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Location and allocation problems in supply chain networks are considered as strategic decisions; because they both require investment and have long-term effects. On the other hand, the supply chain network is not protected against disruptions in the real world. In this paper, a branch and efficiency (B&E) algorithm is developed which integrates a multi-objective optimization model used for designing the supply chain network with an extended data envelopment analysis (EDEA) model. Through this integration, efficient solutions are obtained that lead to minimization of the costs. The objective functions of the optimization model include the operational costs, resilience costs and inequality in satisfying customer demand. Then, the efficiency of the solutions is measured using EDEA in terms of the costs, service level and traffic congestion. The solutions derived from EDEA are added to the multi-objective optimization model based on efficiency cuts. This iterative procedure continues until an efficient design is developed for the supply chain network. The proposed B&E algorithm is implemented on a real case using fuzzy goal programming to illustrate its applicability. The results show that the proposed algorithm has better performance in reducing the costs and measuring efficiency compared to the competing algorithms in the literature. [ABSTRACT FROM AUTHOR]

In marine container drayage (CD), which refers to the transport of goods over a short distance, empty containers (ECs) are required to be transported long distances between a terminal and a shipper/consignee by conventional transportation. This causes truck traffic congestion in harbor districts that increases CO2 emissions (CO2EM). This study addresses the vehicle dispatch problem with respect to the environmental impact of marine CD, considering the tractor dwell time is long at the customer site due to cumbersome container loading/unloading (L/U). We model this problem as an extended version of the vehicle routing problem with precedence constraints to minimize the total CO2 weighted travel distance. To improve CD operation in long L/U time, we propose a new practice for CD operations. Additionally, we develop a mixed integer programing model (MIP) for the new practice representation, and propose a Simulated Annealing (SA) based heuristic approach to solving the new practice instance. By implementing our proposed new operation, in a long L/U time, the CO2EM, EC move and number of tractors required can also be reduced by allowing uncoupled tractor move. [ABSTRACT FROM AUTHOR]

We consider a non-atomic congestion game on a graph, with several classes of players. Each player wants to go from his origin vertex to his destination vertex at the minimum cost and all players of a given class share the same characteristics: cost functions on each arc, and origin-destination pair. Under some mild conditions, it is known that a Nash equilibrium exists, but the computation of such an equilibrium in the multiclass case is an open problem for general functions. We consider the specific case where the cost functions are affine. We show that this problem is polynomially solvable when the number of vertices and the number of classes are fixed. In particular, it shows that the parallel two-terminal case with a fixed number of classes is polynomially solvable. On a more practical side, we propose an extension of Lemke's algorithm able to solve this problem. [ABSTRACT FROM AUTHOR]

This research presents a two-stage stochastic programming model that is used to design and manage a biomass co-firing supply chain network under feedstock supply uncertainty. The model we propose extends current models by taking congestion effects into account. A non-linear cost term is added in the objective function representing the congestion factor which increases exponentially as flow of biomass approaches the capacity of multi-modal facility. We first linearize the model and then use a nested decomposition algorithm to obtain a feasible solution in a reasonable amount of time. The nested decomposition algorithm that we propose combine constraint Generation algorithm with a sample average approximation and Progressive Hedging (PH) algorithm. We apply some heuristics such as rolling horizon algorithm and variable fixing technique to enhance the performance of the PH algorithm. We develop a case study using data from the states of Mississippi and Alabama and use those regions to test and validate the performance of the proposed algorithm. Numerical experiments show that the proposed algorithm can solve large-scale problems with a larger number of scenarios and time periods to a near optimal solution in a reasonable amount of time. Results obtained from the experiments reveal that the delivery cost increases and less hubs with higher capacity are selected if we take congestion cost into account. [ABSTRACT FROM AUTHOR]

