Solution Approach Research Articles

An integrated approach for lot-sizing and storage assignment

In this paper, we study the interaction between the lot-sizing problem and the storage assignment problem. Traditional lot-sizing problems have been studied for decades. However, only recent studies have further considered decisions related to the assignment of items to inventory locations, aiming to better model the complex reality. In our problem, the storage space is divided into several separate locations, and the inventory is assigned to the storage locations taking into account specific compatibility conditions. Relocation of inventory is also possible if needed. In addition to the traditional cost elements from the lot-sizing problem, we consider others related to holding inventory, such as fixed storage costs, handling costs, and relocation costs. We model the problem using a general mathematical model, as well as a transportation reformulation, which provides better lower bounds. We propose several heuristics to solve the problem by splitting it into smaller subproblems, which are then solved sequentially. A series of computational experiments is carried out in order to evaluate the impact of the integration between the lot-sizing and the storage assignment decisions, as well as the behavior of the different solution approaches. The results show that the proposed heuristics are highly effective in finding feasible solutions that are very close to the best solutions, while spending 97% less computational time compared to solving the full mathematical model. When compared to the relax-and-fix heuristic (benchmark), certain versions of the heuristics can find better solutions using less computational effort, underscoring the benefit of employing more specialized heuristics. Additionally, we conduct a sensitivity analysis with the aim of understanding the impact of key input parameters on the problem. The results indicate a significant influence of compatibility levels on the problem complexity. Limited item-item compatibility notably increases complexity, whereas restricted item-location compatibility reduces computational time.

Modulating the elasticity of milk exosome-based hybrid vesicles to optimize transepithelial transport and enhance oral peptide delivery.

To address challenges such as limited loading capacity, restricted targeting precision, and low yield of natural exosomes as drug carriers, the fusion of liposomes and exosomes to create hybrid vesicles has emerged as a viable solution approach. While current research mainly focuses on designing functionalized liposomes, less attention is given to how liposome membrane materials affect the elasticity of these hybrids and their delivery efficiency. This study utilized milk exosomes (mExos) as model exosomes, and generated hybrid vesicles with varying elasticity through the fusion of phospholipids with differing chain lengths, examining the disparities among various hybrid vesicles in their ability to overcoming the gastrointestinal barriers. It was observed that while hard hybrid vesicles exhibited reduced mucus penetration compared to soft hybrid vesicles, they demonstrated a notably higher efficacy in traversing the epithelial cell barrier. The enhanced transepithelial cell capability of hard vesicles can be attributed to their reduced tendency to aggregate in the lysosome through the down-regulated clathrin-mediated endocytosis pathway, as well as by the strengthening of the endoplasmic reticulum-Golgi exocytosis pathway due to their rigid characteristics. In comparison to soft hybrid vesicles, semaglutide (SET) loaded hard hybrid vesicles demonstrated improved in vivo epithelial permeability, enhanced oral bioavailability, and better therapeutic effectiveness. This study could provide valuable insights for determining the optimal elasticity of exosome-liposome hybrid vesicles in the development of oral nanocarriers.

The Author’s Journey - Understanding and improving the authoring process of theory-driven socially intelligent agents

State of the art agent-modelling tools support the creation of powerful Socially Intelligent Agents (SIAs) capable of engaging in social interactions with participants in various roles and environments. However, their deployment demands a laborious authoring task as it is necessary to manually define behaviour rules and create content for different interaction scenarios. While Socially Intelligent Agents (SIAs) research has centered on the user experience, we shift focus to the authors. To understand the challenges faced by authors who create these agents, we performed an innovative analysis of the authoring experience in modern agent modelling tools. One key finding is that, while SIA concepts are generally understandable, emotional-based concepts are not as easily comprehended or used by authors. We propose a hybrid solution approach that culminated in the development of Authoring Assisted FAtiMA-Toolkit. The augmented agent modeling tool incorporates a data-driven authoring assistant to boost author productivity while promoting transparency and authorial control. To evaluate the impact of this framework on the authoring experience, we conducted a user study. Results showed that authors using the Authoring-Assisted FAtiMA-Toolkit were on average able to create more SIA-related content in less time. Our findings suggest that data-augmented, theory-grounded agent modeling tools can support the development of affective social agents by reducing the authoring burden without sacrificing the framework’s clarity or the authors’ control over the content

The integrating of environmental sustainability assessment by using bipolar complex fuzzy soft Aczel-Alsina aggregation operators with EDAS approach.

