Effective Solution Approach Research Articles

Land layout optimisation for virtual land sales in the metaverse: two-dimensional assortment problem

With the growing popularity of the metaverse, the virtual real estate in the metaverse has generated immense investment enthusiasm. Considering the virtual land sale mode allowing the potential buyers to request the purchase of rectangular land parcels of a specified size, this study investigates how to lay out the requested land parcels for minimising the size of the required rectangular open land, which can be formulated completely as the two-dimensional assortment problem (2DAP). Due to the strong NP-hardness of the 2DAP, an effective and efficient heuristic solution approach named binary adjoining algorithm (BAA) is presented for tackling the 2DAPs in large scales. The conducted computational experiments show that the BAA can outperform the state-of-the-art piecewise-linearisation mixed integer linear programming model as well as four existing advanced metaheuristic techniques designed for the 2DAP, in both solution quality and computational time, on the small-size instances. The superiority of the BAA over either foregoing reference method on the large-size instances with up to 60 requested land parcels is also demonstrated.

Unraveling the mechanisms of glioblastoma's resistance: investigating the influence of tumor suppressor p53 and non-coding RNAs.

Glioblastoma (GB) is one of the most fatal CNS malignancies, and its high resistance to therapy and poor outcomes have made it one of the primary challenges in oncology. Resistance to standard therapy, i.e., radio-chemotherapy with temozolomide, is one of the principal causes of the poor prognostic outcomes of GB. Finding the molecular basis of GB resistance to therapy is key to creating effective solution approaches. The general problem of GB resistance is supervised by cancer suppressive protein, p53, and has become a very special interest in molecular research in recent decades. The principal aim of this manuscript is to perform a comprehensive survey on the complex network of interactions developed by p53 with non-coding RNAs (ncRNA) in the context of GB resistance. The present article details the functional aspects of p53 as a cellular stress response protein, including its roles in apoptosis, cell cycle regulation, and DNA repair in glioblastoma (GB), along with the disruption of p53 and its involvement in chemoresistance (CR). It also highlights several classes of ncRNAs, namely microRNAs, long ncRNAs, and circular RNAs, that manipulate p53 signaling in GB-CR. The article likewise explains how disruption in the expression of these ncRNAs can promote GB-CR and how it interacts with essential cellular functions, such as proliferation, apoptosis, and DNA repair. The manuscript also describes the potential of targeting p53 and ncRNAs with their diagnostic and prognostic potential as novel promising therapeutics for GB. Nevertheless, ncRNA-based biomarkers still present challenges for their suitability in GB resistance. However, modern research continues to discover novel prediction targets, potentially enhancing patient outcomes and therapeutic options. Therefore, the neutralization of this intricate regulatory network of GB resistance might have a primary clinical effect in fighting GB resistance therapy and thus might lead to a substantial increase in patient survival and quality of life.

An efficient and robust varying-parameter projection neural network for sparse signal reconstruction

Sparse signal reconstruction plays an important role in many practical applications, and therefore, many scholars strive to study its various effective (approximate) solution approaches, including some projection neural network models with finite-time or fixed-time convergence due to their parallel nature and convenience for hardware implementation. However, we experimentally find that noise has a significant influence on the convergence performance of these models. To overcome this problem, in this paper, we propose a new efficient varying-parameter projection neural network model (VPPNN) using the sliding mode control technique and a novel time-varying parameter for sparse signal reconstruction. Theoretical analysis shows that this new model not only has shorter fixed convergence time under certain conditions but also has noise-tolerance when compared to the existing projection neural network models. Numerical simulation experiments (via sparse signal/image reconstruction) are performed to illustrate the superiority of the new proposed model over the existing ones in terms of the convergence rate and robustness.

Using Simulated Annealing to Solve the Multi-Depot Waste Collection Vehicle Routing Problem with Time Window and Self-Delivery Option

This research introduces the Multi-Depot Waste Collection Vehicle Routing Problem with Time Windows and Self-Delivery Option (MDWCVRPTW-SDO). The problem comes from the waste bank operation implemented in Yogyakarta City, Indonesia. A set of vehicles is dispatched from the waste banks to pick up waste from residents’ locations within the time windows specified by the residents. Residents may be compensated for delivering their waste to a waste bank by themselves. The objective of MDWCVRPTW-SDO is minimizing the sum of investment costs, routing costs, and total compensation paid to the residents. We model this problem as a mixed integer linear programming model and propose Simulated Annealing (SA) as an effective solution approach. Extensive computational experiments confirm that SA is effective to solve MDWCVRPTW-SDO. Moreover, the number of waste banks, compensation paid to residents, and the distribution of residents of each type are crucial for the success of the implementation.

