Multi-objective optimization for through train service integrating train operation plan and type selection

This study focuses on a real-world case that is defined as an extension of the container loading problem (CLP) with weakly heterogeneous types of boxes. A company producing plastic cups wants to increase its efficiency in container loading operations. To solve the problem, a hybrid approach including a static rule to rank orders and integer nonlinear programming (INLP) models is developed. INLP models form box sizes according to the properties of the cups, and determine the position of the boxes in layers while simultaneously minimizing the layer-depth. The presented approach is tested on real-world cases and benchmark data sets. The results show that simultaneously forming boxes and solving the CLP increases the effectiveness of container loading operations.

A traditional hybrid operational mode of a metro system usually combines the express and local trains. The stopping patterns of express trains are generally assumed to be the same. To provide a diverse service, this paper aims to explore the train timetable optimization method when the express trains have different stopping patterns. First, all the trains are categorized into five cases based on the type of train and the order of departure. A passenger-oriented timetabling model for reducing passenger waiting time is established, which can be solved by the Interior Point Method. Numerical examples from Guangzhou Metro Network are conducted with six scenarios, symbolizing different operation strategies. The proposed model was validated through numerical experiments conducted on a Nanjing metro line. The results indicate that the proposed model can efficiently reschedule the timetable in express/local mode with multiple stopping patterns and reduce the passenger waiting time, the most considerable reduction of which comes up to 16.8%, along with a reduction in their extra waiting time of up to 17.3%.

Optimal Design of Heat Exchanger Networks by Using SQP Algorithm Based on GPU Acceleration

In general, it is difficult to directly solve an optimization problem of systems reliability formulated as a nonlinear integer programming (NIP) model. In this paper, we propose a method for solving the NIP problem for the best compromise solution while holding a nonlinear property by using the genetic algorithm. We also report that the optimization problem of systems reliability as a case study is solved using the proposed method. The numerical comparison experiments between the 0-1 LP/0-1 NP formulations are demonstrated and, from the quantitative evaluation, the efficiency of the proposed method is demonstrated. >

The contradiction between transport capacity and passenger demand in urban rail transit is usually prominent during peak hours in some megacities of China, and some passenger flow control measures have been adopted to alleviate passenger congestion. To better save passengers’ travel time when taking passenger flow control measures, this paper proposes an integrated optimization method of bus route adjustment with network-level passenger flow control for urban rail transit, in which the controlled passengers can freely choose to shift to bus or to retain in urban rail transit for pursuing a lower travel cost. With the objectives of minimizing average additional travel time for all affected passengers and maximizing the operating revenue of urban rail transit, an integer non-linear programming model is formulated to determine the inbound passenger volumes and bus adjustment schemes. To solve this proposed model effectively, a multi-objective particle swarm optimization based on dual-population co-evolution is designed. Finally, three sets of numerical experiments, including an integrated optimization experiment and two independent optimization experiments of passenger flow control, are implemented to demonstrate the feasibility and benefits of the proposed method.

This paper investigates an integrated production scheduling and vehicle routing problem with soft time windows and fuzzy travel times, where orders are grouped into batches for production and delivered by a limited number of multi-trip heterogeneous vehicles. A bi-objective mixed integer nonlinear programming (MINLP) model is established, which takes total cost and total early and tardy weighted penalty time as optimization objectives. First, a fuzzy superposition operation is proposed to obtain the fuzzy weighted penalty time, and it is extended to a generalized fuzzy operation law in fuzzy sets and systems. Then, we propose a knowledge-driven co-evolutionary algorithm (KDCEA) to solve this problem. The algorithm fuses a dual-subpopulation co-evolution based on different update strategies and a knowledge-driven strategy based on dynamic knowledge sets and problem-specific knowledge. Finally, the correctness of the MINLP model is verified by the CPLEX solver using the ϵ-constraint method. A computational experiment is conducted based on different scale instances and a real-world case, and the results show the superiority of KDCEA in solving this problem.

Design of multimodal hub-and-spoke transportation network for emergency relief under COVID-19 pandemic: A meta-heuristic approach

To solve the problem of long logistics delivery time in supply chain, a Mixed Integer Non-linear Program (MINLP) model is built by using Mixed Integer nonlinear programming theory. Firstly, the General algebraic modeling system (GAMS) is used to build the model to fully integrate each parameter of logistics transportation, the total distribution time of the supply chain network, the coverage radius of the logistics base, the number of users, the total capacity of the logistics base, the mode of railway and road transportation, the nonlinear programming model is built and solved by DICOPT solver in GAMS. The cost of logistics can be decreased, transportation time can be reduced, and the logistics system's operating efficiency can be increased in the long term with the help of this algorithm. The proper operation of the logistics system is critical in encouraging the supply chain circulation of various industries and has a direct impact on the society's economic development. The optimal logistics distribution plan with 5 logistics bases covered users of 18 and railway capacity of 2. With the same railway capacity and the same total budget, the larger the number of covered users, the greater the total distribution time increases, but the larger the total budget, the growth of the total distribution time slows down significantly. Experiments show that MINLP model can solve the problem of logistics-based layout optimization in nonlinear logistics management.

