A hybrid Tabu Search approach for the design of a paper recycling network

This paper addresses the design of a paper recycling network including external procurement, in-house recycling of paper, technology selection and selling or disposing of co-products. In contrast to the literature, we consider a combined continuous and discrete facility location problem solved by a hybrid Tabu Search approach to enhance candidate facility locations. We provide rules of thumb regarding the length of the tabu list and suggestions regarding runtime boundaries in cases where pre-evaluations are elaborate. The results based on a real-life application case show the potential savings of in-house paper recycling compared to the solely external procurement of recovered paper.     

Fault tolerance modeling for an e-waste recycling supply chain

This paper applies a fault-tolerant method that addresses the problem of steady fulfillment in an e-waste recycling supply chain. Due to the double-ended fluctuations in this supply chain, conventional, optimization based, model predictive control cannot be applied without proper improvement. This paper contributes to the e-waste management literature in the initiation of prudent demand management. The inventory management problem is investigated for a double-ended fluctuation for the e-waste recycling supply chain. It is found that the risk-sensitive predictive control method allows all members to retain a constant inventory level, for which the control target is unclear and the demand fluctuation is high.     

The case of Paratransit - ‘Trotro’ service data as a credible location addressing of road networks in Ghana

Road networks are by far the largest mobility infrastructure in Sub-Sahara Africa, and are key to providing access to economic and social opportunities. Yet the majority of road networks in Ghana and other countries in Sub-Sahara Africa are not geographically referenced, to facilitate their uses for road maintenance and management, transport planning, emergency services, disaster cases, logistics, tourism and other location based services. Consequently, this study takes advantage of paratransit service and the location of landmarks to provide location addressing of road networks in Ghana. The paratransit service, popularly known as Trotro in Ghana is a local transit system that uses an automobile to move people and goods along a prescribed travel route on a road network, with locally known stops where people get on and off the vehicle. The Trotro service is popular in Ghana, yet there is no critical investigation demonstrating its spatial coverage, relevance as a credible location addressing for road networks, or as a comprehensive location data for location based services. This study provides evidence for this case, and investigates the spatial coverage of road networks used by the Trotro vehicles, and the mapping of their service stops together with landmarks in the Asokore Mampong Municipality (AMM) of Ashanti region in Ghana. It was found that the location data from both Trotro service and landmarks covered about 86% of the entire road network in AMM; providing a large coverage of the road network and greater geographic detail. A service area estimation undertaken using the generated location data shows a high geographic accessibility, with travel distances as low as 160 m from any location to the nearest Trotro service or landmark covering about 80% of the road networks. These results show the capability of the generated location data to tackle the problem of spatially unreferenced road networks, and to significantly improve their effective uses. The popularity of paratransit in Sub-Sahara Africa means that this study can be adapted to other countries where the majority of the road networks are not spatially referenced.     

A location-routing problem with disruption risk

This paper considers a location-routing problem in a supply-chain network with a set of producer–distributors that produce a single commodity and distribute it to a set of customers. The production capacity of each producer–distributor varies randomly due to a variety of possible disruptions, and the vehicles involved in the distribution system are disrupted randomly. The goal is to determine the location, allocation and routing decisions that minimize the annual cost of location, routing and disruption, under one of the moderate, cautious or pessimistic risk-measurement policies. Exact formulations and an efficient heuristic are presented for the problem.     

The competitive facility location problem under disruption risks

Two players sequentially locate a fixed number of facilities, competing to capture market share. Facilities face disruption risks, and each customer patronizes the nearest operational facility, regardless of who operates it. The problem therefore combines competitive location and location with disruptions. This combination has been absent from the literature. We model the problem as a Stackelberg game in which the leader locates facilities first, followed by the follower, and formulate the leader’s decision problem as a bilevel optimization problem. A variable neighborhood decomposition search heuristic which includes variable fixing and cut generation is developed. Computational results suggest that high quality solutions can be found quickly. Interesting managerial insights are drawn.     

Electrification of a city bus network—An optimization model for cost-effective placing of charging infrastructure and battery sizing of fast-charging electric bus systems

The deployment of battery-powered electric bus systems within the public transportation sector plays an important role in increasing energy efficiency and abating emissions. Rising attention is given to bus systems using fast charging technology. This concept requires a comprehensive infrastructure to equip bus routes with charging stations. The combination of charging infrastructure and bus batteries needs a reliable energy supply to maintain a stable bus operation even under demanding conditions. An efficient layout of the charging infrastructure and an appropriate dimensioning of battery capacity are crucial to minimize the total cost of ownership and to enable an energetically feasible bus operation. In this work, the central issue of jointly optimizing the charging infrastructure and battery capacity is described by a capacitated set covering problem. A mixed-integer linear optimization model is developed to determine the minimum number and location of required charging stations for a bus network as well as the adequate battery capacity for each bus line. The bus energy consumption for each route segment is determined based on individual route, bus type, traffic, and other information. Different scenarios are examined in order to assess the influence of charging power, climate, and changing operating conditions. The findings reveal significant differences in terms of required infrastructure. Moreover, the results highlight a trade-off between battery capacity and charging infrastructure under different operational and infrastructure conditions. This paper addresses upcoming challenges for transport authorities during the electrification process of the bus fleets and sharpens the focus on infrastructural issues related to the fast charging concept.     

