Multi-objective Integer Linear Programming Research Articles

Exact Zoning Optimization Model for Marine Spatial Planning (MSP)

Marine spatial planning (MSP) has recently attracted more attention as an efficient decision support tool. MSP is a strategic and long-term process gathering multiple competing users of the ocean with the objective to simplify decisions regarding the sustainable use of marine resources. One of the challenges in MSP is to determine an optimal zone to locate a new activity while taking into account the locations of the other existing activities. Most approaches to spatial zoning are formulated as non-linear optimization models involving multiple objectives, which are usually solved using stochastic search algorithms, leading to sub-optimal solutions. In this paper, we propose to model the problem as a Multi-Objective Integer Linear Program. The model is developed for raster data and it aims at maximizing the interest of the area of the zone dedicated to the new activity while maximizing its spatial compactness. We study two resolution methods: first, a weighted-sum of the two objectives, and second, an interactive approach based on an improved augmented version of the ϵ-constraint method, AUGMECON2. To validate and study the model, we perform experiments on artificially generated data. Our experimental study shows that AUGMECON2 represents the most promising approach in terms of relevance and diversity of the solutions, compactness, and computation time.

Efficient solutions to the placement and chaining problem of User Plane Functions in 5G networks

This study attempts to solve the placement and chaining problem of 5G User Plane Functions (UPFs) in a Multi-access Edge Computing (MEC) ecosystem. The problem is formalized as a multi-objective Integer Linear Programming (ILP) model targeted at optimizing provisioning costs and quality of service. Our model takes into account several aspects of the system such as UPF-specific considerations, the Service Function Chain (SFC) requests topology (single and multiple branches), Virtual Network Function (VNF) order constraints, service demands, and physical network capacities. Since the formulated problem is NP-hard, two heuristic solutions are devised to enhance solution efficiency. Specifically, an algorithm called Priority and Cautious-UPF Placement and Chaining (PC-UPC) and a simulated annealing (SA) meta-heuristic are proposed. Through extensive simulation experiments, we evaluated the performance of the proposed solutions. The results revealed that our solutions outperformed the baselines (i.e., two greedy-based heuristics and a variant of the classical SA) and that we had obtained nearly optimal solutions with significant reductions in running time. Moreover, the PC-UPC algorithm can effectively avoid SFC rejections and improve provisioning costs by considering session requirements, current network conditions, and the effects of VNF mapping decisions. Additionally, the proposed SA approach incorporates several mechanisms (e.g., variable Markov chain length and restart–stop) that allow the improvement of not only the quality of the solutions but also their computation time.

Exact solution approaches for integer linear generalized maximum multiplicative programs through the lens of multi-objective optimization

We study a class of single-objective non-linear optimization problems, the so-called Integer Linear Generalized Maximum Multiplicative Programs (IL-GMMP). This class of optimization problems has a significant number of applications in different fields of study including but not limited to game theory, systems reliability, and conservation planning. An IL-GMMP can be reformulated as a mixed integer Second-Order Cone Program (SOCP) and therefore, can be solved effectively by commercial solvers such as IBM ILOG CPLEX, Gurobi, and FICO Xpress. In this study, we show that IL-GMMPs can be viewed as special cases of the problem of optimization over the efficient (or Pareto-optimal) set in multi-objective integer linear programming. Based on this observation, we develop three exact solution approaches with a desirable property: they only solve a finite number of single-objective integer linear programs to compute an optimal solution of an IL-GMMP (which is nonlinear). Through an extensive computational study with 57600 experiments, we compare the performance of all three algorithms using the three main commercial single-objective integer linear programming solvers in the market: CPLEX, Gurobi, and Xpress. We also compare the performance of our algorithms using the mixed integer SOCP solvers of CPLEX, Gurobi, and Xpress. The results show that the choice of a commercial solver impacts the solution time dramatically and that, by the right choice of solver, one of our proposed algorithms is significantly faster than other methods. We also illustrate that although it is possible to linearize IL-GMMPs, commercial solvers struggle to solve such linearized instances.

