Mixed Integer Nonlinear Programming Model Research Articles

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

After suffering from a grid blackout, distributed energy resources (DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points (SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed, and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

Distribution Network Expansion Planning Approach for Large Scale Electric Vehicles Accommodation

The increased power demand incurred by electric vehicles (EVs) has imposed new challenges on the distribution network. One significant responsibility of the distribution network operator (DNO) is to design and expand the distribution network periodically to accommodate the ever-increasing demand. The previous researches mainly use relaxation techniques to enhance the problem tractability, which would involve additional binary variables and incur computation burden to the optimization problem, or even worse, may lead to an intractable problem. This paper develops a mixed-integer non-linear programming (MINLP) model considering various network constraints for the distribution network expansion planning under a high penetration level of EVs. The stochastic characteristics of demands are addressed through set of scenarios. A distributed biased min-consensus algorithm based approach is proposed to solve the MINLP model. Finally, comparison tests are conducted on three different scales of distribution networks to validate the effectiveness of the proposed approach. Simulation results demonstrate that the computation time of the proposed approach is reduced by 42.46% on average for different systems compared with that of the traditional shortest path algorithm based approach.

Location and turbine parameter selection for offshore wind power maximization

Offshore wind turbines can capture more wind than onshore because of their larger structure and location. This higher yield even fails to reduce the high installation and maintenance cost of an offshore wind farm (OWF). Appropriate turbine parameters and installation site selection may maximize the power generation which is a way to trade off these costs. Knowing the wind thrust force, air density, and power coefficient beforehand can help select an appropriate site for turbine location. Once the site is selected, the optimal value of turbine variables such as height and radius can contribute to higher power yield. In this paper, a MINLP (Mixed Integer Non-Linear Programing) model is formulated with these important variables and the optimal values of these variables are determined to maximize the annual power production ([Formula: see text]) from offshore wind farm. The estimated power production, [Formula: see text], is calculated using two methods, mathematical programing method and simulation method. Computational result indicates that mathematical programing method is time consuming but more accurate whereas the accuracy of simulation method is proportional to the number of iterations. Although the result of a simulation can be improved to some extent, it cannot be as accurate as mathematical modeling for this study. These study results have great impact on the managerial decision and long range strategic and technical planning for maximizing power generation from an offshore wind farm.

Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries. However, the optimization model is complex and difficult to build, which is a typical mixed integer nonlinear programming (MINLP) problem. Considering the large scale of the MINLP model, in order to improve the efficiency of the solution, the mixed integer linear programming - nonlinear programming (MILP-NLP) strategy is used to solve the problem. This paper uses the linear blending rules plus the blending effect correction to build the gasoline blending model, and a relaxed MILP model is constructed on this basis. The particle swarm optimization algorithm with niche technology (NPSO) is proposed to optimize the solution, and the high-precision soft-sensor method is used to calculate the deviation of gasoline attributes, the blending effect is dynamically corrected to ensure the accuracy of the blending effect and optimization results, thus forming a prediction-verification-reprediction closed-loop scheduling optimization strategy suitable for engineering applications. The optimization result of the MILP model provides a good initial point. By fixing the integer variables to the MILP-optimal value, the approximate MINLP optimal solution can be obtained through a NLP solution. The above solution strategy has been successfully applied to the actual gasoline production case of a refinery (3.5 million tons per year), and the results show that the strategy is effective and feasible. The optimization results based on the closed-loop scheduling optimization strategy have higher reliability. Compared with the standard particle swarm optimization algorithm, NPSO algorithm improves the optimization ability and efficiency to a certain extent, effectively reduces the blending cost while ensuring the convergence speed.

