, and time dimension of the instant delivery to describe the real-time logistics status. Five key factors are further selected from the above three features to assist in problem modeling and ACO designing. Second, an adaptive instant delivery model is built considering the customer's acceptable delivery time. The acceptable time is calculated by emergency order mark and weather conditions in the event dimension feature. Third, an adaptive ACO algorithm is proposed to obtain the instant delivery order schedules. The parameters of the probability equation in ACO are adjusted according to the extracted key factors. Finally, the Gurobi solver in Python is used to perform numerical experiments on the classical datasets to verify the effectiveness of the instant delivery model. The proposed AACO-RTLF algorithm shows its advantages in instant delivery order scheduling when compared to the other state-of-the-art algorithms.

Solving distributed assembly blocking flowshop with order acceptance by knowledge-driven multiobjective algorithm

In the era of Industry 4.0, industrial artificial intelligence technologies make production planning and scheduling systems more flexible. A new distributed assembly blocking flowshop problem with order acceptance and scheduling decisions (DABFSP_OAS) was investigated in this paper. Specifically, three objectives—the makespan, total energy consumption (TEC), and total profit (TP)—were addressed simultaneously. To address this problem, we established a knowledge-driven non-dominated sorting genetic algorithm-II (KDNSGAII). First, three initialization schemes based on the problem-specific property were introduced to generate diverse initial population. Then, to accelerate the convergence process, we developed multiple Pareto-based crossover and mutation operators. In addition, two novel destructive reinsertion strategies based on product and job sequence length were implemented to enhance the development ability of the algorithm. Finally, the designed strategies were evaluated. Comparisons and discussions showed that the KDNSGAII outperformed the other state-of-art multi-objective algorithms in solving DABFSP_OAS.

Maximum surgical blood order schedule: A key component of perioperative blood management in neurosurgery

Abstract: CONTEXT: Institute specific protocols are needed to reduce unnecessary cross matches. Type and screen policies for surgical procedures which does not require blood and maximum surgical blood order schedule (MSBOS) for procedures which require blood units are the two protocols in use currently. AIM: To compare the effect of maximum surgical blood order schedule in neurosurgery between pre- and post-implementation. SETTINGS AND DESIGN: It is a study of 5 years’ duration (4 years retrospective and 1 year prospective) conducted at the departments of transfusion medicine, neurosurgery and neuroanesthesia. SUBJECTS AND METHODS: The patients above 18 years for whom blood requisition were sent were included, whereas those who underwent emergency procedures, massive transfusion, or had underlying bleeding disorders were excluded. The MSBOS was developed based on 4 years’ retrospective data and the new protocol was followed for 1 year. The transfusion indices were compared between preimplementation and postimplementation. RESULTS: A total of 2462 patients were included in the retrospective period, whereas it was 746 in the prospective group. Overall, reduction of crossmatch to transfused ratio from 3.6 to 2.4 and increase of blood utilization from 27.40% to 41.55% was observed. The patients for whom type and screen were performed increased from 18% in the retrospective period to 75% in the prospective period. Eighteen out of 30 common procedures were managed with T and S and seven of them did not require even a single unit of red blood cells intraoperatively. CONCLUSIONS: Our study highlights the importance of including both diagnosis and procedure to formulate MSBOS and the use of modified formulas “transfusion index for type and screen and transfusion probability for type and screen” for revision of MSBOS.

Optimization scheduling for multi-source water distribution systems in mountainous region based on seagull optimization algorithm

Multi-source water distribution systems (WDSs) are critical to solving the increasing demand for urban water supply. Appropriate management of limited resources necessitates optimization of water scheduling in order to reduce energy consumption. However, certain complexities of applying such systems bring severe challenges to optimal scheduling methods, exemplified in mountain regions, where larger elevation gradients make distribution more complicated than in plain regions. Therefore, this study attempts to present best practices in how to reduce the energy consumption of water supply, especially in complex mountainous regions, through innovation of optimal scheduling methods. Based on the seagull optimization algorithm (SOA), a systematic optimization scheduling method for multi-source WDSs is proposed. The optimization results are compared with those obtained from the genetic algorithm. A case study of such optimization in the mountainous region of C-County, China is presented. Power consumption prior and post optimization is compared. The results show that this optimization scheduling method is both effective and feasible. Annual power consumption can be reduced by significant amounts, savings of 23.3% in this case study, and the optimal solution can be deployed with 40 iteration steps.

A self-learning particle swarm optimization for bi-level assembly scheduling of material-sensitive orders

In modern assembly production, maximizing fulfillment becomes challenging amid resource scarcity and process unpredictability. This study addresses such issues with an effective approach to solving the integrated problem. This confluence presents a scenario where resource allocation mirrors a multidimensional knapsack and job sequencing corresponds to distributed assembly permutation flowshop scheduling, factoring in sequence-dependent setup times. Employing a mixed-integer mathematical model, we capture the configurations and objectives of the integrated problem. A special dual-level cooperative self-learning particle swarm optimization (SLPSO) metaheuristic is developed for improved hierarchical decision-making. Adaptive learning is harnessed to guide dynamic job selection under uncertainty, aiming for the highest total value. To minimize tardiness, a neighborhood search operator customizes sequences for each shop, considering changing setup times between jobs. Benchmark tests have been performed, showing that SLPSO surpasses established genetic algorithms and swarm techniques by 8%–15%. When implemented in an operational traditional pharmaceutical manufacturer, significant improvements were observed. The integrated structure harmonizes planning and scheduling stages, enhancing the algorithm via bidirectional feedback between decisions at various levels under uncertainty, thus providing guidance for real-world production.

