Feasible Schedule Research Articles

Knowledge-enhanced multidimensional estimation of distribution hyper-heuristic evolutionary algorithm for semiconductor final testing scheduling problem

The semiconductor final test scheduling problem (SFTSP), recognized as a crucial bottleneck in the semiconductor production process, holds immense significance for improving both quality control and scheduling efficiency within chip and integrated circuit enterprises. This article introduces the knowledge-enhanced multidimensional estimation of distribution hyper-heuristic evolutionary algorithm (KMEDHEA) for addressing the SFTSP with the aim of minimizing the makespan. First, a single-vector encoding scheme is used to represent feasible solutions, and a problem-specific constrained-separable left-shift decoding scheme is devised to transform these solutions into feasible scheduling schedules. Second, eight simple yet effective heuristics with problem-specific knowledge are developed that served as a suite of low-level heuristics (LLHs) for exploring the problem solution space. Third, the multidimensional estimation of distribution algorithm (MEDA) is employed as the high-level strategy to estimate the correlations and connections of the pre-designed LLHs, thereby guiding the search scope towards high-quality individuals. Finally, critical configurations of parameters are systematically analyzed by conducting a design-of-experiment (DOE) approach. Numerical experiments are conducted on well-known benchmark datasets, and the experimental results demonstrate the superiority of the KMEDHEA versus several state-of-the-art approaches. The best-known solutions are updated for nine out of ten benchmark instances, highlighting the effectiveness and efficiency of the proposed KMEDHEA in solving the SFTSP.

A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

With an increasing adoption of battery-electric bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this paper, a simulated annealing (SA) implementation is developed for a charge scheduling framework for a fixed-schedule fleet of BEBs that utilizes a proportional battery dynamics model, accounts for multiple charger types, allows partial charging, and further considers the total energy consumed by the schedule as well as peak power use. Two generation mechanisms are implemented for the SA algorithm, denoted as the “quick” and “heuristic” implementations, respectively. The model validity is demonstrated by utilizing a set of routes sampled from the Utah Transit Authority (UTA) and comparing the results against two other models: the BPAP and the Qin-Modified. The results presented show that both SA techniques offer a means of generating operationally feasible schedules quickly while minimizing the cost of operation and considering battery health.

A Novel Set-Based Discrete Particle Swarm Optimization for Wastewater Treatment Process Effluent Scheduling.

With the escalating severity of environmental pollution caused by effluent, the wastewater treatment process (WWTP) has gained significant attention. The wastewater treatment efficiency and effluent quality are significantly impacted by effluent scheduling that adjusts the hydraulic retention time. However, the sequential batch and continuous nature of the effluent pose challenges, resulting in complex scheduling models with strong constraints that are difficult to tackle using existing scheduling methods. To optimize maximum completion time and effluent quality simultaneously, this article proposes a restructured set-based discrete particle swarm optimization (RS-DPSO) algorithm to address the WWTP effluent scheduling problem (WWTP-ESP). First, an effective encoding and decoding method is designed to effectively map solutions to feasible schedules using temporal and spatial information. Second, a restructured set-based discrete particle swarm algorithm is introduced to enhance the searching ability in discrete solution space via restructuring the solution set. Third, a constraint handling strategy based on violation degree ranking is designed to reduce the waste of computational resources. Fourth, a Sobel filter based local search is proposed to guide particle search direction to enhance search efficiency ability. The RS-DPSO provides a novel method for solving WWTP-ESP problems with complex discrete solution space. The comparative experiments indicate that the novel designs are effective and the proposed algorithm has superior performance over existing algorithms in solving the WWTP-ESP.