Presentation and implementation multi-objective mathematical models to balance the assembly line

The use of assembly lines is one of the important approaches in mass production of industrial products. Imbalance of assembly lines increases cycle time and idle times, resulting in reduced production rates, line efficiency, and increased system costs, which ultimately lead to low productivity. A hybrid model assembly line is a type of production line on which various models of products are assembled. These assembly lines are increasingly accepted in the industry in order to overcome the diversity of customer demand. The hybrid model assembly line is able to respond quickly to sudden changes in demand for different models of a product without maintaining a large inventory.The purpose of this paper is to present a multi-objective integer linear mathematical programming model for balancing assembly lines, which is solved using the general criteria method. The three objective functions considered in this model are: (1) Minimizing cycle time (2) Minimize the idle time of each station and (3) increase the efficiency of the assembly line. In order to investigate the model, Iran-Shargh Neishabour Company has been considered as a case study. After implementing the proposed model of the paper, the results show the optimal performance of the proposed model and the studied parameters in line balancing have been significantly improved.

Multi-objective optimization for VM placement in homogeneous and heterogeneous cloud service provider data centers

We address the virtual machine placement problem that arises in Cloud Service Providers data centers. We purpose, a Multi-Objective Integer Linear Programming model which aims at optimizing simultaneously the number of hosted virtual machines, the resource wastage and the number of active physical machines (PM) in order to minimize power consumption. This new combination of objectives enables to maximize the client satisfaction rate with minimizing the data center (DC) operational costs. We modelize this problem with a multi-objective integer linear program and solve it through two different methods. The first method computes a unique solution for a given preference order over the objectives whereas the second computes a set of non-dominated solutions. Both methods are compared through extensive simulation scenarios. We consider two DC architectures: homogeneous DCs (i.e., a DC with PMs having the same amount of resources) and heterogeneous DCs. We study the impact of each DC configuration on the performances of the solutions. We show that the second method leads to solutions with a reduction of up to 30% over the number of used PMs and that the heterogeneous DCs outperforms the homogeneous one across all objectives.

Online Virtual Machine Evacuation for Disaster Resilience in Inter-Data Center Networks

With the risk of natural disaster occurrence rising globally, the interest in innovative disaster resilience techniques is greatly increasing. In particular, Data Center (DC) operators are investigating techniques to avoid data-loss and service downtime in case of disaster occurrence. In cloud DC networks, DCs host Virtual Machines (VM) that support cloud services. A VM can be migrated, i.e., transferred, across DCs without service disruption, using a technique known as “online VM migration”. In this article, we investigate how to schedule online VMs migrations in an alerted disaster scenario (i.e., for those disasters, such as tsunami and hurricanes, that grant an alert time to DC operators) where VMs are migrated from a risky DC, i.e., a DC at risk to be affected by a disaster, to a DC in safe locations, within a deadline set by the alert time of the incoming disaster. We propose a multi-objective Integer Linear Programming (ILP) model and heuristic algorithms for efficient online VMs migration to maximize number of VMs migrated, minimize service downtime and minimize network resource occupation. The proposed approaches perform scheduling, destination DC selection and assign route and bandwidth to VM migrations. Compared to baseline approaches, our proposed algorithms eliminate service downtime in exchange of an acceptable additional network resource occupation. Results also give insights on how to calculate the minimum amount of time required to evacuate all VMs with no service downtime. Moreover, since the proposed approaches exhibit different execution times, we design an ‘alert-aware VM evacuation’ tool to intelligently select the most suitable approach based on the number and size of VMs, alert time and available network capacity.

