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One of the main issues in a paper mill is the minimization of trim loss when cutting master reels and stocked reels into reels of smaller required widths. The losses produced in trimming at a paper mill are reprocessed by using different chemicals which contributes to significant discharge of effluent to surface water and causes environmental damage. This paper presents a real-world industrial problem of production planning and cutting optimization of reels at a paper mill and differs from other cutting stock problems by considering production and cutting of master reels of flexible widths and cutting already stocked over-produced and useable leftover reels of smaller widths. The cutting process of reels is performed with a limited number of cutting knives at the winder. The problem is formulated as a linear programming model where the generation of all feasible cutting patterns determines the columns of the constraint matrix. The model is solved optimally using simplex algorithm with the objective of trim loss minimization while satisfying a set of constraints. The solution obtained is rounded in a post-optimization procedure in order to satisfy integer constraints. When tested on data from the paper mill, the results of the proposed model showed a significant reduction in trim loss and outperformed traditional exact approaches. The cutting optimization resulted in minimum losses in paper trimming and a lesser amount of paper is reprocessed to make new reels which reduced the discharge of effluent to the environment. [ABSTRACT FROM AUTHOR]

One of the main issues in a paper mill is the minimization of trim loss when cutting master reels and stocked reels into reels of smaller required widths. The losses produced in trimming at a paper mill are reprocessed by using different chemicals which contributes to significant discharge of effluent to surface water and causes environmental damage. This paper presents a real-world industrial problem of production planning and cutting optimization of reels at a paper mill and differs from other cutting stock problems by considering production and cutting of master reels of flexible widths and cutting already stocked over-produced and useable leftover reels of smaller widths. The cutting process of reels is performed with a limited number of cutting knives at the winder. The problem is formulated as a linear programming model where the generation of all feasible cutting patterns determines the columns of the constraint matrix. The model is solved optimally using simplex algorithm with the objective of trim loss minimization while satisfying a set of constraints. The solution obtained is rounded in a post-optimization procedure in order to satisfy integer constraints. When tested on data from the paper mill, the results of the proposed model showed a significant reduction in trim loss and outperformed traditional exact approaches. The cutting optimization resulted in minimum losses in paper trimming and a lesser amount of paper is reprocessed to make new reels which reduced the discharge of effluent to the environment.

We describe the formulation and development of a supply-chain optimisation model for Fletcher Challenge Paper Australasia (FCPA). This model, known as Paper Industry Value Optimisation Tool (PIVOT), is a large mixed integer program that finds an optimal allocation of supplier to mill, product to paper machine, and paper machine to customer, while at the same time modelling many of the supply chain details and nuances which are peculiar to FCPA. PIVOT has assisted FCPA in solving a number of strategic and tactical decision problems, and provided significant economic benefits for the company. [ABSTRACT FROM AUTHOR]

This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) [1] . They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed MIP formulation contains errors, which are described in this technical note. We develop corrections and improvements to the original formulation and test it with non-parametric statistical tests on the same data sets used to evaluate the original model. The statistical analysis shows that the results of the correction formulation are significantly different from the original model. [ABSTRACT FROM AUTHOR]

This paper proposes a smart system for managing the operations of grid-connected charging stations for electric vehicles (EV) that use photovoltaic (PV) sources. This system consists of a mobile application for EV drivers to make charging reservations, an algorithm to optimize the charging schedule, and a remote execution module of charging operations based on the open charge point protocol (OCPP). The optimal charging schedule was obtained by solving a binary integer programming problem. The merits of our solution are illustrated by simulating different charging demand scenarios. [ABSTRACT FROM AUTHOR]

This article presents several papers received for the November 1974 issue of the periodical "Management Science." Papers received include "Optimization of Traffic Signal Settings in Networks by Mixed-Integer Linear Programming," by N. Gartner, J.D.C. Little and H. Gabbay, "Intesections Cuts From Disjunctive Constraints," by E. Balas, and "Integer Programming," by Charles P. Bonini.

