Binary Linear Programming Model Research Articles

Cross-modal de-deviation for enhancing few-shot classification

Few-shot learning poses a critical challenge due to the deviation problem caused by the scarcity of available samples. In this work, we aim to address deviations in both feature representations and prototypes. To achieve this, we propose a cross-modal de-deviation framework that leverages class semantic information to provide robust prior knowledge for the samples. This framework begins with a visual-to-semantic autoencoder trained on the labeled samples to predict semantic features for the unlabeled samples. Then, we devise a binary linear programming model to match the initial prototypes with the cluster centers of the unlabeled samples. To circumvent potential mismatches between the cluster centers and the initial prototypes, we perform the label assignment process in the semantic space by transforming the cluster centers into semantic representations and utilizing the class ground truth semantic features as reference points. Moreover, we model a linear classifier with the concatenation of the refined prototypes and the class ground truth semantic features serving as the initial weights. Then we propose a novel optimization strategy based on the alternating least squares (ALS) model. From the ALS model, we can derive two closed-form solutions regarding to the features and weights, facilitating alternative optimization of them. Extensive experiments conducted on few-shot learning benchmarks demonstrate the competitive advantages of our CMDD method over the state-of-the-art approaches, confirming its effectiveness in reducing deviation. The code is available at: https://github.com/pmhDL/CMDD.git.

Combined Optimization of Maintenance Works and Crews in Railway Networks

This study develops optimal maintenance schedules for train lines, a critical endeavor ensuring the safety, efficiency, and reliability of railway networks. The study addresses the combined scheduling problem of maintenance works and crews on the railway networks. The baseline scheduling model is initially established with the primary objective of allocating maintenance tasks efficiently while adhering to pertinent constraints, encompassing task grouping and cost minimization. Subsequently, this baseline model is enhanced through the integration of crew scheduling, wherein work crews are strategically assigned to execute predefined tasks, thereby facilitating effective workload distribution. The combined maintenance work and crew scheduling problem is mathematically formulated as a binary linear programming model, enabling the attainment of globally optimal solutions. Comparing the outcomes of our enhanced model, which incorporates both maintenance works and crew schedules, with the baseline model that solely addresses maintenance works, we reveal that task grouping in accordance with predefined conditions leads to reduced overall costs by minimizing maintenance duration during various periods. Additionally, the judicious distribution of workload among the crews ensures comprehensive coverage of all essential tasks. These findings underscore the significance of our proposed approach in enhancing the operational efficacy and economic viability of railway maintenance scheduling, thereby offering valuable insights for practical implementation and future research endeavors.

CD-LwTE: Cost- and Delay-Aware Light-Weight Transcoding at the Edge

The edge computing paradigm brings cloud capabilities close to the clients. Leveraging the edge’s capabilities can improve video streaming services by employing the storage capacity and processing power at the edge for caching and transcoding tasks, respectively, resulting in video streaming services with higher quality and lower latency. In this paper, we propose, a Cost-and Delay-aware Light-weight Transcoding approach at the Edge, in the context of HTTP Adaptive Streaming (HAS). The encoding of a video segment requires computationally intensive search processes. The main idea of is to store the optimal search results as metadata for each bitrate of video segments and reuse it at the edge servers to reduce the required time and computational resources for transcoding. Aiming at minimizing the cost and delay of Video-on-Demand (VoD) services, we formulate the problem of selecting an optimal policy for serving segment requests at the edge server, including (i) storing at the edge server, (ii) transcoding from a higher bitrate at the edge server, and (iii) fetching from the origin or a CDN server, as a Binary Linear Programming (BLP) model. As a result, stores the popular video segments at the edge and serves the unpopular ones by transcoding using metadata or fetching from the origin/CDN server. In this way, in addition to the significant reduction in bandwidth and storage costs, the transcoding time of a requested segment is remarkably decreased by utilizing its corresponding metadata. Moreover, we prove the proposed BLP model is an NP-hard problem and propose two heuristic algorithms to mitigate the time complexity of . We investigate the performance of in comprehensive scenarios with various video contents, encoding software, encoding settings, and available resources at the edge. The experimental results show that our approach (i) reduces the transcoding time by up to 97%, (ii) decreases the streaming cost, including storage, computation, and bandwidth costs, by up to 75%, and (iii) reduces delay by up to 48% compared to approaches.