A quantitative framework for Industry 4.0 enabled Circular Economy

Sustainability and digital transformation represent two of the main trends of the last decade. More in detail, Industry 4.0 (I4.0) and Circular Economy (CE) are two key concepts that are characterizing the present and that will shape the future. From the extant literature review emerges that, although academics identify a possible synergy between these topics, a concrete integrated framework able to achieve CE at systemic level is still missing. The objective of this research is to put in relation the I4.0 and CE paradigms to understand the link between the two topics and envisage a new framework focused on circularity among supply chains. Therefore, a systematic literature review on CE, I4.0 and on their mutual relationship have been conducted to investigate which are the synergies and how the new I4.0 technologies support the implementation of CE at supply chains network level. Hence, a framework in which I4.0 technologies support the adoption of Reduce, Redesign, Recycling and Remanufacturing strategies has been developed. Indeed, starting from the 6Rs’ model (Reduce, Reuse, Recycle, Recover, Redesign, Remanufacture), the focus has been shifted towards these 4Rs since they have been considered the most affected by I4.0. Regarding the use of I4.0 technologies, Cloud Manufacturing (MaaS) and Additive Manufacturing have been deemed the most enabling technologies. Indeed, they allow to respectively aggregate resources and reduce wastes. The framework is supported by a quantitative analysis of economic and environmental impacts that results in the definition of a Multi-Objective Integer Linear Programming (MOILP). The framework works in closing the loop by acting as strategic node able to link different supply chains. It has been developed focusing on discrete manufacturing and replicable in different sectors. Finally, the model identified has been supported by some pilot assessments through semi-structured interviews.

Energy and Cost Footprint Reduction for 5G and Beyond With Flexible Radio Access Network

This paper focuses on Beyond fifth generation (B5G) non-linear data modeling and decision-making tools to optimize cost reduction versus coverage-QoS trade-off, in other words, the number of active Remote Radio Heads or Units (RRHs) needed according to traffic demands. The cost and energy optimization are analytically expressed by modeling the complex relationships between input and output system parameters using realistic scenarios and traffic profiles for low, medium, and high traffic environments. The optimization tool is based on a multi-objective integer linear programming model, designed to reduce the network cost while maintaining a good coverage-QoS and accounting for capacity constraints, User Equipments (UEs), and different slices. Results at 3.6 and 28 GHz are presented by analyzing and comparing several Cloud Radio Access Network (C-RAN) split options in a heterogeneous deployment with Macro-RRHs (MRRHs) and Small-RRHs (SRRHs). Cost reductions ranging from 30 % to 70 % have been obtained depending on the scenario. This proposal allows mobile network operators (MNOs) to achieve further optimization, while providing better network diagnostics.

AI-enabled mobile multimedia service instance placement scheme in mobile edge computing

Leveraging cloud infrastructure to the mobile edge computing helps the mobile users to get real time multimedia services in Fifth Generation (5G) network system. To ensure higher Quality-of-Experience (QoE), faster migration of mobile multimedia service instances is required to cope up with user mobility. By deploying the mobile multimedia service instances proactively in multiple edge nodes (ENs) helps the users to get higher QoE. However, excessive deployment of service replicas might increase the cost of the overall network. To establish trade-off between these two conflicting objectives, we have formulated the problem as a Multi-objective Integer Linear Programming (MILP) by integrating the users’ path prediction model. This problem is proven to be an NP-hard one for large networks, thus we develop an artificial intelligence (AI) based meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to achieve near-optimal solution within polynomial time. The performance analysis results show the significant performance improvement in terms of QoE and user satisfaction as compared to other state-of-the-art works.

A New 2-Phase Optimization-Based Guaranteed Connected Target Coverage for Wireless Sensor Networks

Relying on a limited power source, WSNs present one of their most challenging concerns as energy consumption. In addition, coverage and connectivity are important quality of service metrics in the networks. In this paper, the problem of connected target coverage (CTC) with an optimistic view of energy usage is investigated. A new 2-phase optimization method is proposed that provides full target coverage and connectivity with the user throughout network lifetime. In the first phase of the algorithm, sensors are organized into maximum achievable disjoint cover sets (CSs) using a new multi-objective integer linear programming (ILP) model. Set of remaining nodes that could not be formed into an independent CS, are allocated to the existing CSs as a subset of potential cluster heads (CHs) i.e., for every active CS, CHs are chosen from its potential CH subset. In the second phase, the algorithm activates CSs one after another to gather information from targets and forward them to the user in a hierarchical manner via a modified multi-objective mixed integer linear programming (MILP) model. Both of the ILP and MILP models are solved using branch-and-bound method. The solutions of the optimization models are solved to optimality. The superiority of the proposed method is proven through numerous experiments in different scenarios compared with two of the most related works.

