Multi-commodity Model Research Articles

Application of cloud computing and big data in three-stage dynamic modeling of disaster relief logistics and wounded transportation: a case study.

Collecting and sharing information about affected areas is an important activity for optimal decision-making in relief processes. Defects such as over-sending some items to affected areas and mistakes in transferring injured people to medical centers in accidents are due to improper management of this information. Because cloud computing as a processing and storage platform for big data is independent of the device and location and can also perform high-speed processing, its use in disasters has been highly regarded by researchers. In this environment, a three-stage dynamic procedure for evacuation operations and logistics issues is presented. The first stage of the proposed model is image processing and tweet mining in a cloud center in order to determine the disaster parameters. In stage II, a mixed-integer multi-commodity model is presented for the relief commodity delivery, wounded people transportation with capacity constraints, and locating of the possible on-site clinics and local distribution centers near disaster areas. In stage III, by using a system of equations, detailed vehicle load/unload instructions are obtained. Finally, the effectiveness of the proposed model on the data of an earthquake disaster in Iran is investigated. The results of comparing the proposed approach with a two-stage algorithm show that the total number of unsatisfied demand for all types of commodities in the proposed approach was better than the other. Also, the number of survivors in the three-stage model is significantly higher than in the two-stage one. The better performance of the proposed algorithm is due to the fact that online data is continuously available and that decisions such as sending relief items and dispatching are made more effectively.

Creating conditions for the sustainable development of the liquid

In light of the growing importance of biofuels in the world and Ukraine’s potential of its production, the current research focuses on analyzing the future development of liquid biofuel market and production possibilities in Ukraine. Ukraine has a great competitive advantage in the production of biofuels as availability of the feedstock, fertile soils and supports through investments and know-how from abroad. Whereas the country disadvantageously exports feedstock to Europe for cheaper price and purchase expensive gas and oil instead. Thus, national interest should be shifted from the export of raw material to processing them into final biofuel products. This research aims at analyzing the current status and future projections of the liquid biofuel market in Ukraine until 2030 using the AGMEMOD model - an econometric, dynamic, partial equilibrium, the multi-commodity model. This is a new market for the Ukrainian AGMEMOD model, and it was implemented in the model for the first time. In recent years, Ukraine has worked on the fulfillment of European standards in the sector of biofuels. Ukraine has built a legislative base, which aims to support industry development and offer a large scale of benefits. However, due to high excise duty, low oil prices and no penalties for not achieving established indicators, the biofuel industry currently remains non-operating. The current paper offers the introduction of state aid in form of direct support and tax preferences for liquid biofuels producers to meet the needs of the domestic market in biofuels and to achieve the indicative target of 10% consumption biofuels in the total consumption of motor fuel by transport sector by 2020. The research suggested conditions for the creation of sustainable development of the liquid biofuel market in Ukraine for the long term period. Due to modeling results, it is expected that in the Baseline scenario biofuel production will not face major changes, because the use of raw materials for food and feed consumption, as well as their export, shall remain a more profitable option for Ukrainian producers. However, it is expected to motivate biofuel production by the introduction of a minimum of 10 % biofuel use for the transport sector. In particular, the increase of demand for biofuel by at least 10% shall positively affect the respective domestic market price and, consequently, positively influence on the use of commodities for biofuel production. Therefore, it might be the case that correctly specified domestic policy will trigger the development of biofuel production in Ukraine. This research proposes to introduce the obligatory admixture with traditional gasoline and diesel and return non-excise production of alternative fuel and biodiesel fuel for a certain period (up to 10 years) to achieve the indicative target of 10% biofuels consumption by the transport sector, and then promote the excise duty at an economically justified level of 10-20% of the excise tax rate on traditional gasoline and diesel. All the above measures, such as direct support for liquid biofuels producers, tax incentives for producers and the introduction of a mandatory mixing rate, will contribute to achieving the indicative target of 10 % consumption biofuels by the transport sector.