The act of responsibly engaging with the world is referred to as environmental sustainability. It entails preserving the world's ecosystems and natural resources for the benefit of present and future generations. Being forward-thinking is essential to environmental sustainability because many decisions have long-term effects on the environment. Now, environmental sustainability is dealing with other fields in their way. The environmental sustainability having up-down situations in two dimensions is worthy of investigation. Therefore, it deals in the environment of bipolar complex fuzzy soft (BCFS) Aczel-Alsina aggregation operators (AAAOs). Here, we want to use some suitable and dominant tools like evaluation based on distance from average solution (EDAS) approach. We will also handle the problem of environmental sustainability and will solve it through AAAOs with the EDAS method. Further, we take some materials of environmental sustainability and will solve it by the EDAS method with associated AOs. After this, we select the best material for its stability and loading capability.

Solution Algorithms for the Capacitated Location Tree Problem with Interconnections

This paper addresses the Capacitated Location Tree Problem with Interconnections, a new combinatorial optimization problem with applications in network design. In this problem, the required facilities picked from a set of potential facilities must be opened to serve customers using a tree-shaped network. Costs and capacities are associated with the opening of facilities and the establishment of network links. Customers have a given demand that must be satisfied while respecting the facilities and link capacities. The problem aims to minimize the total cost of designing a distribution network while considering facility opening costs, demand satisfaction, capacity constraints, and the creation of interconnections to enhance network resilience. A valid mixed-integer programming was proposed and an exact solution method based on the formulation was used to solve small- and medium-sized instances. To solve larger instances two metaheuristic approaches were used. A specific decoder procedure for the metaheuristic solution approaches was also proposed and used to help find solutions, especially for large instances. Computational experiments and results using the three solution approaches are also presented. Finally, a case study on the design of electrical transportation systems was presented and solved.

Comparison of different characterization approaches for monoelemental calibration solutions at two national metrology institutes.

Monoelemental calibration solutions are the most common reference in elemental analysis, linking measurement results to the International System of Units (SI). National Metrology Institutes (NMI) prepare these solutions as certified reference materials (CRM) and determine their elemental mass fraction with high accuracy. Characterization with high accuracy is one of the most critical steps in CRM production. This report compares the approaches taken by the NMIs of Türkiye (TÜBİTAK-UME) and Colombia (INM(CO)) in preparing and characterizing cadmium calibration solutions with a nominal value of 1 g kg-1. Each NMI produced CRMs using an independent batch of cadmium calibration solution and assigned mass fraction values to both their own solutions and those of the other NMIs. TÜBİTAK-UME employed a primary difference method (PDM) to assess the purity of a cadmium metal standard, quantifying all possible impurities using a combination of instrumental analytical techniques. The defined purity standard was used for both the gravimetric preparation of the CRM and the calibration of high-performance inductively coupled plasma optical emission spectrometry (ICP-OES) measurements. On the other hand, INM(CO) used gravimetric titration with EDTA to assay cadmium in the CRM solutions. The EDTA salt was previously characterized by titrimetry. Despite the fundamentally different measurement methods and independent metrological traceability paths to the SI, the measurement results exhibited excellent agreement within the stated uncertainties. This comparative analysis demonstrates the effectiveness of varied characterization approaches and underscores the reliability of the cadmium calibration solutions prepared by TÜBİTAK-UME and INM(CO). This collaborative effort highlights the reliability and adaptability of various measurement techniques in fulfilling rigorous metrological standards for elemental calibration solutions. Furthermore, this approach enhances the robustness of the measurements in the field of elemental analysis and contributes traceability to the SI.

