Interrelated Decisions Research Articles

Automatic activity-travel sequence generator using language, grammar, and machine theory

ABSTRACT Activity schedule results from a complex decision-making process characterized by several interrelated decisions. Different facets of an activity schedule such as activity type, timing, duration, etc. influence each other and this makes modeling activity schedules a complex task. This complexity has compelled researchers to explore different approaches for modeling activity schedules, among which two predominant approaches can be identified: the utility-maximization theory based econometric approach and the computational process modeling approach. Despite their advantages and a few successful practical applications, challenges still remain leaving avenues for exploration of new approaches. This paper contributes in this direction by reviewing the relationship between language, grammar, and machines in the context of sequence analysis for activity sequence generation. Following that, the paper presents a stochastic Finite State Machine that can generate activity sequences to match the frequency distribution of sequences from a given data set. Our results show that the proposed algorithm can not only generate activity sequences with a distribution similar to that of original data but can also efficiently generate new patterns not in the original data.

The one-station bike repositioning problem

In bike sharing systems the quality of the service to the users strongly depends on the strategy adopted to reposition the bikes. The bike repositioning problem is in general very complex as it involves different interrelated decisions: the routing of the repositioning vehicles, the scheduling of their visits to the stations, the number of bikes to load or unload for each station and for each vehicle that visits the station. In this paper we study the problem of optimally loading/unloading vehicles that visit the same station at given time instants of a finite time horizon. The goal is to minimize the total lost demand of bikes and free stands in the station. We model the problem as a mixed integer linear programming problem and present an optimal algorithm that runs in linear time in the size of the time horizon.

Enhanced image steganalysis through reinforcement learning and generative adversarial networks

With the surging prominence of digital communication platforms, there has been an intensified emphasis on ensuring robust security and privacy measures. Against this backdrop, image steganalysis has emerged as a critical discipline, employing advanced methods to detect clandestine data within image files. At the core of our research is an innovative exploration into image steganalysis using an amalgamation of enhanced reinforcement learning techniques and online data augmentation. This methodology ensures the meticulous identification of concealed data within images. Our design integrates triple parallel dilated convolutions, enabling concurrent extraction of feature vectors from the input images. Once extracted, these vectors are synthesized, paving the way for subsequent classification tasks. To substantiate the efficacy of our approach, we conducted tests on a comprehensive dataset sourced from BossBase 1.01. Furthermore, to discern the influence of transfer learning on our proposed model, the BOWS dataset was employed. Notably, these datasets present a challenge due to its inherent imbalance. To counteract this, we incorporated an advanced Reinforcement Learning (RL) framework. Herein, the dataset samples are envisioned as states in a sequence of interrelated decisions, with the neural network playing the role of the decision-making agent. This agent is then incentivized or reprimanded based on its accuracy in discerning between the minority and majority classes. To bolster our classification capabilities, we innovatively employed data augmentation using images generated by a Generative Adversarial Network (GAN). Concurrently, a regularization mechanism was instituted to alleviate prevalent GAN-related challenges, such as mode collapse and unstable training dynamics. Our experimental outcomes underscore the potency of our methodology. The results highlight a remarkable capability to discern between pristine and steganographic images, registering an average accuracy rate of 85%.

Stated-Preference Survey Design and Testing in Health Applications.

Following the conceptualization of a well-formulated and relevant research question, selection of an appropriate stated-preference method, and related methodological issues, researchers are tasked with developing a survey instrument. A major goal of designing a stated-preference survey for health applications is to elicit high-quality data that reflect thoughtful responses from well-informed respondents. Achieving this goal requires researchers to design engaging surveys that maximize response rates, minimize hypothetical bias, and collect all the necessary information needed to answer the research question. Designing such a survey requires researchers to make numerous interrelated decisions that build upon the decision context, selection of attributes, and experimental design. Such decisions include considering the setting(s) and study population in which the survey will be administered, the format and mode of administration, and types of contextual information to collect. Development of a survey is an interactive process in which feedback from respondents should be collected and documented through qualitative pre-test interviews and pilot testing. This paper describes important issues to consider across all major steps required to design and test a stated-choice survey to elicit patient preferences for health preference research.

