Order Scheduling Research Articles

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.

Efficacy of Blood Utilization in Elective Surgery for Non-COVID Patients during COVID-19 Outbreaks

Objective: This study aimed to evaluate the pattern of blood transfusion requests and utilization in non-coronavirus disease (COVID) patients having undergone elective surgeries during the COVID-19 pandemic. Material and Methods: The pattern of blood transfusion requests and utilization for elective surgical procedures in six departments of a University Hospital; between January 2020 and December 2021, were retrospectively evaluated. The cross-match-to transfusion (C/T) ratio, transfusion probability (%T), transfusion index (Ti), and maximum surgical blood order schedule (MSBOS) were calculated. Results: A total of 15,030 patients underwent elective surgery. Among the 14,426 units of blood requested, 12,776 (89%) units were cross-matched preoperatively for 5,799 (39%) patients, and an additional 1,650 (11%) units were requested for 394 (2.6%) patients intraoperatively. Among these, 4,588 (32%) units were transfused to 1,710 (11.4%) patients. The overall C/T ratio, %T, and Ti were 2.78, 29.5%, and 0.79, respectively. Blood utilization indices for each department varied substantially according to the type of surgery, with blood utilization indices being unfavorable for 68 (80%) of the 85 procedures. The MSBOS was 0 for 32 procedures. Conclusion: Over-ordering of blood units for elective surgical procedures remained common during the COVID-19 pandemic. The blood utilization indices showed substantial variations according to the type of surgical procedures. The MSBOS has been formulated to assist in future decision-making.

Comparison of MOEAs in an Optimization-Decision Methodology for a Joint Order Batching and Picking System

This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area.

Enhancing energy‐efficient sleep scheduling for Narrowband Internet of Things devices in coordinated 5G networks within smart environments

SummaryThis research project takes on a crucial role in the quickly changing field of integrated 5G networks inside smart environments by concentrating on the creation of an extremely effective sleep scheduling system designed especially for Narrowband Internet of Things (NB‐IoT) devices. This work introduces a unique method for precisely controlling the nodes' sleep schedules through the use of convolutional neural network (CNN) architecture, which optimizes both energy usage and operating patterns. The principal goal still stands to guarantee the extended lifetime of operation and dependability of NB‐IoT devices in the larger framework of intelligent ecosystems driven by synchronized 5G networks. To achieve its objectives, the research explores a number of complex domains and employs cutting‐edge technologies and techniques, such as CNN‐based pattern recognition. This method's real‐time component is essential since it allows for prompt modifications to sleep schedules in order to optimize energy savings. Further boosting the devices' effectiveness and flexibility is continuous contact with a central server, which guarantees that the devices are updated with the most recent data and instructions. Essentially, the main objective of this study is to greatly increase the energy economy and operational lifetime of NB‐IoT devices, which will allow for stable and long‐lasting IoT deployments in the context of 5G networks in intelligent settings. This advancement is not only a boon for businesses and industries leveraging IoT technology but also a substantial step toward building smarter, more energy‐efficient, and resilient smart ecosystems that benefit society as a whole.

NOTE: The Development of Modern Criminal Justice Processes and Institutions in Ethiopia (1907-1974): An Overview

This note briefly discusses the institutional and process developments in Ethiopia’s modern criminal justice. There is a close relationship between political administration and justice administration considering the issue of judicial independence in a historical context. The year 1907 is taken as a starting point in the development of criminal norms and institutions because the Ministry of Justice was established and Afe Negus Nesibu was appointed Minster of Justice. The institutional and normative developments in this period were merely reactive to problems. Twelve judges were appointed to help the Minster of Justice who was entrusted to see all the cases by himself. Procedural rules were introduced as schedules in order to manage the inflow of cases. The enactment of the 1930 Penal Code helped judges determine sentences rather than sending convicts to the governor for sentencing. Regulation of appeal and preliminary processes such as providing a copy of judgement were introduced. The second phase in Ethiopia’s modern criminal justice system started from 1941 onward because this year is a landmark for the establishment of modern legislation and modern criminal justice institutions. The police were established as an investigation institution that executes orders given by the court. Prison was established signaling imprisonment as a mode of punishment. The prosecution institution was established under the Ministry of Justice. Thereafter, several substantive and procedural rules have been adopted governing the initiation of cases, summons, evidence, judgement and appeal. The last institution established during this period was the judicial organ.

