Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach

Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation network within a stadium as a hierarchical directed network. We propose a multi-objective optimization approach to solve the evacuation routing problem on the basis of this hierarchical directed network. This problem involves three objectives that need to be achieved simultaneously, such as minimization of total evacuation time, minimization of total evacuation distance and minimal cumulative congestion degrees in an evacuation process. To solve this problem, we designed a modified ant colony optimization (ACO) algorithm, implemented it in the MATLAB software environment, and tested it using a stadium at the Wuhan Sports Center in China. We demonstrate that the algorithm can solve the problem, and has a better evacuation performance in terms of organizing evacuees’ space-time paths than the ACO algorithm, the kth shortest path algorithm and the second generation of non-dominated sorting genetic algorithm were used to improve the results from the kth shortest path algorithm.     

A polynomial-time heuristic for the quay crane double-cycling problem with internal-reshuffling operations

One of great challenges in seaport management is how to handle containers under reshuffling, called reshuffles. Repositioning reshuffles in a bay (internal reshuffling) can improve the efficiency of quay cranes and help ports to reduce ship turn-around time. This paper studies the quay crane double-cycling problem with internal-reshuffling operations, and presents a fast solution algorithm. To reduce the number of operations necessary to turn around a bay of a vessel, the problem is first formulated as a new integer program. A polynomial-time heuristic is then developed. The analysis is made on the worst-case error bound of the proposed algorithm. Results are presented for a suite of combinations of problem instances with different bay sizes and workload scenarios. Comparisons are made between our algorithm and the start-of-the-art heuristic. The computational results demonstrate that our model can be solved more efficiently with CPLEX than the model proposed by Meisel and Wichmann (2010), and the proposed algorithm can well solve real-world problem instances within several seconds.     

Aircraft line maintenance scheduling and optimisation

With the advent of new technologies and more modern aircraft, many of the maintenance jobs traditionally scheduled for periodic block checks can now be performed in the ‘‘line maintenance” environment, i.e., during layovers between scheduled flights of an aircraft. This flexibility can be exploited to reduce maintenance costs and improve fleet utilisation of an airline. In this paper we introduce and study the Line Maintenance Scheduling Problem (LMSP). The LMSP assigns jobs to available maintenance opportunities, defined by aircraft routes, and sets the starting time for each job. Its objective is to minimise the deviation from this schedule with respect to given due dates for each task, without exceeding resource capacity at the airports at any moment. We formulate the LMSP as a mixed integer programming problem, and describe and compare two solution approaches for this problem: an integrated exact solution algorithm, which solves job assignment and timetabling simultaneously, and a sequential, heuristic approach. We tested our algorithms on a set of instances inspired on data provided by an industry partner. Our experiments show the applicability of both approaches on realistic settings: the exact approach was able to find the optimal solution for all instances, in less than 10 min on average. Our analysis also shows with an example that line maintenance can be more efficient when capacity is spatially spread, even if the total capacity is reduced.     

Stochastic fleet deployment models for public bicycle rental systems

This paper presents two stochastic bike deployment (SBD) models that determine the optimal number of bicycles allocated to each station in a leisure-oriented public bicycle rental system with stochastic demands. The SBD models represent the stochastic demands using a set of scenarios with given probabilities. A multilayer bike-flow time-space network is constructed for developing the models, where each layer corresponds to a given demand scenario and effectively describes bicycle flows in the spatial and temporal dimensions. As a result, the models are formulated as the integer multi-commodity network flow problem, which is characterized as NP-hard. We propose a heuristic to efficiently obtain good quality solutions for large-size model instances. Test instances are generated using real data from a bicycle rental system in Taiwan to evaluate the performance of the models and the solution algorithm. The test results show that the models can help the system operator of a public bicycle system make effective fleet deployment decisions.     

A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district

We propose a multi-depot location-routing model considering network failure, multiple uses of vehicles, and standard relief time. The model determines the locations of local depots and routing for last mile distribution after an earthquake. The model is extended to a two-stage stochastic program with random travel time to ascertain the locations of distribution centers. Small instances have been solved to optimality in GAMS. A variable neighborhood search algorithm is devised to solve the deterministic model. Computational results of our case study show that the unsatisfied demands can be significantly reduced at the cost of higher number of local depots and vehicles.     

