Shop Model Research Articles

Identifying Effective Strategies for Improving Engagement in HIV Prevention and Care Among Transgender Persons in the United States: A Systematic Review.

This systematic review synthesized published literature (2000 - 2023) to identify HIV interventions specifically designed for transgender persons in the United States (PROSPERO registration number: CRD42021256460). The review also summarized strategies for improving outcomes related to the four pillars of the Ending the HIV Epidemic (EHE) initiative in the United States: Diagnose, Treat, Prevent, and Respond. A comprehensive search was conducted using the Centers for Disease Control and Prevention's HIV Prevention Research Synthesis Project database, which included over 120,000 citations from routine systematic searches in CINAHL, EMBASE, Global Health, MEDLINE, PsycInfo, and Sociological Abstracts. Of 23 interventions that met inclusion criteria, 94% focused on transgender women of color and 22% focused on young transgender persons aged 15-29 years old. Most interventions focused on Treat or Prevent, few focused on Diagnosis, and none focused on Respond. Twenty interventions (87%) showed improvement in at least one EHE related outcome and a quarter of these effective interventions were tested with randomized controlled trials. Common strategies observed in effective interventions include the following: engaging the community in intervention development; pilot-testing with the focus population to ensure appropriateness and acceptability; addressing social determinants of health (e.g. stigma, discrimination, violence) through empowerment and gender-affirming approaches; increasing access to care, prevention, and services through co-location and one-stop shop models; and utilizing peer-led counseling, education, support, and navigation. Continuous effort is needed in addressing gaps, including more research for transgender men and rural settings and for how best to adopt and adapt best practices for subgroups of transgender population.

Modeling and scheduling a triply-constrained flow shop in biomanufacturing systems

The crude protein purification automated workstation has recently resolved the bottlenecks induced by manual operations, paving the way for high-throughput protein biomanufacturing. However, its three interacted constraints consisting of batch processing machines, limited buffer, and transportation present challenges for systematic scheduling. Here, we develop a triply-constrained flow shop model, enabling optimization in scheduling the crude protein purification automated workstation. A batching genetic algorithm is designed, where the flexible decoding resolves contradictions between the triple constraints, and the hybrid population initialization enhances performance by incorporating flow-shop heuristic and batching branch-and-bound. Computational experiments are conducted on 27 instances of varying problem scales ranging from small to large, demonstrating a notable 9.18 % reduction in makespan and enhanced stability when compared to three advanced meta-heuristics. Furthermore, the mechanism of how batching settings, including capacities and layouts, impact the makespan is revealed, offering managerial insights. This marks the first demonstration of modeling and scheduling crude protein purification automated workstations, signifying a significant advancement in biomanufacturing systems.

Cost-Effectiveness and Budget Impact Analysis of the Implementation of Differentiated Service Delivery Models for HIV Treatment in Mozambique: a Modelling Study.

In 2018, the Mozambique Ministry of Health launched guidelines for implementing differentiated service delivery models (DSDMs) to optimize HIV service delivery, improve retention in care, and ultimately reduce HIV-associated mortality. The models were fast-track, 3-month antiretrovirals dispensing, community antiretroviral therapy groups, adherence clubs, family approach and three one-stop shop models: adolescent-friendly health services, maternal and child health, and tuberculosis. We conducted a cost-effectiveness analysis and budget impact analysis to compare these models to conventional services. We constructed a decision tree model based on the percentage of enrolment in each model and the probability of the outcome (12-month retention in treatment) for each year of the study period-three for the cost-effectiveness analysis (2019-2021) and three for the budget impact analysis (2022-2024). Costs for these analyses were primarily estimated per client-year from the health system perspective. A secondary cost-effectiveness analysis was conducted from the societal perspective. Budget impact analysis costs included antiretrovirals, laboratory tests and service provision interactions. Cost-effectiveness analysis additionally included start-up, training and clients' opportunity costs. Effectiveness was estimated using an uncontrolled interrupted time series analysis comparing the outcome before and after the implementation of the differentiated models. A one-way sensitivity analysis was conducted to identify drivers of uncertainty. After implementation of the DSDMs, there was a mean increase of 14.9 percentage points (95% CI: 12.2, 17.8) in 12-month retention, from 47.6% (95% CI, 44.9-50.2) to 62.5% (95% CI, 60.9-64.1). The mean cost difference comparing DSDMs and conventional care was US$ -6 million (173,391,277 vs. 179,461,668) and -32.5 million (394,705,618 vs. 433,232,289) from the health system and the societal perspective, respectively. Therefore, DSDMs dominated conventional care. Results were most sensitive to conventional care interaction costs in the one-way sensitivity analysis. For a population of 1.5 million, the base-case 3-year financial costs associated with the DSDMs was US$550 million, compared with US$564 million for conventional care. DSDMs were less expensive and more effective in retaining clients 12 months after antiretroviral therapy initiation and were estimated to save approximately US$14 million for the health system from 2022 to 2024.