Abstract 5284: Immune surveillance of chemotherapy-induced mutant subclones in CRC

Abstract Molecular alterations generated by DNA repair deficiencies and DNA damaging agents can be detected by the host immune system and this can restrict cancer growth. We have previously reported that temozolomide (TMZ) is able to induce MMR defects and hypermutability leading to immunotherapy clinical benefit in the subset of CRCs characterized by inactivation of O6-methylguanine methyltransferase (MGMT). We reasoned that combination of chemotherapeutic agents could similarly impair DNA repair function, increase mutational burden and trigger cancer immunogenicity in the larger group of MMR proficient (MMRp)/MGMT wild type (MGMTwt) CRCs. For this reason, we investigated the impact of treatment with TMZ and cisplatin (CDDP), as well as other commonly used cytotoxic agents in CRC such as 5-fluorouracil, irinotecan and oxaliplatin, on the immunogenomic features of murine CRC cells. We performed a two-step experiment: first, murine CRC models were exposed in vitro to chemotherapy agents used alone or in combination in a pulsatile schedule in order to mimic drug exposure in the clinical setting (priming phase); second, the cells were challenged in vivo with subcutaneous injection in immune-deficient and -proficient syngeneic mice (editing phase). Whole exome sequencing (WES) was performed at baseline and after both the priming and the editing phases to identify mutational signatures, predict neoantigens and assess immunoediting. We found that combinatorial treatment of MMRp/MGMTwt CRC cells with CDDP and TMZ causes clonal and subclonal hypermutability, eventually leading to tumor rejection in syngeneic mice. By following the fate of chemotherapy-induced mutations and predicted neoantigens in primed tumors grown in immunocompromised or immunocompetent mice we found that the majority of chemo-induced mutant clones were immunologically deleted in the CDDP-TMZ combination arm. The same effect was not observed when CDDP and TMZ were administered as monotherapy or when CRC cells were primed with the 5-fluorouracil-oxaliplatin-irinotecan (FOLFOXIRI) triplet. Mutational signature profiling of the immunologically deleted mutations was consistent with the previous exposure to chemotherapy in the priming phase. Moreover, the extent of the immunoediting was related to the clonality of the chemo-induced mutations, underlining the relevance of clonal abundance of immunogenic mutations in immune recognition and surveillance. These results indicate that rational combinations of commonly used cytotoxic agents can be exploited to promote cancer cell subclonal and clonal hypermutability. This approach can be used to promote cancer immunogenicity with implications for the design of new chemo-immunotherapy strategies in CRC. Citation Format: Pietro Paolo Vitiello, Paolo Battuello, Rosaria Chilá, Vittorio Battaglieri, Vito Amodio, Gaia Grasso, Federica Di Nicolantonio, Giovanni Crisafulli, Giovanni Germano, Alberto Bardelli. Immune surveillance of chemotherapy-induced mutant subclones in CRC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5284.

Optimization for Customized Bus Stop Planning, Order Schedule, and Routing Design in On-Demand Urban Mobility

Optimization for Customized Bus Stop Planning, Order Schedule, and Routing Design in On-Demand Urban Mobility

A novel implicit decision variable classification approach for high-dimensional robust multi-objective optimization in order scheduling

This paper efficiently addresses the high-dimensional robust order scheduling problem. A novel algorithm named dynamic cooperative coevolution based on an implicit decision variable classification approach (DCC/IDVCA) is developed to search for robust order schedules. To significantly reduce the computational resources required for solving the high-dimensional robust order scheduling problem, we propose decomposing the original decision variables through implicit classification methods. First, a novel estimation method is introduced to evaluate the weighted contribution of variables to robustness. This method utilizes historical information, including the variation of the overall mean effective fitness and the frequency of variables being classified into highly robustness-related subcomponents in previous cycles, for evaluating their weighted contribution to robustness. Then, based on the corresponding weighted robustness contributions, the original variables are classified into highly and weakly robustness-related variables. Finally, these two types of variables are decomposed into highly and weakly robustness-related subgroups within a dynamic cooperative coevolution framework and optimized separately. In the experimental section, the proposed algorithm is applied to two practical order scheduling problems in discrete manufacturing industry. The experimental results demonstrate that the proposed algorithm achieves competitive outcomes compared to state-of-the-art high-dimensional robust multi-objective optimization algorithms.