Controller Placement for Resilient Network State Synchronization in Multi-Controller SDN

In a multi-controller SDN, the network state is replicated or distributed between the controllers requiring repeated synchronization. This synchronization cost depends on controller locations, as well as needs to be low and resilient to network failures. Leveraging a Steiner tree-based inter-controller latency model, a multi-objective integer linear program is presented to deduce the controller placement optimizing (a) synchronization cost in failure-free scenarios, and, (b) resilience against single-link failures. The proposed approach incurs low computational complexity and post-failure network reconfiguration cost, while achieving near-optimal post-failure synchronization cost. We extensively evaluate the proposed model over real networks, and demonstrate its effectiveness.

Research on fault repair method of all-optical network based on SDN

All-optical network, as a new backbone network, is featured with high speed and large capacity transmission. It may be out of order due to various faults while providing high-performance transmission service, thus more effective fault repairing methods are required. A routing and wavelength assignment method based on SDN is designed and analyzed from the perspective of service function chaining in this paper. A multi-objective integer linear programming model based on impairment-aware and scheduling time is constructed by combining the unified control of control plane with the resource allocation mode of service function virtualization. Meanwhile, an improved Firefly Algorithm is adopted to solve the model for obtaining a better scheduling scheme, so as to the resources are allocated on-demand in a more flexible and efficient way, which effectively improved the self-recovery capability of the network. In the simulation experiments, Through the comparison between the method proposed and methods based on centralization and distribution, method proposed in the paper is superior to the compared ones in the indexes of survivability, blocking probability, link recovery time, and presents a better scheduling performance, makes the system has stronger ability of self-healing in the face of failure.

On Solving Fully Rough Multi-Objective Integer Linear Programming Problems

In this paper a suggested algorithm to solve fully rough multi-objectiveinteger linear programming problem [FRMOILP] is described. In orderto solve this problem and find rough value efficient solutions anddecision rough integer variables by the slice-sum method with thebranch and bound technique, we will use two methods, the first one isthe method of weights and the second is e- Constraint method. The basicidea of the computational phase of the algorithm is based onconstructing two LP problems with interval coefficients, and then to fourcrisp LPs. In addition to determining the weights and the values of e-constraint. Also, we reviewed some of the advantages and disadvantagesfor them. We used integer programming because many linearprogramming problems require that the decision variables are integers.Also, rough intervals (RIs) are very important to tackle the uncertaintyand imprecise data in decision making problems. In addition, theproposed algorithm enables us to search for the efficient solution in thelargest range of possible solutions range. Also, we obtain N suggestedsolutions and which enables the decision maker to choose the bestdecisions. Finally, two numerical examples are given to clarify theobtained results in the paper.

Towards a sustainable reconfigurable manufacturing system (SRMS): multi-objective based approaches for process plan generation problem

Nowadays to meet the continuously growing worldwide demand for capital and consumer goods while considering the associated economic, environmental, and social aspects, new generation of manufacturing systems are developed and adopted. The goal is to respond to changing market needs while minimising adverse effects on the environment. Reconfigurable manufacturing system (RMS) – due to its flexibility and characteristics – can increase the system sustainability and responsiveness to satisfy the market needs. This paper considers an environmental oriented multi-objective process plan generation problem for a sustainable reconfigurable manufacturing system (SRMS). Three criteria are minimised respectively the sustainability-metric value, the total production time and the total production cost. As a new criterion, the sustainability-metric value considers both liquid hazardous waste and greenhouse gas emissions (GHG). First, a multi-objective integer linear programming (MOILP) model is proposed. Second, due to the problem complexity, three approaches are adapted respectively a posteriori approach called augmented ε-constraint (AUGECON) and two evolutionary approaches namely, non-dominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm II (SPEA-II). Finally, due to the lack of real world data and to illustrate the applicability of the three approaches, a simple numerical example is presented and the obtained numerical results are analysed.

Generating the efficient set of MultiObjective Integer Linear plus Linear Fractional Programming Problems

The problem of optimizing a linear plus linear fractional function is an important field of search, it is a difficult problem since the linear plus linear fractional function doesn’t possess any convexity propriety. In this paper, we propose a method that generates the set of the efficient solutions of multiobjective integer linear plus linear fractional programming problem. Our method consists in Branch-and-Bound exploration combined with cutting plane technique that allows to remove from search inefficient solutions. The cutting plane technique takes into account the inefficiency of a solution in another problem that implies the inefficiency of that solution in our problem and uses this link to reduce the exploration’s domain.

