Strategy for Locating People to Reduce the Transmission of COVID-19 Using Different Interference Measures

COVID-19 is generally transmitted from person to person through small droplets of saliva emitted when talking, sneezing, coughing, or breathing. For this reason, social distancing and ventilation have been widely emphasized to control the pandemic. The spread of the virus has brought with it many challenges in locating people under distance constraints. The effects of wakes between turbines have been studied extensively in the literature on wind energy, and there are well-established interference models. Does this apply to the propagation functions of the virus? In this work, a parallel relationship between the two problems is proposed. A mixed-integer linear programming (MIP) model and a mixed-integer quadratic programming model (MIQP) are formulated to locate people to avoid the spread of COVID-19. Both models were constructed according to the distance constraints proposed by the World Health Organization and the interference functions representing the effects of wake between turbines. Extensive computational tests show that people should not be less than two meters apart, in agreement with the adapted Wells–Riley model, which indicates that 1.6 to 3.0 m (5.2 to 9.8 ft) is the safe social distance when considering the aerosol transmission of large droplets exhaled when speaking, while the distance can be up to 8.2 m (26 ft) if all the droplets in a calm air environment are taken into account.

A note on bi-objective optimization for sustainable supply chain network design in omnichannel

PurposeThis paper aims to correct the existing mixed integer programming (MIP) model proposed by Yadav et al. (2019) [“Bi-objective optimization for sustainable supply chain network design in omnichannel.”, Journal of Manufacturing Technology Management, Vol. 30 No. 6, pp. 972–986].Design/methodology/approachThis paper first presents a counterexample to show that the existing MIP model is incorrect and then proposes an improved mixed integer linear programming (MILP) model for the considered problem. Last, a numerical experiment is conducted to test our improved MILP model.FindingsThis paper demonstrates that the formulations of the facility capacity constraints and the product flow balance constraints in the existing MIP model are incorrect and incomplete. Due to this reason, infeasible solutions could be identified as feasible ones by the existing MIP model. Hence, the optimal solution obtained with the existing MIP model could be infeasible. A counter-example is used to verify our observations. Computational results verify the effectiveness of our improved MILP model.Originality/valueThis paper gives a complete and correct formulation of the facility capacity constraints and the product flow balance constraints, and conducts other improvements on the existing MIP model. The improved MILP model can be easily implemented and would help companies to have more effective distribution networks under the omnichannel environment.

Two-Stage Robust Optimization Model for Uncertainty Investment Portfolio Problems

Investment portfolio can provide investors with a more robust financial management plan, but the uncertainty of its parameters is a key factor affecting performance. This paper conducts research on investment portfolios and constructs a two-stage mixed integer programming (TS-MIP) model, which comprehensively considers the five dimensions of profit, diversity, skewness, information entropy, and conditional value at risk. But the deterministic TS-MIP model cannot cope with the uncertainty. Therefore, this paper constructs a two-stage robust optimization (TS-RO) model by introducing robust optimization theory. In case experiments, data crawler technology is used to obtain actual data from real websites, and a variety of methods are used to verify the effectiveness of the proposed model in dealing with uncertainty. The comparison of models found that, compared with the traditional equal weight model, the investment benefits of the TS-MIP model and the TS-RO model proposed have been improved. Among them, the Sharpe ratio, Sortino ratio, and Treynor ratio have the largest increase of 19.30%, 8.25%, and 7.34%, respectively.

The Use of Unmanned Aerial Vehicles for Dynamic Site Layout Planning in Large-Scale Construction Projects

Construction sites are increasingly complex, and their layout have an impact on productivity, safety, and efficiency of construction operations. Dynamic site layout planning (DSLP) considers the adjustment of construction facilities on-site, on an evolving basis, allowing the relocation of temporary facilities according to the stages of the project. The main objective of this study is to develop a framework for integrating unmanned aerial vehicles (UAVs) and their capacity for effective photogrammetry with site layout planning optimisation and Building Information Modelling (BIM) for automating site layout planning in large construction projects. The mathematical model proposed is based on a mixed integer programming (MIP) model, which was employed to validate the framework on a realistic case study provided by an industry partner. Allocation constraints were formulated to ensure the placement of the facilities in feasible regions. Using information from the UAV, several parameters could be considered, including proximity to access ways, distances between the facilities, and suitability of locations. Based on the proposed framework, a layout was developed for each stage of the project, adapting the location of temporary facilities according to current progress on-site. As a result, the use of space was optimised, and internal transport costs were progressively reduced.