About the Cover Image

This issue's cover image: As part of the same AHRC-funded research project about gay male “pig” masculinities that led to the conference behind this special issue, I had the pleasure of working with an incredible film crew to produce a short experimental documentary titled Oink!. Having originally planned to shoot a feature-length documentary, the first COVID-19 lockdown was eventually announced to come into force one week after we had planned to start filming in Berlin. Adding to all the anxieties associated with a new, not fully-known, pandemic, we had to change our filming schedule and concept in order to respond to the virus. As such, a film that we had planned to film over a whole month and across a variety of locations, private and public, to document a gay male sex culture in which exchanges of bodily fluids have a core role, we were forced to condense filming into the last week before lockdown and to cut out all the plans to film in sex clubs and dark rooms. The film thus gained an unexpected layer of nostalgia for a very recent time when bodies had been able to touch and exchange fluids (once again) without fear. For the most part, the bodies in the film are alone in their private spaces, and intimacy and the intensity of sex becomes something that is only talked about, visually alluded to, but never shown. In that context, the photo in the cover of this issue, a production photo I took during filming, is somewhat charged with that very longing. It no longer just depicts a man looking for a hookup on a mobile phone app, but it also signals the painful desire for human contact—for touch, sex, and intimacy—at a time when public health measures were about to be put in place that would restrict the satisfaction of that important human need. I would like to thank Liz Rosenfeld, Rob Eagle, Rufai Ajala, and Liam Byrne for having gone through that journey with me. And special thanks to Giovanni, depicted in the cover image, for his hospitality and candor.

Assesment of Muscular Endurance Owing to Ladder Training: An Experiment

The aim of the research was to investigate the impact of ladder training on football players’ muscle endurance. Thirty football players from Bengaluru, Karnataka were randomly chosen as subjects, ranging in age from 18 to 21, in order to fulfil the objectives of the current study. Randomly, the subjects were split into two equal groups. Group II served as the control group, and Group I served as the experimental group (training on ladders). Before the study was administered, the subjects were informed of the requirements for the experiment procedures, testing, and exercise schedule in order to gain their full cooperation and the effort required on their part. The experimental period lasted for six weeks. Following the experimental treatment, the thirty participants underwent tests to measure their muscle endurance. To determine the significance of the mean differences, the pre- and post-test scores were statistically analysed using Analysis of Covariance (ANCOVA). A fixed 0.05 level of significance was applied to all cases when testing hypotheses.It was observed that the six weeks of experimental group have significantly improved the muscular enduranceof football players.

An integrated vehicle routing model to optimize agricultural products distribution in retail chains

The Vehicle Routing Problem (VRP) represents a thoroughly investigated domain within operations research, yielding substantial cost savings in global transportation. The fundamental objective of the VRP is to determine the optimal route plan that minimizes the overall distance traveled. This study employs VRP to address the challenge of distributing fresh agricultural products within retail chains. It introduces an integrated bi-objective VRP model that concurrently optimizes resource allocation, order scheduling, and route planning. The proposed model incorporates two objective functions with the goals of minimizing total distribution costs and ensuring timely product deliveries to retail outlets. Real-world characteristics are integrated to enhance practical applicability. All solution algorithms and the developed VRP model undergo testing using data from one of Sri Lanka's largest retail chains. Numerical experiments showcase the efficiency of the proposed algorithm in solving real-world VRP problems. Moreover, the proposed VRP model achieves a 19% reduction in daily distribution costs, including a 24% saving in fuel costs. This not only provides financial benefits but also contributes to the reduction of the carbon footprint of retail chains. The model ensures on-time deliveries to 95% of retail outlets, which is crucial for maintaining the quality of fresh food. The findings of this study underscore the significant cost savings, enhanced sustainability, and improved quality associated with the efficient distribution of fresh agricultural products within retail chains.