A novel economic scheduling of multi-product deterministic demand for co-production air separation system with liquid air energy storage

Air separation units (ASUs) integrated with liquid air energy storage (LAES) have the potential to balance grid demand and improve production profits. This study aims to enhance the demand response of a novel co-production air separation system (NCASS) by maximizing the energy arbitrage of a LAES system. We developed a mixed-integer nonlinear programming model to minimize operating costs by using a proxy model to represent the ASU production space and a black box model to represent the material and energy flow balance of LAES. The model is based on rigorous mathematical programming and, considers material and energy flow balance, analyzes the production and delivery processes and constraints for each product, and matches equipment start-up and shutdown time constraints. The case study shows that the model achieves a high level of matter and energy matching between the distillation process and the LAES process, maximizing the LAES peak-shaving capacity and improving system economics. During the 24 h schedule period, the total electricity consumption of NCASS increases by 18.94% during the valley electricity period and decreases by 20.59% during the flat and peak electricity period. The economic benefits increase by 9.51 k$, accounting for 14.29% of the total costs. This study contributes to the wider application of NCAS with LAES technology for sustainable energy management.

A MINLP model for the layout design of subsea oil gathering - transportation system in deep water oil field considering avoidance of subsea obstacles and pipe intersections

This paper focuses on the layout design of subsea oil gathering-transportation system in deep water oil field. In such a system, subsea manifolds are applied to gather and transport the produced fluids from subsea wells to floating processing terminals. A mixed integer nonlinear programming (MINLP) model is proposed, constrained by a series of operation and production requirements, aiming to minimize the total layout cost. Through the model, the pipe line network topology structure which reflects the allocations among the subsea wells, manifolds and processing terminals, the routes of pipes, as well as the size of the facilities could all be figured out. Two key contributions are made through this work. First, avoiding pipe intersections and subsea obstacles are integrated simultaneously, making the proposed model closer to practical situations. Second, a decomposition strategy based on Delaunay triangulation and gradient descent is constructed, achieving high quality initial solution and stable iteration process. The results of case studies indicate the validity, feasibility and stability of the proposed model and the solution method. Besides, the effect of manifold numbers on the layout optimization result is analyzed through the model, indicating its flexibility in the layout design analysis.

Queueing-based formulation model in a public transit network considering energy storage technology and demand charges

Battery Electric Buses (BEBs) are quickly emerging as a viable option for dramatically lowering transportation-related emissions. Finding optimal locations of charging stations for BEBs can facilitate the development of electric transit networks that provide continuous services. Using fast charging along with Energy Storage (ES) charger technologies has also garnered attention to mitigate the peak power demand. A consensus regarding two types of chargers may result in more investment in the electric transit network, which resulted in considering installing fast chargers in designing an electric transit network. While this study aims to consider two types of fast- and ES-chargers, along with demand charges and waiting times at terminal stops simultaneously, to minimize the total costs of the electric transit network. The queuing model integrated with the Mixed-Integer Non-Linear Programming (MINLP) model is proposed to locate the optimal placements of charging stations in the electric transit network. To validate the model, a benchmark Mumford0 network is applied. Results indicated that the proposed charging location model could efficiently determine optimal locations of fast- and ES-chargers across the electric transit network. Also, the results confirmed that considering ES chargers could significantly save 16.6 % of the total costs of the Mumford0 transit network and reduce the total demand charge imposed on the network by the fast charger. More interestingly, by purchasing more chargers, transit agencies can decide on an acceptable waiting time that would be comfortable for BEBs arriving at terminal stops.

Optimal Placement and Sizing of D-STATCOMs in Electrical Distribution Networks Using a Stochastic Mixed-Integer Convex Model

This paper addresses the problem regarding the optimal placement and sizing of distribution static synchronous compensators (D-STATCOMs) in electrical distribution networks via a stochastic mixed-integer convex (SMIC) model in the complex domain. The proposed model employs a convexification technique based on the relaxation of hyperbolic constraints, transforming the nonlinear mixed-integer programming model into a convex one. The stochastic nature of renewable energy and demand is taken into account in multiple scenarios with three different levels of generation and demand. The proposed SMIC model adds the power transfer losses of the D-STATOMs in order to size them adequately. Two objectives are contemplated in the model with the aim of minimizing the annual installation and operating costs, which makes it multi-objective. Three simulation cases demonstrate the effectiveness of the stochastic convex model compared to three solvers in the General Algebraic Modeling System. The results show that the proposed model achieves a global optimum, reducing the annual operating costs by 29.25, 60.89, and 52.54% for the modified IEEE 33-, 69-, and 85-bus test systems, respectively.