Accelerating Benders Decomposition for sustainable food closed-loop supply chain network under uncertainty: a case study

PurposeThe purpose of this study was to address waste management in the food supply chain (FSC) through the integration of inspection processes in production and distribution centers under uncertain conditions, aiming to enhance sustainability across environmental, economic and social dimensions. The study introduces a sustainable forward and reverse FSC network using a closed-loop supply chain network approach to prevent the transfer of spoiled products, ultimately providing competitive advantages to stakeholders.Design/methodology/approachA robust multi-objective mathematical programming model is proposed, incorporating inspection processes to manage perishable products effectively. The model is solved using the Augmented Epsilon Constraint technique implemented in GAMS software, providing Pareto-optimal solutions tailored to decision-makers’ preferences. Furthermore, the methodology is applied in a real-world case study and solved with the Benders Decomposition algorithm to validate its practicality and effectiveness.FindingsThe proposed methodology effectively minimizes waste and enhances sustainability in the FSC by optimizing decision-making processes under uncertainty. The illustrative examples and real case study demonstrate the efficiency of the model and solution approach, highlighting the significant role of inspection in improving all three dimensions of sustainability.Practical implicationsThe study offers valuable insights into and tools for food industry managers to make informed strategic and tactical decisions. By addressing waste management through advanced supply chain modeling, the research helps organizations reduce costs, improve sustainability and gain a competitive edge in the market.Originality/valueThis research is novel in its focus on integrating inspection into the FSC network and addressing uncertainty through robust mathematical modeling. It contributes to the existing literature by demonstrating the impact of inspection on sustainability in FSCs and providing practical solutions for real-world implementation.

A decomposition algorithm for distributionally robust chance-constrained programs with polyhedral ambiguity set

Abstract In this paper, we study a distributionally robust optimization approach to chance-constrained stochastic programs to hedge against uncertainty in the distributions of the random parameters. We consider a general polyhedral ambiguity set under finite support. We develop a decomposition-based solution approach to solve the model and use mixing inequalities to develop custom feasibility cuts. In addition, probability cuts are also developed to handle the distributionally robust chance constraint. Finally, we present a numerical study to illustrate the effectiveness of the proposed decomposition-based algorithm and showcase the results for Wasserstein ambiguity set, total variation distance ambiguity set, and moment-based ambiguity set as special cases of the polyhedral ambiguity set.

Enhanced remediation of oil-contaminated mineral sand by the controlled release of a surfactant from calcium alginate capsules

ABSTRACT The remediation of oil-contaminated soil poses significant environmental challenges, often necessitating innovative approaches for effective and sustainable solutions. This study focuses on the synthesis, characterisation, and application of biodegradable capsules loaded with surfactant for enhanced oil remediation of a clean sand. By controlling the release properties of capsules, the research aims to overcome the limitations of conventional surfactant-based remediation methods, such as rapid washout and reduced efficacy over time. The study outlines a systematic approach, beginning with the synthesis of capsules containing surfactant cargo using the ionic gelation technique, and charactering the capsules' physical attributes. Subsequently, laboratory washing tests were designed to evaluate the efficiency of capsules in enhancing the remediation of oil-contaminated sand, comparing their performance with conventional surfactant application methods. The results show that the capsules enhanced the efficiency of oil removal during the washing process by the controlled release of surfactants. With the use of capsules, oil removal rates of the contaminated sand increased from 29.56% (blank group) to 59.90%, 68.67%, and 76.72% (at 5%, 10%, and 15%, capsule content (mass of capsules by 100 g mass of sand), respectively). Compared to the direct use of surfactants, the capsule approach exhibited higher performance, which increased oil removal efficiency by 95.9%, 97.0%, and approximately 100%, respectively. A release mechanism of higher oil removal ability is proposed based on the measurement of the washing water's surface tension. The findings highlight the potential of polymeric capsules for oil remediation, paving the way for the development of self-remediation approaches in contaminated sands.

Damage location in mechanical structures by multi-objective pattern search

Abstract We propose a multi-objective global pattern search algorithm for the task of locating and quantifying damage in flexible mechanical structures. This is achieved by identifying eigenfrequencies and eigenmodes from measurements and matching them against the results of a finite element simulation model, which leads to a nonsmooth nonlinear bi-objective parameter estimation problem. A derivative-free optimization algorithm is required since the problem is nonsmooth and also because complex mechanical simulation models are often solved using commercial black-box software. Moreover, the entire set of non-dominated solutions is of interest to practitioners. Most solution approaches published to date are based on meta-heuristics such as genetic algorithms. The proposed multi-objective pattern-search algorithm provides a mathematically well-founded alternative. It features a novel sorting procedure that reduces the complexity in our context. Test runs on two experimental structures with multiple damage scenarios are used to validate the approach. The results demonstrate that the proposed algorithm yields accurate damage locations and requires moderate computational resources. From the engineer’s perspective it represents a promising tool for structural health monitoring.

