Hybridization of tabu search with feasible and infeasible local searches for periodic home health care logistics

This paper addresses a periodic vehicle routing problem encountered in home health care (HHC) logistics. It extends the classical Periodic Vehicle Routing Problem with Time Windows (PVRPTW) to three types of demands of patients at home. Demands include transportation of drugs/medical devices between the HHC depot and patients׳ homes, delivery of special drugs from the hospital to patients, and delivery of blood samples from patients to the lab. Each patient requires a certain number of visits within a planning horizon and has a set of possible combinations of visit days. Daily routing should meet time window constraints associated with patients, the hospital and the lab. The problem consists in determining the visit days of each patient and vehicle routes for each day in order to minimize the maximal routing costs among all routes over the horizon. We propose a Tabu Search method combined with different local search schemes including both feasible and infeasible local searches. The proposed approaches are tested on a range of instances derived from existing Vehicle Routing Problem with Time Window (VRPTW) benchmarks and benchmarks on special cases of our problem. Numerical results show that local search scheme starting with an infeasible local search with a small probability followed by a feasible local search with high probability is an interesting hybridization. Experiments with field data from a HHC company show that the proposed approach reduces the total cost and better balances the workloads of vehicles.     

Integrated framework for robustness analysis using ratio-based efficiency model with application to evaluation of Polish airports

We consider a problem of evaluating efficiency of Decision Making Units (DMUs) based on their deterministic performance on multiple consumed inputs and multiple produced outputs. We apply a ratio-based efficiency measure, and account for the Decision Maker׳s preference information representable with linear constraints involving input/output weights. We analyze the set of all feasible weights to answer various robustness concerns by deriving: (1) extreme efficiency scores and (2) extreme efficiency ranks for each DMU, (3) possible and necessary efficiency preference relations for pairs of DMUs, (4) efficiency distribution, (5) efficiency rank acceptability indices, and (6) pairwise efficiency outranking indices. The proposed hybrid approach combines and extends previous results from Ratio-based Efficiency Analysis and the SMAA-D method. The practical managerial implications are derived from the complementary character of accounted perspectives on DMUs׳ efficiencies. We present an innovative open-source software implementing an integrated framework for robustness analysis using a ratio-based efficiency model on the diviz platform. The proposed approach is applied to a real-world problem of evaluating efficiency of Polish airports. We consider four inputs related to the capacities of a terminal, runways, and an apron, and to the airport׳s catchment area, and two outputs concerning passenger traffic and number of aircraft movements. We present how the results can be affected by integrating the weight constraints and eliminating outlier DMUs.     

Linear solution schemes for Mean-SemiVariance Project portfolio selection problems: An application in the oil and gas industry

We study the Mean-SemiVariance Project (MSVP) portfolio selection problem, where the objective is to obtain the optimal risk-reward portfolio of non-divisible projects when the risk is measured by the semivariance of the portfolio׳s Net-Present Value (NPV) and the reward is measured by the portfolio׳s expected NPV. Similar to the well-known Mean-Variance portfolio selection problem, when integer variables are present (e.g., due to transaction costs, cardinality constraints, or asset illiquidity), the MSVP problem can be solved using Mixed-Integer Quadratic Programming (MIQP) techniques. However, conventional MIQP solvers may be unable to solve large-scale MSVP problem instances in a reasonable amount of time. In this paper, we propose two linear solution schemes to solve the MSVP problem; that is, the proposed schemes avoid the use of MIQP solvers and only require the use of Mixed-Integer Linear Programming (MILP) techniques. In particular, we show that the solution of a class of real-world MSVP problems, in which project returns are positively correlated, can be accurately approximated by solving a single MILP problem. In general, we show that the MSVP problem can be effectively solved by a sequence of MILP problems, which allow us to solve large-scale MSVP problem instances faster than using MIQP solvers. We illustrate our solution schemes by solving a real MSVP problem arising in a Latin American oil and gas company. Also, we solve instances of the MSVP problem that are constructed using data from the PSPLIB library of project scheduling problems.     

