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When a large-scale disaster occurs, each evacuee should move to an appropriate refuge in a speedy and safe manner. Most of the existing studies on the refuge assignment consider the speediness of evacuation and refuge capacity while the safety of evacuation is not taken into account. In this paper, we propose a refuge assignment scheme that considers both the speediness and safety of evacuation under the refuge capacity constraint. We first formulate the refuge assignment problem as a two-step integer linear program (ILP). Since the two-step ILP requires route candidates between evacuees and their possible refuges, we further propose a speedy and reliable route selection scheme as an extension of the existing route selection scheme. Through numerical results using the actual data of Arako district of Nagoya city in Japan, we show that the proposed scheme can improve the average route reliability among evacuees by 13.6% while suppressing the increase of the average route length among evacuees by 7.3%, compared with the distance-based route selection and refuge assignment. In addition, we also reveal that the current refuge capacity is not enough to support speedy and reliable evacuation for the residents.

The optimal PMU placement problem is placing the minimum number of PMUs in the network to ensure complete network observability. It is an NP-complete optimization problem. PMU placement based on cost and critical nodes is solved separately in the literature. This paper proposes a novel approach, a degree of centrality in the objective function, to combine the effect of both strategies to place PMUs in the power network optimally. The contingency analysis and the effect of zero-injection buses are solved to ensure the reliability of network monitoring and attain a minimum number of PMUs. Integer linear programming is used on the IEEE 7-bus, IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, and IEEE 118-bus systems to solve this problem. The results are evaluated based on two performance measures: the bus observability index (BOI) and the sum of redundancy index (SORI). On comparison, it is found that the proposed methodology has significantly improved results, i.e., a reduced number of PMUs and increased network overall observability (SORI). This methodology is more practical for implementation as it focuses on critical nodes. Along with improvement in the results, the limitations of existing indices are also discussed for future work.

Phasor measurement units (PMUs) provide synchronized measurements to enhance power system monitoring, strategically placed to achieve full network observability with minimal cost. In this paper, the PMU placement problem for critical buses is addressed using integer linear programming, taking into account both PMU contingencies and the impact of zero-injection buses. The primary contribution is the development of a multi-stage approach to place PMUs on critical buses. Moreover, it is demonstrated that considering PMU contingencies inherently accounts for line contingencies. Furthermore, a new performance metric, the Bus Coverage Index (BCI), is proposed to evaluate the effectiveness of the placement strategy. This index overcomes the limitations of existing indices, such as the Sum of Redundancy Index (SORI) and Bus Observability Index (BOI). The results are tested on various IEEE benchmark systems under four different cases, showing significantly improved results in terms of network observability and minimized number of PMUs. In Case 1, SORI values improved significantly for the IEEE 7 and IEEE 118 bus systems, while in Case 2, enhancements were observed in the IEEE 30 and IEEE 118 systems. Case 3 demonstrated consistency in results across systems. Notably, in Case 4, the number of required PMUs was reduced in the IEEE 30, IEEE 57, IEEE 118, and New England 39 bus systems, with complete network observability.

The Internet Shopping Optimization Problem with multiple item Units (ISHOP-U) is a recently proven NP-Hard variant of the classical ISHOP, which considers buying more than one unit of the same product. In this work, we propose a new set of instances where the prices of the products follow a Gaussian distribution, which is more realistic in a competitive market than the original instances with random uniform prices. We compute the optimal values of the previous uniform and new Gaussian instances using an Integer Programming formulation in CPLEX. In addition, we also propose two new low-complexity heuristics, the first not metaheuristics approaches proposed for the ISHOP-U, which use a linear representation instead of the original matrix candidate solution, achieving better results than the previous Evolutionary Algorithms for the ISHOP-U from the state-of-the-art.

In this work, we present a resource allocation scheme for managing trade-offs between total throughput maximisation and system fairness in a non-orthogonal multiple access (NOMA) system for 5G networks. Our proposed approach is designed to improve throughput and fairness as performance metrics of NOMA in 5G networks. We apply integer linear programming for user pairing and adopt particle swarm optimisation as the power allocation scheme for reducing resource allocation complexity. To formulate the multi-objective problem, we use scalarisation of multi-objective optimisation, which exhibits flexibility in assigning different weights to a single objective—in the case of this study, either sum rate or fairness. Moreover, the problem is formulated with a penalty function to prevent optimisation violating the constraints of the optimisation function. Simulation results show that the proposed model outperformed the conventional approach by at least 17% in terms of throughput maximisation and fairness rate.

