Multigraph Modeling for Urban Distribution of Emergency Commodities with Semisoft Time Windows under Uncertainty

In this paper, we studied a stochastic bi-objective mathematical model for effective and reliable rescue operations in multigraph network. The problem is addressed by a two-stage stochastic nonlinear mixed-integer program where the reliability of routes is explicitly traded-off with total weighted completion time. The underlying transportation network is able to keep a group of multiattribute parallel arcs between every pair of nodes. By this, the proposed model should consider the routing decision in logistic planning along with the path selection in an uncertain condition. The first stage of the model concerns with the vehicle routing decisions which is not involved with random parameters; besides, the second stage of the model involves with the departure time at each demand node and path finding decisions after observation of random vectors in the first stage considering a finite number of scenarios. To efficiently solve the presented model, an enhanced nondominated sorting genetic algorithm II (NSGA-II) is proposed. The effectiveness of the introduced method is then evaluated by conducting several numerical examples. The results implied the high performance of our method in comparison to the standard NSGA-II. In further analyses, we investigated the beneficiary of using multigraph setting and showed the applicability of the proposed model using a real transportation case.

Optimization-Based Proposed Solution for Water Shortage Problems: A Case Study in the Ismailia Canal, East Nile Delta, Egypt

Water conflicts in transboundary watersheds are significantly exacerbated by insufficient freshwater sources and high water demands. Due to its increasing population and various development projects, as well as current and potential water shortages, Egypt is one of the most populated and impacted countries in Africa and the Middle East in terms of water scarcity. With good future planning, modeling will help to solve water scarcity problems in the Ismailia canal, which is one of the most significant branches of the Nile River. Many previous studies of the Nile river basin depended on quality modeling and hydro-economic models which had policy or system control constraints. To overcome this deficit position and number, the East Nile Delta area was investigated using LINDO (linear interactive, and discrete optimizer) software; a mathematical model with physical constraints (mass balances); and ArcGIS software for canals and water demands from the agriculture sector, which is expected to face a water shortage. Using the total capital (Ismailia canal, groundwater, and water reuse) and total demand for water from different industries, the software measures the shortage area and redistributes the water according to demand node preferences (irrigation, domestic, and industrial water demands). At the irrigation network’s end, a water deficit of 789.81 MCM/year was estimated at Al-Salhiya, Ismailia, El Qantara West, Fayed, and Port Said. The model was then run through three scenarios: (1) the Ismailia Canal Lining’s effect, (2) surface water’s impact, and (3) groundwater’s impact. Water scarcity was proportional to lining four sections at a length of 61.0 km, which is considered to be optimal—based on the simulation which predicts that the Ismailia canal head flow will rise by 15%, according to scenarios—and the most effective way to reduce water scarcity in the face of climate change and limited resources as a result of the increasing population and built-in industrial projects in Egypt.

Modeling and Optimization in Resource Sharing Systems: Application to Bike-Sharing with Unequal Demands

The imbalanced distribution of shared bikes in the dockless bike-sharing system (a typical example of the resource-sharing system), which may lead to potential customer churn and lost profit, gradually becomes a vital problem for bike-sharing firms and their users. To resolve the problem, we first formulate the bike-sharing system as a Markovian queueing network with higher-demand nodes and lower-demand nodes, which can provide steady-state probabilities of having a certain number of bikes at one node. A model reduction method is then designed to reduce the complexity of the proposed model. Subsequently, we adopt an operator-based relocation strategy to optimize the reduced network. The objective of the optimization model is to maximize the total profit and act as a decision-making tool for operators to determine the optimal relocation frequency. The results reveal that it is possible for most of the shared bikes to gather at one low-demand node eventually in the long run under the influence of the various arrival rates at different nodes. However, the decrease of the number of bikes at the high-demand nodes is more sensitive to the unequal demands, especially when the size of the network and the number of bikes in the system are large. It may cause a significant loss for operators, to which they should pay attention. Meanwhile, different estimated values of parameters related with revenue and cost affect the optimization results differently.

Modelling and Optimization in Resource Sharing Systems: Application to Bike Sharing with Unequal Demands

Although the dockless bike-sharing system, which can be regarded as a typical example of the resource-sharing system, has been increasingly popular for years with people especially in China, the imbalanced distribution of shared bikes gradually becomes a major problem for both bike-sharing companies and their customers. To solve the imbalance problem, we aim to investigate the long-term performance of a system under the influence of some key factors (with an emphasis on the unequal demand between different nodes), which can guide us to discover the causes of the problem and offer several valuable suggestions to the operators. According to the fundamental principle of a dockless bike-sharing system, we propose a model reduction method to reduce the complexity of the theoretical network models, which are developed based on the Markovian queueing theory with the consideration of higher-demand nodes and lower-demand nodes. The theoretical network models provide us with steady-state probabilities of having a certain number of bikes at one node, which are used as an important part of the optimization model for solving the imbalance problem by carrying out an operator-based relocation strategy. The objective of the optimization model is to maximize the total profit and determine the optimal relocation frequency. It is found that most of the shared bikes are possible to gather at one low-demand node eventually in the long run under the influence of the different arrival rates at different nodes, but the decrease of the number of bikes at the high-demand nodes is more sensitive to the unequal demands and may cause a great loss for operators, which should be payed attention to especially when solving the relocation problems.