A DQN based approach for large-scale EVs charging scheduling

This paper addresses the challenge of large-scale electric vehicle (EV) charging scheduling during peak demand periods, such as holidays or rush hours. The growing EV industry has highlighted the shortcomings of current scheduling plans, which struggle to manage surge large-scale charging demands effectively, thus posing challenges to the EV charging management system. Deep reinforcement learning, known for its effectiveness in solving complex decision-making problems, holds promise for addressing this issue. To this end, we formulate the problem as a Markov decision process (MDP). We propose a deep Q-learning (DQN) based algorithm to improve EV charging service quality as well as minimizing average queueing times for EVs and average idling times for charging devices (CDs). In our proposed methodology, we design two types of states to encompass global scheduling information, and two types of rewards to reflect scheduling performance. Based on this designing, we developed three modules: a fine-grained feature extraction module for effectively extracting state features, an improved noise-based exploration module for thorough exploration of the solution space, and a dueling block for enhancing Q value evaluation. To assess the effectiveness of our proposal, we conduct three case studies within a complex urban scenario featuring 34 charging stations and 899 scheduled EVs. The results of these experiments demonstrate the advantages of our proposal, showcasing its superiority in effectively locating superior solutions compared to current methods in the literature, as well as its efficiency in generating feasible charging scheduling plans for large-scale EVs. The code and data are available by accessing the hyperlink: https://github.com/paperscodeyouneed/A-Noisy-Dueling-Architecture-for-Large-Scale-EV-ChargingScheduling/tree/main/EV%20Charging%20Scheduling.

A rolling horizon for rolling stock maintenance scheduling problem with cyclical activities

The rolling stock maintenance scheduling problem addressed here involves scheduling a set of cyclical preventive maintenance activities while considering resource capacity and geographically dispersed depots. The objective is to minimize both corrective and preventive maintenance costs using reliability features. We propose to use rolling horizon to decompose the problem into several successive subproblems such that each one tackles the scheduling of only the next cycle of activities instead of all cycles. The scheduled dates to be fixed should be as close as possible to their respective deadlines. Each train can have three status (in service, in maintenance, out of service). The activities deadlines are determined depending on component failure rate which has to be respected otherwise the train becomes out of service. An integer linear programming (ILP) model is proposed to solve the subproblem using a time oriented modeling to get an accurate maintenance cost (preventive and corrective) and respect a minimum of available trains. The rolling horizon allows to build a feasible schedule within a predefined planning horizon for moderate size problems with an interesting trade-off between computation time and solution quality.

Weekly BH009 in patients with squamous cell carcinoma of the head and neck (SCCHN) and ovarian cancer: Interim phase I results.

e17545 Background: BH009 is a novel polysorbate 80-free formulation of docetaxel that will eliminate hypersensitivity reactions associated with polysorbate 80. BH009 has completed a bioavailability study compared to Taxotere and was found to have a better safety profile to Taxotere with the potential for higher doses. As a result, we conducted a phase I dose escalation study to determine the maximum tolerated dose (MTD), and the safety profile of a weekly BH009 regimen in the treatment of patients with advanced SCCHN and Ovarian cancer. Methods: This phase I study assessed safety of BH009 in patients with advanced SCCHN and Ovarian cancer. BH009 was administered at escalating doses (starting dose 40 mg/m2) as a 1-h i.v. infusion for three consecutive weeks every 28 days without dexamethasone premedication. Results: Up to January 2024, 11 patients were treated with BH009 (4 at 40 mg/m2, 7 at 50 mg/m2). 2 patients with SCCHN, and 9 patients with ovarian cancer. 100% of ovarian cancer patients were recurrent platinum-resistant ovarian cancer (PROC) patients. 82% (9/11) of patients had received prior taxanes, of whom 33% (3/9) had received prior docetaxel. There were no DLT observed in 40 mg/m2 dose groups, and one DLT was observed in the 50 mg/m2 group (grade 3 febrile neutropenia). The MTD for BH009 is expected to exceed 50 mg/m2, which would be higher than that of Taxotere under the same dosing regimen (≥50mg/m2 vs 42mg/m2*), and the overall tolerability was better than Taxotere. In this study, treatment-related adverse events (TRAEs) occurred in 100% (11/11) patients. The most common grade 3/4 TRAEs were Leukopenia (36%, n=4), Anaemia (36%, n=4), and Neutropenia (55%, n=6). Non-hematologic toxicities were generally mild with no grade 4 toxicity observed. No AEs that led to death were related to BH009. Up to January 2024, the DCR is 50% (3/6) after 2 courses of treatment. The ORR in 6 evaluable patients was 33% (2/6, 1 patient with SCCHN at 40 mg/m2, 1 patient with ovarian cancer at 50 mg/m2). One patient (17%, 1/6) with ovarian cancer at 50 mg/m2 dose group received a complete response to the target lesion and the non-target lesion was partially responded after 2 courses of BH009, and this patient had been previously treated with docetaxel, carboplatin, and bevacizumab. Conclusions: The weekly BH009 for three consecutive weeks every 28 days is a feasible schedule with a favorable toxicity profile, and the overall tolerability is better than Taxotere. BH009 has the potential to increase the dose of administration and improve the clinical efficacy. Dose escalation is ongoing. Clinical trial information: NCT06232863 . [Table: see text]