Analytical Solution Approach for Geomechanical Response of 3D Geological Model

Summary Understanding geomechanical response to pore pressure changes in subsurface structures is crucial for safe drilling and carbon storage operations. One of the most recognized analytical geomechanical models to understand overburden geomechanical responses to reservoir pore pressure changes is Geertsma's analytical solution, which used the ‘nucleus of strain’ concept to quantify displacements and changes in stress field in the surroundings as a result of reservoir depletion. However, the Geertsma solution is limited to isotropic conditions, no contrast between reservoir and surroundings, and homogeneous pore pressure changes, which do not fully capture heterogeneity and anisotropy of real-world subsurface with horizontally layered structures. Shale, as one of the most common caprock, exhibits vertically transverse isotropic (VTI) properties. Our work aims to enhance the ability of analytical modelling to accommodate more realistic scenarios for considering topographic changes in the subsurface. It is based on a semi-analytical solution that extends the Geertsma solution to accommodate static VTI properties with five independent elastic stiffnesses in the surroundings of the reservoir. This extension significantly enhances the ability of the model to represent the vertical stiffness variations characteristic of realistic subsurface conditions. We have applied the extended Geertsma solution through linear superposition to adapt our model for practical use in scenarios with inhomogeneous pore pressure distributions and three-dimensional variations in rock stiffness. We validated deformation results from our linear superposition approach by comparing them with those from a finite element numerical method under multiple scenarios. This helped evaluate the performance of the approach and identify its key limitations. After validation, we applied our approach to the Valhall field, which is known for its significant subsidence and considering complex topography. The practical application highlighted the approach's capability to accurately model realistic subsidence changes, demonstrating its utility for complex geomechanical assessments and the potential to be linked with geophysical monitoring.

Free and forced vibrations of circular plates made of functionally graded graphene origami-enabled auxetic metamaterials

Presented herein is a numerical investigation into the free and forced vibrational characteristics of circular plates made of functionally graded graphene origami-enabled auxetic metamaterials (FG-GOEAMs). The material properties of FG-GOEAM are estimated using genetic programing-assisted micromechanical models considering two graphene content distribution patterns. The effects of graphene content, size and folding degree on the material properties are taken into account by the models. The formulation of the paper is in the context of first-order shear deformation plate theory, and the governing equations of motion are achieved utilizing Hamilton’s principle. In the solution approach, the variational differential quadrature (VDQ) method is employed by which the variational statement of problem is directly derived and discretized using matrix differential and integral operators. Selected numerical results are presented to study the effects of graphene origami (GOri) content, folding degree, and distribution pattern on the free and forced vibration responses of FG-GOEAM circular plates with clamped and hinged boundary conditions.

Mobile home delivery parcel lockers

The demand for last-mile parcel delivery services is expected to increase. Great efforts are therefore being made to develop new ideas for more efficient last-mile delivery. One of the more recent concepts are Mobile Parcel Lockers (MPL), which can be easily repositioned to meet the actual daily demand for deliveries. In this work, we introduce the Mobile Home Delivery Parcel Locker (MHDPL) problem. Therein, a single vehicle can provide both, MPL service and Attended Home Deliveries (AHD), the current industry standard. Hence, the MHDPL problem integrates two characteristics: the simultaneous delivery of many customers as with MPLs, and the delivery of particularly remote customers in a short time as with AHDs. We present a mathematical formulation and propose an Iterated Local Search (ILS) as a solution approach. Since our ILS works on a time-space network, we introduce new problem and network-specific operators. The performance of the ILS is evaluated and the efficiency of the new service is analyzed in terms of various instance and customer characteristics, comparing it to dedicated MPL and AHD services. Our computational analysis reveals that the combination of services has economic potential and satisfies customer preferences more effectively. According to our study, an average MHDPL vehicle can serve 21% more customers than a comparable AHD vehicle.