Radical hospitality: Innovative programming to build community and meet the needs of people who use drugs at a government-sanctioned overdose prevention site in San Francisco, California

BackgroundThe Tenderloin Center (TLC), a multi-service center where people could receive or be connected to basic needs, behavioral health care, housing, and medical services, was open in San Francisco for 46 weeks in 2022. Within a week of operation, services expanded to include an overdose prevention site (OPS), also known as safe consumption site. OPSs have operated internationally for over three decades, but government-sanctioned OPSs have only recently been implemented in the United States. We used ethnographic methods to understand the ways in which a sanctioned OPS, situated in a multi-service center, impacts the lives of people who use drugs (PWUD). MethodsWe conducted participant observation and in-depth interviews June–December 2022. Extensive field notes and 39 in-depth interviews with 24 TLC guests and 15 TLC staff were analyzed using an inductive analysis approach. Interviewees were asked detailed questions about their experiences using and working at the TLC. ResultsTLC guests and staff described an atmosphere where radical hospitality—welcoming guests with extraordinary warmth, generosity, and unconditional acceptance—was central to the culture. We found that the co-location of an OPS within a multi-service agency (1) allowed for the culture of radical hospitality to flourish, (2) yielded a convenient one-stop shop model, (3) created a space for community building, and (4) offered safety and respite to guests. ConclusionsThe co-location of an OPS within a multi-service drop-in center is an important example of how such an organization can build positive sociality among PWUD while protecting autonomy and reducing overdose mortality. Overdose response and reversal is an act of relational accountability in which friends, peers, and even strangers intervene to protect and revive one another. This powerful intervention was operationalized as an anti-oppressive, horizontal activity through radical hospitality with a built environment that allowed PWUD to be both social and safe.

Workload Control in Flow Shops with Bottleneck Shifting and Process Time Variability

Abstract Manufacturing industries struggle to devise precise planning and scheduling solutions due to unpredictable business situations. Additionally, uncertainties in production such as machine breakdowns, labour absenteeism, cycle time deviations, etc., would further deteriorate production plans and lead to uncertainty in decision-making processes. Flow shops with bottlenecks are particularly susceptible to these disturbances. Moreover, the random variations in cycle time variations can cause the bottleneck to shift between different stages. Literature indicates that conventional job release methods are ineffective in addressing these difficulties. In contrast, workload control methods would provide better solutions. Hence, a flow shop model has been developed and simulated using the variables like process time variations and bottleneck shifting on the discrete-event simulation software. The flow shop model incorporates realistic shop characteristics which are subjected to random process time variations, so as to assess the performance. The outcomes of the experimentation demonstrate that order release methods play a pivotal role in improving the performance of flow shops in more volatile situations.

Exact algorithm and machine learning-based heuristic for the stochastic lot streaming and scheduling problem

This article presents a probabilistic variant of the classic Lot Streaming and Scheduling Problem (LSSP), in which the arrival times of products are stochastic. The LSSP involves a multi-product lot streaming problem and a sublot scheduling problem with a flow shop model and sequence-dependent setup times. Although the deterministic LSSP has been studied in the literature, the problem with stochastic arrival times of products has not been explored. In this article, we first derive some properties of the LSSP solution and propose closed-form expressions to compute the objective function of a given solution under three commonly used stochastic distributions. Based on these expressions, we develop a new exact Dynamic Programming (DP) algorithm and propose an efficient DP-based heuristic algorithm. Additionally, we build a machine learning model to predict whether a DP transition needs to be considered in the heuristic to improve its efficiency. Our computational study of test instances with various arrival time distributions shows that our algorithms can achieve promising results. Furthermore, we find that the machine learning model can simultaneously reduce the computational complexity and improve the algorithm’s accuracy.