Mitigating Co-Activity Conflicts and Resource Overallocation in Construction Projects: A Modular Heuristic Scheduling Approach with Primavera P6 EPPM Integration

This paper proposes a heuristic approach for managing complex construction projects. The tool incorporates Primavera P6 EPPM and Synchro 4D, enabling proactive clash detection and resolution of spatial conflicts during concurrent tasks. Additionally, it performs resource verification for sufficient allocation before task initiation. This integrated approach facilitates the generation of conflict-free and feasible construction schedules. By adhering to project constraints and seamlessly integrating with existing industry tools, the proposed solution offers a comprehensive and robust approach to construction project management. This constitutes, to our knowledge, the first dynamic digital twin for the delivery of a complex project.

Local-search based heuristics for advertisement scheduling

In the MAXSPACE problem, given a set of ads A, one wants to place a subset A' of A into K slots B_1, ..., B_K of size L. Each ad A_i in A has size s_i and frequency w_i. A schedule is feasible if the total size of ads in any slot is at most L, and each ad A_i in A' appears in exactly w_i slots. The goal is to find a feasible schedule that maximizes the space occupied in all slots. We introduce MAXSPACE-RDWV, a MAXSPACE generalization with release dates, deadlines, variable frequency, and generalized profit. In MAXSPACE-RDWV each ad A_i has a release date r_i >= 1, a deadline d_i >= r_i, a profit v_i that may not be related with s_i and lower and upper bounds w^min_i and w^max_i for frequency. In this problem, an ad may only appear in a slot B_j with r_i <= j <= d_i, and the goal is to find a feasible schedule that maximizes the sum of values of scheduled ads. This paper presents some algorithms based on meta-heuristics GRASP, VNS, and Tabu Search for MAX\-SPACE and MAXSPACE-RDWV. We compare our proposed algorithms with Hybrid-GA proposed by Kumar et al (2006). We also create a version of Hybrid-GA for MAXSPACE-RDWV and compare it with our meta-heuristics. Some meta-heuristics like VNS and GRASP+VNS have better results than Hybrid-GA for both problems. In our heuristics, we apply a technique that alternates between maximizing and minimizing the fullness of slots to obtain better solutions. We also applied a data structure called BIT to the neighborhood computation in MAXSPACE-RDWV and showed that this enabled our algorithms to run more iterations.

Factors associated with compliance with weekly iron and folic acid supplementation among school adolescent girls in Debub Achefer district, northwest Ethiopia: school-based cross-sectional study.

Iron deficiency anemia is a public health problem among adolescents that could be addressed by weekly Iron Folic Acid Supplementation (IFAS). The Ethiopian government piloted weekly IFAS in schools, where itseffectiveness depends on compliance. We assessed the determinants of compliance with the weekly IFAS in Ethiopia. A school-based survey was conducted in 506 adolescent girls on weekly IFAS. Compliance was considered when girls reported WIFAS for at least three months without discontinuation. Bivariable and multivariable logistic regression models were modeled, with odds ratios reported. Out of 506, 25.8% had limited access to educational resources, and 79.4% had no information on IFAS. Among these, 47.9% (95% CI: 45.5-49.9%) had poor compliance with weekly IFAS. Non-compliance was mainly due to school absenteeism (55.9%). Important predictors of poor compliance were adolescent girls' marital status (AOR = 5.21; 1.55-17.6), academic standing (AOR = 4.37; 2.20-8.70), family income (AOR = 1.85; 1.09-3.15), access to health education materials (AOR = 1.57; 1.02-2.40), problems with IFAS (AOR = 2.44; 1.26-4.74), a discouraging home environment for the program (AOR = 2.27; 1.54-3.34), and a lack of knowledge of the IFAS program (AOR = 1.40; 0.97-2.03). Compliance with weekly IFAS is optimal, which could be improved via strong adherence support and feasible supplementation schedules.