An optimal approach for a set covering version of the refueling-station location problem and its application to a diffusion model

This article deals with the refueling-station location problem for alternative fuel vehicles in a traffic network. Alternative fuel vehicles can be characterized by the vehicle range that limits the travelable distance with fuel at full capacity. I propose an efficient formulation of the refueling-station location problem using an optimal property and prove that the problem is NP(Non-deterministic Polynomial)-complete in the strong sense. I consider a special case of the refueling-station location problem in which the construction costs are equal for all nodes. In this case, the problem is to determine refueling station locations to minimize the total number of stations, while making the possible multiple predetermined origin–destination round-trips. I propose an optimal algorithm applicable when no refueling stations currently exist in a traffic network and a dynamic programming based algorithm applicable when a set of refueling stations already exists. I apply the algorithms to a traffic network to study the diffusion of refueling stations and predict the speed and range of station establishment. The computational experiments show that the speed of diffusion depends on the vehicle range and the sequence of the origin–destination demands considered in the diffusion process.     

Robust hub network design problem

This paper presents a robust formulation for the uncapacitated single and multiple allocation hub location problem where demand is uncertain and its distribution is not fully specified. The proposed robust model is formulated as a mixed integer nonlinear program and then transformed into a mixed integer conic quadratic program. An efficient linear relaxation strategy is proposed which is found to deliver the optimal solutions for all the cases considered in this paper. Numerical experiments suggest location of more number of hubs when accounting for demand uncertainty using robust optimization compared to the deterministic setting.     

A dynamic hazard-based system of equations of vehicle ownership with endogenous long-term decision factors incorporating group decision making

The transportation system affects all aspects of our daily lives including relatively long-term decisions on work and home location choice and automobile ownership decisions. The interdependency existing among these three decisions jointly influences household mobility and overall travel patterns. Therefore, a dynamic modeling framework that can account for the effects of interdependencies between vehicle transaction behavior and residential and job location choices is highly desirable. These decisions are made in the household level while individuals’ decisions influence the overall outcome; therefore, it is also important to incorporate a group decision making process within such modeling frameworks.This study introduces a dynamic model for vehicle ownership, residential mobility, and employment relocation timing decisions. These decisions are modeled at the individual level and then sequentially aggregated to the household level if it is required. A hazard-based system of equations is formulated and applied in which work location and residential location changes are included as endogenous variables in the vehicle transaction model while other important factors such as land-use and built environment variables, household dynamics, and individuals’ socio-demographics are also considered.     

Charging station location problem of plug-in electric vehicles

A novel model for the charging station location problem of plug-in electric vehicles is proposed in this paper. With the objective of minimizing the total cost, the proposed model simultaneously handles the problem of where to locate the charging stations and how many chargers should be established in each charging station. Furthermore, the model is expanded to consider a more general case. A genetic algorithm-based method is proposed for solving the expanded model. The results show that a better location scheme can be obtained using the expanded model.     

Modelling a rail/road intermodal transportation system

This paper deals with the problem of optimally locating rail/road terminals for freight transport. A linear 0-1 program is formulated and solved by a heuristic approach. The model is applied to the rail/road transportation system in the Iberian Peninsula. Five planning scenarios are considered. It is shown that modal shares are very sensitive to the cost of rail and to that of track gauge changes at the Spanish border. Conversely, the location of the terminals has little or no impact on the market shares of the combined traffic, but location changes in the Peninsula generate consequences on the entire European transportation system.     

Reliable emergency service facility location under facility disruption,en-route congestion and in-facility queuing

The planning of emergency service facility location, especially for those expecting high demand and severe conditions, requires consideration of victims’ en-route travel, in-facility service quality, and reliability of these service facilities themselves. This paper first presents a scenario-based stochastic mixed-integer non-linear program (MINLP) model that integrates facility disruption risks, en-route traffic congestion and in-facility queuing delay into an integrated facility location problem. We derive lower and upper bounds to this highly complex problem by approximating the expected total system costs across the normal and all probabilistic facility disruption scenarios. This allows us to develop a more tractable approximate MINLP formulation and a Lagrangian Relaxation (LR) based solution approach. The relaxed sub-problem for location and service allocation decisions is further reformulated into a second-order conic program. Numerical experiments show that the approximate model and LR solution approach are capable of overcoming the computational difficulties associated with the problem. Interesting findings and managerial insights are obtained from a series of sensitivity analyses, e.g., regarding the importance of considering in-facility queuing in location design, and the significance of resource pooling on the optimal facility deployment.     