A Qualitative Analysis of Barriers to Accessing HIV Prevention Services During an HIV Outbreak among Persons who Inject Drugs in West Virginia.

In response to an increase in HIV diagnoses among persons who inject drugs (PWID) in Kanawha County, West Virginia, West Virginia Bureau for Public Health and CDC conducted a qualitative assessment in Kanawha County to inform HIV outbreak response activities. Interviews with 26 PWID and 45 community partners were completed. Transcribed interviews were analyzed to identify barriers to accessing HIV prevention services among PWID using the risk environment framework. Participants identified numerous political, physical, social, and economic community-level barriers that influenced access to HIV prevention services among PWID. Political factors included low community support for syringe services programs (SSPs); physical factors included low SSP coverage, low coverage of HIV testing outreach events, low HIV preexposure prophylaxis availability, and homelessness; social factors included stigma and discrimination; economic factors included community beliefs that SSPs negatively affect economic investments and limited resources for HIV screening in clinical settings. Individual-level barriers included co-occurring acute medical conditions and mental illness. Community-level interventions, such as low-barrier one-stop shop models, are needed to increase access to sterile syringes through comprehensive harm reduction services.

Heuristic Approach For Weighted Two Stage Flow Shop Model In A String Of Disjoint Job Block Under No Idle Constraint

The current paper investigates a two-stage flow shop scheduling model with no idle restriction, in which jobs are scheduled to run in a string of two job blocks that are disjoint in nature, and the jobs processing time is correlated with probabilities. Owing to inherent usefulness as well as relevance in real-world situations, jobs' weight has additionally included. In order to eliminate machine idle time and cutting machine cost of rental, the reason for the conduct of the study is to provide a heuristic algorithm which, once put into practice, processes jobs in optimal way, guarantees in smallest conceivable make span. Multiple computational examples generated in MATLAB 2019a serve as testament to efficacy of the proposed strategy operates. The outcomes are contrasted with the current methods that Johnson and Palmer have demonstrated.

Assessing the ‘forgotten fundamental’ in policy advisory systems research: Policy shops and the role(s) of core policy professionals

AbstractDuring the past 30 years, research on policy analytical capacity's multidimensional nature and the evolution of policy advisory systems (PASs) has both increased knowledge of these processes and structures and opened new avenues of inquiry. While it is clear that changes in PASs in many countries have occurred ‐ featuring processes such as the increased externalisation and politicisation of policy advice ‐ studies of changes among the roles played by core policy professionals in advice provision have lagged. One aspect of this question concerns the nature and extent of changes in this ‘forgotten fundamental’ of advice systems related to how these professionals are arrayed within ‘policy shops’—that is organisational units identified in the 1960s and 1970s as the main organisational home of policy professionals in government. Whether or not such shops have changed from the central‐integrated model identified in early studies and, if so, how, remain outstanding and foundational questions. Recent research in Canada has mapped the distribution of policy professionals at the central and provincial level and found more types of analysts and venues than in earlier eras— which range from the ‘classical’ integrated policy shops of the 1960s and 1970s which remain in central agencies and single‐purpose line departments to the much more 'distributed' patterns found in many departments dealing with multiple or complex controversial issues. Using Canadian data, this study outlines the development of these organisational types and their distribution in government and discusses the implications of these changes for better understanding the work, and needs, of core professionals in policy advice systems. .Points for practitioners ‘Policy professionals’ or public employees specifically tasked with policy analysis in government are key players in policy advisory systems despite the addition of more external and internal actors in policy advice systems in recent years. How these advisors and analysts are organised in government, whether they work in clusters or small groups, and how they interact with other civil servants and policy‐makers are a key determinant of their activities and influence in policy‐making. In the policy sciences, work in the 1960s and 1970s established ‘the policy shop’, that is relatively small centrally located organisational units employing mainly policy analysts, as the main home of policy professionals in government. These units often enjoyed a monopoly in analysis and played a key role in policy‐making. However, research on these organisations has not kept up with changes in advisory relations within and outside of governments and the impact such changes have had on the influence and activities of core professionals. Recent work by the authors looking at the large Canadian province of Ontario has identified more than a single type of arrangement of analysts in the current era. This paper expands this analysis to both small and large jurisdictions in Canada, including the federal government, and develops two detailed case studies of the current organisation of policy professionals in smaller central agencies like Ministries of Justice as well as in larger omnibus Ministries of the Environment. The study finds a ‘distributed’ model of policy shops—in which multiple policy units exist throughout the agency—to be dominant in both cases and in all the governments examined and suggests this is now the new normal, replacing the dominance of the older more integrated central shop model. The predominance of the distributed model contributes to the fragmentation of policy advice already underway in government due to the growth in the number of external advisors such as consultants and think tanks, and internal ones such as political staffers.