A common problem encountered in paper-production facilities is that of allocating customer orders to machines so as to minimize the total cost of production. It can be formulated as a dual-angular integer program, with identical machines inducing symmetry. While the potential advantages of decomposing large mathematical programs into smaller subproblems have long been recognized, the solution of decomposable integer programs remains extremely difficult. Symmetry intensifies the difficulty. This paper develops an approach, based on the construction of tight subproblem bounds, to solve decomposable dual-angular integer programs and successfully applies it to solve the problem from the paper industry. This method is of particular interest as it significantly reduces the impact of symmetry. [ABSTRACT FROM AUTHOR]

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In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large. [ABSTRACT FROM AUTHOR]

This paper studies the non-progressive spread of influence with threshold model in social networks. Consider a graph G with a threshold function t on its vertex set. Spread of influence is a discrete dynamic process as follows. For a given set of initially infected vertices at time step 0 each vertex v gets infected at time step i, i = 1, if and only if the number of infected neighbors are at least t (v) in time step i-1. Our goal is to find the minimum cardinality of initially infected vertices (perfect target set) such that eventually all of the vertices get infected at some time step l. In this paper an upper bound for the convergence time of the process is given for graphs with general thresholds. Then an integer linear programming for the size of minimum perfect target set is presented. Then we give a lower bound for the size of perfect target sets with strict majority threshold on graphs which all of the vertices have even degrees. It is shown that the later bound is asymptotically tight. [ABSTRACT FROM AUTHOR]

To improve cargo loading efficiency and achieve diverse needs of companies for the loading process, this paper innovatively establishes a multiple objective mixed integer programming model for the three-dimensional multiple bin-size bin packing problem (3D-MBSBPP). The model is designed to maximize container space utilization rate and cargo load balance, minimize container usage costs, and incorporates some practical constraints. On this basis, we propose a novel dynamic feedback algorithm based on spatial corner fitness (SCF_DFA) to solve this model, which consists of three stages. Specifically, Stage 1 employs a heuristic algorithm based on spatial corner fitness to optimize the search of the remaining spaces. Stage 2 employs a container type selection algorithm to dynamically adjust and optimize container types. Stage 3 uses an improved genetic algorithm to improve the quality of the solutions of the first two stages. We demonstrate the effectiveness of the proposed algorithm through comparative experiments on benchmark instances, and apply it to solve the real-life instances for the 3D-MBSBPP. The results show that the proposed algorithm can make the average container space utilization rate reach 85.38%, which is 1.48% higher than that of baseline method, while the loading results obtained are more balanced, indicating the advantages of the SCF_DFA in solving 3D-MBSBPP. Furthermore, we conduct ablation experiments to confirm the effectiveness of each component within the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a method for identifying causal relationships between strategic objectives within a strategy map of a Balanced Scorecard. Strategy maps are modeled as a network of strategic objectives (nodes) and causal relationships (directed arcs). The nodes are also grouped into clusters that represent the perspectives of a Balanced Scorecard: (a) Finances, Clients, Internal Processes and Growth and Learning. The method uses the Analytic Network Process (ANP) to establish the importance of every relationship and uses a multi-objective integer linear programming model to select the relationships to be included within a strategy map of a company. The method provides a method that optimizes the selection of the relationships to be included in a strategy map. An illustration of the application of the method in a manufacturing company is presented. [ABSTRACT FROM AUTHOR]