A Network-Based Solution for the Fair Student-to-School Allocation

This paper presents a mathematical model for planning in public schools, considering the satisfaction of the total demand for education, fair students' access to schools, and the total travel distance. Given a set of schools, the aim of the model is to find an optimal allocation of students such that school should is available to each group of students. The model includes information about geographical prioritised access zones which should be covered by each school. The objective function essentially seeks to minimise the total distance travelled by students. We propose a multi-commodity programming model for solving this problem. The model is used to analyse the effects of different allocation policies on the fairness of student allocation to schools. Results show that a fair distribution of students of each social class to each school is obtained, while the total distance being travelled is minimised.

Large-Scale Model and Solution for Integrated Maintenance Routing and Tail Assignment Problem in Airline Industry

The airline planning and scheduling functions are generally performed in sequential manner which makes the schedule yield to disruptions. This paper presents an integrated maintenance routing and tail assignment problem (IMRTAP), a paradigm of embedding the tactical and operational planning. This is a relatively new approach in airline planning where the aircraft routing is re-optimised with the consideration of tail assignment and semi-integration of crew scheduling. This aids in the design of a more robust schedule with up-to-date operational information regarding the aircraft. The problem is formulated as a combination of set partition and multi-commodity network model. The model is solved using a modified restrict-and-relax as an efficient strategy to tackle large-scale instances. Six real-world instances are used to test the model with the problem size as large as 50 lakh variables in the largest instance. In addition to efficient solving, perturbation is introduced to the problem to deal with degeneracy in large problems.

Structural Electricity Models and Asymptotically Normal Estimators to Quantify Parameter Risk

ABSTRACTWe estimate a structural electricity (multi-commodity) model based on historical spot and futures data (fuels and power prices, respectively) and quantify the inherent parameter risk using an average value at risk approach (‘expected shortfall’). The mathematical proofs use the theory of asymptotic statistics to derive a parameter risk measure. We use far in-the-money options to derive a confidence level and use it as a prudent present value adjustment when pricing a virtual power plant. Finally, we conduct a present value benchmarking to compare the approach of temperature-driven demand (based on load data) to an ‘implied demand approach’ (demand implied from observable power futures prices). We observe that the implied demand approach can easily capture observed electricity price volatility whereas the estimation against observable load data will lead to a gap, because – amongst others – the interplay of demand and supply is not captured in the data (i.e., unexpected mismatches).

Efficiency evaluation of a new approach for cargo routing with empty containers repositioning

The cargo routing and empty repositioning problems are important to growth of world market and competitive differentials of the container transporting. This paper aims to evaluate the efficiency of a new approach for an integrated model to cargo routing and empty containers repositioning. We formulated the proposed optimization model as binary-mixed linear programming based on a network multi-commodity model additionally with capacity constraints. Considering the complexity of this problem compared to models available in the literature, the results proved the model efficiency in computational time and main measures of demand and capacity. We understand the model as efficient to solve problems of large instances, assisting real decisions with robustness. The solving time increases significantly with increasing demand, occurring more containers repositioned in order to more profitable cargos can viability a repositioning decision. The main complexity of the model is related to integer variables of cargo routing decisions.

Mixed integer optimization for the combined capacitated facility location-routing problem

Given a set of potential facilities (with individual opening cost and finite capacity) and a set of client demands, the capacitated Facility Location Problem (cFLP) consists in selecting a set of facilities to open and performing an assignment of demands to open facilities that minimize the sum of the facility opening cost, supplying cost and transportation/connection cost. This problem shares many similarities with the one consisting in minimizing the cost for opening a set of servers or replicas for storing a set of data objects (files) and connecting clients to facility locations so as to satisfy their demands. The multi-source dimension of the problem translates the situation where the same data object may be available simultaneously at different facility locations. When various data objects with different properties are available at (possibly multiple) opened facility locations, the problem shares the characteristics of the multi-product or multi-commodity model. On the other hand, the combination of the cFLP with the network design problem, referred to as the Capacitated Facility Location-Network Design Problem (cFLNDP), has been subject to several studies since the early 2000s. This (class of) problem(s) was initially motivated by the observation that when the (physical) network topology is determined endogenously, it may be more effective to change the configuration of the underlying network physical topology instead of locating additional facilities in order to account for the spatio-temporal variability in clients demands. However, in the context of communication networks, one does not require to physically rewire the network to obtain a new logical topology. Hence, in this paper we combine the capacitated multi-source multi-product facility location with the traffic routing problem and propose a mixed-integer program formulation. As dynamic routing offers the flexibility to re-configure routing tables entries and adapt routing decisions, our combined model also enables to dynamically re-allocate demands upon facility failure.