Semiconductor capacity expansion based on forecast evolution and mini-max regret strategy for smart production under demand uncertainty

Semiconductor industry has continuously migrated for advanced technology nodes with capital intensive capacities to fulfil the demands and maintain competitive advantages. Owing to demand fluctuation and shortening product life cycles, capacity expansion strategy is crucial for maintaining capital effectiveness and profitability. Limitations of the existing capacity expansion approaches can be traced in part to the lack of a framework within which the interrelated decisions can be integrated in light of demand uncertainty along the long lead time for capacity planning, while the forecasted demand changes in the rolling horizon. The research objective of this study is to develop a rolling horizon capacity expansion model based on forecast evolution with minimax regret strategy (MMR) to reduce the risks of capacity shortage and surplus to improve capacity utilization and capital effectiveness. In particular, this study applied both additive and multiplicative martingale models of forecast evolution (MMFE) to generate sets of possible demand scenarios for minimax regret capacity expansion model. To estimate the validity of the proposed approach, different types of demand scenarios based on realistic data from a leading semiconductor company in Taiwan are employed. A simulation model that integrates mathematical programming model is constructed using realistic demand scenarios to compare the performance of the proposed approach with conventional approaches. The results of the proposed MMFE + MMR approach have shown practical viability to provide a more robust capacity strategy to reduce supply chain risks while integrating the collaborative planning decisions for smart production.

Technician Collaboration and Routing Optimization in Global Maintenance Scheduling for Multi-Center Service Networks

With the popularization of service-oriented manufacturing, the current operation & maintenance (O&M) has shifted from traditional in-house maintenance to proactive outsourcing maintenance. It is paramount for an original equipment manufacturer (OEM) to provide timely and cost-effective maintenance schemes to geographically distributed customer enterprises. Interestingly, transportation theories could be combined to facilitate multi-location O&M management. In this paper, a transportation-oriented cross-regional opportunistic maintenance (TCOM) policy is developed for solving O&M optimizations and planning real-time schemes for the multi-center service network. The optimization model of this TCOM policy addresses several inter-related decisions: (1) most suitable maintenance times for each leased machine, (2) cost-effective arrangements of technician teams to perform maintenance tasks, and (3) optimal service routes for required teams. We not only integrate maintenance grouping and technician routing, but also investigate the new issues arising from the collaborative sharing of technician teams belonging to different maintenance centers. Numerical examples show that this TCOM policy can achieve significant cost-saving in cross-regional maintenance grouping and multi-location routing optimization for OEMs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical problem of computing an optimal maintenance policy for the multi-center service network of geographically distributed production systems to achieve a timely and cost-effective O&M management. We consider individual machine degradations, complex maintenance opportunities and network logistics optimization to establish a global maintenance scheme for the multi-center service network. The existing approaches are to schedule the preventive maintenance (PM) for a single machine or multi-unit leased system, which is unilateral and cannot be applied to multi-location production systems. Although the network opportunistic maintenance policy has been proposed in the literature for maintenance grouping and technician routing problem, it still does not consider the limited workforce resource and the collaborative sharing of technician teams from different maintenance centers. To fill this literature gap, in this paper, we develop the TCOM policy to determine the optimal maintenance timetable for leased machines, as well as the cost-effective service route of technician teams belonging to multiple maintenance centers, so as to conduct timely and cost-effective PM on multi-location production systems. Compared with the conventional maintenance policy, our adaptive policy greatly reduces the total outsourcing maintenance cost in long-term O&M services.

Konsistensi Pembuatan Norma Hukum dengan Doktrin Judicial Activism dalam Putusan Judicial Review

This study intends to explain the consistency of the Constitutional Court (MK) in making new legal norms by using the doctrine of judicial activism and to explain the factors that underlie the consistency of the Constitutional Court in making new legal norms through normative juridical research by explaining the principles, principles, and analysis of interrelated decisions. This study concludes that the Constitutional Court is inconsistent because it only grants and makes new legal norms in the Constitutional Court Decision Number 5/PUU-V/2007. Meanwhile, in the Constitutional Court's Decision Number 53/PUU-XV/2017, the Constitutional Court refused to make a new norm even though the two cases created discrimination and limited public participation in politics. The inconsistency factors include: 1) jurisprudence factors, 2) the application cannot convince the majority of the judges of the Constitutional Court, and 3) the paradigm factor of judges.

Smart semiconductor manufacturing for pricing, demand planning, capacity portfolio and cost for sustainable supply chain management

ABSTRACT Leading nations have re-emphasised the importance of semiconductor manufacturing to supply chain safety and resilience. Limitations of the existing studies can be traced in part to the lack of a framework within which the interrelated decisions for corporate strategic planning can be coordinated while integrating the associated actions. This research aims to review related studies involved in the decision elements of the proposed Pricing-Demand-Capacity-Capital Expenditure-Cost-Return (PDCCCR) framework and discuss the influence relationships among pricing, demand planning, capacity portfolio and cost structure for the overall financial return to derive the implications for smart manufacturing. Thus, a decision framework is proposed to integrate collaborative decisions for international interests and practical implications. As computational capability is increasing, the interrelated strategic decisions for pricing, demand planning, capacity portfolio and cost structure can be considered simultaneously via the proposed framework at the digital decision platform to coordinate flexible decisions for an agile and sustainable supply chain.