This paper proposes a two‐stage stochastic scheduling model for urban distribution network resilience enhancement against ice storms, which coordinates mobile deicing equipment routing and distributed energy resources dispatching. An improved line ice thickness prediction model and a photovoltaic power generation prediction method in accordance with conditional generative adversarial networks are proposed to provide data boundaries for scheduling strategy. Facing the uncertainty of line failure, a two‐stage scenario‐based distribution network optimization model is established. At first stage, the mobile deicing equipment routing strategy is decided to mitigate the impact caused by ice storms. The Monte‐Carlo simulation method is introduced to describe the uncertainty of line failure due to ice acceleration. For the second stage, based on the results of photovoltaic forecasting and possible distribution line failure scenario generated by Monte‐Carlo simulation method, the optimal distributed energy resources dispatching strategy can be obtained through the mixed integer programming. The proposed model is simplified to a mixed integer linear programming model that can be solved by a commercial solver. The test results on the modified IEEE 33‐node system and modified 69‐node system demonstrate that the proposed method can effectively improve the resilience performance of urban distribution network under ice storms. [ABSTRACT FROM AUTHOR]

When a system of one-sided max-plus linear equations is inconsistent, the approximate solutions within an admissible error bound may be desired instead, particularly with some sparsity property. It is demonstrated in this paper that obtaining the sparsest approximate solution within a given L8 error bound may be transformed in polynomial time into the set covering problem, which is known to be NP-hard. Besides, the problem of obtaining the sparsest approximate solution within a given L1 error bound may be reformulated as a polynomial-sized mixed integer linear programming problem, which may be regarded as a special scenario of the facility location-allocation problem. By this reformulation approach, this paper reveals some interesting connections between the sparsest approximate solution problems in max-plus algebra and some well known problems in discrete and combinatorial optimization. [ABSTRACT FROM AUTHOR]

Fog computing (FC) is considered one of the smart and effective solutions for service provisioning to the Internet of Everything (IoE) layer. IoE layer means the platforms including homes, vehicles, infrastructures, and alike others. FC supports the IoE layer in a smart and charismatic manner by providing services near the smart devices, from vehicles to other mobilities, and fitting the response and delay time requirements. In the proposed paper, the authors focused on discussing vehicular fog computing (VFC) and how FC supports smart vehicles operating on the IoT layer. In the previous decade, scheduling algorithms are proposed by different scholars such as RTFRS, FCFS, ILP, and others to improve the working and efficacy of VFC. In the proposed paper, an analysis is done on the VFC scheduling algorithms published in the last three years in accordance with areas such as traditional, meta-heuristic, deep reinforcement learning, fuzzy logic, heuristic, and integer linear programming. The analysis is done to examine the areas of scheduling such as task scheduling, resource allocation, and others on which the existing solutions are working. This helps in inspecting the gap across which in future further work can be done. [ABSTRACT FROM AUTHOR]

In the multi-domain network scenario, in order to improve the survivability of service function chain (SFC) in the face of network failure, most methods solve this problem through virtual network function (VNF) backup mechanism. However, the traditional multi-domain SFC deployment method lacks a SFC partition mechanism for backup resource consumption and does not consider the isolation and privacy requirements between different network domains. In view of the above problems, this paper proposes a reliability partition scheme based on reinforcement learning in SFC partition stage, which can ensure that VNF is backed up while maintaining good load balancing and low inter-domain transmission delay, and improve the reliability of SFC. Then, this paper proposes a VNF backup mechanism with minimum resource fluctuation in the VNF mapping stage and uses the integer linear programming (ILP) model to determine the backup scheme of each VNF, so as to ensure the minimum fluctuation of resource occupancy of the entire network. Finally, this paper proposes a multi-domain SFC deployment and backup algorithm based on Federated learning (FA-MSDB). The experimental results indicate that FA-MSDB can effectively improve the survival rate of SFC, reduce the overall transmission delay, and ensure good inter-domain and intra-domain load balance. [ABSTRACT FROM AUTHOR]

Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical and practical importance. Additionally, the interpretability of these models presents a persistent challenge. This paper proposes two innovative dimensionality reduction models based on integer programming (DRMBIP). These models assess compactness through the correlation of each indicator with its class center, while separation is evaluated by the correlation between different class centers. In contrast to DRMBIP-p, the DRMBIP-v considers the threshold parameter as a variable aiming to optimally balances both compactness and separation. This study, getting data from the Global Health Observatory (GHO), investigates 141 indicators that influence life expectancy. The findings reveal that DRMBIP-p effectively reduces the dimensionality of data, ensuring compactness. It also maintains compatibility with other models. Additionally, DRMBIP-v finds the optimal result, showing exceptional separation. Visualization of the results reveals that all classes have a high compactness. The DRMBIP-p requires the input of the correlation threshold parameter, which plays a pivotal role in the effectiveness of the final dimensionality reduction results. In the DRMBIP-v, modifying the threshold parameter to variable potentially emphasizes either separation or compactness. This necessitates an artificial adjustment to the overflow component within the objective function. The DRMBIP presented in this paper is adept at uncovering the primary geometric structures within high-dimensional indicators. Validated by life expectancy data, this paper demonstrates potential to assist data miners with the reduction of data dimensions. To our knowledge, this is the first time that integer programming has been used to build a dimensionality reduction model with indicator filtering. It not only has applications in life expectancy, but also has obvious advantages in data mining work that requires precise class centers. [ABSTRACT FROM AUTHOR]

Under the modern environment, the reconstruction of enterprise's core competitiveness depends not only on capital and technical strength, but also on the overall strength of its human resources. At the same time, effective allocation and rational use of talents are needed to create good performance for enterprises. Enterprise human resource management is the key part of the whole enterprise management. At the same time, it is also a necessary preparation for the continuous development and innovation of enterprises. In the whole process of human resource management, the core work is person-post matching. Only by promoting the reasonable implementation of person-post matching can other management work be carried out smoothly. This paper expounds two major elements in human resource management, namely, the concept and measurement of person-post matching and the principle of person-post matching. And the factors in the matching of people and posts are analyzed. This paper probes into the implementation of person-post matching in enterprise human resource management. Based on this, this paper puts forward a depth model of accurate matching between people and posts based on ability perception. On the basis of studying the optimization of human resource scheduling, this paper takes into account three factors: resource constraints, heterogeneity of employee efficiency and time sequence relationship, and uses integer linear programming theory to model the system with the shortest construction period as the goal. The research shows that the accuracy of this algorithm can reach about 94%, which is about 8% higher than the traditional algorithm. It has certain superior performance. This will provide some reference for related researchers. [ABSTRACT FROM AUTHOR]

Advances in image processing technology have made it possible to measure the surface shape of the road ahead while driving. A new semi‐active suspension control method considering the forward road surface shape is proposed. A vehicle model equipped with a semi‐active suspension can be expressed as an mixed logical dynamical model. When the shape of the road ahead can be measured, the information on future disturbances is available before the vehicle undergoes. In this paper, the finite time optimization problem for the mixed logical dynamical model is formulated to consider the future disturbances as a mixed integer quadratic programming problem in the same way as the conventional control problem without future disturbance. However, the mixed integer quadratic programming problem is hard to obtain the control action within the control cycle period required in the real‐time vibration control with general computers for vehicles. In this paper, the reduction of the computational load is achieved by constructing an approximation function of the designed controller. A neural network is adopted for the approximation. The performance evaluation of the proposed method is evaluated by simulations. In the simulation study, the proposed method achieves better ride comfort with the equivalent suspension stroke compared to the traditional methods. [ABSTRACT FROM AUTHOR]

Within Edge and Fog computing, edge and fog nodes must be optimally located at the network edge to minimise the network's overall latency. This survey addresses all aspects of these nodes' placement problems. Literature on edge and fog nodes' placement is collected from reputable databases (IEEE Xplore digital library, Scopus, ScienceDirect, and Web of Science) using a search query. Manual search using keywords and the snowball method is also used to get as many related papers as possible. According to defined inclusion criteria, retrieved documents are filtered to 64 articles for eight years (2015–2022). Depending on the optimisation method used, literature is classified into six categories. The first relies on Integer programming, accounting for 20.3% (13/64). The second category depends on heuristic and metaheuristic methods, accounting for 20.3% (13/64). The third category depends on hybrid methods between the two aforementioned categories accounting for 18.7% (12/64). Forth category depends on clustering methods, accounting for 11% (7/64). The fifth category depends on reinforcement learning, accounting for 6.3% (4/64). And the final category depends on the hybrid methods between two or more methods mentioned above, accounting for 23.4% (15/64). Papers have been analysed to get information like the optimisation problem, the method used for solving it, considered parameters, objectives, constraints, implementation tools, and evaluation methods. [ABSTRACT FROM AUTHOR]