Refining trip starting and ending locations when estimating travel-demand at large urban scale

Demand estimation is an important step for multiple applications in urban studies. However, the level of accuracy required depends on the objective of the study. In dynamic traffic microsimulation, the estimation of demand needs to be accurate, as it is intended to describe individuals' trips on a very small-scale. In this case, poor estimation of trip initiation, path, and endings could result in the wrong estimation of the city-block scale's traffic state. Estimating demand at a large-scale with high-resolution is not only very challenging because it requires a large volume of data from multiple sources, but the underlying mathematical problem is considerable and thus hard to solve.In this paper, we address the issue of trip starts and ends when modeling large perimeters. We propose to enhance the location of trip initiation and termination by merging heterogeneous and large public datasets. To do so, we develop a series of algorithms that identify fine-mesh areas where trips could reliably start or end and we share the estimated demand within these sub-areas, following the distribution of trip purposes (Home, Work, Shop, etc.).The method is deployed in Lyon city, France, and validated on an extraction of it. Micro-simulation results show that the demand, once accurately distributed, changes the overall network's performance, confirming the significant influence of trip endings and starts on the overall traffic dynamics.     

The capacitated plant location problem with customers and suppliers matching

This paper introduces a new problem called the capacitated plant location problem with customer and supplier matching (CLCSM). The product distribution from plants to customers and the material supply from suppliers to plants are considered together. We merge a distribution trip and a supply trip into one triangular trip for saving allocation cost. Vehicles from plants visit a customer and a supplier for each trip. We provide a heuristic solution procedure based on Lagrangian relaxation. Computational results indicate that the proposed heuristic solution procedure is shown to be efficient yielding optimal or near-optimal solutions for randomly generated instances.     

A heuristic approach to logistics network design for end-of-lease computer products recovery

This paper discusses the logistics network design for end-of-lease computer products recovery by developing a deterministic programming model for systematically managing forward and reverse logistics flows. Due to the complexity of such network design problem, a two-stage heuristic approach is developed to decompose the integrated design of the distribution networks into a location–allocation problem and a revised network flow problem. The applicability of the proposed method is illustrated in a numerical study. Computational experiments demonstrate that high-quality solutions are obtained while modest computational overheads are incurred.     

Incorporating location,routing and inventory decisions in supply chain network design

This paper for the first time presents a novel model to simultaneously optimize location, allocation, capacity, inventory, and routing decisions in a stochastic supply chain system. Each customer’s demand is uncertain and follows a normal distribution, and each distribution center maintains a certain amount of safety stock. To solve the model, first we present an exact solution method by casting the problem as a mixed integer convex program, and then we establish a heuristic method based on a hybridization of Tabu Search and Simulated Annealing. The results show that the proposed heuristic is considerably efficient and effective for a broad range of problem sizes.     

The optimization model for the location of maritime emergency supplies reserve bases and the configuration of salvage vessels

This paper studies the location–allocation–configuration problem of emergency resources in a maritime emergency system and it proposes a discrete nonlinear integer-programming model, which integrates the location, allocation and the configuration problem. The model is converted into a two-stage model keeping the calculation logic. It designs a hybrid heuristic algorithm and a genetic algorithm. The test results show that the hybrid heuristic algorithm is more efficient than the genetic algorithm, the sensitivity analysis studies the influence of some parameters to the final solution and the Uncertainty–Sensitivity justification tool is used to evaluate the assumptions.     

The time dependent vehicle routing problem with time windows: Benchmark problems, an efficient solution algorithm, and solution characteristics

An algorithm that can tackle time dependent vehicle routing problems with hard or soft time windows without any alteration in its structure is presented. Analytical and experimental results indicate that average computational time increases proportionally to the number of customers squared. New replicable test problems that capture the typical speed variations of congested urban settings are proposed. Solution quality, time window perturbations, and computational time results are discussed as well as a method to study the impact of perturbations by problem type. The algorithm efficiency and simplicity is well suited for urban areas where fast running times may be required.     

A three-level supply chain network design model with risk-pooling and lead times

This paper considers a single-sourcing network design problem for a three-level supply chain consisting of suppliers, distribution centers (DC’s) and retailers, where risk-pooling strategy and DC-to-supplier dependent lead times are considered. The objective is to determine the number and locations of suppliers and DC’s, the assignment of each location-fixed DC to a supplier and that of each retailer to a DC, which minimizes the system-wide location, transportation, and inventory costs. The problem is formulated as a nonlinear integer programming model, for which a two-phase heuristic solution algorithm is derived based on the Lagrangian relaxation approach. Numerical experiments show that the proposed heuristic is effective and also efficient.     

Fleet deployment, network design and hub location of liner shipping companies

A mixed integer linear programming formulation is proposed for the simultaneous design of network and fleet deployment of a deep-sea liner service provider. The underlying network design problem is based on a 4-index (5-index by considering capacity type) formulation of the hub location problem which are known for their tightness. The demand is elastic in the sense that the service provider can accept any fraction of the origin–destination demand. We then propose a primal decomposition method to solve instances of the problem to optimality. Numerical results confirm superiority of our approach in comparison with a general-purpose mixed integer programming solver.