Developing a multi-commodity multi-period mathematical model based on the travelling salesman problem for solving bike sharing rebalancing problem

Developing a multi-commodity multi-period mathematical model based on the travelling salesman problem for solving bike sharing rebalancing problem

Unifiable multi-commodity kinematic wave model

Abstract In the literature, many kinematic wave models have been proposed for multi-class vehicles on multi-lane roads; however, there lacks an explicit model of unifiable multi-commodity traffic, in which different commodity flows can have different speeds and violate the first-in-first-out (FIFO) principle, but there exists a speed-density relation for the total traffic. In this study, we attempt to fill the gap by constructing and solving a unifiable multi-commodity kinematic wave model. We first construct commodity speed-density relations based on generic generating functions. Then for two commodities we discuss the properties of the unifiable kinematic wave model and analytically solve the Riemann problem with a combination of total and commodity kinematic waves. We propose a unifiable multi-commodity Cell Transmission Model (CTM) with a general junction model for numerical simulations of network traffic flows, which are unifiable but may violate the FIFO principle. We prove that the CTM is well-defined under an extended CFL (Courant et al., 1928) condition. With examples we verify the consistency between the analytical and numerical solutions and demonstrate the convergence of the CTM. We conclude with several follow-up research directions for unifiable multi-commodity kinematic wave models.

Minimizing cost of empty container repositioning in port hinterlands, while taking repair operations into account

Shipping companies are striving to optimize their empty container repositioning strategies which also contribute to reduced congestion and environmental improvements. In this paper we propose a multi-commodity model that makes an explicit distinction between flows of non-damaged containers, on the one hand, and flows of damaged containers, on the other. The model is tailored for the repositioning of these containers in the representative setting of a network of off-dock empty depots, ocean terminals, and inland terminals. In our case study, cost savings of up to 17% are found, depending on the composition of the network, container type, and particular evacuation and repositioning strategy. In particular, directly transporting containers from inland terminals to other inland terminals (direct repositioning) results in cost savings of up to 15% for dry containers and up to 17% for reefer containers. Furthermore, the total costs might be optimized by actually preventing the container failure from occurring possibly leading to considerable additional cost reductions. Finally, exporting damaged containers might seem to be the optimal solution from a regional cost perspective, but, this does not necessarily lead to total cost optimization from the global perspective.

Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair

Natural disasters such as earthquakes impose destructive effects in the form of human injuries and damage to properties each year. Damage caused by the earthquake can disrupt traffic and highway systems, block vehicles and relief operations and make distribution operations difficult. Therefore, the repair of damaged roads in the least possible time so that distribution of relief can be done is a significant natural phenomenon after the disaster. In this study, a new mathematical integer nonlinear multi-objective, multi-period, multi-commodity model is suggested to locate the distribution centers, for timely distribution of vital relief to the damaged areas, vehicles routing and emergency roadway repair operations. It minimizes the travel time and total cost and increases reliability of the routes. To solve the designed problem, two meta-heuristic algorithms, namely non-dominated sorting genetic algorithm-II (NSGAII) and multi-objective particle swarm optimization (MOPSO), are offered. Then, the accuracy of mathematical models and efficiency of algorithms are assessed through numerical examples in detail.

Designing a flexible multi-commodity distribution network in a multi-level supply chain

In response to the market competitive conditions today, organizations are increasingly striving to deliver products to customers in the correct volumes, at the right time, and in the right place. An important success factor in this competitive environment is employing an appropriate and efficient distribution network that is designed around the objectives of optimum numbers, locations, and capacities of production and distribution centers and an optimum strategy for the timely transfer of products from the plant or the warehouse to the customer.The present study aims to investigate the problem of designing a flexible distribution network in a multi-level supply chain by assuming direct shipment from the manufacturer to the customer. The problem is considered as a multi-commodity, static, and certain model in the form of mixed integer programming, with each of the production and distribution facilities having multi-capacity levels and their numbers considered as variables in the model. The restriction of distribution radius for production plant and distribution centers (warehouses) has been considered. Since the distribution network design problem is an NP-hard problem, a meta-heuristic approach based on the particle swarm optimization method was adopted to solve the problem. To evaluate the performance of the proposed approach, 32 test problems were solved. Comparison of the results indicates the efficiency of the proposed method.