A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling

We propose an efficient evolutionary multi-objective optimization approach to the capacitated facility location–allocation problem (CFLP) for solving large instances that considers flexibility at the allocation level, where financial costs and CO₂ emissions are considered simultaneously. Our approach utilizes suitably adapted Lagrangian Relaxation models for dealing with costs and CO₂ emissions at the allocation level, within a multi-objective evolutionary framework at the location level. Thus our method assesses the robustness of each location solution with respect to our two objectives for customer allocation. We extend our exploration of selected solutions by considering a range of trade-offs for customer allocation.     

Branch-and-price algorithm for the location-routing problem with time windows

This study proposes a branch-and-price algorithm to solve the Location-Routing Problem with Time Windows (LRPTW) which has never been attempted with the exact solutions before. The problem is solved by the simplex algorithm in the master problem and elementary shortest path problems with resource constraint corresponding to column generation in the subproblem until only the non-negative reduced cost columns remain. The proposed algorithm can solve many testing instances effectively. The computational results and the effect of time windows are also compared and discussed.     

A generalized algebraic model for optimizing inventory decisions in a multi-stage complex supply chain

In this paper, we deal with more generalized inventory coordination mechanism in an n-stage, multi-customer, non-serial supply chain, where we extend and generalize pervious works that use algebraic methods to optimize this coordinated supply chain. We establish the recursive expressions for this multi-variable optimization problem. These expressions are used for the derivation of the optimal replenishment policy and the development of the solution algorithm. Further, we describe a simple procedure that can help in sharing the coordination cost benefits to induce all stages to adopt the inventory coordination mechanism. We provide a numerical example for illustrative purposes.     

A multi-start heuristic approach for the split-delivery vehicle routing problem with minimum delivery amounts

We propose a new multi-start solution approach for the split-delivery vehicle routing problem with minimum delivery amounts (SDVRP-MDA). Initial solutions are generated by both node-insertion and route-addition procedures with a single parameter to control the restart. These solutions are then improved by a variable neighborhood descent metaheuristic with a novel search operator inspired by node-ejection chains. We test the proposed approach with 32 benchmark instances for four different minimum delivery fractions. Using the proposed algorithm, out of 128 cases tested, we find 81 best known solutions and 34 new best solutions; overall, we find 43 new best solutions.     

A milk collection problem with blending

A milk collection problem with blending is introduced. A firm collects milk from farms, and each farm produces one out of three possible qualities of milk. The revenue increases with quality, and there is a minimum requirement at the plant for each quality. Different qualities of milk can be blended in the trucks, reducing revenues, but also transportation costs, resulting in higher profit. A mixed integer-programming model, a new cut, and a branch-and-cut algorithm are proposed to solve medium-sized instances. A three-stage heuristic is designed for large instances. Computational experience for test instances and a large-sized real case is presented.     

Tactical crew planning in railways

Tactical crew capacity planning problem in railways involves finding the minimum number of crews in a region required to operate a predetermined set of train duties satisfying the strict day-off requirement for crew. For the single-region problem, we develop two solution approaches based on a space–time network representation: the sequential approach and the integrated approach. We also study the multi-regional capacity planning problem where we minimize total system-wide capacity by simultaneously considering multiple regions within a neighborhood search algorithm based on our solution methods for the single-region problem. We present the computational study on problem instances from Turkish State Railways.     

Optimization of postal express line network under mixed driving pattern of trucks

Details about the movement of trucks on postal express lines are investigated to improve the performances of mail distribution. A mixed driving pattern of trucks is introduced to minimize the transportation cost of a postal express line network with a service level requirement. We formulate this problem as a mixed p meeting depots location with shipment scheduling problem and build a MINLP model. A two-level tabu search procedure based on shipment grouping method is developed. Through a series of computational experiments and sensitivity analysis on different instances, some managerial insights of the network under mixed driving pattern are revealed.     