A hybrid metaheuristic to optimize electric first-mile feeder services with charging synchronization constraints and customer rejections

This paper addresses the on-demand meeting-point-based feeder electric bus routing and charging scheduling problem under charging synchronization constraints. The problem considered exhibits the structure of the location routing problem, which is more difficult to solve than many electric vehicle routing problems with capacitated charging stations. We propose to model the problem using a mixed-integer linear programming approach based on a layered graph structure. An efficient hybrid metaheuristic solution algorithm is proposed. A mixture of random and greedy partial charging scheduling strategies is used to find feasible charging schedules under the synchronization constraints. The algorithm is tested on instances with up to 100 customers and 49 bus stops/meeting points. The results show that the proposed algorithm provides near-optimal solutions within less one minute on average compared with the best solutions found by a mixed-integer linear programming solver set with a 4-hour computation time limit. A case study on a larger sized case with 1000 customers and 111 meeting points shows the proposed method is applicable to real-world situations.

Collaborative optimization of depot location, capacity and rolling stock scheduling considering maintenance requirements

Generally, when optimizing a rolling stock schedule, the locations of the depots, or places in the network where the composition changes and maintenance occurs, are assumed known. The locations where maintenance is performed naturally influence the quality of any resulting rolling stock schedules. In this paper, the problem of selecting new depot locations and their corresponding capacities is considered. A two-stage mixed integer programming approach for rolling stock scheduling with maintenance requirements is extended to account for depot selection. First, a conventional flow-based model is solved, ignoring maintenance requirements, to obtain a variety of rolling stock schedules with multiple depot locations and capacity options. Then, a maintenance feasible rolling stock schedule can be obtained by solving a series of assignment problems by using the schedules found in the first stage. The proposed methodology is tested on real-life instances, and the numerical experiments of different operational scenarios are discussed.

Energy-aware cloud manufacturing service selection and scheduling optimization

ABSTRACT Cloud Manufacturing Service Selection and Scheduling (CMSSS) is vital for optimizing resource allocation and meeting task requirements. However, inattention to the preheating process of manufacturing equipment has resulted in wasted energy. To reduce manufacturing energy consumption and ensure the Quality of Service (QoS), this paper establishes a bi-level programming model for CMSSS, quantifies the preheating energy consumption of manufacturing equipment by task cohesion, and proposes an energy-aware cloud manufacturing service selection and scheduling approach. The approach selects the service composition for a task from candidate service sets, schedules subtasks to avoid service occupancy conflicts and maximises task cohesion to reduce the preheating energy consumption of manufacturing equipment by Energy-aware Scheduling Generation Scheme (ESGS). Finally, it integrates ESGS into Non-dominated Sorting Genetic Algorithm II (NSGA-II) to determine the optimal task execution solution. Experimental results show that ESGS has a better Pareto front than the previous Feasible Scheduling Generation Scheme (FSGS) under seven types of QoS weights. With almost the same QoS satisfaction level, ESGS consumes, on average, 2% to 8% less energy than FSGS for preheating manufacturing equipment. In cloud manufacturing scenarios with preheating processes, ESGS can meet the QoS requirements of demanders as FSGS but with a better energy economy.

Developing a prototype system of computer-aided appointment scheduling: A radiology department case study.

Scheduling patient appointments in hospitals is complicated due to various types of patient examinations, different departments and physicians accessed, and different body parts affected. This study focuses on the radiology scheduling problem, which involves multiple radiological technologists in multiple examination rooms, and then proposes a prototype system of computer-aided appointment scheduling based on information such as the examining radiological technologists, examination departments, the patient's body parts being examined, the patient's gender, and the patient's age. The system incorporated a stepwise multiple regression analysis (SMRA) model to predict the number of examination images and then used the K-Means clustering with a decision tree classification model to classify the patient's examination time within an appropriate time interval. The constructed prototype creates a feasible patient appointment schedule by classifying patient examination times into different categories for different patients according to the four types of body parts, eight hospital departments, and 10 radiological technologists. The proposed patient appointment scheduling system can schedule appointment times for different types of patients according to the type of visit, thereby addressing the challenges associated with diversity and uncertainty in radiological examination services. It can also improve the quality of medical treatment.

