This paper presents a comprehensive review of the last two decades of research on Kidney Exchange Programs (KEPs), systematically categorizing and classifying key contributions to provide readers with a structured understanding of advancements in the field. The review highlights the evolution of KEP methodologies and lays the foundation for our contribution. We propose three mathematical models aimed at improving both the quantity and quality of kidney transplants. Model 1 maximizes the number of transplants by focusing on compatibility based on blood type and PRA, without additional constraints. Model 2 introduces a minimum Human Leukocyte Antigen (HLA) compatibility threshold to enhance transplant quality, though this leads to fewer matches. Model 3 extends the problem to a Multi-Agent Kidney Exchange Program (MKEP), pooling incompatible donor-recipient pairs across multiple agents, resulting in a higher number of successful transplants while ensuring fairness across agents. Sensitivity analyses demonstrate trade-offs between transplant quantity and quality, with Model 3 striking the optimal balance by leveraging multi-agent collaboration to improve both the number and quality of transplants. These findings underscore the potential benefits of more integrated kidney exchange systems.

Facing current client expectations for high quality, timely order response, and multiple shipments of various needed merchandise, today’s producers must simultaneously satisfy external requirements and operate internally with minimum overall expenses and capacity constrained. Aiming to help present-day producers achieve the operational goals mentioned above, this work develops a decisional scheme to determine the best manufacturing-delivery policy for a multi-item economic production quantity (EPQ) system with multi-shipment, quality assurance, overtime, postponement, and external source. Combining a production postponement strategy in our multi-item batch fabricating procedures intends to first make all required standard/common parts for various client-needed merchandise and make finished goods in the 2nd phase. Two fabricating-uptime-shortening strategies are adopted: contracting out a proportion of the standard part’s batch and overtime-making of finished goods. We include screening and rework tasks in fabricating procedures to help us remove the identified scraps and correct the repairable faulty items. The quality-assured finished batches are divided into multiple equal-amount shipments transported to meet client requests. The overall manufacturing-transportation relevant expenses, including quality and uptime-expedited costs, are mathematically modeled and minimized using optimization methodology to help derive the best manufacturing-delivery operating policy. Moreover, we offer an illustration to validate the results and our research scheme’s capability numerically. This work mainly contributes to the literature by presenting a practical decision-making model. It enables the producers to expose numerous crucial problem-related managerial insights to facilitate producers in deciding the most appropriate manufacturing-delivery policy to meet clients’ multi-criteria demands.

Providing K-12 students with proper science, technology, engineering, and math (STEM) education is important to ensuring an innovative and prosperous economy. A highly skilled STEM workforce can lead to increased productivity and competitiveness, which can lead to a host of new ideas being researched and developed. STEM workers make added-value products, build bridges and roads, and conduct lifesaving medical research, among other important activities. The use of virtual reality (VR) technology for both education and workforce training has grown in recent years. VR technology can accelerate these processes at maximum efficacy and minimum costs and can have a significant impact on productivity gains, earnings, new jobs, innovation through research and development, and high-growth industries. This paper presents the development of a series of VR modules using the Unity game engine, the HTC VIVE Pro VR headset, and the Hi5 VR glove for the purposes of K-12 STEM education. Specifically, these developed modules have been designed to instruct K-12 students on topics related to motion and heat, with future goals to expand the modules to cover topics related to light, magnetism, electricity, radioactivity, sound, and waves. This paper will cover the methodology and design considerations that went into developing these modules, with a focus on how these modules relate to various learning strategies as well as with existing research on the use of VR in K-12 education.

This study provides a comprehensive analysis of supply chain management practices based on survey responses from a sample of enterprises. Through descriptive statistics, hypothesis testing, predictive modeling, advanced analytics techniques such as classification, clustering, and association rule mining, the research offers valuable insights into key areas of collaboration, quality management, technology adoption, agility, risk management, and customer responsiveness within supply chains. The findings highlight the importance of strategic integration, proactive problem-solving, customer-centric practices, and agility in meeting changing demands. The study also identifies distinct profiles of practice adoption and reveals intricate relationships between different supply chain practices. Overall, the research contributes to a deeper understanding of supply chain dynamics and offers actionable insights for improving operational performance and strategic decision-making.