A Hybrid Genetic—GRASP Algorithm Using Lagrangean Relaxation for the Traveling Salesman Problem

Hybridization techniques are very effective for the solution of combinatorial optimization problems. This paper presents a genetic algorithm based on Expanding Neighborhood Search technique (Marinakis, Migdalas, and Pardalos, Computational Optimization and Applications, 2004) for the solution of the traveling salesman problem: The initial population of the algorithm is created not entirely at random but rather using a modified version of the Greedy Randomized Adaptive Search Procedure. Farther more a stopping criterion based on Lagrangean Relaxation is proposed. The combination of these different techniques produces high quality solutions. The proposed algorithm was tested on numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the algorithms of the DIMACS Implementation Challenge are also presented.     

A multiperiod auto-carrier transportation problem with probabilistic future demands

In this paper we study the problem of delivering finished vehicles from a logistics yard to dealer locations at which they are sold. The requests for cars arrive dynamically and are not announced in advance to the logistics provider who is granted a certain time-span until which a delivery has to be fulfilled. In a real-world setting, the underlying network is relatively stable in time, since it is usually a rare event that a new dealership opens or an existing one terminates its service. Therefore, probabilities for incoming requests can be derived from historical data. The study explores the potential of using such probabilities to improve the day-to-day decision of sending out or postponing cars that are ready for delivery. Apart from the order selection, we elaborate a heuristic to optimize delivery routes for the selected vehicles, whereby special loading constraints are considered to meet the particular constraints of car transportation via road. In a case study, we illustrate the value of introducing probabilistic information to the planning process and compare the quality of different configurations of our approach.     

A hybrid genetic algorithm for train sequencing in the Korean railway

This article addresses the train-sequencing problem encountered in the Korean railway. It first presents a mixed integer programming model for the problem, in which the mileage must be balanced for each train route, while various field constraints must be satisfied, including overnight stay capacity and maintenance allocation restrictions. Then, it proposes a hybrid genetic algorithm as a solution approach to the problem. The proposed algorithm utilizes a modified elite group technique along with two heuristic procedures based on the mixed integer programming model. Finally, the proposed solution approach is tested with real-world data from the Korean railway. Numerical experiments under different conditions indicate that the proposed solution approach to the train-sequencing problem is promising.     

Phased local search for the maximum clique problem

This paper introduces Phased Local Search (PLS), a new stochastic reactive dynamic local search algorithm for the maximum clique problem. (PLS) interleaves sub-algorithms which alternate between sequences of iterative improvement, during which suitable vertices are added to the current clique, and plateau search, where vertices of the current clique are swapped with vertices not contained in the current clique. The sub-algorithms differ in their vertex selection techniques in that selection can be solely based on randomly selecting a vertex, randomly selecting within highest vertex degree or randomly selecting within vertex penalties that are dynamically adjusted during the search. In addition, the perturbation mechanism used to overcome search stagnation differs between the sub-algorithms. (PLS) has no problem instance dependent parameters and achieves state-of-the-art performance for the maximum clique problem over a large range of the commonly used DIMACS benchmark instances.     

Satellite downlink scheduling problem: A case study

The synthetic aperture radar (SAR) technology enables satellites to efficiently acquire high quality images of the Earth surface. This generates significant communication traffic from the satellite to the ground stations, and, thus, image downlinking often becomes the bottleneck in the efficiency of the whole system. In this paper we address the downlink scheduling problem for Canada׳s Earth observing SAR satellite, RADARSAT-2. Being an applied problem, downlink scheduling is characterised with a number of constraints that make it difficult not only to optimise the schedule but even to produce a feasible solution. We propose a fast schedule generation procedure that abstracts the problem specific constraints and provides a simple interface to optimisation algorithms. By comparing empirically several standard meta-heuristics applied to the problem, we select the most suitable one and show that it is clearly superior to the approach currently in use.     