Scheduling with cardinality dependent unavailability periods

We consider non-preemptive scheduling problems on parallel identical machines where machines change their status from being available to being unavailable and vice versa along the time horizon. The particular form of unavailability we consider is when the starting time of each downtime depends upon the cardinality of the job subset processed on that machine since the previous downtime. We consider the problem of minimizing the makespan in such scenarios as well as its dual problem where we have a fixed common deadline of 1 and the goal is to minimize the number of machines for which there is a feasible schedule. We develop an EPTAS for the first variant and an AFPTAS for the second variant.

An optimization and discrete event simulation framework for evaluating delivery performance in Swedish wood supply chains under stochastic weather variations

ABSTRACT Delivery performance in wood supply chains is affected by carrying capacity on forest roads. Stochastic weather variations affect carrying capacity of roads and accessibility of harvested wood on landings. In Sweden, annual harvest scheduling is usually based on four seasons and road accessibility classes are set to match these seasons. In reality, road accessibility changes depend on weather variations not season. This causes a risk of poor delivery performance. Delivery performance is evaluated using two performance indicators, backorders (orders not delivered on time) and lead time (time from harvesting to delivery to mill). A data-analytic framework, combining optimization and simulation, is proposed to assess and evaluate these performance indicators for the wood supply chain under stochastic weather variations. An optimization model is used to generate a feasible harvest schedule for expected weather. This is thereafter evaluated given various weather scenarios using a discrete-event simulation model. The combination of optimization and discrete-event simulation is used to assess delivery performance over time, and changes in road accessibility caused by weather variation. An expected decrease in road accessibility implies a stable delivery performance, while an unexpected decrease causes a reduced delivery performance.

Multi-AGV-Driven Pallet-Picking Scheduling Optimization (MADPSO): A Method for Flexible Multi-Level Picking Systems

In the context of increasingly competitive shipbuilding, the flexible multi-level picking system, composed of high-rise shelves, Automated Guided Vehicles (AGVs), and picking stations, has been of gradual interest because of its advantages in operation efficiency, system flexibility, and system robustness. Compared with other simple-level systems, the flexible multi-level picking system has a more complex coupling temporal relationship, which makes the scheduling optimization of shipbuilding automated collaborative order picking (SACOP) extremely difficult. In order to avoid the dilemma of finding a feasible and optimal collaborative scheduling scheme under the constraints of a complex temporal relationship, this paper proposed a multi-AGV-driven pallet-picking scheduling optimization (MADPSO) method, which takes the AGV scheduling scheme as the direct solution and modifies it to a feasible solution under the reasonably designed interaction strategy of stacker, AGV, and the interaction strategy of picking station, AGV. Furthermore, taking the minimum energy consumption and operation time as the optimization objectives, a multi-objective optimization mathematical model was established to describe MADPSO, and an improved NSGA-III algorithm was designed to solve the problem. Finally, several experiments were conducted in various scenarios and verified that using MADPSO can achieve a comprehensive optimization index improvement of 52.02–75.66% compared with traditional picking methods, which has a certain reference significance for shipyards.

SCOP: Schrödinger Control Optimal Planning for Goal-based Wealth Management

We consider the problem of optimization of contributions of a financial planner such as a working individual towards a financial goal such as retirement. The objective of the planner is to find an optimal and feasible schedule of periodic installments to an investment portfolio set up towards the goal. Because portfolio returns are random, the practical version of the problem amounts to finding an optimal contribution scheme such that the goal is satisfied at a given confidence level. This paper suggests a semi-analytical approach to a continuous-time version of this problem based on a controlled backward Kolmogorov equation (BKE) which describes the tail probability of the terminal wealth given a contribution policy. The controlled BKE is solved semi-analytically by reducing it to a controlled Schrödinger equation and solving the latter using an algebraic method. Numerically, our approach amounts to finding semi-analytical solutions simultaneously for all values of control parameters on a small grid, and then using the standard two-dimensional spline interpolation to simultaneously represent all satisficing solutions of the original plan optimization problem. Rather than being a point in the space of control variables, satisficing solutions form continuous contour lines (efficient frontiers) in this space.