Utilizing energy transition to drive sustainability in cold supply chains: a case study in the frozen food industry

In alignment with the ever-growing interest in adopting sustainable practices, this paper devises a cold supply chain (CSC) planning model that integrates the three pillars of sustainability into the decision-making process while accounting for the shift towards clean energy sources. Interrelated decisions pertaining to production-distribution strategy, backorder and inventory levels, choice of truck type, and selection of third-party logistics (3PLs) providers are jointly optimized. For global CSCs in specific, such decisions are particularly sensitive to the energy sources of the refrigerated facilities and the accompanying levels of CO2 emissions generated. As such, a multi-objective mixed-integer non-linear programming (MINLP) model is developed and then solved via the weighted-sum method. In essence, the model seeks to operationalize sustainability goals by considering the rapidly evolving transition in energy sources across different regions when deciding on which 3PLs to engage in a contractual agreement with while adjusting the production and distribution strategy accordingly. The practical relevance of the model is illustrated using a case study drawn from the North American frozen food industry. The conducted trade-off analysis indicates the possibility of obtaining a drastic improvement of 86% in jobs’ stability levels (social measure) with a maximum cost increase of around 9% as compared to the economic measure. Furthermore, the analysis reveals that it is possible to reduce 71% of CO2 emissions while attaining 63% reduction in worker variations at the expense of only 4.47% cost increase once compared to solely optimizing the economic objective.

Удосконалення механізмів управління фінансовим потенціалом територіальних громад з урахуванням європейського досвіду

The purpose of the article is a theoretical-methodological substantiation along with development of practical recommendations for improving existing and developing innovative approaches to managing the financial potential of territorial communities on the basis of sustainable development, taking into account European experience. Using the methods of scientific synthesis, comparison and systematization, the theoretical-methodological foundations of managing the financial potential of territorial communities on the basis of sustainable development and European integration are summarized. To develop ways to improve the efficiency of managing the financial potential of the development of territorial communities, taking into account European experience, abstract-logical and statistical-economic methods of scientific research were used. The article proves that the decentralization reform contributed to changing the system of organization of local authorities through the creation of amalgamated territorial communities, which received a wider range of powers in making managerial decisions, as well as additional sources of formation of financial resources. It is defined that the key among them are the budget, investment, credit, and grant ones. The experience of the European Union countries has shown that the application of an integrated approach to achieve the goals of sustainable development of territorial communities by promoting creation of jobs, research and innovation in the field of infrastructural development of territories, cooperation between cities and villages, implementation of technical assistance measures is also relevant for our domestic realities. An important aspect of managing the financial resources of the EU Member States is the targeted nature of the use of funds and the availability of an effective system of financial management on the ground. Based on the experience of the EU Member States, key goals, principles and levers of managing the financial potential of territorial communities are formulated, focused on the optimal formation, distribution and use of their financial resources, as well as on the adoption of a set of interrelated decisions that will contribute to their sustainable development.

Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment

Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation. To get a successful CMS design, addressing the interrelated decisions simultaneously is important. In this article, a new nonlinear mixed-integer programming model is presented which comprehensively considers solving the integrated dynamic cell formation and inter/intracell layouts in continuous space. In the proposed model, cells are configured in flexible shapes during the planning horizon considering cell capacity in each period. This study considers the exact information about facility layout design and material handling cost. The proposed model is an NP-hard mixed-integer nonlinear programming model. To optimize the proposed problem, first, three metaheuristic algorithms, that is, Genetic Algorithm (GA), Keshtel Algorithm (KA), and Red Deer Algorithm (RDA), are employed. Then, to further improve the quality of the solutions, using machine learning approaches and combining the results of the aforementioned algorithms, a new metaheuristic algorithm is proposed. Numerical examples, sensitivity analyses, and comparisons of the performances of the algorithms are conducted.