An analytical approach to prototype vehicle test scheduling

The test planning group within Ford׳s Product Development division develops schedules for building prototype vehicles and assigning them to departments in charge of different vehicle components, systems and aspects (e.g., powertrain, electrical, safety). These departments conduct tests at pre-production phases of each vehicle program (e.g., 2015 Ford Fusion, 2016 Ford Escape) to ensure the vehicles meet all requirements by the time they reach the production phase. Each prototype can cost in excess of $200 K because many of the parts and the prototypes themselves are hand-made and highly customized. Parts needed often require months of lead time, which constrains when vehicle builds can start. That, combined with inflexible deadlines for completing tests on those prototypes introduces significant time pressure, an unavoidable and challenging reality. One way to alleviate time pressure is to build more prototype vehicles; however, this would greatly increase the cost of each program. A more efficient way is to develop test plans with tight schedules that combine multiple tests on vehicles to fully utilize all available time. There are many challenges that need to be overcome in implementing this approach, including complex compatibility relationships between the tests and destructive nature of, e.g., crash tests. We introduce analytical approaches for obtaining efficient schedules to replace the tedious manual scheduling process engineers undertake for each program. Our models and algorithms save test planners׳ and engineers׳ time, increases their ability to quickly react to program changes, and save resources by ensuring maximal vehicle utilization.     

A new hybrid heuristic algorithm for the Precedence Constrained Production Scheduling Problem: A mining application

In this work we address the Precedence Constrained Production Scheduling Problem (PCPSP), the problem of scheduling tasks in such a way that total profit is maximized, while satisfying conditions such as precedence constraints among tasks and side constraints. A motivation for addressing this problem comes from open-pit mining industry, where the PCPSP seeks to maximize the net present value of an ore deposit by selecting the blocks (tasks) to extract, their extraction periods and their processing options, while satisfying constraints as precedences among blocks, limited availability of operational resources and maximum and/or minimum allowable concentrations of ore-grade or pollutants. Since real-world models have millions of blocks and constraints, the monolithic problem is computationally intractable. This article presents a hybrid heuristic algorithm that combines a rolling horizon decomposition with a block preselection procedure, allowing near-optimal solutions to be quickly determined. The proposed heuristic was tested on all the PCPSP instances of the MineLib library and has shown a significant improvement over the previous reported results. Moreover, a good feasible solution has been found for the instance W23, for which no solution has been previously reported.     

A reactive tabu search algorithm for the vehicle routing problem with simultaneous pickups and deliveries

The vehicle routing problem with pickups and deliveries (VRPPD) extends the vehicle routing problem (VRP) by allowing customers to both send and receive goods. The main difficulty of the problem is that the load of vehicles is fluctuating rather than decreasing as in the VRP. We design a reactive tabu search metaheuristic that can check feasibility of proposed moves quickly and reacts to repetitions to guide the search. Several new best solutions are found for benchmark problems.     

A memetic algorithm for the patient transportation problem

This paper addresses a real-life public patient transportation problem derived from the Hong Kong Hospital Authority (HKHA), which provides ambulance transportation services for disabled and elderly patients from one location to another. We model the problem as a multi-trip dial-a-ride problem (MTDARP), which requires designing several routes for each ambulance. A route is a sequence of locations, starting and terminating at the depot (hospital), according to which the ambulance picks up clients at the origins and delivers them to the destinations. A route is feasible only if it satisfies a series of side constraints, such as the pair and precedence constraints, capacity limit, ride time, route duration limit and time windows. Owing to the route duration limit, in particular, every ambulance is scheduled to operate several routes during the working period. To prevent the spread of disease, the interior of the ambulances needs to be disinfected at the depot between two consecutive trips. The primary aim of the problem investigated herein is to service more requests with the given resources, and to minimize the total travel cost for the same number of requests. In this paper, we provide a mathematical formulation for the problem and develop a memetic algorithm with a customized recombination operator. Moreover, the segment-based evaluation method is adapted to examine the moves quickly. The performance of the proposed algorithm is assessed using the real-world data from 2009 and compared with results obtained by solving the mathematical model. In addition, the proposed algorithm is adapted to solve the classic DARP instances, and found to perform well on medium-scale instances.     