This study aims to optimize a hybrid multi-item fabricating-shipping integrated system incorporating scrap, adjustable rate, and postponement. In present-day competitive market environments, there is a clear client demand trend for various goods, shorter lead time, and expected quality. To satisfy the client’s needs, the management of manufacturing firms requires an effective and efficient plan to fabricate various high-quality goods in an expedited period, under limited capacity, and with minimal operating expenses. Inspired by facilitating production management to determine the best fabricating scheme/plan to achieve their operational goals, this work proposes an exploratory postponement model with quality assurance and uptime reduction strategies for their decision-making. By employing a two-phase making scheme, the required standard components are first made in the 1st phase, and multiple finished merchandise is fabricated in the 2nd phase. The study suggests strategies of contracting out a part of the common parts’ batch and adopting an adjusted/expedited making rate in the 2nd phase to considerably reduce both phases’ production uptimes. During both fabricating processes, the screening tasks identify/remove scrapped/faulty goods to ensure each finished batch’s quality. Equal-amount multiple shipments of end merchandise are transported to the clients in fixed time-interval. Optimization methodology and mathematical analyses support us in deriving the model’s expected annual operating cost and deciding the optimal production-transportation policy. A numerical illustration helps verify our model’s applicability and reveals important managerial insights into the studied problem to facilitate management in decision-making.

When making a batch production decision for a buyer-vendor coordination system, the management must simultaneously consider the operating expenses incurred in in-house manufacturing and inventory, finished goods’ shipping, and stock holding at the retailer end. Achieving the operational goals of desirable quality, minimal production disruption, and shortening fabrication time help minimize overall in-house operating costs and maximize customer satisfaction. This work builds an operating cost minimization model for buyer-vendor coordination batch system with scrap, breakdowns, overtime, multi-shipment, and an external source to assist the management in optimizing their production-delivery plan. Removing inevitable scrap items ensures product quality, and correction action on stochastic equipment breakdown prevents unacceptable production delays. Implementing partial overtime and adopting an external source expedites in-house manufacturing time. Model construction and cost analysis enable us to decide the operating expense function. Then, we verify the function’s convexity and decide our model’s best manufacturing runtime with the differential calculus and a proposed algorithm. Furthermore, the numerical demonstrations are used to exhibit our work’s applicability and show what kinds of crucial in-depth information can be disclosed and made accessible to the production planners for their decision-making.

As the simple assembly line balancing problem type 1 (SALBP1) has been proven to be NP-hard, heuristic and metaheuristic approaches are widely used for solving middle to large instances. Nevertheless, the characteristics (fitness landscape) of the problem’s search space have not been studied so far and no rigorous justification for implementing various metaheuristic methods has been presented. Aiming to fill this gap in the literature, this study presents the first comprehensive and in-depth Fitness Landscape Analysis (FLA) study for SALBP1. The FLA was performed by generating a population of 1000 random solutions and improving them to local optimal solution, and then measuring various statistical indices such as average distance, gap, entropy, amplitude, length of the walk, autocorrelation, and fitness-distance among all solutions, to understand the complexity, structure, and topology of the solution space. We solved 83 benchmark problems with various cycle times taken from Scholl’s dataset which required 83000 local searches from initial to optimal solutions. The analysis showed that locally optimal assembly line balances in SALBP1 are distributed nearly uniformly in the landscape of the problem, and the small average difference between the amplitudes of the initial and optimal solutions implies that the landscape was almost plain. In addition, the large average gap between local and global solutions showed that global optimum solutions in SALBP1 are difficult to find, but the problem can be effectively solved using a single-solution-based metaheuristic to near-optimality. In addition to the FLA, a new mathematical formulation for the entropy (diversity) of solutions in the search space for SALBP1 is also presented in this paper.