Efficient Reallocation of BESS in Monopolar DC Networks for Annual Operating Costs Minimization: A Combinatorial-Convex Approach

This article deals with the solution of a mixed-integer nonlinear programming (MINLP) problem related to the efficient reallocation of battery energy storage systems (BESS) in monopolar direct current (DC) grids through a master–slave optimization approach. The master stage solves the integer nature of the MINLP model, which is related to the nodes where the BESS will be located. In this stage, the discrete version of the vortex search algorithm is implemented. To determine the objective function value, a recursive convex approximation is implemented to solve the nonlinear component of the MINLP model (multi-period optimal power flow problem) in the slave stage. Two objective functions are considered performance indicators regarding the efficient reallocation of BESS in monopolar DC systems. The first objective function corresponds to the expected costs of the annual energy losses, and the second is associated with the annual expected energy generation costs. Numerical results for the DC version of the IEEE 33 bus grid confirm the effectiveness and robustness of the proposed master–slave optimization approach in comparison with the solution of the exact MINLP model in the General Algebraic Modeling System (GAMS) software. The proposed master–slave optimizer was programmed in the MATLAB software. The recursive convex solution of the multi-period optimal power flow problem was implemented in the convex discipline tool (CVX) with the SDPT3 and SEDUMI solvers. The numerical reductions achieved with respect to the benchmark case in terms of energy loss costs and energy purchasing costs were 7.2091% and 3.2105%, which surpassed the results reached by the GAMS software, with reductions of about 6.0316% and 1.5736%.

Dynamic zoning of the network using cooperative transmission and maintenance planning: A solution for sustainability of water distribution networks

Sustainability approaches in transmission and maintenance planning of network utilities with different maintenance zones, which involve increasing the reliability of the transfer while employing maintenance teams fairly, pose decision-making challenges. For these utilities, the best model to employ is the maximum-flow model, which considers network reliability and destination stocks. Therefore, the crucial point is to understand how zone owners can cooperate to maximize reliable transmission and increases maintenance income while considering fairness in allocating maintenance teams to maintenance centers over several periods. A Mixed-Integer Nonlinear Programming (MINLP) model is presented in this paper for maximizing the reliability in network utility issues, allocating maintenance teams, and exploring edge statuses. Cooperative Transmission and Maintenance (CTM) planning is a dynamic, multi-duration decision-making problem. The problem scale in this study is significantly enlarged to separate the lines into active, inactive, and in-repair sets, and consequently, a genetic algorithm is proposed. This study examines these approaches and analyses them (including collaborative outputs and managerial insights) in the context of three major water pipeline zones. Based on sensitivity analysis for 81 collaborative scenarios, the highest and lowest synergies were 83.61% and 36.7%, respectively. Finally, several collaborative income-sharing strategies based on game theory are investigated.

Risk-constrained energy management of an isolated multi-energy microgrid enhanced with hydrogen storage

Hydrogen energy has proven to be a key player in transitioning towards a carbon-neutral society. However, integrating it into existing multi-energy microgrids (MEMGs) is challenging due to uncertainties in hydrogen vehicle (HV) demand, gas and electricity demand, renewable energy output, and prices. To address this, a computationally feasible risk-constrained energy management model is developed using the information gap decision theory (IGDT) method. This model provides both robust and opportunistic strategies. The MEMG system includes electricity, hydrogen, and gas subsystems, and allows for conversions between power to hydrogen (P2H), hydrogen to power (H2P), hydrogen to gas (H2G), and gas to power (G2P). Liquified natural gas (LNG) is used as an external energy source to improve system security. The study also explores the use of hydrogen storage systems (HSS) and renewable energy for sustainability. While the problem is a mixed integer nonlinear programing model, it is converted into a mixed integer linear programing model to ensure global optimality. The proposed risk-based energy management model was tested on a case study and results showed a 2 % decrease in profit for the robust strategy and a 4 % increase in profit for the opportunistic strategy, with final profits of $4876.102 (robust) and $5171.624 (opportunistic).