A dynamic programming algorithm for order picking in robotic mobile fulfillment systems

AbstractThe order scheduling and rack sequencing problem deals with the order picking process in robotic mobile fulfillment systems: automated guided vehicles lift and transport movable storage racks to picking stations to supply items requested by customer orders which are put together in cardboard boxes on a workbench of limited capacity. To efficiently operate the station, the sequence in which racks visit the station one after another and the intervals at which customer orders are scheduled must be coordinated. We present a dynamic programming algorithm for the order scheduling and rack sequencing problem at a single picking station minimizing the number of rack visits. Despite monolithic mixed integer linear programming formulations, our approach appears to be the first combinatorial solution method for the problem in literature. A computational study demonstrates the effectiveness of the approach.

Formulations and heuristic for the long-term preventive maintenance order scheduling problem

This paper studies the long-term preventive maintenance order scheduling problem (LTPMOSP). The problem consists of deciding which maintenance should be scheduled, assigning each maintenance simultaneously to a team and a machine over the planning horizon, and determining when each maintenance should be performed. The goal of the problem is to minimize the number of teams activated and the sum of penalties for maintenance not scheduled. This paper presents two new formulations for the problem, where the first formulation is based on time-indexed variables, and the second formulation combines time-indexed variables with precedence variables. Furthermore, to solve large-size instances, an algorithm based on the GRASP metaheuristic is proposed. The results of the computational experiments show that the new formulations significantly outperform the literature formulation by finding new optimal solutions for some instances with up to 600 maintenance orders. The second formulation stands out by finding a more significant number of optimal solutions. The results also show a superiority of the GRASP algorithm over heuristic methods in the literature, generating better upper bounds in expressively less time, resulting in a schedule with a greater percentage of maintenance planned to be performed.

Deep reinforcement learning driven cost minimization for batch order scheduling in robotic mobile fulfillment systems

Robotic Mobile Fulfillment Systems (RMFS) are extensively employed in modern warehouses. In the era of booming e-commerce, this parts-to-picker model significantly reduces warehouse costs and enhances operational efficiency. The primary focus of this article is on the scheduling of batch orders, which involves the simultaneous allocation of mobile robots and picking stations to orders to meet their requirements, with the aim of minimizing processing costs. While the optimization of RMFS has been extensively investigated by scholars in recent years., research on categorized storage remains limited. Furthermore, combinatorial optimization in scenarios with large orders, numerous picking stations, and multiple mobile robots presents an ongoing and significant challenge. To overcome these challenges, we introduce the Enhanced Deep Reinforcement Learning Method for Batch Order Scheduling (EDRL-OBOS), designed to minimize operational costs in RMFS. Our proposed algorithm, EDRL-OBOS, features a uniquely designed state space that encompasses the allocation of all orders, with the variable portion of the state space diminishing as the algorithm evolves. Moreover, we substitute traditional actions with heuristic rules, significantly enhancing the efficiency of the algorithm. Additionally, we establish a reward function based on a greedy algorithm, ensuring that our method surpasses conventional algorithms. As the number of episodes progresses, the EDRL-OBOS algorithm assigns picking stations and sequences of mobile robots to each order until all orders within the state space are fulfilled, subsequently determining the reward. Subsequently, all states are reset to their initial conditions, and the order allocation scheme is optimized through the learning experience from the previous episode, until convergence is reached. Ultimately, we conduct simulation experiments by varying the number of orders, picking stations, and mobile robots. The results indicate that the EDRL-OBOS algorithm achieves a maximum cost reduction of 22.89%, a minimum of 19.66%, and an average of 21.57%, across various scenarios. At the same time, the computation of our algorithms is superior to that of traditional algorithms.

Maximum surgical blood ordering schedule for elective surgical procedures in Omdurman teaching hospital, Sudan

BackgroundThe need for blood during a surgical procedure is greater than what blood banks are able to provide. There is an excessive amount of blood being ordered for elective surgeries, surpassing the actual requirements. Only 30% of the cross matched blood is actually used in these surgeries. The accuracy of estimating the transfusion needs before a surgical procedure can be determined by looking at the cross match to transfusion ratio and the transfusion index. “These indicators play a crucial role in developing the maximum surgical blood ordering schedule; in this study, these indicators were tested.”Aim of studyIs to determine the efficiency of blood ordering and transfusion practices for patients undergoing elective surgeries.MethodsThis study is a prospective cross-sectional hospital-based study done at Omdurman Teaching Hospital-Sudan. Conducted for the duration of 6 months period from July to December 2019.The study participants were patients who underwent elective surgical procedures in general surgery and Urology departments as total coverage sample over a period of study duration. Ethical clearance obtained from ethical committee of Sudan Medical Specialization Board.ResultsTwo hundreds seven patients included in this study, the amount of blood units requested were 443-unit, cross matching for 98.6% (n 437) of units were done. Only 100 unit were Transfused (22,8%). The calculated CT ratio was 4.4, transfusion index was 1.6 and transfusion probability was 29.9%.ConclusionTransfusion probability and transfusion index of the present study were optimal but comparatively higher than the standard guidelines as most of the cross matched blood was not utilized.