The present research explores the collective influence of quality assurance and postponement on a hybrid multiproduct replenishing-delivery decision-making. Assume the required multiproduct has a standard (common) component, and our replenishing-delivery model has incorporated a two-phase postponement strategy. The first phase makes all standard components and hires an external supplier to partially provide the required parts to cut short the needed uptime. In contrast, the second phase fabricates the finished multiproduct in sequence. To ensure the desired merchandise quality, we apply a quality-assurance action to the in-house processes to screen and remove scrap items and rework the repairable defects in both stages. Upon completing each merchandise, these products are transported to the customer in n fixed-quantity shipment in fixed-time intervals. We employ math modeling and formulating approaches to gain the overall supply-chain operating expenses comprising subcontracting, fabricating, stock holding, transportation, and customer holding costs. By minimizing system operating expenses, this research determines the optimal replenishing-delivery policy. Lastly, we give a numerical example to demonstrate our study’s applicability and usefulness/capability for facilitating managerial decision-making.

The efficient management of reverse supply chains, particularly the collection and remanufacturing of defective products, plays a critical role in reducing production costs and determining the final pricing of remanufactured products. While existing research extensively explores warranty policies and maintenance services to enhance customer satisfaction and profitability, the integration of vehicle routing for product collection and sustainability advertising strategies remains underexplored. Addressing this gap, this study introduces a comprehensive two-stage reverse supply chain model that captures the interactions between manufacturers (MFRs) and remanufacturers (RMFRs) through a Stackelberg game framework. Methods: The proposed model incorporates interactive production constraints, vehicle routing problem (VRP) for optimizing collection logistics, and sustainability advertising to influence consumer behavior towards remanufactured products. Utilizing mixed nonlinear programming (MINLP) and nonlinear programming (NLP) techniques, the model simultaneously optimizes pricing strategies, collection efforts, and advertising investments for both MFRs and RMFRs. Numerical analyses are conducted to solve the optimization problems, accompanied by sensitivity analyses to evaluate the impact of key parameters such as production costs, defect rates, and routing constraints. The numerical results demonstrate that increases in production costs for MFRs lead to higher selling prices, thereby reducing their profit margins and negatively impacting RMFR profitability due to decreased demand for remanufactured products. Sensitivity analysis reveals that higher defect rates (α ≥ 0.8) significantly diminish overall supply chain profitability by lowering customer acceptance of RMPs. Additionally, expanding the allowable vehicle routing distance L effectively reduces collection costs, enhancing RMFR profits and enabling greater investment in sustainability advertising. The study shows that the integration of VRP and advertising strategies proves crucial in balancing cost efficiencies and market competitiveness, ultimately fostering a more sustainable and profitable reverse supply chain.

This study examines the joint impact of outsourcing, overtime, multi-delivery, rework, and postponement on a multiproduct fabrication problem. A growing/clear trend in today’s customer requirements turned into rapid response and desired quality of multi-merchandises and multiple fixed-amount deliveries in equal-interval time. To satisfy customers’ expectations, current manufacturing firms must effectively design/plan their multiproduct production scheme with minimum fabrication-inventory-shipping expenses and under confined capacity. Motivated by assisting manufacturing firms in making the right production decision, this study develops a decision-support delayed-differentiation model considering multi-shipment, rework, and dual uptime-reducing strategies (namely, overtime and outsourcing). Our delayed-differentiation model comprises stage one, which makes all common/standard parts of multi-end-merchandises, and stage two, which produces multiple end merchandise. For cutting making times, the study proposes subcontracting a portion of the common/standard part’s lot size and adopting overtime-making end merchandise in stage two. The screening and reworking tasks identify and repair faulty items to ensure customers’ desired quality. The finished lots of end merchandise are divided into a few equal-amount shipments and distributed to customers in equal-interval time. We employ mathematical derivation and optimization methodology to derive the annual expected fabrication- inventory-shipping expense and the cost-minimized production-shipping policy. A numerical demonstration is presented to exhibit our research scheme’s applicability and exposes the studied problem’s critical managerial insights, which help the management make beneficial decisions.