Design of a reliable multi-modal multi-commodity model for hazardous materials transportation under uncertainty

In this paper, we propose a new mathematical model for designing a reliable hazardous material (HAZMAT) transportation network (RHTND) on the basis of hub location topology under uncertainties, in which hub nodes may be disrupted by external events, as well as HAZMATs incidents. Hub locations and HAZMAT transportation routes using different transportation modes are simultaneously optimized to obtain minimum risk of incidents. A mixed integer nonlinear programing model is developed. To cope with the uncertainties in the model, we provide a solution framework based on an integration of the well-known chance-constrained programing with a possibilistic programing approach. Small size problems are solved to optimality. In order to solve large size instances, a meta-heuristic algorithm was applied and its performance is evaluated in comparison with a new lower bound approach through analysis of a real case-study of a HAZMAT transportation network.

Abolishing biofuel policies: Possible impacts on agricultural price levels, price variability and global food security

In this paper we assess the impact of abolishing biofuel policies (mandates, tax credits, import and export tariffs) on agricultural price levels and price variability as well as some aspects related to global food security. For the analysis we employ a recursive-dynamic agricultural multi-commodity model within a stochastic framework. Results of the 10-years forward looking scenario indicate that the removal of biofuel policies would have a significant effect on price variability of biofuels, but only a marginal impact on the variability of agricultural commodity prices. Without biofuel policies, global biofuel demand would decrease by 25% for ethanol and 32% for biodiesel. Moreover, prices would only moderately decrease for ethanol feedstock commodities like wheat and coarse grains, while prices for biodiesel feedstock commodities, specifically vegetable oils, would be more affected. Due to competing uses of crop production such as feed and industrial use, abolishing biofuel policies would not necessarily lead to an increase in global food security, as food use increases would remain low for most crops and regions.

Design of a Reliable Bi-objective Relief Routing Network in the Earthquake Response Phase

One of the most critical issues in areas which have recently faced natural disasters is transportation of both injured people to medical centers and relief commodities to their desired destinations. In such situation, due to disruptions, some routes may lose all or part of their capacities and not be able to accept as much vehicles as before. Consequently, the relief vehicles face difficulties to follow their itinerary. Such critical circumstance calls for finding reliable solutions. This study develops a new bi-objective, multi-mode and multi-commodity model, in which the reliability is taken into account to help managers in making more reliable decisions. To solve this model, the TH method is used in order to find the equivalent single objective model and this new model is exactly solved by coding in GAMS software.

A two stage heuristic algorithm for the integrated aircraft and crew schedule recovery problems

Airline disruptions incurred huge cost for airlines and serious inconvenience for travelers. In this paper, we study the integrated aircraft and crew schedule recovery problem. A two stage heuristic algorithm for the integrated recovery problem is proposed. In the first stage, the integrated aircraft recovery and flight-rescheduling model with partial crew consideration is built. This model is based on the traditional multi-commodity network model for the aircraft schedule recovery problem. The objective of this model also includes minimization of the original crew connection disruption. In the second stage, the integrated crew schedule recovery and flight re-scheduling model with partial aircraft consideration is built. We proposed a new multi-commodity model for the crew schedule recovery. The main advantage of such model is that it is much more efficient to integrate the flight-scheduling and aircraft consideration. New constraints are incorporated to guarantee that the aircraft connections generated in the stage 1 are still feasible. Two stages are run iteratively until no improvement can be achieved. Experimental results show that our method can provide better recovery solutions compared with the benchmark algorithms.