A bi-criterion sequence-dependent scheduling problem with order deliveries.

The manufacturing sector faces unprecedented challenges, including intense competition, a surge in product varieties, heightened customization demands, and shorter product life cycles. These challenges underscore the critical need to optimize manufacturing systems. Among the most enduring and complex challenges within this domain is production scheduling. In practical scenarios, setup time is whenever a machine transitions from processing one product to another. Job scheduling with setup times or associated costs has garnered significant attention in both manufacturing and service environments, prompting extensive research efforts. While previous studies on customer order scheduling primarily focused on orders or jobs to be processed across multiple machines, they often overlooked the crucial factor of setup time. This study addresses a sequence-dependent bi-criterion scheduling problem, incorporating order delivery considerations. The primary objective is to minimize the linear combination of the makespan and the sum of weighted completion times of each order. To tackle this intricate challenge, we propose pertinent dominance rules and a lower bound, which are integral components of a branch-and-bound methodology employed to obtain an exact solution. Additionally, we introduce a heuristic approach tailored to the problem's unique characteristics, along with three refined variants designed to yield high-quality approximate solutions. Subsequently, these three refined approaches serve as seeds to generate three distinct populations or chromosomes, each independently employed in a genetic algorithm to yield a robust approximate solution. Ultimately, we meticulously assess the efficacy of each proposed algorithm through comprehensive simulation trials.

Energy Management System for a Smart Green Nanogrid feeding a Research Laboratory with Autonomous Mobile Robots

This paper proposes a mixed-integer linear programming optimization model used to define an energy management system tailored for nanogrids in buildings, integrating renewable energy sources, battery energy storage systems and task-executing autonomous mobile robots. Focused on a nanogrid to be realised at the Savona Campus of the University of Genoa, the energy management system optimizes power flows and robot task scheduling in order to minimize the operating costs, the curtailment of the photovoltaic source and the number of unperformed tasks. Its novelty lies in combining energy and task planning constraints, offering significant potential for sustainable building energy management.

Using AquaCrop for Irrigation and water productivity assessment of Table grapes in arid region of Mexico

The aim of this work is to use the AquaCrop model for irrigation and water productivity assessment of Table grapes in arid region of Mexico during 2005 and 2006 cropping seasons. The irrigation efficiency was investigated by comparing the irrigation scheduling design used by the farmer to the AquaCrop model recommendations. Data analysis showed that the farmer irrigates almost every day, which results in the water content in the root zone always exceeding the soil moisture at field capacity (FC). This generates substantial losses of water through deep percolation. By using the AquaCrop model, the optimization of irrigation water scheduling in order to avoid both water stress and deep percolation was about 547 mm and 510 mm, which it is about half of what was applied by the farmer (1006 mm and 930 mm) during 2005 and 2006, respectively. This large difference, lost through deep percolation, reduces the water productivity (WP) by about 45%.

Optimization of Static Patient Admission Scheduling using the Variable Neighborhood Search Method

Planning and resource management are important aspects of a company's operational sustainability. With good management, companies can achieve their targets while minimizing operational costs. The same goes for hospitals. Various challenges related to resources are experienced by hospitals, such as scheduling nurses, patient surgeries, and patient appointments. Therefore, this paper aim at optimizing patient admission scheduling in order to improve hospital resource efficiency. To be more specific, patient admission scheduling, also known as the Patient Admission Scheduling Problem (PASP), is a scheduling problem that considers patient preferences and needs, bed availability, resource efficiency, and utilization. This problem is highly relevant, especially for large hospitals. The number of rooms, specialists, and facilities makes manual scheduling extremely difficult. It is due to the varied preferences, needs, and lengths of stay for patients in hospital. Various methods, both heuristic and exact, have been proposed, including the use of integer programming methods. However, for a large search space, this method requires a very long computational time. To address the PASP, this study applies the Variable Neighborhood Search (VNS) algorithm and random selection as optimization algorithms. The method was chosen because it has been proven effective to solve some combinatorial optimization problems in prior studies. Seven types of neighborhoods are implemented to find the best combinations in optimizing the PASP. The results show that the VNS algorithm outperforms the random selection algorithm, as it is able to generate 5 out of 7 solutions that are better, reducing penalties by 27.84% to 55.29%. The expected impact of this study is to increase the hospital patient satisfaction whereas in the same time minimize the operational cost.