Placement of SDN controllers based on network setup cost and latency of control packets

Software-defined networking (SDN) was developed to simplify the management of computer networks. In the SDN architecture, the control layer is completely decoupled from the data layer. Moreover, network software configuration is also possible. To setup an SDN, you need a device called controller, which is not available in traditional networks. In the control layer, there are several controllers that control the data layer switches. The type, number and place of these controllers directly affect the cost and average latency of control packets. Since achieving the lowest setup cost and the minimum latency of the control packets (between switches and controllers, and among controllers) is of great importance at the same time, in this paper, a binary linear programming model is proposed to achieve the trade-off between them in wide area networks(WAN). The model determines the type, place and minimum number of required controllers by receiving the place of the switches, the amount of processing capacity of the controllers and the setup cost of each controller. Accordingly, it reduces the setup cost, average latency between switches and controllers as well as average latency among controllers in comparison with previous methods. The results of the study show that the model has the lowest setup cost and the minimum average latency of control packets (switch-to-controller and controller-to-controller) in controllers placement compared to the previous methods.

Design model of an IEEE 802.11ad infrastructure for TSN-based industrial applications

This paper addresses two main aspects related to the application of the IEEE 802.11ad technology in an industrial environment, i.e., to guarantee a low latency and reliable data delivery while reducing the number of the deployed Access Points (APs). First, we discuss the parameter settings of the IEEE802.11ad MAC which guarantee a low delay by exploiting the synchronous service periods (SPs) mechanism. Then, we propose a binary linear programming model to enhance the reliability of the system under the Frame Replication and Elimination for Reliability (FRER). The model minimizes the number of deployed APs by taking into account the constraints on the available synchronous SPs. We also propose two heuristics, based on the described mathematical model, in order to efficiently solve the design problem, then compare the computational performance of the proposed approaches. The reported computational results highlight the complementary features of the compared methods and provide useful guidelines about the usage of the proposed approaches.

A novel grey multi-objective binary linear programming model for risk assessment in supply chain management

Robust and resilient agri-food supply chain management (AFSCM) is paramount to agribusinesses, given the many challenges and risks that this increased demand will bring in the coming decades. Interruptions caused by various risks to this crucial supply chain network, particularly in emerging economies, can put the lives of millions in danger, not to mention creating devastating impacts on the economy and the environment. Even so, there are only a limited number of quantitative risk management studies in the AFSCM literature. In this study, an integrated modified risk mitigation matrix (M-RMM) is developed to analyze the mitigation strategies for dealing with various risks in the context of the agri-food supply chain. The M-RMM is integrated with the grey multi-objective binary linear programming (GMOBLP) model to obtain the optimal risk mitigation strategies related to the three objective functions of risk, cost, and time minimization. The proposed model is a useful tool for formulating sustainable business policies and reducing food waste, and acquiring a context-specific (i.e., a developing economy), sector-specific (i.e., the agri-food processing sector), and multi-product (i.e., fresh and non-perishable) approach. The findings reveal that continuous training and development and vulnerability analysis of IT systems are the most effective risk mitigation strategies to lessen the impacts of lack of skilled personnel, sub-standard leadership, failure in IT systems, insufficient capacity to produce quality products, and poor customer relationships. The findings assist practitioners in managing risks in supply chains.

A robust model for emergency supplies prepositioning and transportation considering road disruptions

Proper prepositioning of emergency supplies can dramatically improve the efficiency of emergency response work. However, the uncertainties of emergency demands and road conditions bring difficulties to the prepositioning of emergency supplies. This paper proposes a two-stage robust model to locate emergency supply points and preposition the corresponding storage amount of emergency supplies, in which we presented two budget sets to describe the uncertainties of demands and road conditions, respectively. The innovative use of variables in the model to limit road capacities addresses the representation of different road interruption scenarios. We proposed an algorithm based on Benders decomposition by transforming the second-stage model into a binary linear programming model. Computational experiments based on the Sioux Falls network demonstrate the validity of the model and algorithm. We conducted sensitivity analyses for some important parameters in the model, such as two uncertainty control parameters, unit transportation cost, budget for the construction of emergency supply points, etc. We find that the uncertainty of road disruptions has a greater impact on the model than the uncertainty of demands. In addition, when the control parameter of the road disruptions exceeds a certain threshold, its influence on the model remains essentially constant.