Quadratic optimization over a discrete pareto set of a multi-objective linear fractional program

ABSTRACT In the present paper, an exact algorithm is proposed for maximizing a quadratic function over the efficient set of a multi-objective integer linear fractional programming problem. This method uses a branch and bound search in the decision space and a cutting plane which will significantly shorten the search. A sequence of quadratic programs are solved, the feasible region is iteratively partitioned with two branching constraints or reduced using a cutting plane. We establish theoretical results which prove the effectiveness of this exact method. For illustration, a numerical example and some computational experiments are reported.

User mobility and Quality-of-Experience aware placement of Virtual Network Functions in 5G

Virtual Network Functions (VNFs) in cloud servers of Fifth Generation (5G) network systems are responsible for executing offloaded codes from mobile users. Placement of VNFs in the cloud is very complicated to get on-time execution service due to many reasons including users’ mobility and resource heterogeneity, which often cause VNF relocations from one data center to another. Minimizing service delay (i.e., maximizing user Quality-of-Experience) for the user applications and the number of VNF relocations are the two main design goals of VNF placement problem; however, they do oppose each other. In this paper, we have formulated the above problem as a Multi-objective Integer Linear Programming (MILP), which is proven to be an NP-hard one. The proposed optimization framework trades-off between the number of VNF relocations and user Quality-of-Experience. We then develop an Artificial Intelligence based meta-heuristic Ant Colony Optimization (ACO) algorithm to achieve sub-optimal placement of VNFs within polynomial time. The performance analysis results, carried out in Cloudsim, depict that the proposed system outperforms the state-of-the-art works significantly in terms of user satisfaction and VNF relocation overhead.

On solving fuzzy rough multiobjective integer linear fractional programming problem

On solving fuzzy rough multiobjective integer linear fractional programming problem

Optimal resource utilisation algorithm for visible light communication‐based vehicular ad‐hoc networks

With the advent of visible light communication (VLC) and vehicular ad-hoc networks (VANETs), the realisation of the intelligent transportation system (ITS) is becoming a reality. In high-mobility networks such as VANETs packet transmission failure may occur frequently if network lifetime, connectivity, and load balancing are not considered during next-hop link selection. Optimal resource utilisation can address the network lifetime, network connectivity, and network load balancing problems in ITS. This study investigates the problem of optimal resource allocation for VLC-based VANETs. The authors present a multi-objective integer linear programming (ILP) model that optimises resource utilisation in VLC-based VANETs, wherein the objectives are maximising network lifetime, load balancing, and network connectivity. Illustrative numerical results demonstrate that their solution achieves promising results, however, for high vehicle density, the problem is intractable. Therefore, they proposed a heuristic algorithm that addresses the problem when vehicle density is high in a network. They demonstrate the benefits of the proposed algorithm by comparing their solution against optimal resource utilisation unaware strategy, wherein resources are assigned randomly without considering resource optimisation. Their experimental study shows that both the ILP model and the heuristic algorithm increase network performance in terms of maximising network lifetime, connectivity, and load balancing.

Economic and Environmental Optimisation Framework for Carbon Capture Utilisation and Storage Supply Chain

Carbon capture utilisation and storage (CCUS) is widely recognised as a key mitigation technology that can significantly reduce carbon dioxide (CO2) emission. This study develops an optimisation framework focused on the economic and environmental aspects of Northeast China CCUS supply chain. The overall network is economically optimised over a 20 years’ time horizon to provide the geographic location and scale of capture and sequestration sites as well as the most convenient transport routes. The resulting problem is a multi-objective mixed integer linear programming (MILP) problem, whose objectives include minimising its total annual cost and minimising life cycle greenhouse gas (GHG) emission. The economic and environmental performances are optimised by e-constraint method. To demonstrate the application of the proposed model, a realistic case study from Northeast China involving 16 sources and 3 sinks is illustrated. The results provide valuable insights to adopt more sustainable strategic alternatives in the design of CCUS supply chain.