A computational comparison of cargo prioritization and terminal allocation problem models

Network function virtualization (NFV) brings the potential to provide the flexible implementation of network functions and reduce overall hardware cost by running service function chains (SFCs) on commercial off-the-shelf servers with many-core processors. Towards this direction, both academia and industry have spent vast amounts of effort to address the optimal placement challenges of NFV middleboxes. Most of the servers usually are equipped with Intel X86 processors, which adopt Non-Uniform Memory Access (NUMA) architecture. However, existing solutions for placing SFCs in one server either ignore the impact of hardware architecture or overlook the dependency between middleboxes. Our empirical analysis shows that the placement of virtual network functions (VNFs) with interdependency in a server needs more particular consideration. In this paper, we first manage the optimal placement of VNFs by jointly considering the discrepancy of cores in different NUMA nodes and interdependency between network functions (NFs), and formulate the optimization problem as a Non-Linear Integer Programming (NLIP) model. Then we find a reasonable metric to describe the dependency relation formally. Finally, we propose a heuristic-based backtracking placement algorithm (BPA) to find the near-optimal placement solution. The evaluation shows that, compared with two state-of-art placement strategies, our algorithm can improve the aggregate performance by an average of 20% or 45% within an acceptable time range.

Network Function Virtualization (NFV) offers service delivery flexibility and reduces overall costs by running service function chains (SFCs) on commodity servers with many cores. Existing solutions for placing SFCs in one server treat all CPU cores as equal and allocate isolated CPU cores to network functions (NFs). However, advanced servers often adopt Non-Uniform Memory Access (NUMA) architecture to improve the scalability of many-core systems. CPU cores are grouped into nodes, incurring performance degradation due to cross-node memory access and intra-node resource contention. Our evaluation shows that randomly selecting cores to place NFs in an SFC could suffer from 39.2 percent lower throughput comparing to an optimal placement solution. In this article, we propose Octans, an NFV orchestrator to achieve maximum aggregate throughput of all SFCs in many-core systems. Octans first formulates the optimization problem as a Non-Linear Integer Programming (NLIP) Model. Then we identify the key factor for problem solving as evaluating the throughput drop of an NF caused by other NFs in the same SFC or different SFCs, i.e., performance drop index, and propose a formal and accurate prediction model based on system level performance metrics. Finally, we propose two online algorithms to quickly find near-optimal placement solutions for one-time and incremental deployment. Extensive evaluation on a prototype implementation shows that Octans significantly improves the aggregate throughput comparing to two state-of-the-art placement solutions by 27.1 ~ 45.2 percent for one-time deployment and by 20.9 ~ 38.1 percent for incremental deployment, with very low prediction errors. Moreover, Octans could quickly find a near-optimal placement solution with tiny optimality gap.

Preventive maintenance (PM) is a maintenance program with activities created at a determined interval or according to certain principles, designed to reduce the likelihood of failure or deterioration of item performance. This aims to improve overall reliability and system availability. In this research, a preventive maintenance schedule (PMS) was designed for electricity and desalination of water in power plants, subject to meeting relevant constraints. The proposed methodology is used to generate a PMS for the boilers, turbines, and distillers. A nonlinear integer programming (NLIP) model was employed to address this problem. The results of the proposed method were compared with the PMS for a power station in Kuwait. The results were better in terms of the volume of production and in terms of the gap between the available production and demand in order to continue providing consumers with electricity and water without a shortage in the event of a breakdown in equipment. It produces an improvement of 12.12% and 16.58% respectively, for water and electricity. Furthermore, the sensitivity and robustness of the proposed method were analysed by increasing the maintenance duration for some equipment, increasing the demand, and adding various additional conditions. In addition, a comparison of additional conditions with a binary problem method in terms of computer time for the search for an optimal solution was carried out, where the model provided an optimal solution in a reasonable time. Among the most important benefits that the user can obtain for this technique are extending the life of the equipment, increasing efficiency, and reducing expenses.

Optimizing communication network geodiversity for disaster resilience through shielding approach

Flexible train capacity allocation for an overcrowded metro line: A new passenger flow control approach

In this work, a new multi-objective mixed integer nonlinear programming (MINLP) model based on Pinch Technology principles is presented. The proposed framework is able to simultaneously maximize the heat recovery, minimize the total heat exchange surface while guaranteeing the heat transfer feasibility. The formulation adopted for the heat integration problem significantly reduces the number of binary variables when dealing with variable temperatures and flowrates, thus enhancing the solution efficiency. The model is then augmented with the introduction of a novel approach for the total area estimation not requiring heavy disjunctions nor complex tailor-made algorithms. A study on the optimization of an air separation unit (ASU) is finally performed to show the potential and effectiveness of the proposed framework.