Virtual Target Selection for a Multiple-Pursuer–Multiple-Evader Scenario

This paper considers an N-pursuer–M-evader scenario involving L virtual targets. The virtual targets serve as an intermediary target for the pursuers, allowing the pursuers to delay their final assignment to the evaders. However, upon reaching the virtual target, the pursuers must decide which evader to capture. It is assumed that there are more pursuers than evaders and that the pursuers are faster than the evaders. The objective is two-part: first, assign each pursuer to a virtual target and evader such that the pursuer team’s energy is minimized, and, second, choose the virtual targets’ locations for this minimization problem. The approach taken is to consider the Apollonius geometry between each pursuer’s virtual target location and each evader. Using the constructed Apollonius circles, the pursuer’s travel distance and maneuver at a virtual target are obtained. These metrics serve as a gauge for the total energy required to capture a particular evader and are used to solve the joint virtual target selection and pursuer–evader assignment problem. This paper provides a mathematical definition of this problem, the solution approach taken, and an example. Following the example, a Monte Carlo analysis is performed, demonstrating the efficacy of the algorithm and its suitability for real-time applications.

Evaluating the potential of green roofs for greywater treatment

Abstract Greywater, comprising household wastewater excluding toilet waste, represents a significant portion of daily wastewater. Effective greywater treatment is crucial to mitigate environmental pollution, especially when greywater flows directly into water bodies without clear controls, discharge limits, and treatment requirements. Nature-based solution (NBS) approaches, such as green roofs, are gaining popularity for their urban benefits and potential for greywater treatment. However, in Malaysia, the application and literature on green roofs are limited, primarily focusing on stormwater management. The potential for greywater treatment remains understudied, despite numerous successful implementations worldwide. Hence, this study aims to explore the effectiveness of green roofs in improving greywater quality. Two green roof models were constructed: one using commercial materials and the other using recycled coconut waste. Greywater samples from washing and kitchen activities were tested, with influent and effluent samples analyzed for key water quality parameters including pH, turbidity, BOD, COD, TSS, TN, and TP. Both green roof models demonstrated notable effectiveness in reducing BOD concentrations, achieving removal efficiencies between 12% to 33%. The resulting BOD levels ranged from 21 to 25 mg/L, which is slightly above the limit of Standard A but compliant with Standard B of the Environmental Quality Act (EQA) 1974. While green roofs show potential in reducing BOD from greywater, their effectiveness in treating other water quality parameters remains limited. Addressing these shortcomings through system enhancements and integrated treatment approaches can lead to more robust and reliable greywater treatment solutions.

Livelihood and household clean cooking challenges in rural India: A community-based participatory approach for solutions

Clean cooking in rural India faces bottlenecks impeding the attainment of sustainable development goal 7 despite various government interventions to propagate clean cooking. This hindrance often has a direct variation with the livelihood challenges of the people alongside some other factors. Following this, a participatory research approach was employed to identify the challenges, the interlinkages with livelihood and the sustainable solutions to improve livelihood through clean cooking solutions. In this work, the study was focused on a rural village of Guptapada, Khordha District of Odisha state in India. Challenges revealed from the study include the ineffectiveness of Pradhan Mantri Ujjwala Yojana (PMUY) and other government schemes, low awareness, lack of access to capital and the need for entrepreneurial skill and capacity development. Based on these, potential sustainable solutions were highlighted to improve livelihood in the village by developing clean cooking interventions. It also redirects governments, agencies and institutions working on developmental projects in rural communities to leverage the opportunity provided by clean cooking to improve the livelihood of the rural dwellers, ensuring sustainability through a participatory research approach.

Solar Heating as Sustainable Solution in Dyeing Industries: A Compromise Solution Approach Integrated Machine Learning

Solar Heating as Sustainable Solution in Dyeing Industries: A Compromise Solution Approach Integrated Machine Learning