Multi-Terminal Berth and Quay Crane Joint Scheduling in Container Ports Considering Carbon Cost

As container ports become increasingly important to the global supply chain, a growing number of ports are improving their competencies by consolidating multiple terminal resources internally. In addition, in the context of energy conservation and emission reduction, ports measure competitiveness not only in terms of terminal size, throughput and service level, but also in terms of low energy consumption and low pollution. Therefore, a nonlinear mixed-integer programming model considering the cost of carbon is developed for the multi-terminal berth and quay crane joint robust scheduling problem under uncertain environments to minimize the sum of expectation and variance of total cost under all randomly generated samples. The model considers the water depth and interference of quay cranes, etc. The expected vessel arrival time and the average operational efficiency plus relaxation are used as their actual values when scheduling. Finally, an improved adaptive genetic algorithm is developed by combining the simulated annealing mechanism, and numerical experiments are designed. The results show that the joint berth and quay crane scheduling with uncertainties and a multi-terminal coordination mechanism can effectively reduce the operating cost, including carbon costs and the vessel departure delay rate, and can improve resource utilization. Meanwhile, the scheduling with the multi-terminal coordination mechanism can obtain more significant improvement effects than the scheduling with uncertainties.

Optimizing Renewable Injection in Integrated Natural Gas Pipeline Networks Using a Multi-Period Programming Approach

In this paper, we propose an optimization model that considers two pathways for injecting renewable content into natural gas pipeline networks. The pathways include (1) power-to-hydrogen or PtH, where off-peak electricity is converted to hydrogen via electrolysis, and (2) power-to-methane, or PtM, where carbon dioxide from different source locations is converted into renewable methane (also known as synthetic natural gas, SNG). The above pathways result in green hydrogen and methane, which can be injected into an existing natural gas pipeline network. Based on these pathways, a multi-period network optimization model that integrates the design and operation of hydrogen from PtH and renewable methane is proposed. The multi-period model is a mixed-integer non-linear programming (MINLP) model that determines (1) the optimal concentration of hydrogen and carbon dioxide in the natural gas pipelines, (2) the optimal location of PtH and carbon dioxide units, while minimizing the overall system cost. We show, using a case study in Ontario, the optimal network structure for injecting renewable hydrogen and methane within an integrated natural gas network system provides a $12M cost reduction. The optimal concentration of hydrogen ranges from 0.2 vol % to a maximum limit of 15.1 vol % across the network, while reaching a 2.5 vol % at the distribution point. This is well below the maximum limit of 5 vol % specification. Furthermore, the optimizer realized a CO2 concentration ranging from 0.2 vol % to 0.7 vol %. This is well below the target of 1% specified in the model. The study is essential to understanding the practical implication of hydrogen penetration in natural gas systems in terms of constraints on hydrogen concentration and network system costs.

Optimal Integration of D-STATCOMs in Radial and Meshed Distribution Networks Using a MATLAB-GAMS Interface