This paper introduces a simultaneous optimization approach to synthesizing work and heat exchange networks (WHENs). The proposed work and heat integration (WHI) superstructure enables different thermodynamic paths of pressure and temperature-changing streams. The superstructure is connected to a heat exchanger network (HEN) superstructure, enabling the heat integration of hot and cold streams identified within the WHI superstructure. A two-step solution strategy is proposed, consisting of initialization and design steps. In the first step, a thermodynamic path model based on the WHI superstructure is combined with a model for simultaneous optimization and heat integration. This nonlinear programming (NLP) model aims to minimize operating expenditures and provide an initial solution for the second optimization step. In addition, hot and cold streams are identified, enabling additional model reduction. In the second step of the proposed solution approach, a thermodynamic path model is combined with the modified HEN model to minimize the network's total annualized cost (TAC). The proposed mixed integer nonlinear programming (MINLP) model is validated by several examples, exploring the impact of the equipment costing and annualization factor on the optimal network design. The results from these case studies clearly indicate that the new synthesis approach proposed in this paper produces solutions that are consistently similar to or better than the designs presented in the literature using other methodologies. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the deployment plan for standby points of professional rescue vessels based on the data of maritime incidents in the Beihai area of China. The primary objective is to achieve multi-level and multiple coverage of the jurisdictional waters of the Beihai Rescue Bureau. Models including the coverage quality of the jurisdictional waters, the coverage quality in high-risk areas, the maximum coverage of jurisdictional areas, and the maximum coverage of high-risk areas are constructed and solved using 0–1 integer programming. The optimal plan for eight standby points and their corresponding deployment plans for rescue vessels are obtained. A comparison with the current site selection plan of the Beihai Rescue Bureau validates the superiority of the proposed deployment plan for rescue vessel standby points in this paper. [ABSTRACT FROM AUTHOR]

Renovating old neighborhoods for the benefit of people has become increasingly important in urban renewal. Nevertheless, old neighborhood renovations are currently considered a group decision-making issue under public participation, involving diverse decision-making subjects. Conflicts within a group are a common problem during group decision-making. In this paper, conflict is examined in the decision-making process for renovating old neighborhoods and novel ideas are provided for quantifying conflict. Public participation in old neighborhood renovations is assessed using conflict degree calculations in group decision-making. Based on the preferences of decision-making experts, a MIP DEA–DA (Mixed Integer Programming Data Envelopment Analysis–Discriminant Analysis) based partial binary tree cyclic clustering model is constructed for clustering experts, and an aggregated group conflict indicator and an aggregated conflict vector are computed, allowing for the quantification of conflict during the renovation process of the old neighborhood based on actual situations. Results indicate that there is primarily a conflict between the benefits of decision-making subject interests and the professionalism of decision-making renovations. This paper contributes to improving public participation, promoting the application of group decision-making theory in old neighborhood renovation, reducing conflict between decision-makers, and speeding up urban renewal. [ABSTRACT FROM AUTHOR]