Motivated by the strategic importance of congestion management, in this paper we present a model to design hub-and-spoke networks under stochastic demand and congestion. The proposed model determines the location and capacity of the hub nodes and allocate non-hub nodes to these hubs while minimizing the sum of the fixed cost, transportation cost and the congestion cost. In our approach, hubs are modelled as spatially distributed M/G/1 queues and congestion is captured using the expected queue lengths at hub facilities. A simple transformation and a piecewise linear approximation technique are used to linearize the resulting nonlinear model. We present two solution approaches: an exact method that uses a cutting plane approach and a novel genetic algorithm based heuristic. The numerical experiments are conducted using CAB and TR datasets. Analysing the results obtained from a number of problem instances, we illustrate the impact of congestion cost on the network topology and show that substantial reduction in congestion can be achieved with a small increase in total cost if congestion at hub facilities is considered at the design stage. The computational results further confirm the stability and efficiency of both exact and heuristic approaches. [ABSTRACT FROM AUTHOR]

The production and distribution planning problem, where incomplete information from the manufacturer factories is available to the distributor, is prevalent in the real world. Yet, researchers have not focused sufficiently on this field of research. Therefore, our paper offers a two-level multi-objective optimization of production and distribution planning for the two-stage supply chain based on the Data Envelopment Analysis method, which minimizes both the cost and the traffic congestion caused by the establishment of distributor warehouses. In addition, transparency through the blockchain technology and uncertain conditions through stochastic and fuzzy programming are considered in the supply chain design. The proposed model is first transformed by using Karush–Kuhn–Tucker into single-level linear programming and then solved by fuzzy goal programming. Next, the obtained solutions are measured and sorted in terms of efficiency. In this regard, the solutions are categorized in terms of cost-based strategies, traffic congestion, demand satisfaction, and optimality. For the purpose of enabling comprehensive supply chain management, the solutions are aggregated according to various strategies and the results show that two solutions are the most efficient, one focusing on cost and the other focusing on traffic congestion. [ABSTRACT FROM AUTHOR]

Peak-hour week-day traffic congestion is a common challenge in urban mobility. Promotion of commuter cycling can help in alleviating this problem in many cities. This paper takes a data analytics approach to propose policies for promoting commuter cycling in Singapore. It uses farecard data to assess the commuter cycling potential and develops a data-driven approach to policy making. A spatio-temporal analysis of farecard data helps in finding patterns in the potential demand for first-mile as well as end-to-end cycling. This analysis is used to suggest policies like cycling towns to promote first-mile cycling and cycling regions to enable end-to-end cycling by linking together the cycling towns. Furthermore, an optimization model is developed to make efficient choice of cycling towns and links for a given budget so as to maximize the potential number of commuter cyclists. [ABSTRACT FROM AUTHOR]

The sensor location problem is that of locating the minimum number of traffic sensors at intersections of a road network such that the traffic flow on the entire network can be determined. In this paper, we provide a new necessary condition on the location of these sensors to enable the traffic flow throughout the network to be computed. This condition is not sufficient in general, but we show that for a large class of problem instances, the condition is sufficient. Many typical road networks are included in this category, and we show how our condition can be used to inform traffic sensor placement. [ABSTRACT FROM AUTHOR]

This research investigates the traffic police routine patrol vehicle (RPV) assignment problem on an interurban road network through a series of integer linear programs. The traffic police RPV's main task, like other emergency services, is to handle calls-for-service. Emergency services allocation models are generally based on the shortest path algorithm however, the traffic police RPV also handles other roles, namely patrolling to create a presence that acts as a deterrence, and issuing tickets to offenders. The RPVs need to be located dynamically on both hazardous sections and on roads with heavy traffic in order to increase their presence and conspicuousness, in an attempt to prevent or reduce traffic offences, road accidents and traffic congestion. Due to the importance of the traffic patrol vehicle's location with regard to their additional roles, allocation of the RPVs adheres to an exogenous, legal, time-to-arrival constraint. We develop location-allocation models and apply them to a case study of the road network in northern Israel. The results of the four models are compared to each other and in relation to the current chosen locations. The multiple formulations provide alternatives that jointly account for road safety and policing objectives which aid decision-makers in the selection of their preferred RPV assignments. The results of the models present a location-allocation configuration per RPV per shift with full call-for-service coverage whilst maximizing police presence and conspicuousness as a proxy for road safety. [ABSTRACT FROM AUTHOR]