Optimizing Medical Enterprise’s Operations Management considering Corporate Social Responsibility under Industry 5.0

Corporate social responsibility (CSR) is the concrete practice of sustainable development at the enterprise level, emphasizing the human value in the production process. The proposal of Industry 5.0 begins to take the coordinated development between human and various production factors as one of the key points of corporate sustainable management. Therefore, this paper studies the optimization of medical enterprise’s operations management considering enterprise CSR under Industry 5.0. A mixed-integer programming (MIP) model is developed to maximize the CSR with the consideration of the impact of precision medical technologies such as surgical robots (SRs) and 3D bone printing on employee social welfare, corporate profits, social environment, and customer surplus value. An improved variable neighborhood tabu search (IVNTS) algorithm which combines the variable neighborhood tabu search (VNTS) algorithm and simulated annealing (SA) algorithm is designed to solve the model, and numerical experiments are analyzed to verify the effectiveness of the proposed IVNTS. The research aids medical enterprise to make reasonable operations management decisions, while providing a reference for the government to draft and implement related policies, thereby achieving sustainable social development.

Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice that experiences a peak in demand during the summer. The lot-sizing problem reduces the ordering and the total inventory holding costs using a mixed-integer programming (MIP) model. Both the lot size and cycle time are variable over the planning horizon. To obtain results faster, a dynamic programming (DP) model was developed, and run using R software. The model was run every week to update the plan according to the current inventory size. The DP model was run on a personal computer 35 times to represent dynamic planning. The CPU time was only a few seconds. Results showed that initial planning is difficult to follow, especially after week 30, and the service level was only 92%. Dynamic planning reached a higher service level of 100%. This study is the first to investigate discrete delivery times, opening the door for further investigations in the future in other industries.

Optimization of Collection and Consolidation Operations in Cross-Border Multi-modal Distribution Networks

This study introduces a model to solve a dynamic network optimization model on a heterogeneous graph. We use this model to optimize the collection and consolidation operations on a cross-country multi-modal distribution network. The model's dynamic objects are trucks, trailers, orders, unvisited collection and customs check points. Information about dynamic objects is extracted from a real-time database. The model's static objects include objects that are known in advance, such as warehouses. The constraints of the problem include due dates, vehicle capacity, availability of vehicles, and precedence constraints of visiting locations. We propose a mixed-integer programming model and provide a solution using matheuristics. We decompose the master MIP model into subproblems that can be solved to optimality with LP solvers. We also reduce the graph complexity by variable fixing due to optimized subproblems or by bounding the maximum number of paths to be selected due to the solutions of priority-based bin packing algorithms. Finally, we convert the resulting problem into a bipartite matching problem by expanding the graph nodes which can then be solved in polynomial time. We implement our solution method on real-time data retrieved from the tracking system of a third-party logistics company. Experiments show that our solution method significantly outperforms other heuristics in terms of solution quality which is measured with respect to lateness, empty kilometers traveled, travel times, number of required/used vehicles, load factors, and ratio of served orders.

Optimization of Collection and Consolidation Operations in Cross-Border Multi-modal Distribution Networks

This study introduces a model to solve a dynamic network optimization model on a heterogeneous graph. We use this model to optimize the collection and consolidation operations on a cross-country multi-modal distribution network. The model's dynamic objects are trucks, trailers, orders, unvisited collection and customs check points. Information about dynamic objects is extracted from a real-time database. The model's static objects include objects that are known in advance, such as warehouses. The constraints of the problem include due dates, vehicle capacity, availability of vehicles, and precedence constraints of visiting locations. We propose a mixed-integer programming model and provide a solution using matheuristics. We decompose the master MIP model into subproblems that can be solved to optimality with LP solvers. We also reduce the graph complexity by variable fixing due to optimized subproblems or by bounding the maximum number of paths to be selected due to the solutions of priority-based bin packing algorithms. Finally, we convert the resulting problem into a bipartite matching problem by expanding the graph nodes which can then be solved in polynomial time. We implement our solution method on real-time data retrieved from the tracking system of a third-party logistics company. Experiments show that our solution method significantly outperforms other heuristics in terms of solution quality which is measured with respect to lateness, empty kilometers traveled, travel times, number of required/used vehicles, load factors, and ratio of served orders.

DEVELOPING SUPPLY CHAIN NETWORK WITH PIECEWISE LINEAR TRANSPORTATION COST FOR A SMALL-AND-MEDIUM ENTERPRISE (SME) IN CILEGON

This study proposed a supply chain network for determining suppliers’ location in which the transportation costs are a piecewise linear function. The supply chain network consists of a production facility, suppliers, and customers. These types of costs are found in the fields of transportation, logistics, and purchasing discount. First, the supply chain network is formulated as the mixed-integer non-linear programming (MINLP) because piecewise linear transportation cost makes the model non-linear. Then, the model is transformed into a mixed-integer programming (MIP) model using the convex-combination method to overcome this nonlinearity. The model was used for solving the problem faced by a small and medium enterprise (SME) in Cilegon. The MIP was solved using the CPLEX software. Sensitivity analysis was carried to provide the SME with several alternatives in handling the suppliers’ location problem