Many papers in the field of integer linear programming (ILP, for short) are devoted to problems of the type max { c ⊤ x : A x = b , x ∈ Z ≥ 0 n } , where all the entries of A, b, c are integer, parameterized by the number of rows of A and ‖ A ‖ max . This class of problems is known under the name of ILP problems in the standard form, adding the word "bounded" if x ≤ u , for some integer vector u. Recently, many new sparsity, proximity, and complexity results were obtained for bounded and unbounded ILP problems in the standard form. In this paper, we consider ILP problems in the canonical form max { c ⊤ x : b l ≤ A x ≤ b r , x ∈ Z n } , where b l and b r are integer vectors. We assume that the integer matrix A has the rank n, (n + m) rows, n columns, and parameterize the problem by m and Δ (A) , where Δ (A) is the maximum of n × n sub-determinants of A, taken in the absolute value. We show that any ILP problem in the standard form can be polynomially reduced to some ILP problem in the canonical form, preserving m and Δ (A) , but the reverse reduction is not always possible. More precisely, we define the class of generalized ILP problems in the standard form, which includes an additional group constraint, and prove the equivalence to ILP problems in the canonical form. We generalize known sparsity, proximity, and complexity bounds for ILP problems in the canonical form. Additionally, sometimes, we strengthen previously known results for ILP problems in the canonical form, and, sometimes, we give shorter proofs. Finally, we consider the special cases of m ∈ { 0 , 1 } . By this way, we give specialised sparsity, proximity, and complexity bounds for the problems on simplices, Knapsack problems and Subset-Sum problems. [ABSTRACT FROM AUTHOR]

On the basis of predecessors' coordination optimization of active and reactive power in distribution network, For the necessity of the optimal operation in the distribution network, part of power generated from photovoltaic (PV) cannot be sold to users, and cannot enjoy subsidies. Similarly, the network loss in the power transmission will also bring a certain economic loss. This paper comprehensively considers the economic loss caused by the network loss and PV abandon of the distribution system, and establishes a model to minimize the economic loss. To solve this problem efficiently, the method of DistFlow equation and mixed integer second order cone programming (MISOCP) is used to solve the problem, in this method, the original mixed integer nonlinear programming non-convex problem is transformed into a convex problem, which makes the optimization problem easy to solve. The modified IEEE 33 and IEEE 69 distribution networks are tested by the above method. The optimized results are able to meet the target and have very small relaxation gaps, and the voltage level is also optimized. This coordinated optimization approach helps to optimize the economic operation for active distribution networks with PVs. [ABSTRACT FROM AUTHOR]

This paper explores a multi-objective, multi-period integrated routing and scheduling problem under uncertain conditions for distributing relief to disaster areas. The goals are to minimize costs and maximize satisfaction levels. To achieve this, the proposed mathematical model aims to speed up the delivery of relief supplies to the most affected areas. Additionally, the demands and transportation times are represented using fuzzy numbers to more accurately reflect real-world conditions. The problem was formulated using a fuzzy multi-objective integer programming model. To solve it, a hybrid algorithm combining a multi-objective ant colony system and simulated annealing algorithm was proposed. This algorithm adopts two ant colonies to obtain a set of nondominated solutions (the Pareto set). Numerical analyses have been conducted to determine the optimal parameter values for the proposed algorithm and to evaluate the performance of both the model and the algorithm. Furthermore, the algorithm's performance was compared with that of the multi-objective cat swarm optimization algorithm and multi-objective fitness-dependent optimizer algorithm. The numerical results demonstrate the computational efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Power distribution networks should strive for reliable delivery of energy. In this paper, we support this endeavor by addressing the Maintenance Resources Allocation Problem (MRAP). This problem consists of scheduling preventive maintenance plans on the equipment of distribution networks for a planning horizon, seeking the best trade-offs between system reliability and maintenance budgets. We propose a novel integer linear programming (ILP) formulation to effectively model and solve the MRAP for a single distribution network. The formulation also enables flexibility to suit new developments, such as different reliability metrics and smart-grid innovations. Then we develop a straightforward ILP formulation to address the MRAP for several distribution networks which takes the advantages of exchanging maintenance information between local agents and upper management. Using a general-purpose ILP solver, we performed computational experiments to assess the performance of the proposed approaches. Optimal maintenance trade-offs were achieved with the new formulations for real-scale distribution networks within short running times. To the best of our knowledge, this is the first time that the MRAP is optimally solved using ILP, for single or multiple distribution networks. [ABSTRACT FROM AUTHOR]