A two-stage hybrid ant colony algorithm for multi-depot half-open time-dependent electric vehicle routing problem

This article presents a detailed investigation into the Multi-Depot Half-Open Time-Dependent Electric Vehicle Routing Problem (MDHOTDEVRP) within the domain of urban distribution, prompted by the growing urgency to mitigate the environmental repercussions of logistics transportation. The study first surmounts the uncertainty in Electric Vehicle (EV) range arising from the dynamic nature of urban traffic networks by establishing a flexible energy consumption estimation strategy. Subsequently, a Mixed-Integer Programming (MIP) model is formulated, aiming to minimize the total distribution costs associated with EV dispatch, vehicle travel, customer service, and charging operations. Given the unique attributes intrinsic to the model, a Two-Stage Hybrid Ant Colony Algorithm (TSHACA) is developed as an effective solution approach. The algorithm leverages enhanced K-means clustering to assign customers to EVs in the first stage and employs an Improved Ant Colony Algorithm (IACA) for optimizing the distribution within each cluster in the second stage. Extensive simulations conducted on various test scenarios corroborate the economic and environmental benefits derived from the MDHOTDEVRP solution and demonstrate the superior performance of the proposed algorithm. The outcomes highlight TSHACA’s capability to efficiently allocate EVs from different depots, optimize vehicle routes, reduce carbon emissions, and minimize urban logistic expenditures. Consequently, this study contributes significantly to the advancement of sustainable urban logistics transportation, offering valuable insights for practitioners and policy-makers.

An epsilon-constraint-based exact multi-objective optimization approach for the ship schedule recovery problem in liner shipping

Time management is crucial for liner shipping services. A variety of unexpected events can disrupt liner shipping schedules. A real-time port capacity analysis and rescheduling the original ship operations would be necessary to counteract the negative effects of such disruptions. Different ship schedule recovery options can be adopted in response to disruptive events (e.g., ship sailing speed adjustment, skipping of disrupted ports). However, shipping lines face conflicting decisions when selecting ship schedule recovery options. As an example, the commonly-used ship speeding-up option could effectively reduce delays during the voyage but would increase the fuel cost. Similarly, the skipping of disrupted ports may substantially decrease the associated delays but would incur additional costs associated with supply chain disruptions and misconnected cargo. Nevertheless, there is a lack of analytical methods that enable the evaluation of competing objectives in ship schedule recovery and effective multi-objective solution approaches. Therefore, this study proposes a novel multi-objective model for ship schedule recovery that aims not only to minimize the total late ship arrivals at ports but also to minimize the total profit loss due to disruptive events that may occur at sea and/or at ports. An epsilon-constraint-based exact optimization algorithm is adopted to obtain optimal Pareto Fronts. The computational experiments conducted for a real-life transit route demonstrate that the adopted exact optimization algorithm is able to generate Pareto Fronts in a timely manner. Moreover, the conducted sensitivity analyses provide interesting insights regarding the effects of different disruption types and unit fuel costs on ship schedule recovery.

Freight-on-Transit for urban last-mile deliveries: A strategic planning approach

We study a delivery strategy for last-mile deliveries in urban areas which combines freight transportation with mass mobility systems with the goal of creating synergies contrasting negative externalities caused by transportation. The idea is to use the residual capacity on public transport means for moving freight within the city. In particular, the system is such that parcels are first transported from origins (central distribution centers) to drop-in stations, which are stop locations on public vehicle itineraries. Then, they are transported on public vehicles to drop-out stations, from where they are delivered to destination by green vehicles (such as bikes, drones, porters, etc.). The system is known as Freight-On-Transit (FOT). In this paper, we focus on the strategic decisions related to defining the public transportation network that will take part in the delivery system, i.e., which public vehicle lines and stop locations will be included (and thus equipped for the service). We propose different formulations for the problem and effective heuristic solution approaches based on column generation. We perform exhaustive tests aimed at providing managerial insights on the performance and the efficiency of the system.