Ambiguity of the definition of permutation flow shops in the presence of missing operations

The majority of flow shop studies focused so far on permutation schedules, even though non-permutation schedules might offer better results at the expense however of additional computational effort. It is therefore crucial to determine whether the use of non-permutation schedules is justified by the potential improvements that might be obtained. This question becomes particularly interesting in the case of the flow shop problem with missing operations. But, before investigating this question, we must first clarify the definition of a permutation within this context. The objective of this paper consists in bringing out the ambiguity of the definition of a permutation within the flow shop model with missing operations. To overcome this ambiguity, we propose another way of looking at a permutation that includes the classical understanding. Even though, these two definitions may produce different solutions, we present cases where they are equivalent with respect to the makespan criterion.

10 Year Longitudinal Evaluation of the Spilstead Model of Milieu Intervention.

Although international research has defined best-practice intervention for children from vulnerable families as integrated and comprehensive, limited implementation and longitudinal evaluation of this approach has been conducted. The Spilstead Model (SM) of early years milieu intervention provides a uniquely integrated one stop shop model of care incorporating a comprehensive range of best-practice programs within a trauma-informed approach. Results from an initial evaluation involving 23 families (mean child age 3.7 years) indicated large effect size improvements 12 months post entry in family functioning as well as child development and emotional wellbeing (ES 0.8 -1.46, p < 0.001). This study aimed to evaluate the sustainability of these outcomes for both children and families via follow-up of the initial study co-hort 10 years post the initial evaluation. The study targeted families who participated in the original evaluation. Clinician and parent-rated adolescent measures paralleled the original assessments of parent, child and family functioning. Qualitative evaluation was also conducted via a semi-structured interview with parents.83% of the original sample participated. Mean youth age was 13.2 years. Results indicated sustained improvements in parent-child relationship, child-wellbeing and reduction of parent stress with large effect size (1.14 - 1.92 p < 0.001). On average 73% of the adolescents scored within the normal range on each measure of functioning. Few had repeated school grades or been suspended. None had been arrested. Emerging themes from the qualitative evaluation confirmed the value of the integrated model. The results further support the value of the one stop shop Spilstead Model and have the potential to inform international policy and practice.

Managers' and providers' perspectives on barriers and facilitators for the implementation of differentiated service delivery models for HIV treatment in Mozambique: a qualitative study.

In 2018, Mozambique's Ministry of Health launched a guideline for a nationwide implementation of eight differentiated service delivery models to optimize HIV service delivery and achieve universal coverage of HIV care and treatment. The models were (1) Fast-track, (2) Three-month Antiretrovirals Dispensing, (3) Community Antiretroviral Therapy Groups, (4) Adherence Clubs, (5) Family-approach, and three one-stop shop models for (6) Tuberculosis, (7) Maternal and Child Health, and (8) Adolescent-friendly Health Services. This study identified drivers of implementation success and failure across these differentiated service delivery models. Twenty in-depth individual interviews were conducted with managers and providers from the Ministry of Health and implementing partners from all levels of the health system between July and September 2021. National-level participants were based in the capital city of Maputo, and participants at provincial, district and health facility levels were from Sofala province, a purposively selected setting. The Consolidated Framework for Implementation Research (CFIR) guided data collection and thematic analysis. Deductively selected constructs were assessed while allowing for additional themes to emerge inductively. The CFIR constructs of Relative Advantage, Complexity, Patient Needs and Resources, and Reflecting and Evaluating were identified as drivers of implementation, whereas Available Resources and Access to Knowledge and Information were identified as barriers. Fast-track and Three-month Antiretrovirals Dispensing models were deemed easier to implement and more effective in reducing workload. Adherence Clubs and Community Antiretroviral Therapy Groups were believed to be less preferred by clients in urban settings. COVID-19 (an inductive theme) improved acceptance and uptake of individual differentiated service delivery models that reduced client visits, but it temporarily interrupted the implementation of group models. This study described important determinants to be addressed or leveraged for the successful implementation of differentiated service delivery models in Mozambique. The models were considered advantageous overall for the health system and clients when compared with the standard of care. However, successful implementation requires resources and ongoing training for frontline providers. COVID-19 expedited individual models by loosening the inclusion criteria; this experience can be leveraged to optimize the design and implementation of differentiated service delivery models in Mozambique and other countries.