Various metaheuristic algorithms inspired by nature have been designed to deal with a variety of practical optimization problems. As an excellent metaheuristic algorithm, the improved particle swarm optimization algorithm based on grouping (IPSO) has strong global search capabilities. However, it lacks a strong local search ability and the ability to solve constrained discrete optimization problems. This paper focuses on improving these two aspects of the IPSO algorithm. Based on IPSO, we propose an improved particle swarm optimization algorithm based on variable neighborhood search (VN-IPSO) and design a 0-1 integer programming solution with constraints. In the experiment, the performance of the VN-IPSO algorithm is fully tested and analyzed using 23 classic benchmark functions (continuous optimization), 6 knapsack problems (discrete optimization), and 10 CEC2017 composite functions (complex functions). The results show that the VN-IPSO algorithm wins 18 first places in the classic benchmark function test set, including 6 first places in the solutions for seven unimodal test functions, indicating a good local search ability. In solving the six knapsack problems, it wins four first places, demonstrating the effectiveness of the 0-1 integer programming constraint solution and the excellent solution ability of VN-IPSO in discrete optimization problems. In the test of 10 composite functions, VN-IPSO wins first place four times and ranks the first in the comprehensive ranking, demonstrating its excellent solving ability for complex functions. [ABSTRACT FROM AUTHOR]

This paper establishes a multivariate function model for natural human load-carrying walking in some typical scenarios such as college equipment and material relocation by students and a large amount of identical freight relocation in commercial activities. For classified material relocation needs and constraints, we obtain the relationship between walking speed and load weight for a single person, as well as the time cost for different round trips. By establishing an integer programming model with the minimum total transportation time cost and shelf life as the objective function and the requirements of negative weight and speed as the constraint conditions, we reach the optimal item allocation methods considering time cost and shelf life. We discover that there is an approximate linear relationship between the change in natural walking speed and travel time when the load is small, thus obtaining the time cost of student transportation under different round-trip situations. The Monte Carlo simulation algorithm, which is more efficient compared with other methods such as the integer programming method, is used to obtain the optimal allocation scheme that meets the efficiency and quality requirements. The analysis methods and results can be used as guidance for task scheduling optimization for material relocation in educational organizations as well as commercial agencies. [ABSTRACT FROM AUTHOR]

Demand fluctuates in actual production. When manufacturers face demand under their maximum capacity, suspension shifts are crucial for cost reduction and on-time delivery. In this case, suspension shifts are needed to minimize idle time and prevent inventory buildup. Thus, it is essential to integrate suspension shifts with scheduling under an uncertain production environment. This paper addresses the two-stage hybrid flow shop scheduling problem (THFSP) with suspension shifts under uncertain processing times, aiming to minimize the weighted sum of earliness and tardiness. We develop a stochastic integer programming model and validate it using the Gurobi solver. Additionally, we propose a dual-space co-evolutionary biased random key genetic algorithm (DCE-BRKGA) with parallel evolution of solutions and scenarios. Considering decision-makers' risk preferences, we use both average and pessimistic criteria for fitness evaluation, generating two types of solutions and scenario populations. Testing with 28 datasets, we use the value of the stochastic solution (VSS) and the expected value of perfect information (EVPI) to quantify benefits. Compared to the average scenario, the VSS shows that the proposed algorithm achieves additional value gains of 0.9% to 69.9%. Furthermore, the EVPI indicates that after eliminating uncertainty, the algorithm yields potential improvements of 2.4% to 20.3%. These findings indicate that DCE-BRKGA effectively supports varying decision-making risk preferences, providing robust solutions even without known processing time distributions. [ABSTRACT FROM AUTHOR]