A Fuzzy Multi-Criteria Decision Making Methodology for Job Evaluation

Aims: In this study, the integrated methods Hesitant Fuzzy Analytic Hierarchy Process (HF-AHP), Fuzzy COmplex Proportional Assessment (F-COPRAS) and Fuzzy Technique for Order Performance to Ideal Solution (F-TOPSIS) were used for job evaluation studies in a food company. Background: There has been a decline in employee performance in the company. Unfair wages and unequal workload were identified as the reasons for the failure. Therefore, it has been observed that the staff turnover rate in the company is quite high. Objective: The objective is to determine a fair wage policy that will increase employee satisfaction by stratifying with job evaluation analysis between positions. Methods: The experts of Human Resources Department determined eight competency evaluation criteria for job evaluation studies in the proposed approach. Based on their judgments on these criteria, the competencies were rated using a linguistic scale and the weighting values were calculated using HF-AHP method. These values are inputs for the next stage. Employees were ranked using F-COPRAS and F-TOPSIS methods. Results: This study showed that the integrated method can be an effective alternative solution approach for calculating the weighting values and ranking of competencies in job evaluation studies. Conclusion: It has been shown that the use of the strata created as a result of this study is a great facilitator in determining employee pay policies.

Barriers to organic waste management in a circular economy

Organic waste disposal methods, notably landfilling, not only deplete resources but also contribute to environmental challenges. This research looks at potential barriers to organic waste management solutions. The objective of this study is to identify the barriers to organic waste management solutions from an actor's perspective, and to explore their causal relationships to overcome the organic waste management problem from a system perspective. Several key challenges were identified regarding organic waste management solution, the current intervention overview indicates that promoting and tracking attention towards “value to waste” would be an effective solution approach. Waste collection fees, unethical behavior, and a lack of engagement and commitment in activities show a subsequent effect on consumer-household solutions, which are currently acting as priority barriers in this research. In order to have a better understanding of this complex issue, a detailed knowledge of barriers (leading to organic waste) is discovered and evaluated with the application of fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL). The data for this research has been taken in the context of a developing economy like India. This work can provide structural support to the managers by knowing the cause (influencing) and effect-group (influencing) barriers to the effective implementation of an organic waste management system in a circular economy context.

The maintenance conversion scheduling problem: Models and insights

AbstractThe maintenance conversion scheduling problem (MCSP) is faced by organizations such as those in the airline, defense, heavy equipment, and transportation industries switching from an asset maintenance program with longer, less‐frequent service visits to one with shorter, more frequent visits. One example is the United States Air Force (USAF) High Velocity Maintenance program piloted at the Warner Robbins Air Logistics Center on the C‐130 aircraft line. The USAF MCSP is complex, as planners must schedule significantly more aircraft depot maintenance visits during the conversion period and must determine the timing and specific order in which each aircraft completes a repetitive sequence of maintenance visits. The conversion is expected to yield a stable long‐term maintenance schedule, while balancing annual depot workload and operating within reasonable flow times and work‐in‐process levels. While practically important, this problem has received little to no attention in the literature. Therefore, this research formalizes the general MCSP within an optimization framework, shows the MCSP is NP‐complete, proposes a computationally effective solution approach, and shows that a balanced long‐term schedule depends critically on the conversion period schedule. Our solutions are markedly better than USAF proposed schedules and underscore the value of leveraging synergies between asset availability and maintenance efficiency. Our approach moves the focus away from batch scheduling toward a smoother, more uniform, mixed‐model type schedule that yields more stable maintenance operations and a more consistent, predictable level of aircraft readiness. The manuscript concludes with a discussion of the main theoretical and practical implications of our work.

A branch-and-price algorithm for nanosatellite task scheduling to improve mission quality-of-service

Satellite scheduling is concerned with the assignment of start and finish times for tasks while considering the mission objective, available resources, and constraints. This scheduling problem is especially critical in nanosatellites, given that resources are more scarce than in traditionally-sized spacecrafts. Despite having its own set of constraints and being increasingly deployed, nanosatellite task scheduling has received little attention in the literature. In this paper, we advance the state-of-the-art by devising an effective solution approach for a nanosatellite task scheduling problem. We resort to the Dantzig-Wolfe decomposition to explore the special structure of an existing mixed-integer linear programming (MILP) formulation, decomposing it by tasks, which results in a novel profile-based formulation for the problem. To solve the resulting formulation, we propose a branch-and-price (B&P) algorithm that is suitable for the scheduling of a large number of tasks over an expanded time horizon. Furthermore, we design a dynamic programming algorithm for generating feasible schedules instead of solving the pricing subproblem with an MILP solver, among other algorithmic strategies. Computational experiments using instances that represent realistic scenarios showed that the B&P algorithm is considerably more efficient in solving large instances when compared to commercial solvers. Overall, the proposed solution strategy empowers the decision maker to plan more complex missions, in an optimal or nearly-optimal manner and within a reasonable computation time.