In the field of the automotive area as well as industrial control, real-time communication requires deterministic delivery with low delay and bounded jitter. Real-time communication in these networks requires transmission schedule and routing, which is an NP-hard problem. In this paper, we present an offline routing and scheduling method based on integer linear programming (ILP), with a flow preprocessing step to explore the period correlation of time-triggered (TT) traffic in time-sensitive networking (TSN). First, a multiperiod flow routing and scheduling algorithm based on flow classification is proposed to improve the scheduling success rate and reduce execution time. The flow classification technique obtained a more fine-grained TT traffic classification, which can be superimposed on any routing and scheduling algorithms. Second, an adaptive period compensation scheduling algorithm based on flow classification is proposed in simple network architecture conditions. The evaluations demonstrate that the proposed algorithms improve scheduling success rate and reduce execution time compared with baseline methods in all test cases. In addition, we can adapt our different proposed algorithms in different network architecture conditions to schedule various flows with different periods and sizes.

This study presents a novel, two-stage algorithm that minimizes the number of machines and operators required to produce multiple product types repeatedly in cyclic scheduling. Our algorithm treats the problem of minimum machines as a bin packing problem (BPP), and the problem of determining the number of operators required is also modeled as the BPP, but with constraints. The BPP is NP-hard, but with suitable heuristic algorithms, the proposed model allocates multiple product types to machines and multiple machines to operators without overlapping setup times (machine interference). The production schedule on each machine is represented as a circle (donut). By using lower bounds, it is possible to assess whether the number of machines required by our model is optimal; if not, the optimality gap can be quantified. The algorithm has been validated using real-world data from an industrial facility producing 17 types of products. The results of our algorithm led to significant cost savings and improved scheduling performance. The outcomes demonstrate the effectiveness of the proposed algorithm in optimizing resource utilization by reducing the number of machines and operators required. Although this study focuses on a manufacturing system, the model can also be applied to other contexts.

Excess commuting, defined as the inefficiency resulting from spatial mismatches between residential and employment locations, poses significant challenges for urban planning and transportation systems. This study uses big data from individual vehicle trips collected in Tampa, Florida, to quantify excess commuting more accurately than traditional zonal approaches. Through the application of Linear Programming (LP) and Integer Linear Programming (ILP) models, this research measures minimum and actual commuting patterns across different spatial scales—census tract, block group, and individual trip levels. The findings reveal a clear scale effect associated with the Modifiable Areal Unit Problem (MAUP), as smaller spatial units consistently yield shorter minimum commuting distances and times and the ILP model at the individual trip level yields the least. By directly analyzing actual trips rather than simulated data, this approach provides a more precise and realistic assessment of excess commuting. The results underscore the values of methodological improvements and individual-level data in refining our understanding of excess commuting and supporting more efficient urban planning and policymaking.

This paper proposes a fast optimization method for synthesizing thinned planar antenna arrays. A 0-1 integer linear programming (ILP) model was proposed for the antenna array optimization. This model mainly aims to minimize the peak sidelobe level (PSLL) and consider the design requirements of narrow beamwidth and high directivity, finally obtaining the optimal distribution of the turned “ON” element positions in the aperture. Several cases of planar array designs with different aperture sizes and scan angles were provided in the paper and compared with other popular algorithms. Numerical results showed that the new method can effectively optimize the thinned planar arrays, including large-scale arrays, while significantly reducing the computational cost and time.

In 2016, Saudi Arabia published its vision for the year 2030, which is based on the Kingdom’s geographical, financial, social and religious potentials. Developing the logistics sector and improving the Kingdom’s ranking in the World Bank’s Logistics Performance Index (LPI) to the twenty-fifth rank was one of the most targeted success factors. Unfortunately, Saudi Arabia’s rank in this index has declined over the past years, until it reached the fifty-fifth rank as per the last published LPI report. This research proposes a set of logistics hubs (LHs) which are located at key multi-logistics areas within the regional trading zones of the country. A spatial model was implemented on a macro level to integrate multi-logistical, infrastructural and natural geographic information system (GIS) layers, and highlight their intersections as initial feasible areas. Subsequently, an optimization model based on integer linear programming (ILP) was used to maximize the number of allocated LHs and minimize the overall distances between allocated LHs and international trading nodes considering multiple factors. More than 80 selected subject matter experts (SMEs) from 9 different countries have participated in World Bank’s driven surveys that assess the contribution of the LHs’ allocation proposal on Saudi Arabia’s LPI ranking. An improvement of about 10% in the LPI overall score with a 20 rank promotion has been estimated as per the SMEs responses. These results demonstrate that investing in logistics infrastructure and ranking in LPI are perfectly, positively and highly related.