Rapid-cycle systems modeling to support evidence-informed decision-making during system-wide implementation

BackgroundTo “model and simulate change” is an accepted strategy to support implementation at scale. Much like a power analysis can inform decisions about study design, simulation models offer an analytic strategy to synthesize evidence that informs decisions regarding implementation of evidence-based interventions. However, simulation modeling is under-utilized in implementation science. To realize the potential of simulation modeling as an implementation strategy, additional methods are required to assist stakeholders to use models to examine underlying assumptions, consider alternative strategies, and anticipate downstream consequences of implementation. To this end, we propose Rapid-cycle Systems Modeling (RCSM)—a form of group modeling designed to promote engagement with evidence to support implementation. To demonstrate its utility, we provide an illustrative case study with mid-level administrators developing system-wide interventions that aim to identify and treat trauma among children entering foster care.MethodsRCSM is an iterative method that includes three steps per cycle: (1) identify and prioritize stakeholder questions, (2) develop or refine a simulation model, and (3) engage in dialogue regarding model relevance, insights, and utility for implementation. For the case study, 31 key informants were engaged in step 1, a prior simulation model was adapted for step 2, and six member-checking group interviews (n = 16) were conducted for step 3.ResultsStep 1 engaged qualitative methods to identify and prioritize stakeholder questions, specifically identifying a set of inter-related decisions to promote implementing trauma-informed screening. In step 2, the research team created a presentation to communicate key findings from the simulation model that addressed decisions about programmatic reach, optimal screening thresholds to balance demand for treatment with supply, capacity to start-up and sustain screening, and availability of downstream capacity to provide treatment for those with indicated need. In step 3, member-checking group interviews with stakeholders documented the relevance of the model results to implementation decisions, insight regarding opportunities to improve system performance, and potential to inform conversations regarding anticipated implications of implementation choices.ConclusionsBy embedding simulation modeling in a process of stakeholder engagement, RCSM offers guidance to realize the potential of modeling not only as an analytic strategy, but also as an implementation strategy.

The school bus routing problem with student choice: a bilevel approach and a simple and effective metaheuristic

AbstractThe school bus routing problem (SBRP) involves interrelated decisions such as selecting the bus stops, allocating the students to the selected bus stops, and designing the routes for transporting the students to the school taking into account the bus capacity constraint, with the objective of minimizing the cost of the routes. This paper addresses the SBRP when the reaction of students to the selection of bus stops is taken into account, that is, when students are allowed to choose the selected bus stop that best suits them. A bilevel optimization model with multiple followers is formulated, and its transformation into a single‐level mixed integer linear programming (MILP) model is proposed. A simple and effective metaheuristic algorithm is also developed to solve the problem. This algorithm involves solving four MILP problems at the beginning, which can be used to obtain tight upper bounds of the optimal solution. Extensive computational experiments on SBRP benchmark instances from the literature show the effectiveness of the proposed algorithm in terms of both the quality of the solution found and the required computing time.

Intuitionistic Fuzzy Sets and Dynamic Programming for Multi-objective Non-linear Programming Problems

The practical applications of engineering designs are often concerned by the presence of interrelated decisions. Naturally, these decisions are induced by solving many conflicting and incommensurable objectives. To deal with the interlinked decisions as well as the paradox natures among objectives, this paper presents an integrated approach based on dynamic programming approach (DPA) and intuitionistic fuzzy set (IFS) denoted as DPA-IFS for solving multi-objective optimization problem (MOOP). In DPA-IFS, the principle of DPA aims to generate an efficient solution of MOOP. In contrast, IFS aims to handle conflicting natures among the objective functions by means of the satisfaction (maximization the degree of membership) and dissatisfaction (minimization the degree of non-membership) concepts. The illustration is investigated by numerical illustrations taken from the literature. Furthermore, a new closeness strategy-based distance function is introduced to measure the worth of a satisfactory solution. The proposed methodology is validated on the IEEE 30-bus with six generators as a real test paradigm in the electrical power system. The results obtained by the DPA-IFS show the superior results than those obtained by different approaches.

СТРАТЕГИЧЕСКИЕ АСПЕКТЫ УПРАВЛЕНИЯ СОЦИАЛЬНЫМ РАЗВИТИЕМ ПЕРСОНАЛА ОРГАНИЗАЦИЙ

The main aim of the research is to distinguish scientific approaches to determining the essence of the management of social development of staff and the justification of interrelated management decisions aimed at developing strategic planning and forecasting in the personnel management system based on the principles of social responsibility. The subject of the study is the management system of social development of the personnel of economic entities. Research design is the basis of the theoretical and methodological research was the scientific works of domestic and foreign scientists concerning the strategic management of social development of personnel, as well as the concept of corporate social responsibility of business. Research results: the concept of strategic planning for the social development of personnel is presented, a methodological approach to evaluating the effectiveness of personnel management based on the principles of corporate responsibility is proposed, which allows determining the level of employee compliance.