Knowledge-transfer based genetic programming algorithm for multi-objective dynamic agile earth observation satellite scheduling problem

The multi-objective dynamic agile earth observation satellite scheduling problem (MO-DAEOSSP) aims to schedule a set of real-time arrival requests and form a reasonable observation plan to satisfy various criteria. According to the requirements in practical applications, the total profit and the average image quality of scheduled requests are taken as optimization goals in this study. Compared to manually designed heuristics and iterative-based methods used in previous research, genetic programming based hyper heuristics (GPHH) can automatically evolve high-quality heuristic rules (HRs) for real-time scheduling without being highly dependent on expert knowledge. In this paper, a knowledge-transfer based multi-objective GPHH framework (KT-MOGP) is proposed, equipped with a heuristic-based simulation considering the idle monitoring, to evolve non-dominated HRs for solving MO-DAEOSSP. The heuristic-based simulation generates feasible schedules and returns fitness values for given HRs, which are the individuals evolved by KT-MOGP. KT-MOGP applies a knowledge transfer mechanism to accelerate convergence. Once a source problem is trained, its non-dominated solutions are extracted and their feature importance is transferred to guide the initialization of another target problem, by which the knowledge generated during the training process can be fully utilized. Experimental results on three sets of instances show that KT-MOGP outperforms the existing GPHH-based method and that the evolved HRs are competitive compared to several classical constructive heuristics and multi-objective evolutionary algorithms. The results also show the effectiveness of the proposed knowledge transfer-based initialization. To the best of our knowledge, this study is the first attempt to consider both multi-objective scenarios and real-time arrival requests.

An Integer Linear Programming Model for the Examination Timetabling Problem: A Case Study of a University in Thailand

This paper considers the examination timetabling problem (ETTP) at the College of Industrial Technology (CIT) of the King Mongkut’s University of Technology North Bangkok in Thailand. A new integer linear programming (ILP) formulation for the ETTP at the CIT is presented. The objectives were to minimize both the number of examination days (the main objective) and the number of rooms used throughout the entire examination period (the secondary objective). In this paper, a course can have multiple sections, and an examination room can accommodate exams for more than one course section. To illustrate the proposed ILP model, real data on courses acquired from the CIT were used to generate two test problems: a small problem and a large problem. The small problem included 32 courses with 69 sections. The large problem included 73 courses with 341 sections, which was the real data required for generating the midterm examination timetable for all first‐year courses in the first semester of 2021. Both problems were solved using the CPLEX solver software. The results show that the proposed model could find an optimal examination timetable for the small problem with a computational time of 2 min and 46 s. It also could find a good feasible midterm examination timetable that satisfied the requirements of the CIT for the large problem within the 2‐h time limit, much less time than that compared to manual scheduling by the CIT’s administrative staff. The obtained midterm examination timetable required five examination days and could reduce 104 examination rooms compared to assigning each course section to a separate examination room. The proposed ILP model can be used in a real‐life situation and can be a good option to generate an optimal schedule or a good feasible schedule for examinations at the CIT or other institutions that have similar requirements.

Generalizing Horn's conditions for preemptive scheduling on identical parallel machines via network flow techniques

AbstractWe study the use of flow‐based algorithmic and proof techniques applied to preemptive scheduling of jobs on parallel identical machines. For the classical problem in which the jobs have individual release dates and must be finished by a common deadline, we present and prove unified necessary and sufficient conditions for the existence of a feasible schedule by examining the structure of minimum cuts in a special network. We then study an enhanced model that allows the presence of additional resources, provided that some jobs at any time of their processing require one unit of a particular resource. We extend our argument developed for the classical case to this enhanced model. The generalized necessary and sufficient conditions for the existence of a feasible schedule are presented and proved using the max‐flow/min‐cut reasoning.