Research on efficient automated guided vehicle transportation based on improved A* and DQN algorithms in solar cell shop

Efficient logistics is the goal pursued by production enterprises. Planning reasonable transportation routes quickly and formulating crossroads traffic strategies are effective methods to achieve efficient logistics. In this paper, for the problems of fast path planning and crossroads traffic in solar cell shop, firstly, the task, goal and constraint models are established. Secondly, the turning penalty length and the multi-minimum equal path straight rule are introduced, and the A* algorithm is improved to quickly plan the automatic guided vehicle (AGV) task path. Thirdly, each crossroads is regarded as an agent and the model, including state, action, reward and strategy, is built. The crossing strategy using deep Q network (DQN) learning is proposed. Finally, an information interaction mechanism between Python and Plant simulation is proposed to realize dynamic simulation analysis, which verifies the superiority of the proposed method.

Bi-Objective Flow Shop Scheduling with Equipotential Parallel Machines

Scheduling is very important concept in each and every field of life especially in case of manufacturing process. Actually, a production schedule is the time table that tells the time at which an assignment will be processed on various machines. The Schedule also gives the information about starting and finishing of a work on one machine. This paper also deals with the theory of Scheduling. The main attraction of this study is the optimization done on like Parallel machines with the help of Fuzzy Processing Times. Here the problem of optimization on Two Stage Flow Shop Model has been taken into consideration. This paper reveals an algorithm using Branch and bound method for scheduling on three like parallel machines available at initial stage and solo machine at next stage having processing period of all works as fuzzy triangular numbers involving transportation time from first stage to second stage. Algorithm provides an optimal sequence of jobs for minimizing make span as well as the unit operational cost of each job on all three parallel machines. Numerical example has also been discussed for elaborating this situation. The proposed model is the extension of model presented by Deepak Gupta and Sonia Goel [18].

A String of Disjoint Job Block with Processing Time Associated with Probability in Two-Stage Weighted Open Shop Model

Open-shop scheduling problem (OSSP) is a well-known topic with wide industrial applications which belongs to one of the vital issues in the field of engineering. This paper deals with a two-stage open shop scheduling problem in which the processing time of jobs is allied with probabilities. The concept of a string of two job blocks which are disjoint in nature is considered so that the first block covers the jobs with a fixed route and the second block covers the jobs with an arbitrary path. Further, the weights of jobs are also introduced due to their applicability and relative importance in the real world. The objective of this study is to propose a heuristic which on execution, provides an optimal or near-optimal schedule to diminish the makespan. Several numerical illustrations are produced in MATLAB 2018a to demonstrate the effectiveness of the proposed approach, and to confirm the performance, the results are compared with the existing methods developed by Johnson and Palmer.

A Sequential Multi-Staged Approach for Developing Digital One-Stop Shops to Support Energy Renovations of Residential Buildings

Buildings account for 40% of the European Union’s energy consumption. Deep energy renovation of residential buildings is key for decarbonization and energy poverty alleviation. However, renovation is occurring at far below the needed pace and depth. In this context, building renovation one-stop shops, which bring all project phases under one roof and provide advice, support, and finance to households, are highlighted as a promising solution. Nevertheless, this model is still absent or under-developed in most European countries and remains understudied in the scientific literature. Therefore, the present research goals are as follows: (i) to provide a critical review of emerging one-stop shop models; (ii) to streamline the deployment of building renovation digital one-stop shops by piloting a sequential multi-staged approach for Portuguese households and proposing it for replication elsewhere; and (iii) to compare case-study insights with other one-stop shops and discuss the notion in the context of the European Renovation Wave. In total, for the Portuguese case-study, five steps were conducted. The first three—stakeholder mapping, expert interviews, and customer journey—aimed to gather intel on the local energy renovation market. The results from these stages informed the design of the platform (fourth step). Finally, a post-launch market consultation survey gathered user feedback (fifth step). Insights from this study suggest that digital one-stop shops, while providing a helpful tool to close information gaps and activate specific audiences, may be insufficient on their own. As such, a more comprehensive set of instruments supporting households is needed to accelerate building renovation.