Hierarchical Operation Management of Electric Vehicles for Depots With PV On-Site Generation

Solar photovoltaic (PV) generation is a promising energy cost-saving selection for depots that use electric vehicles (EVs) to deliver goods; however, the intermittency of power output renders it challenging to realize reliable and economical operations. This paper proposes a two-stage hierarchical operation management method of EVs for depots with on-site PV generation. In the day-ahead stage, considering real road networks and battery swapping mode, a coordinated scheduling model of delivery service and charging/discharging for EVs is constructed to minimize the overall energy cost and logistics delivery cost by using the forecasted PV power output. Moreover, an effective solution approach based on the natural aggregation algorithm and Floyd algorithm is developed for the model. In the actual operation stage, a model prediction control (MPC)-based rolling horizon operation strategy is proposed to correct EVs’ charging/discharging decisions with the realization of real-time PV power output, aiming at to minimize the deviation in the actual and day-ahead scheduled interactive power between the depot and upper grid. Finally, simulations are conducted to validate the proposed methods.

Centralized collaborative production scheduling with evaluation of a practical order-merging strategy

We study a collaborative scheduling problem where the dominant manufacturer (DM) outsources several processes to collaborative manufacturers (CMs). We consider multiple types of products defined by different collaborative production networks which may share some common CMs. The DM assigns the orders to the CMs and coordinates the production scheduling among the CMs. We analyse a practical order-merging strategy which combines a subset of same-type orders throughout the production aming at the cost benefit by continuous processing. Such a strategy is easy to implement but may increase the scheduling cost because of increased deviation between the completion time and the required delivery window of some orders. To this end, a heuristic algorithm based on a learning mechanism and ant colony optimisation is proposed for solving the collaborative scheduling problem under the order-merging plans. Simulation case study was carried out to compare the costs under different merging options. We conclude that merging more orders could lead to a better cost in a few cases, and the overall system performance is more sensitive to the merging schemes when the network capacity is tighter due to disruption. Our study provides an effective solution approach and managerial insights for collaborative scheduling problems with practical order-merging strategies.

Combining workload balance and patient priority maximisation in operating room planning through hierarchical multi-objective optimisation

Previous analysis suggested the opportunity to consider the preferences of different stakeholders (hospital, patients, doctors and nurses) through the adoption of both patient priority maximisation and workload balance as performance criteria. The aim of this paper is to develop an effective and efficient solution approach for the operating room planning and scheduling capable to take into account the patient priority maximisation and workload balance criteria at the same time. This work is inspired by the need of a deeper understanding of the quality of the solutions obtained when a combination of the two criteria leads the OR planning decisions. Starting from a hierarchical multi-objective optimisation model for the combined master surgical scheduling and surgical cases assignment problems, we develop a class of new multi-neighbourhood local search based matheuristic algorithms, whose main feature is to exploit an ad hoc neighbourhood to generate better solutions in a significant shorter running time. A broad quantitative analysis on new realistic instances proves the effectiveness and the efficiency of the proposed matheuristic algorithms as well as to evaluate the quality of the computed solution from an operating room management perspective.

AC network-constrained unit commitment via conic relaxation and convex programming

To address the AC network-constrained unit commitment problem, we propose an effective three-step solution approach. We first solve a DC (mixed-integer linear) approximation of the original problem to identify an initial solution and the set of transmission lines likely to be congested. Secondly, with the linear solution as a starting point, we solve a mixed-integer second-order conic relaxation of the original problem using an active set strategy regarding transmission constraints. Finally, using the solution of the relaxed problem as a starting point, we solve a set of increasingly accurate continuous convex optimization problems to ensure full AC feasibility. We show the effectiveness of the proposed approach using the IEEE 24-bus test system and an Illinois 200-bus system with 49 generators and 245 lines.

Non-Gray Radiative Heat Transfer in Two-Dimensional Cylindrical System

Radiative heat transfer in a concentric cylinder system with a participating medium is demonstrated to be inherently two-dimensional, even for the consideration of heat transfer to the total inner or outer surface with uniform boundary conditions. For the evaluation of two-dimensional nongray radiative heat transfer, the zonal exchange factor, together with the concept of point mean beam length, is shown to be an effective solution approach. Optimal point mean beam length for radiative heat transfer between a differential area and a finite area with different geometrical configurations within the concentric cylindrical enclosure is generated. Numerical data for an example calculation is presented to illustrate the characteristics of nongray two-dimensional radiative heat transfer.