This paper proposes an interconnection of the MATLAB and GAMS software interfaces, which were designed based on a master-slave methodology, to solve the mixed-integer nonlinear programming (MINLP) model problem associated with the problem regarding the optimal location and sizing of static distribution compensators (D-STATCOMs) in meshed and radial distribution networks, considering the problem of optimal reactive power flow compensation and the fact that the networks have commercial, industrial, and residential loads for a daily operation scenario. The objective of this study is to reduce the annual investment and operating costs associated with energy losses and the installation costs of D-STATCOMs. This objective function is based on the classical energy budget and the capacity constraints of the device. In the master stage, MATLAB software is used to program a discrete version of the sine-cosine algorithm (DSCA), which determines the locations where the D-STATCOMs will be installed. In the slave stage, using the BONMIN solver of the GAMS software and the known locations of the D-STATCOMs, the MINLP model representing the problem under study is solved to find the value of the objective function and the nominal power of the D-STATCOMs. To validate the effectiveness of the proposed master-slave optimizer, the 33-node IEEE test system with both radial and meshed topologies is used. With this test system, numerical comparisons were made with the exact solution of the MINLP model, using different solvers in the GAMS software, the genetic-convex strategy, and the discrete-continuous versions of the Chu and Beasley genetic algorithm and the salp swarm optimization algorithm. The numerical results show that DSCA-BONMIN achieves a global solution to the problem under study, making the proposed method an effective tool for decision-making in distribution companies.

Development of a MATLAB-GAMS Framework for Solving the Problem Regarding the Optimal Location and Sizing of PV Sources in Distribution Networks

This paper addresses the planning problem regarding the location and sizing of PV generators in distribution networks with a radial topology. This problem is mathematically modeled using a mixed integer nonlinear programming (MINLP) model, which seeks to reduce the total annual operating costs of the system for a planning horizon of 20 years. The objective function used in this paper comprises three elements: (i) the energy purchase costs at the substation node (i.e., the main supply node), (ii) the investment costs for the integration of PV generators, and (iii) the costs associated with the operation and maintenance of these devices. To solve this problem, the interconnection of MATLAB and GAMS software is proposed, while using a master–slave methodology, with which a high-quality solution to this problem is achieved. In the master stage, the MATLAB software is used as a tool to program a discrete version of the sine–cosine algorithm (DSCA), which determines the locations where the PV generators are to be installed. In the slave stage, using one of the solvers of the GAMS software (BONMIN) with the known locations of the PV generators, the MINLP model representing the problem to be studied is solved in order to find the value of the objective function and the nominal power of the PV generators. The numerical results achieved in the IEEE 33- and 69-node systems are compared with the mixed-integer conic programming model solution reported in the specialized literature, thus demonstrating the efficiency and robustness of the proposed optimization methodology.

Operational Reliability Assessment of Distribution Network With Energy Storage Systems

In this article, a novel approach that considers the time-varying load restoration capability is proposed for operational reliability assessment of distribution networks. To evaluate the operational reliability, two indices are firstly defined as the minimal load loss under the worst-case fault contingency in the upcoming time interval. To search for the optimal remedial actions for load restoration, a sequential network reconfiguration (NR) model is then proposed considering the reconfigurability of feeders and capability of emergent power support of energy storage systems (ESSs). Since this model is a complex nonlinear mixed integer programming model, an improved greedy searching-based algorithm is proposed to compute the optimal remedial actions and to further evaluate the risk of load loss. Comparative case studies are presented in this article to demonstrate that the load level, the expectation of the state-of-charge of ESS and the failure duration have the impacts on the system operational reliability. Besides, the proposed sequential NR model can be used to enhance the reliability of distribution networks by enabling an effective sequential load transfer strategy under emergent conditions. Unlike traditional reliability assessment, the proposed method provides the operators with the real-time awareness of the operational reliability level considering the real-time condition of distributed resource equipped feeders and worst-case emergency constraints.