Layout Optimization for a Large-Scale Grid-Connected Solar Power Plant

A solar power plant provides green electricity to the public via a power grid. As governments worldwide have pledged to reduce carbon emissions and achieve carbon neutrality, large-scale grid-connected solar power plants are booming. Developing such a plant requires significant investment, a large proportion of which covers construction costs. Such costs, together with the energy yield, critically depend on the plant’s layout. The layout planning of a solar power plant involves a series of complex optimization problems such as district partitioning, photovoltaic (PV) component location, and cable routing problems in a solar power plant. These problems have received limited attention in the literature and are highly challenging because they involve large-scale instances, complex design principles, and complicated physical constraints. Motivated by our collaborative projects with an electrical engineering company in China, this paper specifically focuses on the integrated location and routing (ILR) problem, which involves locating service ways, inverters, combiner boxes, and routing cables to connect them. We develop exact algorithms to effectively solve the ILR problem via a decomposition framework (leading to a variant of Benders decomposition (BD)), which is proven to produce an optimal solution. We also develop an exact branch-and-cut scheme to solve each subproblem in the decomposition framework by incorporating cutting planes and separation algorithms. Our solution approach is evaluated on 50 real-world data instances via extensive numerical experiments. Compared with the manual method based on greedy heuristics used in practice, our approach reduces the total cost by approximately 20%. Our decomposition method also achieves an average gap of 0.02% between the obtained lower and upper bounds, significantly smaller than the 16.08% gap achieved with the traditional BD. History: Accepted by Alice E. Smith and David Alderson, Area Editors for Network Optimization: Algorithms & Applications. Funding: This work was partially supported by the Research Grants Council of Hong Kong [Grant 15501221], the National Natural Science Foundation Program of China [Grants 72122006, 72471100, and 72131008], Huazhong University of Science and Technology Double First-Class Funds for Humanities and Social Sciences (Digital Intelligence Decision Optimization Innovation Team), the Interdisciplinary Research Program of HUST [Grant 5003300129], and the Fundamental Research Funds for the Central Universities [Grant 2023WKFZZX101]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0223 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0223 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Code and Data Repository for Layout Optimization for a Large-Scale Grid-Connected Solar Power Plant

The goal of this repository is to share data and results related to the Integrated Location and Routing (ILR) problem within a large-scale solar power plant, solved using our exact solution approach.

OPTIMIZATION PROBLEM FOR NUMBER OF LOGIC GATES NEEDED TO IMPLEMENT MULTIPLE BOOLEAN FUNCTIONS USING DECODER

Abstract. Background. Computer circuits enable almost every piece of electronics to function. An opportunity for their optimization was found in one of the ways to implement a circuit, which involves a decoder. Decoders are basic circuit components whose behavior makes it easy to implement multiple Boolean functions. Functions are just rules by which informational signals are being processed in the circuit. To implement a function Logic gatEs (LEs) are used. LEs group multiple input signals and process them using binary logic operators like AND or NOR. Purpose. Develop a computer program that, based on provided functions, will find a solution associated with approximately minimal number of logic gates. Relevance. Prevalence of circuits increases the value of small optimizations, since any improvement gets multiplied by millions of circuits produced. Optimization at the logic level is being explored, which will retain value regardless of the physical properties of the circuit. Additionally, the theory of optimizing LE usage for circuits built on decoders is examined, which will alleviate future research. Methods. Development of the theory arose from an understanding that input signals can be grouped using LEs consciously. This way, LE outputs can be used in more than one function, which results in reduction of total number of LEs. It becomes evident that the problem can be restated in terms of sets. Similarity to known NP-hard problems emerges, which prompts usage of existing solution approaches. Among them, the greedy algorithm is chosen for its simplicity in program implementation. The connection between the problem and digital circuitry is weakened by the introduction of special evaluation functions that work with sets. So, the problem is reduced to several mathematical rules, which then are used as the appropriate parts of the greedy algorithm. Development of knowledge necessary to create the target program is thus concluded. Results. The target program is successfully implemented and can be seen in full at https://github.com/Gleb-05/MakeFuncForDC. A test is conducted on 1000 sets of four pseudorandom eight-term functions. It is estimated that the implementations proposed by the program require on average 0.75 of the initial number of LEs. The average running time is estimated at 2 milliseconds. In contrast, brute-force search for the same problem is theorized to run for years. Conclusions. A working program that finds an almost optimal solution is successfully created and tested. Its performance makes it potentially useful in the industrial scale of circuit manufacturing. Since the program essentially offers workload management, its possible applications go beyond the domain of digital electronics. Additionally, the theory of optimizing the use of LEs is presented for the first time. It can be used as the basis for future research on the implementation of multiple Boolean functions using decoder.