New benchmark algorithm for hybrid flowshop scheduling with identical machines

With many industrial applications in the production sector, the hybrid flowshop scheduling problem (HFSP) has received wide recognition in the scheduling literature. Given the NP-hard nature of the HFSP, which is characterized by highly intractable solution spaces, effective solution approaches are of particular interest to facilitate its real-world use cases. This study proposes a new benchmark metaheuristic, the Chaos-enhanced Simulated Annealing (CSA) algorithm to minimize makespan in the HFSP with identical machines. A recently published testbed is used to evaluate the performance of CSA against that of the upper bounds/best-known solutions in the literature. In addition to improving the upper bounds, the computational results revealed that CSA performed very well in terms of computational efficiency and stability. The proposed CSA can serve as a strong benchmark algorithm for developing more algorithms to effectively and efficiently solve HFSPs and its extensions.

Pricing for a product with network effects and mixed logit demand

AbstractWe consider a pricing problem for a single product that experiences network effects. Demand is described by a consumer choice model in which each individual chooses between purchasing the product and not purchasing the product. We assume that there are multiple segments in the population of potential buyers, and that individuals' intrinsic values for the product and sensitivities to the network effect (ie, the extent to which their values are affected by how many others buy the product) vary across segments. The demand model may be viewed as a version of the mixed multinomial logit model, modified to incorporate network effects. We formulate and analyze an optimization problem that aims to find the seller's revenue‐maximizing price. In settings with an arbitrary number of demand segments, we present a simple, effective heuristic solution approach. In settings with two segments, we obtain a solution method that outputs provably near‐optimal prices. We close with an extensive numerical study.

Solving the winner determination problem in combinatorial auctions for fractional ownership of autonomous vehicles

AbstractThe introduction of autonomous vehicles (AVs) to consumer markets will expedite the trend of car sharing and enable co‐owning or co‐leasing a car. In this paper, we consider a combinatorial auction market for fractional ownership of AVs, which is unique in two aspects. First, items are neither predefined nor discrete; rather, items are continuous time slots defined by bidders. Second, the spatial information of bidders should be incorporated within the winner determination problem (WDP) so that sharing a vehicle is indeed a viable plan. The consideration of spatial information increases the computational complexity significantly. We formulate the WDP, which plays a critical role in various auction designs and pricing schemes, for both discrete‐ and continuous‐time settings. In terms of social welfare maximization, we show that the continuous‐time model is superior to the discrete‐time model. We provide a conflict‐based reformulation of the continuous‐time model, for which we develop an effective solution approach based on a heuristic and maximal clique based reformulations. Using samples of the 2010–2012 California Household Travel Survey, we verify that the proposed solution methods provide effective computational tools for the combinatorial auction with bidder‐defined items.

A Mathematical Programming Model and Enhanced Simulated Annealing Algorithm for the School Timetabling Problem


 Despite significant research efforts for School Timetabling Problem (STP) and other timetabling problems, an effective solution approach (model and algorithm) which provides boundless use and high quality solution has not been developed. Hence, this paper presents a novel solution approach for solving school timetabling problem which characterizes the problem-setting in the timetabling problem of the high school system in Nigeria. We developed a mixed integer linear programming model and meta-heuristic method - Enhanced Simulated Annealing (ESA) algorithm. Our method incorporates specific features of Simulated Annealing (SA) and Genetic Algorithms (GA) in order to solve the school timetabling problem. Both our solution approach and SA approach were implemented using Matrix Laboratory 8.6 software. In order to validate and demonstrate the performance of the developed solution approach, it was tested with the highly constrained school timetabling datasets provided by a Nigerian high school using constraints violation, simulation time and solution cost as evaluation metrics. Our developed solution approach is able to find optimal solution as it satisfied all the specified hard and soft constraints with average simulation time of 37.91 and 42.16 seconds and solution cost of 17.03 and 18.99, respectively, for JSS and SSS to the problem instance. A comparison with results obtained with SA approach shows that the developed solution approach produced optimal solution in smaller simulation time and solution cost, and has a great potential to solve school timetabling problems with satisfactory results. The developed ESA algorithm can be used for solving other related optimization problems.