Parallel assembly lines worker assignment and balancing problem: A mathematical model and an artificial bee colony algorithm

In today’s competitive world, assembly lines are one of the most important industrial manufacturing systems, which can accommodate the massive increase in customer demands and product range, for the production industry. One possible way to increase the capacity and efficiency of assembly lines is to design a parallel assembly line system, consisting of two or more assembly lines placed next to each other. In the literature, the assembly lines are usually balanced according to tasks with fixed processing times without considering worker–task compatibility. In real-life applications, however, since the processing time of each task may vary according to the capability of each worker, the assignment of tasks to stations in the assembly line depends on the skills of the worker assigned to the related station. Considering the worker and task assignments together for each assembly line will provide a more realistic perspective. This paper, to the best of our knowledge, is the first study that considers the minimization of joint cycle time for the worker assignment and assembly line balancing problem in parallel assembly lines (PALWABP-II). Accordingly, a binary linear mathematical programming (BLP) model is developed and an artificial bee colony (ABC)-based solution approach is proposed for medium and large-sized problems. A new set of test problems is presented to the literature for a computational experiment. In addition, a summary of the literature is presented. An example problem is solved using the BLP model. In order to evaluate the effectiveness of the proposed model and approach, they are compared with the classical particle swarm optimization (PSO) algorithm and ten different priority rules. The computational results on test problems prove the effectiveness of the proposed ABC algorithm for the PALWABP-II.

Selecting Bus Stops for Accessibility Improvements for Riders with Physical Disabilities

Bus stops are key links in the journeys of riders with disabilities. Inaccessible bus stops prevent people with physical disabilities from using fixed-route bus services, thus limiting their mobility. Due to limited budgets, transit agencies must select bus stops for which their improvements, as part of the effort to comply with the Americas with Disabilities Act (ADA), can maximize the overall benefits to riders with physical disabilities. In this paper, an analytic hierarchy process (AHP) was applied to combine the factors affecting the benefits to riders with physical disabilities, and a binary linear programming model was used to identify bus stops for ADA improvements based on budgetary and construction cost constraints. As an application example, the optimization model was applied to the 5,034 bus stops in Broward County, Florida. Compared to the usual approaches, the optimization model provides a more objective platform on which to identify bus stops for ADA improvements.

Evaluating the Security and Economic Effects of Moving Target Defense Techniques on the Cloud

Moving Target Defense (MTD) is a proactive security mechanism that changes the attack surface with the aim of confusing attackers. Cloud computing leverages MTD techniques to enhance the cloud security posture against cyber threats. While many MTD techniques have been applied to cloud computing, there has so far been no joint evaluation of the effectiveness of MTD techniques with respect to security and economic metrics. In this paper, we first introduce mathematical definitions for the combination of three MTD techniques: Shuffle, Diversity, and Redundancy. Then, we utilize four security metrics – namely, system risk, attack cost, return on attack, and reliability – to assess the effectiveness of the combined MTD techniques applied to large-scale cloud models. Second, we focus on a specific context based on a cloud model for e-health applications to evaluate the effectiveness of the MTD techniques using security and economic metrics. We introduce (1) a strategy to effectively deploy the Shuffle MTD technique using a virtual machine placement technique, and (2) two strategies to deploy the Diversity MTD technique through operating system diversification. As deploying the Diversity technique incurs costs, we formulate the optimal diversity assignment problem (O-DAP), and solve it as a binary linear programming model to obtain the assignment that maximizes the expected net benefit.

The carbon footprint of Italian schools meals: An optimal choice of dishes in vegan, vegetarian, and omnivorous menus.

This study aims to assess the carbon footprint associated with vegan, vegetarian, and omnivorous menus for primary school lunches in Italy. For this purpose, healthy and acceptable menus with minimal greenhouse gas emissions have been designed by a binary linear programming model. The results show that the adoption of a specific diet may help in reducing the carbon footprint of menus, but it is the optimal selection of dishes that ultimately makes the difference. Interestingly enough, the optimal choice of dishes and the restriction of meat consumption in omnivorous menus can lead up to a 40% emission reduction compared to the current school lunch menu of the municipality of Rome. Moreover, the optimal choice of dishes in vegan menus provides the menu with the lowest carbon footprint among all kinds of diets.

Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix Binary Linear Programming model

Nowadays, the buildings have an important role on high demand of electricity energy. Therefore, the energy management of the buildings may have significant influence on reducing the electricity consumption. Moreover, Electric Vehicles (EVs) have been considering as a power storage devices in Smart Buildings (SBs) aiming to reduce the cost and consuming energy. Here, an energy management framework is proposed in which by considering the flexibility of the contracted power of each apartment, an optimal charging-discharging scheduled for EVs and Battery Energy Storage System (BESS) is defined over long time period to minimize the electricity cost of the building. The proposed model is design by a Mixed Binary Linear rogramming formulation (MBLP) that the charging and discharging of EVs as well as BESS in each period is treated as binary decision variables. In order to validate the model, a case study involving three scenarios are considered. The obtained results indicate a 15% reduction in total electricity consumption cost and consumption energy by the grid over a one year. Finally, the impact of capacity and charge/discharge rate of BESS on the power cost is analyzed and the optimal size of the BESS for assumed SB in the case study is also reported.

Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix Binary Linear Programming model

Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix Binary Linear Programming model

Managing the Gate Assignment Problem in the Hub Airport with Satellite Halls: A Transfer Demand-Oriented Approach.

This study focuses on managing the gate assignment in the hub airport with both main terminal and satellite halls. We first formulate the gate assignment problem (GAP) as a binary linear programming model with multi-objective functions, where the practical constraints, e.g., gate time conflict and gate compatibility, are considered. Then, we incorporate the impact of gate assignment on transfer passengers and formulate the transfer demand-oriented gate assignment problem (TGAP) as a nonlinear model. A linearization approach and a heuristic approach are designed to solve the TGAP model. A case study is conducted based on the practical data of the Shanghai Pudong International Airport, where a comparison between the results of GAP and TGAP by the two proposed approaches is demonstrated. It shows that the proposed TGAP model and solution approaches can not only enhance the service for transfer passengers but also improve the gate utilization efficiency in the hub airport.

Subsea field layout optimization (part II)–the location-allocation problem of manifolds

The location-allocation problem of manifolds, which is a part of subsea field layout optimization, directly affects the flowline cost. This problem has always been studied as a mixed-integer nonlinear programming (MINLP) problem, or an integer linear programming (ILP) problem when there are location options for the facilities. Making a MINLP model is surely convenient to interpret the optimization problem. However, finding the global optimum of the MINLP model is very hard. Hence, practically, engineers use approximation algorithms to search a good local optimum or give several good location options based on their experience and knowledge to reduce the MINLP model into an ILP model. Nevertheless, the global optimum of the original MINLP model is no longer guaranteed. In this study, enlightened by the graphic theories, we propose a new method in which we reduce the MINLP model into an ILP model---more precisely, a binary linear programming (BLP) model---without compromise of achieving global optimum, but also with extremely high efficiency. The breakthrough in both efficiency and accuracy of our method for the location-allocation problem of manifolds and wellheads is well demonstrated in various cases with comparison to the published methods and the commercial MINLP solver from LINDO. Besides, we also provide our results for larger-scale problems which were considered infeasible for the commercial MINLP solver. More generally, our method can be regarded as a specific MINLP/NIP (nonlinear integer programming) solver which can be used for many other applications. This work is the second of a series of papers which systematically introduce an efficient method for subsea field layout optimization to minimize the development cost.

Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance

In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, demonstrating their practicality by applying them to partition signed networks of collaboration and opposition in the US House of Representatives. These models guarantee a globally optimal network partition and can be practically applied to signed networks containing up to 30,000 edges. In the US House context, we find that a three-cluster partition is better than a conventional two-cluster partition, where the otherwise hidden third coalition is composed of highly effective legislators who are ideologically aligned with the majority party.