분산 자동차 시스템에서의 실시간 비주기 태스크 스케줄링

The research in distributed automotive systems have mainly been centered around periodic tasks scheduling. In this paper, we present an algorithm using synthetic utilization that can check the schedulability of aperiodic local tasks and aperiodic end-to-end tasks with precedence relation in flow shop model. If the scheduling algorithm for aperiodic end-to-end tasks executed in a multiple stage pipeline is applied to the flow shop model, sometimes the actually schedulable tasks are decided to be not schedulable because of the fact that the actually unschedulable tasks are decided to be schedulable. The algorithm presented in this paper solves this problem, and the simulation shows that the schedulability increases 10%.

A choice experiment for testing the energy-efficiency mortgage as a tool for promoting sustainable finance

The challenges currently facing the EU in the energy sector include increasing import dependence, limited diversification, high and volatile energy prices, decarbonization, and slow progress in energy efficiency. EU energy policy has provided a wide range of measures to achieve an integrated energy market and sustainability of the building sector. Various incentives and financial instruments have been promoted and financed by governments to help consumers in energy retrofit processes. These include direct investments and fiscal, financial, and market instruments. Public measures have been widely studied but private initiatives have not. In this study, the energy-efficiency mortgage (EEM) is investigated as a credit scheme to attract young people to a real estate market for sustainable buildings, characterized by high sale prices. A choice experiment (CE) was modelled to investigate the preferences of potential young buyers of a new home. The results of this exploratory survey showed an appreciation of the purchase of new A-rated properties according to the Energy Performance Certificate, compared to those that need to be retrofitted or not retrofitted. Consumers like the option of a home energy efficiency renovation being fully managed by a third party (i.e., following the one-stop shop model). The key appeal of energy efficiency for consumers ranges from its ability to support better energy management, to better property value management. The EEM seems to be a promising tool to stimulate investments in energy efficiency and to promote the accessibility and affordability of housing in the sustainable housing market. The results of this study can help private financial institutions to propose appropriate credit plans, which limit the risk of default by the borrower. They can also help public incentive policymakers to propose complementary instruments to EEM.

A new hybridization of adaptive large neighborhood search with constraint programming for open shop scheduling with sequence-dependent setup times

In recent years, researchers have been paying special attention to scheduling problems with setup times and costs, aiming to adopt more realistic assumptions. This paper aims at presenting a new hybridization of an adaptive large neighborhood search (ALNS) with constraint programming (CP) as a local search phase for open shop scheduling with non-anticipatory sequence-dependent setup time. An integer linear programming model is presented based on the classic open shop model, and a new CP model is proposed with non-anticipatory setup times. The proposed CP model has not been addressed in the revised literature and outperforms all other exact methods. The objective function adopted is makespan minimization, and we use the relative deviation as performance criteria. Since the problem under study is NP-hard, we test many approximations and exact algorithms to obtain high-quality solutions in acceptable computational times. The extensive computational experience shows that the proposed hybridization of metaheuristic and constraint programming is promising for solving large-sized instances.

EFFECTIVE ALLOCATION OF MANPOWER IN THE PRODUCTION OF PRECAST CONCRETE ELEMENTS WITH THE USE OF METAHEURISTICS

Planning problems are particularly important for the production processes of precast reinforced concrete elements. Currently used modeling of these processes is based on the flow shop problem. Flow shop models are usually used in Enterprise Resource Planning systems, which, however, may not take into account the specifics of the production of such elements. The article presents a new model for scheduling the production of reinforced concrete prefabricated elements, which is distinguished by the possibility of carrying out activities by more than one working group. An additional new constraint is the possibility of parallel performance of some works, which may occur during their production. Also, there will be an individual order of elements assumed for each of the activities. New objective functions will be considered – the sum of idle times of working groups and the total type changes of precast components. The presented scheduling model contains an NP-hard discrete optimization problem. For this reason, metaheuristics were used in the article to solve optimization problems: the simulated annealing algorithm and the tabu search algorithm. Verification of the results obtained with the use of these algorithms confirmed their high efficiency. The application of the presented scheduling model illustrates a practical case study showing the effectiveness of the used algorithms.