Min–Max exact and heuristic policies for a two-echelon supply chain with inventory and transportation procurement decisions

We study the problem in which one supplier delivers a product to a set of retailers over time by using an outsourced fleet of vehicles. Since the probability distribution of the demand is not known, we provide a Min–Max approach to find robust policies. We show that the optimal Min-Expected Value policy can be very poor in the worst case. We provide a Min–Max Dynamic Programming formulation that allows us to exactly solve the problem in small instances. Finally, we implement a Min–Max Matheuristic to solve benchmark instances and show that it is very effective.     

Mixed truck delivery systems with both hub-and-spoke and direct shipment

This paper studies a mixed truck delivery system that allows both hub-and-spoke and direct shipment delivery modes. A heuristic algorithm is developed to determine the mode of delivery for each demand and to perform vehicle routing in both modes of deliveries. Computational experiments are carried out on a large set of randomly generated problem instances to compare the mixed system with the pure hub-and-spoke system and the pure direct shipment system. The experiment results show that the mixed system can save around 10% total traveling distance on average as compared with either of the two pure systems.     

Multi-period heterogeneous vehicle routing considering carbon emission trading

This study considers the problem of determining heterogeneous vehicle routes in each period of a given planning horizon while satisfying service combinations, customer demands and vehicle capacities. The objective is to minimize the sum of vehicle operation costs and carbon emission trading cost/benefit, where the trading cost is incurred to purchase the carbon emission right if the total emission exceeds an upper limit in each period, while the trading benefit can be obtained by selling the right in each period, otherwise. A mixed integer programming model is developed to formulate the problem mathematically. Then, a tabu search algorithm is proposed that incorporates the characteristics of the heterogeneous and the period vehicle routing problems while considering the amount of carbon emission in each period. Computational experiments were done on modified benchmark instances and additional random instances, and the results show that the multi-period approach outperforms the existing single-period one in overall average. In particular, the test results show that the multi-period approach can reduce carbon emission more significantly than the single-period one without sacrificing the total cost.     

Arc routing problems to restore connectivity of a road network

After a disaster, restoring accessibility in the affected area is critical for response operations. We study two arc routing problems for clearing blocked roads. The first problem minimizes the time to reconnect the road network, while the second maximizes the total benefit gained by reconnecting network components within a time limit. For each problem, we develop a mixed integer programming formulation and two versions of a heuristic algorithm. We conduct computational experiments on Istanbul data and instances adapted from the literature. The heuristics achieve near-optimal or optimal solutions quickly in most of the tested instances.     

A bi-objective model for planning and managing rail-truck intermodal transportation of hazardous materials

We propose a bi-objective optimization framework for routing rail-truck intermodal shipments with hazardous materials, when shippers and receivers have access to alternate intermodal terminals. A tabu-search based solution methodology is developed, which together with the optimization framework is applied to realistic size problem instances to gain managerial insights. Our analysis indicates that drayage accounts for a significant portion of transport risk and that it can be reduced by scheduling direct and faster trains; and, that the mix of intermodal trains depends on the interest of the decision-makers, where the resulting traffic can facilitate planning emergency response systems.     

Combining Lagrangian heuristic and Ant Colony System to solve the Single Source Capacitated Facility Location Problem

The facility location problems have been applied extensively in practice. We describe a Multiple Ant Colony System (MACS) to solve the Single Source Capacitated Facility Location Problem (SSCFLP). Lagrangian heuristics have been shown to produce good solutions for the SSCFLP. A hybrid algorithm, which combines Lagrangian heuristic and Ant Colony System (ACS), LH–ACS, is developed for the SSCFLP. The performance of the proposed methods are tested on two sets of benchmark instances and compared with other heuristic algorithms in the literature. The computational results indicate that both MACS and LH–ACS are effective and efficient for the SSCFLP and competitive with other well-known algorithms.     

A stochastic programming winner determination model for truckload procurement under shipment uncertainty

We propose a two-stage stochastic integer programming model for the winner determination problem (WDP) in combinatorial auctions to hedge the shipper’s risk under shipment uncertainty. The shipper allows bids on combinations of lanes and solves the WDP to determine which carriers are to be awarded lanes. In addition, many other important comprehensive business side constraints are included in the model. We demonstrate the value of the stochastic solution over one obtained by a deterministic model based on using average shipment volumes. Computational results are given that indicate that moderately sized realistic instances can be solved by commercial branch and bound solvers in reasonable time.     

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.