Non-Real-Time Network Traffic in Software-Defined Networking: A Link Bandwidth Prediction-Based Algorithm

Network traffic control is the process of managing, prioritizing, controlling or reducing the network traffic by the network scheduler. High utilization of link bandwidth is very significant for network control and maintenance in Software-Defined Networking (SDN). When we get the accurate link bandwidth predictions for T time periods of the future in a specific network topology, the residual link bandwidth could be determined by the link bandwidth capacity and corresponding prediction values. Given the non-real-time request pairs, this process can be transformed into a multi-commodity flow model. But the traditional multi-commodity model has not introduced the time dimension. In this paper, the model associated with the time dimension is to complete the transmission of the non-real-time network traffic. However, in consideration of the large scale of the problem, a heuristic algorithm on the basis of greedy strategy is proposed to schedule the non-real-time network traffic properly. The experiments show that the heuristic algorithm is superior to global optimization in computing speed and the single path resulting from heuristic algorithm occupies fewer links in the network topology for the non-real-time network traffic.

Beyond Ricardian Model: An Optimal Commodity Distribution Based on Absolute Advantage for Multi-Country Multi-Commodity

Ricardo’s comparative advantage model asserts that international trade will make every single participantcountry better off if they traded goods in which they have comparative advantage. Tian (2008) has generalizedand expanded this Ricardo's 2-country, 2-commodity comparative advantage into a multi-country,multi-commodity model. His methodology, however, occasionally fails to achieve optimal commoditydistribution or to facilitate international trade even if it succeeds in optimal distribution, for it results in a highdegree of difference in countries’ commodity prices. This paper proposes an algorithm that selects countries’comparatively advantageous goods for multi-country, multi-commodity model based on their absolute advantage.The proposed algorithm simply selects a commodity with the maximum absolute advantage - the minimum price- from each country and reassigns commodities of over-assigned countries to under-assigned countries. Thisabsolute advantage model (AAA) is found to be much simpler than Tian’s comparative advantage method and toyield superior results.

Flattening and redistribution of the CAP direct payments for the EU27 regions

 The paper presents key results regarding a possible reform of the Common Agricultural Policy direct payments, based on a scenario analysis by the CAPRI (Common Agricultural Policy Regionalized Impact) modelling system. Combining aggregate programming models at the NUTS 2 level with a global spatial multi-commodity model, it enables depicting the impacts of different policy and economic scenarios from regional to the global scale. The paper discusses simulated impacts on farm income and agricultural markets from implementing the European flat rate hectare payment corrected for the purchasing power disparities across the Member States while reducing the overall budget outlays for direct payments by 50% and dismantling the remaining coupled support to ruminants. The results are an outcome of a comparative static analysis against a reference scenario which assumes the Health Check policy in 2020. The model results suggest a drop of the agricultural gross value added by 9% at the aggregate EU27 level compared to the reference scenario. Impacts differ between the Member States groups, Member States and regions, depending on the share of premiums in the income from agriculture, specialization and competitiveness of production. The largest reduction is projected for the suckler cow herd, dropping by 6% compared to the reference scenario. The drop is caused by removing the coupled support and affecting mostly the herds in Spain and France.    

New Methodology for Transportation Investment Decisions with Consideration of Project Interdependencies

A new methodology for transportation project evaluation and selection explicitly addresses issues of networkwide impacts of a project and interdependencies of multiple projects. The methodology consists of two key elements: a multicommodity minimum-cost network model to estimate networkwide benefits of a single project or multiple interdependent projects and a hypergraph Knapsack model to identify the best subcollection of projects to yield maximized overall benefits. The proposed methodology is applied in a computational study with data on roadway network design, geometrics, travel demand, and traffic operations as well as the scope and costs of six major projects proposed for investment in the Loop area of Chicago, Illinois. The study reveals that the overall benefits with consideration of project networkwide impacts and their interdependencies are significantly lower by 38% to 64% as compared with those benefits established without consideration of project interdependencies, and the rate of increase in overall benefits follows a diminishing trend with higher budget levels; these findings suggest that an optimal level of investment could be established. The proposed methodology may be refined by developing a decision rule to identify a reasonably small number of subcollections of projects guaranteed to contain the optimal solution so that it could practically support large-scale transportation investment decisions.