Fractional-order delayed resonator with order scheduling

Fractional-order delayed resonator with order scheduling

Intelligent Order Release In Matrix-Structured Assembly Systems

Due to routing and sequence flexibility, matrix-structured assembly systems require new approaches for order release. A prescriptive scheduling and booking of orders on specific assembly stations cannot meet the flexibility requirements. Therefore, this paper presents a novel approach for capacity-oriented order release considering a multidimensional load evaluation. An agent-based simulation environment is used to investigate its performance and compare it to ConWIP control. It can be shown that the novel approach is able to perform intelligent release decisions while leading to better utilization of assembly stations and outperforming other order release procedures.

Real-time processing and optimization strategies for IoT data streams

Abstract With the development of industrial IoT and the arrival of smart manufacturing, the field of edge computing has gained more and more attention. However, traditional industrial computing scenarios relying on industrial clouds make data latency a greater challenge. In this paper, for the contradiction between edge devices and task resource allocation encountered in edge computing scenarios in smart manufacturing, we propose an industrial internet task scheduling model for smart manufacturing and introduce a scheduling node state matrix to realize the state management of each scheduling subtask. Aiming at the problem of multiple tasks seizing resources in a complex, intelligent manufacturing environment, the study combines the caching mechanism to realize the task offloading computational processing of order scheduling, in which the caching mechanism is used to solve the problem of computational resource limitations at the edge. It is found through simulation that when the computational task factor ξk =2 is larger, more offloading power is allowed to be transmitted to the edge ser ver for computation. For computational tasks with smaller task factor ξk , the device tends to allocate more computational rate to that computational task. Eventually the data queue length will be continuously reduced and the data queue is concentrated in the interval of very small values, this result verifies that the task scheduling algorithm is able to perform task scheduling efficiently and reduce the latency.

Nuclear and Joint Family System: Youth Perspective in Quetta, Balochistan

Every human being is a part of either a joint family or a nuclear family. Both types of families have multiple positive and negative consequences on individuals' lives, as previous literature represented various advantages and disadvantages of joint and nuclear families. According to Khatoon, A. (2008), in a joint family, the children's self-decision is discouraged due to the dominancy of the family head in all their choices. The aim of the present study was to analyze the actual experiences and perceptions of youth regarding joint and nuclear families in Quetta City. Even though the existence of joint and nuclear families has a strong influence on individuals' lives, there is still no scholarly work on them. Henceforth, the study selected a qualitative approach to analyze the experiences of youth (B.S. students) from the University of Balochistan. One Focus Group Discussion(FGDs) and nine interviews were conducted on the basis of data saturation through convenient sampling. Data collection tools included interview guide schedules in order to obtain rich pieces of information for targeted phenomena. Further data was analyzed through a thematic analysis tool. However, the findings of the present study portrayed the experiences and perceptions of respondents belonging to both family systems.

Heuristic and swarm intelligence algorithms for work-life balance problem

Employee satisfaction significantly influences the success of business. This emphasises on the importance of employees managing their work, family and personal lives to maintain their physical and mental well-being. This is especially crucial in health-care sector, where physical and mental well-being directly affects the quality of out-coming services provided. Work-life balance, defined as the challenge of striking a reasonable equilibrium between work, family, and personal life, is gaining more attention. However, many studies do not adequately consider employee preferences when addressing this issue. This study introduces a mathematical model for work-life balance problem prioritising the worker preferences focusing on healthcare workers as a special case where personnel preferences are integrated into decision-making. The model has been comparatively solved with population-based algorithms for optimising weekly personnel schedules in order to make them more suitable for work-life balance. The population-based heuristic algorithms used for optimising the schedules are swarm intelligence algorithms; namely ant colony and particle swarm optimisation algorithms. The proposed approach allows the employees to opt their working hours and periods in the work-place, flexibly. We demonstrated with comparative analysis that the produced results with swarm intelligence algorithms evidently outperform one of the state-of-art works done with genetic algorithms, which proves the strength of the proposed problem solvers.