Evacuations are massive operations that create heavy travel demand on road networks some of which are experiencing major congestions even with regular traffic demand. Congestion in traffic networks during evacuations, can be eased either by supply or demand management actions. This study focuses on modeling demand management strategies of optimal departure time, optimal destination choice and optimal zone evacuation scheduling (also known as staggered evacuation) under a given fixed evacuation time assumption. The analytical models are developed for a system optimal dynamic traffic assignment problem, so that their characteristics can be studied to produce insights to be used for large-scale solution algorithms. While the first two strategies were represented in a linear programming (LP) model, evacuation zone scheduling problem inevitable included integers and resulted in a mixed integer LP (MILP) one. The dual of the LP produced an optimal assignment principle, and the nature of the MILP formulations revealed clues about more efficient heuristics. The discussed properties of the models are also supported via numerical results from a hypothetical network example. [ABSTRACT FROM AUTHOR]

Car pooling is one method that can be easily instituted and can help to resolve a variety of problems that continue to plague urban areas, ranging from energy demands and traffic congestion to environmental pollution. Although car pooling is becoming more common, in practice, participant matching results are still being obtained by an inefficient manual approach, which may possibly result in an inferior solution. In the past, when car pooling studies have been done the problem has been treated as either a to-work problem (from different origins to the same destination) or return-from-work problem (from the same origin to different destinations). However, in this study we employ a time-space network flow technique to develop a model for the many-to-many car pooling problem with multiple vehicle types and person types. The model is formulated as an integer multiple commodity network flow problem. Since real problem sizes can be huge, it could be difficult to find optimal solutions within a reasonable period of time. Therefore, we develop a solution algorithm based on Lagrangian relaxation, a subgradient method, and a heuristic for the upper bound solution, to solve the model. To test how well the model and the solution algorithm can be applied to real world, we randomly generated several examples based upon data reported from a past study carried out in northern Taiwan, on which we performed numerical tests. The test results show the effectiveness of the proposed model and solution algorithm. [ABSTRACT FROM AUTHOR]

In this paper we address the Sensor Location Problem, that is the location of the minimum number of counting sensors, on the nodes of a network, in order to determine the arc flow volume of all the network. Despite the relevance of the problem from a practical point of view, there are very few contributions in the literature and no combinatorial analysis is performed to take into account particular structure of the network. We prove the problem is $$\cal N \cal P$$ -complete in different cases. We analyze special classes of graphs that are particularly interesting from an application point of view, for which we give low order polynomial solution algorithms. [ABSTRACT FROM AUTHOR]

This paper deals with the transit passenger origin-destination (O-D) estimation problem by using updated passenger counts in congested transit networks and outdated prior O-D matrix. A bilevel programming approach is extended for the transit passenger O-D updating problem where the upper-level problem seeks to minimize the sum of error measurements in passenger counts and O-D matrices, while the lower level is the stochastic user equilibrium assignment problem for congested transit networks. The transit assignment framework is based on a frequency-adaptive transit network model in this paper, which can help determine transit line frequencies and the network flow pattern simultaneously in congested transit networks. A heuristic solution algorithm is adapted for solving the transit passenger O-D estimation problem. Finally, a numerical example is used to illustrate the applications of the proposed model and solution algorithm. [ABSTRACT FROM AUTHOR]

Radio traffic congestion occurs in a radio network when too many users simultaneously transmit on the same channel within close geographical proximity. Such congestion can be prevented by imposing channel loading constraints, which restrict the number of users that can operate using the same channel at one instance in time. Channel loading constraints are a variety of non-binary constraint that can be represented as subsets of transmitters which cannot operate on the same channel. This makes the computational cost in generating and storing them significant. In this paper we present an efficient technique for making an assignment when channel loading constraints are imposed. The method is suitable for implementation on large problem instances. We also propose a technique for finding a lower bound on the minimum span of channels required. The methods we introduce are well suited to the channel loading problem as they eliminate the need for explicit generation of the constraints involved. [ABSTRACT FROM AUTHOR]

The Ant System is a distributed metaheuristic that combines an adaptive memory with a local heuristic function to repeatedly construct solutions of hard combinatorial optimization problems. In this paper, we present an improved ant system algorithm for the Vehicle Routing Problem with one central depot and identical vehicles. Computational results on fourteen benchmark problems from the literature are reported and a comparison with five other metaheuristic approaches for solving Vehicle Routing Problems is given. [ABSTRACT FROM AUTHOR]