An integrated approach for optimizing location-inventory and location-inventory-routing problem for perishable products

The grocery market, especially the perishable product market is under amazing developments in China, accounting for half of overall food expenditure. However, the supply chain of perishable products has encountered serious problems, including significant freight losses, high inventory and transportation costs, etc. To solve these problems, the current paper proposes an integrated approach for simultaneously optimizing the decisions involved in the supply chain, typically including location, inventory, and routing strategy. A three-echelon supply chain including suppliers, DCs and retailers is structured in this paper, with the impacts of whether allowing direct shipment from supplier to retailer being considered. Two typical problems are investigated: location-inventory problem (LIP) and location-inventory-routing problem (LIRP). Mixed integer nonlinear programming model for LIP is proposed while Mixed integer linear programming models for LIRP is proposed, with the objective of minimizing the overall costs of supply chain, which are composed of infrastructure construction costs, transportation costs, inventory costs, and freight losses. CPLEX is used for LIP model solution and a two-stage heuristic algorithm based on Simulated Annealing is designed to solve the LIRP model. The proposed mathematical models and solution method are then applied to a real supply chain network for fresh milk. The optimized results suggest that considering the direct shipment from supplier to retailer is beneficial in terms of reducing the overall supply chain costs by 31%, with 43% reductions in transportation costs, 69.4% reductions in freight losses, 99.4% increases in inventory costs. With the developments of e-retailers for perishable products, allowing the direct shipment from supplier to retailer will bring more benefits in the future.

Optimal controlled-islanding with AC power flow constraints: A benders-decomposition approach

The controlled-islanding process aims to prevent large devastating blackouts through an optimal splitting of a power network into smaller islands followed by corrective control actions. Generally, the controlled-islanding problem is cast as a mixed-integer non-linear programming (MINLP) model, which can be prohibitively cumbersome, particularly when it accommodates AC power flow constraints. This paper exploits the Benders decomposition to enhance the mathematical tractability of the problem. The AC power flow constraints are converted to equality constraints using proper auxiliary variables. These new constraints are applied to form a modified Jacobian matrix. The modified equations are embedded in the Benders subproblem involving slack variables in a linear iterative-based optimization structure. Hence, the obtained solution is totally free of any approximation or linearization error. The objective function of the controlled-islanding problem is a combination of total load shedding and generation curtailment of the network. This objective function implicitly mitigates the stability concerns in the islands, which can threaten their steady operation. As opposed to a unique value, adaptive lower bound are considered for the steady-state frequency deviation of islands. This adaptive setting prevents an incomplete frequency recovery after the islanding, considering the reserve capacity of each island. The proposed method yields more practically sound results than a standard mixed-integer linear programming (MILP) model with DC or linearized AC power flow constraints as voltage and apparent power quantities are retained in the permissible ranges. The effectiveness of the proposed algorithm is examined via the IEEE 30-bus test system and a 76-bus case as a part of an actual power system. The time-domain simulation results are also provided to support the accuracy of the frequency stability model captured in the optimization problem.

Optimizing investment and transportation decisions for the European natural gas supply chain

We address the European natural gas supply chain with several tiers, including producers, mid-streamers, and consumers, where natural gas and Liquefied Natural Gas (LNG) could be traded via long-term contracts or spot markets. This network problem is formulated as a non-linear mixed-integer programming model which provides the optimal production and export decisions of producers, import and storage decisions of mid-streamers, and infrastructure investment decisions of European Union (EU) countries with respect to new pipelines and LNG regasification terminals that maximize the total social welfare in the EU natural gas market over a five-year horizon. We conduct several case studies to examine this network under different conditions. We first compare the actual and optimal decisions to provide insights. Then, we examine the effect of infrastructure decisions on social welfare. Our results reveal that new infrastructure investments increase the total social welfare by nearly three billion. In addition, we examine its sensitivity to the exclusion of Russian gas supply from the market with and without the infrastructure decisions. Results suggest that if Russian gas supply is excluded from the market, then the social welfare and cumulative natural gas consumption of 26 EU countries decrease by 10% and 15%, respectively and that considering infrastructure investments on LNG terminals and pipelines would reduce supply risk of consumer countries.

Joint optimization decision of service provider selection and CODP positioning based on mass customization in a cloud logistics environment

PurposeBy introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.Design/methodology/approachThis paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.Findings(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.Originality/valueThe originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.