Industry 3.5 for optimizing chiller configuration for energy saving and an empirical study for semiconductor manufacturing

Since many industries may not be able to migrate for Industry 4.0 directly, Industry 3.5 is proposed as a hybrid strategy. While facility is crucial for product quality for hightech manufacturing, manufacturers have focused mainly on facility stability and paid less attention for power saving. In practice, the operation of chillers relies on domain experts leading to inconsistent decisions and operational inefficiency. Although a number of studies have proposed intelligent facilities with similar visions of Industry 4.0, they may not be ready to be adopted in the operating facilities. Furthermore, limitations of existing approaches can be traced in part to the lack of an integrated framework within which interrelated decisions can be modeled and related to the optimal configuration of chillers. Following Industry 3.5 stragey, this study aims to develop an approach that integrates a cooling load forecasting model based on SARIMAX model, a prediction model for the operation efficiency of the chillers, and chiller health evaluation model to optimize the combination of operating chillers to fulfill the cooling load demand and minimize the electricity consumption given the existing infrastructure. To estimate the validity, an empirical study was conducted in a leading semiconductor company for carbon reduction and green production. The results have shown its practical viability and the developed solution has been implemented in this company with significant power saving.

Inventory routing under stochastic supply and demand

It is well known that the integrated optimization of multiple and inter-related decisions in a supply chain can bring important benefits to companies. In this spirit, the inventory routing problem focuses on jointly optimizing inventory replenishment and vehicle routing decisions in a distribution context. In practice, the presence of uncertainty often further complicates the problem. We address a stochastic inventory routing problem under the consideration that both the product supply and the customer demands are uncertain. We propose a two-stage stochastic programming formulation, where routing decisions are made in the first stage, while delivery quantities, inventory levels and specific recourse actions are determined in the second stage. In this context, we analyze different recourse mechanisms such as lost sales, backlogging and an additional source for the product in a capacity reservation contract setting. We provide managerial insights from the results of computational experiments using instances based on a benchmark test set. In particular, we study the response mechanisms of the optimal solutions for different levels of uncertainty and cost configurations. Furthermore, we observe that supply and demand uncertainty have different effects on the value of taking the uncertainty into account. We also study the effect of incorporating a service level. Finally, we propose a heuristic solution method which is based on the progressive hedging algorithm and provides high-quality solutions within reasonable running times for problems with a large number of scenarios.

A learning-based metaheuristic for a multi-objective agile inspection planning model under uncertainty

In this paper, we present an agile integrated inspection-operation planning model wherein inspection actions are planned alongside the machining operations to make the production process agile. Such an agile integrated plan can respond quickly to inspection-machining needs while still controlling costs and quality. A tri-objective mixed-integer nonlinear programming (TMINLP) model is developed for planning the integrated process in a serial multi-stage production (MSP) system. This model addresses several inter-related decisions; (1) what is the most appropriate inspection process for a quality characteristic, (2) at which stage the inspection of these quality characteristics should be performed, (3) how these inspections should be performed, (4) which inspection tools should be used, and (5) which machine should operate on products. The three objectives are: (1) minimizing the total manufacturing cost, (2) minimizing the number of nonconforming products shipped, and (3) minimizing the total manufacturing time for each product. We also address the uncertainty of manufacturing parameters and equipment disruptions. To solve the model, a novel learning-based metaheuristic is developed based on Multi-Objective Differential Evolution (MODE) algorithm, k-Means clustering method, and an Iterated Local Search (ILS) algorithm. The proposed learning-based metaheuristic algorithm is then integrated with the Taguchi Loss Function and Monte Carlo methods to address the input parameters’ uncertainty. The proposed model and solution algorithm are validated through a set of experiments against optimal solutions, and benchmarked against four existing well-known approaches, i.e. NSGA-II, MODE and two learning-based metaheuristics. The proposed approach is applied to a real industrial case and insights are provided.

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

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

An analysis of the importance of both destruction and creation to economic growth

A growth model is studied in which the destruction (or exit) and creative (or research) decisions are decoupled. This approach emphasizes that different agents make these interrelated decisions. The growth rate equals the product of a measure of the destruction and creation rates. The determinants of income mobility, income inequality, the lifespan of a firm, and the growth rate are studied. The equilibrium can either yield too high or low a level of innovation, but the destruction rate may also be too high or low. A non-linear tax/subsidy scheme, which alters the innovation and exit decisions, can improve welfare.