Network equilibrium models have recently been enhanced by explicitly incorporating link capacity constraints, resulting in queueing network equilibrium traffic patterns. Unfortunately, queueing delays at equilibrium are not unique, and thus models suffer deficiencies that limit their applications in network traffic control and management. This article proposes an entropymaximizing method to determine the most likely equilibrium traffic queueing pattern in a capacity constrained network. Queue-weighting factors or queueing probabilities are estimated based on the location of the saturated links in the network, and incorporated into an entropymaximizing model of the queueing distribution. The proposed method would significantly enhance the application of the current queueing network equilibrium models in the design and operation of congested traffic systems. [ABSTRACT FROM AUTHOR]

An analytical model for the determination of the number and locations of time points as well as the amount of slack times in transit schedule design is developed. The model considers a bus route with a special passenger demand pattern in which all boarding passengers coordinate their arrivals at each stop in such a way that they never miss their intended bus, and therefore designing the schedule separately, a single run at a time, becomes possible. The model employs the dynamic programming method to deal with the trade-offs among various cost components associated with the schedule quantitatively, and yet is flexible enough to incorporate the existing rules of thumb as well as transit operators' policies. Numerical examples that illustrate the applications of the model are given. The model, although not quite applicable to bus routes with general passenger demand patterns, is useful in the analysis of the contributing factors to the design of an economical, reliable, and operational transit schedule, and is likely to be adaptable for more realistic cases. [ABSTRACT FROM AUTHOR]

This paper formulates two dynamic network traffic assignment models in which O-D desires for the planning horizon are assumed known a priori: the system optimal (SO) and the user equilibrium (UE) time-dependent traffic assignment formulations. Solution algorithms developed and implemented for these models incorporate a traffic simulation model within an overall iterative search framework. Experiments conducted on a test network provide the basis for a comparative analysis of system performance under the SO and UE models. [ABSTRACT FROM AUTHOR]

The problem of locating flow-intercepting facilities on a network with probabilistic customer flows and with facility set-up costs is studied in this paper. Two types of models are investigated, namely the double-counting model and the no-double-counting model (double-counting refers to multiple interceptions of the same customer). For each model, a nonlinear integer programming formulation is first obtained via the theory of Markov chains, and an equivalent linear integer program is then derived. A simple greedy heuristic is proposed for solving both models and a worst-case bound is established, which is shown to be tight under certain conditions. [ABSTRACT FROM AUTHOR]

We consider an example to show that the minimum instantaneous cost path principle, as suggested in Friesz et al. [1] for generalising Wardrop's first principle to the dynamic state, may cause some drivers' routes to ‘loop’. These looping routes traverse the same link more than once - indeed, in our example six times. [ABSTRACT FROM AUTHOR]

A basic issue in the planning of urban public transport is the determination of headways or inter-dispatch times. During each season, i.e. distinct time-period whose demand characteristics are constant, the following tradeoff must be considered. Dispatching too many vehicles on a route causes high operating costs, while too few vehicles may result in unsatisfactory levels of service. An appropriate policy on headways will help to balance resources between lines (routes) in peak-demand hours and will influence the total number of buses acquired by a transit company. Previous practice in industry usually bases the planning of headways upon satisfying service criteria on a ‘most-congested segment’. This approach reduces the problem from that of studying a route to that of a single segment (stop), but thereby fails to account for other important information about the line's characteristics. In this article, we develop two new service criteria which consider the line as a whole: (1) ‘crowding-overdistance’ takes into account discomfort resulting from a vehicle carrying too many passengers, and the corresponding distance travelled; and (2) ‘probability-of-failure’, the frequency with which a waiting passenger fails to board due to lack of space. COD will be analyzed using simulation. POF will be related to a time-dependent Markov chain that is ‘inhomogeneous’ in terms of distance along the route. Optimal headways are those which dispatch the smallest number of buses while meeting the particular service criterion. Models based on each of the two criteria are illustrated and applied to a number of routes of the Israeli transit company, DAN. [ABSTRACT FROM AUTHOR]