Application of Linear Programming in Oil Production Distribution Networks: Literature Review

In the oil production distribution network, there are three main nodes, namely the supply node, the transshipment node, and the demand node. The pattern of distribution of oil production from the supply node to the demand node is very diverse and can affect the costs to be incurred. Therefore, a company must be able to determine the right distribution pattern, so that the costs incurred are optimal. This paper intends to conduct a study of how to determine and calculate the distribution patterns and the minimum total costs incurred, using the primal-dual linear program method. Based on the results of the case analysis, it is known that the number of supply commodities will be the same as the number of demand commodities, but the distribution from each source does not necessarily have the same capacity and costs. Therefore, the distribution pattern is determined based on the existing cost and capacity, so that cost optimization can be achieved.

A Novel Approach for Determination of Reliability of Covering a Node from K Nodes

In this paper, a stochastic problem of multicenter location on a graph was formulated through the modification of the existing p-center problem to determine the location of a given number of facilities, to maximize the reliability of supplying the system. The system is represented by a graph whose nodes are the locations of demand and the potential facilities, while the weights of the arcs represent the reliability, i.e., the probability that an appropriate branch is available. First, k locations of facilities are randomly determined. Using a modified Dijkstra’s algorithm, the elementary path of maximal reliability for every demand node is determined. Then, a graph of all of elementary paths for demand node is formed. Finally, a new algorithm for calculating the reliability of covering a node from k nodes (k—covering reliability) was formulated.

Optimal Control Strategy to Distribute Water Through Loop-Like Planar Networks

In this article, loop like planar networks formed by circular cross sectioned conduits with possibly different geometric measurements are studied to supply the required amount of isothermal water within the optimal time and through the shortest path. The flow optimization procedure is controlled by time varying pressures at nodes throughout the network for given specifications about pressure value at multiple demanding and single supply nodes. The flow governing equation is solved analytically to correlate transient flow rate and pressure and then studied using analogous electrical circuit. For each possible path between source and demand node, minimum equivalent flow impedance criterion is considered to pick the optimum path. This sets a multi-objective dynamic flow optimization algorithm and the same is executed under the assumption of fully developed and laminar flow. The optimum flow impedance can further be used to measure the pumping power as the cost of flow of a particular path. The algorithm can be extended to reduce the water wastages by controlling pressures efficiently.

Research on makers’ knowledge space construction by libraries

Purpose The purpose of this paper is to figure out the exact knowledge demand of makers. Furthermore, the paper aims to construct the knowledge space based on makers’ knowledge demand. Design/methodology/approach The first step of this research is to explore the makers’ knowledge demand by the methods of field investigation, non-interventional study and in-depth interviews. On that basis, the elements, principles, framework and mechanism of the knowledge space were discussed. The grounded theory and NVivo software were used in the data analysis. Findings The investigation shows that interest and competition are the main motivations for makers’ knowledge demand. The findings also indicate that the characteristics are active knowledge needs, “informal learning” preferences, divergent thinking, close community ties and interdisciplinary. According to the makers’ knowledge demand node conceptual model, the content of makers’ knowledge demand is principle knowledge, empirical knowledge and knowledge situation. Based on the above findings, the elements, principles, operation framework and inherent operation mechanism of the knowledge space construction are systemically described. Originality/value This study gives precise details about makers’ knowledge demand and the construction of makers’ knowledge space by libraries. This is the first research that comprehensively explores the knowledge demand of makers. The findings can help the library plan and implement the construction of makerspace, and also improve the service for makers.

Bayesian Network Approach to Customer Requirements to Customized Product Model

Customizing products based on customer needs is an irreversible trend, and many companies strive to provide customized products to customers in less time. Customer requirements are a key factor in the company's ability to provide customized products. In order to better meet customer needs, solve the problem of incomplete and inaccurate expression, and improve the correlation between customized product performance and customer demand, a customized product method based on Bayesian network is proposed. First, the company built a custom product model based on Bayesian networks. According to the model, the customer selects some nodes and their related information. Then, we can accurately predict the final product model through the link tree algorithm, test the customer demand node to determine the focus of the customer's needs, and optimize the model. Finally, an example of a multifunctional nursing bed is used to illustrate the effectiveness of the method.

Location-Routing Problem for Relief Distribution in the Early Post-Earthquake Stage from the Perspective of Fairness

To address the shortage of relief in disaster areas during the early stages after an earthquake, a location-routing problem (LRP) was studied from the perspective of fairness. A multi-objective model for the fair LRP was developed by lexicographic order object optimal method in consideration of the urgent window constraints, partial road damage, multimodal relief delivery, disaster severity, and vulnerability of each demand node when its demand is not satisfied. The goals of this model are to minimize (1) the maximum loss of demand node, (2) the total loss of demand node, and (3) the maximum time required for the demand node to receive relief. A hybrid heuristic algorithm was proposed to solve the model. Finally, the utility and fairness of the model and algorithm were demonstrated by a case study during the first day after the great Wenchuan earthquake in China.