Collaborative Operation Optimization Scheduling Strategy of Electric Vehicle and Steel Plant Considering V2G

With the shortage of fossil fuels and the increasingly serious problem of environmental pollution, low-carbon industrial production technology has become an effective way to reduce industrial carbon emissions. Electrified steel plants based on electronic arc furnaces (EAF) can reduce most carbon emissions compared with traditional steel production methods, but the production steps have fixed electricity consumption behavior, and impact loads are easily generated in the production process, which has an impact on the stability of the power system. EV has the characteristics of a mobile energy storage unit. When a large number of EVs are connected to the power grid, they can be regarded as distributed energy storage units with scheduling flexibility. Through the orderly scheduling of EVs, the spatial–temporal transfer of EV charging and discharging load can be realized. Therefore, the EV situated in the steel plant’s distribution network node has the capacity to be utilized by providing peak shaving and valley filling services for the steel production load. This study proposes an operation optimization scheduling method for EVs and steel plants. Taking the lowest overall operating cost as the objective, an optimal scheduling model considering EVs operation, steel plant, and distributed generator is established. Based on the IEEE-33 node distribution network model considering distributed generators, the proposed model is simulated and analyzed, and the effectiveness of the EV steel plant operation optimization scheduling strategy is investigated.

Logic-based Benders decomposition for order acceptance and scheduling on heterogeneous factories with carbon caps

We study an integrated order acceptance and scheduling problem for heterogeneous factories with carbon caps, order-dependent processing speeds, and setup times. Three joint decisions including order selection, order assignment and order sequencing are made to maximize profits, i.e., total revenue minus total production and tardiness cost. Firstly, we develop a mixed integer programming model to simultaneously optimize the three decisions and propose several dominance rules to enhance the model. Secondly, due to the particular structure of the problem, we propose a logic-based Benders decomposition (LBBD) method that decomposes the complicated original problem into a master problem and a number of subproblems. The master problem aims to determine order acceptance and order assignment, and each subproblem is for determining order sequencing in each factory. A branch and bound algorithm is then designed to quickly solve the subproblems. The branch and check framework is implemented to accelerate the solving process of the LBBD. Finally, numerical experiments verify that the proposed dominance rules play a promising role in enhancing the model, and the results of the algorithm comparison experiments show the significant advantages of proposed LBBD algorithm combined with the branch and check framework. Sensitivity experiments reveal that carbon cap serves as a major constraint on order acceptance.

Blockchain design for optimal joint production and maintenance over multiple periods for oil-filling production lines

Joint maintenance and production planning result in enhanced system efficiency and a significant reduction in expensive costs. Therefore, this research proposed an integrated blockchain design of two optimization models for the joint scheduling and sequencing of production orders and maintenance jobs over multiple periods with probabilistic and stochastic parameters for the oil-filling process. In the scheduling model, the objective function was to minimize total production and maintenance costs. Further, the sequencing model aimed to minimize the total overtime cost and the sum start times of jobs and orders. The proposed models were implemented on five production lines of five-liter lube oil bottles. Results showed that the developed optimization models effectively achieved concurrent production and maintenance planning at minimal total cost. In practice, this results in effective resource utilization, and saving costly production and maintenance costs. In conclusion, the developed blockchain design is valuable in developing, managing, and controlling optimal joint production and maintenance planning of oil-filling production lines.

A simulation-based genetic algorithm for a semi-automated warehouse scheduling problem with processing time variability

For warehouse operations, efficiently scheduling the available resources is crucial to improve the productivity and customer satisfaction. This paper proposes a simulation-based evolutionary algorithm for order scheduling and multi-robot task assignment in a robotic mobile fulfillment system. The algorithm proactively deals with the effects of the processing time variability by evaluating schedules based on both its system performance as well as its robustness under uncertain conditions. The algorithm implements an efficient resource allocation method and a variance reduction technique to reduce the overall computational burden. The experimental results show that the techniques to reduce the computational time are effective and can significantly reduce the amount of simulations required for the fitness evaluation. If a candidate schedule is allocated insufficient simulation replications it can lead to an inaccurate estimate of its long-term average performance. This could lead to an average performance loss of 7.3 %. Furthermore, the proactive scheduler is able to generate schedules that are more robust compared to deterministically generated. A reduction in the average operational cost of about 5 % can be reached, compared to a deterministically generated schedule. The paper reveals the relevance of identifying and modeling uncertainty when designing schedules in an operational system, rather than looking for optimal schedules for ideal scenarios.