Reduction of energy consumption and delay of control packets in Software-Defined Networking

The energy consumption of network devices is a significant part of the network's energy costs. Recently, Software-Defined Networking (SDN) has been suggested as a suitable and stable solution to decrease energy consumption. The main concern is how to manage resources for optimum energy consumption in SDN. Hence, this paper proposes a binary linear programming model. This model manages (on or off devices) switches, controllers, and SDN links based on the number of flows at the network edge, the processing capability of the controllers, the distance between switches and controllers, and the traffic. Therefore, with this model, the lowest energy consumption, and the lowest delay of control packets is achieved. The results show that this model, on average, has saved 40% of energy consumption, and it has also had up to 80% decrement when there is less network traffic. In addition, this model has decreased 68% of the delay of controlling packets with 70% decrement in less traffic.

A Linear Programming Relaxation DEA Model for Selecting a Single Efficient Unit with Variable RTS Technology

The selection-based problem is a type of decision-making issue which involves opting for a single option among a set of available alternatives. In order to address the selection-based problem in data envelopment analysis (DEA), various integrated mixed binary linear programming (MBLP) models have been developed. Recently, an MBLP model has been proposed to select a unit in DEA with variable returns-to-scale technology. This paper suggests utilizing the linear programming relaxation model rather than the MBLP model. The MBLP model is proved here to be equivalent to its linear programming relaxation problem. To the best of the authors’ knowledge, this is the first linear programming model suggested for selecting a single efficient unit in DEA under the VRS (Variable Returns to Scale) assumption. Two theorems and a numerical example are provided to validate the proposed LP model from both theoretical and practical perspectives.

Bir İhtisas Hastanesinde Ameliyathane Programının Optimizasyonu: Bir Vaka Uygulaması

The operations management in health services has recently become extremely important since hospitals’ main objective is to provide high-quality health services to patients while reducing their costs and improving their financial assets for their survival and growth. In this respect, operating rooms are of great interest to the hospitals since they are the hospital’s largest cost and revenue center. However, despite their criticality in health services, they are generally bottleneck resources in hospitals. Therefore, increasing the efficiency of operating rooms results in improved patient satisfaction and reduction in the cost of surgical operations. At this stage, operating room scheduling is the key to accomplish these two conflicting goals at a reasonable level simultaneously. It includes the timing of each operation as well as the assignment of surgical resources to each operation such as operating room and surgical teams over a few days or a week. Operating room surgical schedules often present logistical difficulties in terms of assigning doctors to specific operating rooms. Due to a wide variety of factors such as room availability, working hours in a week, doctor preferences, and operating room capabilities, surgical scheduling can prove to be challenging. In many hospital administrators manually modify the assignments on a case-by-case basis, which makes it difficult and time-consuming when dealing with surgical schedules. In this paper, we try to implement a linear programming procedure, which transforms the operating room schedule into a working schedule that dynamically changes weekly; and can be programmed to incorporate different scenarios within the hospital-based on specific hospital parameters. With the application of a binary linear programming model on illustrative problems by using Excel Solver Add-in, we demonstrate the advantages of using such an approach in optimizing static and dynamic surgical assignments weekly in order to meet a specific goal.

Simultaneous node and link districting in transportation networks: Model, algorithms and railway application

Partitioning a large network into multiple subnetworks is adopted extensively in various practical applications involving the architecture of distributed management. In this study, we consider strategic simultaneous node and link districting to partition the nodes and links of a given transportation network into a fixed number of mutually disjoint districts, based on the districting criteria of integrity, contiguity, balance, and independence. We first formulate the resulting problem as a mixed integer linear programming model to minimize the weighted sum of the total size deviation of districts and total cooperation between districts. An improved network flow-based technique is proposed to incorporate the complicated contiguity criterion by using a polynomial number of constraints. Valid inequalities, which break the symmetry within and between districts, are designed to strengthen the model. To solve this challenge problem, we reformulate it as a binary linear programming model, and develop a column generation-based algorithm to find tight lower bounds and good-quality solutions. Then, an iterative search algorithm is designed to obtain good-quality solutions rapidly. Furthermore, a more efficient hybrid algorithm is proposed by using the results of the iterative search algorithm to initialize the column generation-based algorithm. We assess the proposed model and algorithms by using various scales of instances derived from the train dispatcher desk districting problem, which is a practical application of the investigated problem in the context of railway. The computational results reveal that our approaches outperform existing approaches and a commercial solver, and our best algorithm can solve almost all the investigated instances to optimality within a considerably short average computation time. The districting solutions of our approaches are also better than the empirical solutions designed by railway managers mainly based on experience.