大规模灾害中基于聚类的医疗物资联合运送优化

应急响应中,往往出现救援物资供应节点与需求节点距离太远、关键道路损毁导致难以及时通过车辆运送物资到灾区等情景,此时直升飞机逐渐被用来运送关键应急资源（如医疗物资及医护人员）。然而,大规模灾害中难以使用直升飞机运送医疗物资到每个医疗救助点,通常考虑灾民的聚集性选择一定数量的应急中转点,以接收直升飞机运送的医疗物资,之后采用车辆运送物资到其覆盖的医疗救助点。针对该问题,提出一种基于聚类的两阶段医疗物资联合运输方法：第一阶段根据医疗救助点分布,采用模糊C-均值算法（FCM）进行应急中转点选择和医疗救助点划分,并针对FCM划分中存在的剩余容量不均衡问题,考虑容量约束提出一种改进划分方法（FCMwCC）,构建“直升飞机-车辆”医疗物资联合运输网络结构;第二阶段建立基于聚类的运送路线优化模型,确定从应急中转点到医疗救助点的具体运送路线。数值实验验证了提出方法和算法的有效性。     

Districting for parcel delivery services – A two-Stage solution approach and a real-World case study

This paper studies a real-world problem arising in the context of parcel delivery. Given a heterogeneous set of resources, i.e., different drivers and different vehicles, the problem for each day consists of assigning a driver and a vehicle to each customer requiring service. Two conflicting aspects must be taken into account. On the one hand, service consistency is desirable, meaning that a customer should always be served by the same driver. On the other hand, daily demand fluctuations and tight resource constraints prohibit fixed resource assignments. With the aim of finding a reasonable compromise between these aspects, we propose a novel two-stage districting approach, which establishes delivery districts in the first stage and adapts them to the daily demand realizations in the second stage. For the first stage problem we propose three models that differ in the level of detail of their input data, their expected compliance with service consistency and the driver’s contractual working times, and their computational effort. Our two-stage approach merges the two dominant approaches in the literature, which either determine a priori routes and then adapt them on a daily basis, or derive fixed service regions for drivers. We present a case study based on a real-world data set. The results highlight the differences between the three first stage models and show that only few adaptations of the districts are necessary in the second stage to achieve feasible daily delivery tours along with a very good workload balance for drivers. We also analyze the effects of a homogeneous vs a heterogeneous fleet, of full time drivers vs full and part time drivers, and of the location of the depot and the length of the planning horizon.     

A lower bound for the adaptive two-echelon capacitated vehicle routing problem

Adaptive two-echelon capacitated vehicle routing problem (A2E-CVRP) proposed in this paper is a variant of the classical 2E-CVRP. Comparing to 2E-CVRP, A2E-CVRP has multiple depots and allows the vehicles to serve customers directly from the depots. Hence, it has more efficient solution and adapt to real-world environment. This paper gives a mathematical formulation for A2E-CVRP and derives a lower bound for it. The lower bound is used for deriving an upper bound subsequently, which is also an approximate solution of A2E-CVRP. Computational results on benchmark instances show that the A2E-CVRP outperforms the classical 2E-CVRP in the costs of routes.     

有顾客时间窗和发货量变化的紧急车辆调度研究

对紧急车辆调度系统进行了研究,探讨了紧急车辆调度问题实现的关键技术.对有顾客时间窗和发货量变化的紧急车辆调度问题,运用了禁忌算法(TS)进行优化.算法基于实数编码,应用GENI插入法产生初始解和进行邻域操作,设计了三种邻域,利用容量约束控制单条路径配送点数,采用惩罚函数处理时间窗约束,通过设计虚拟车场等方法实现了车辆的紧急调度.本文给出了一个具有代表性的算例试验结果,算例结果及其分析表明了此方法对优化紧急车辆调度问题的有效性.     

The value of VMI beyond information sharing in a single supplier multiple retailers supply chain under a non-stationary (Rn,Sn) policy

This study aims to determine the value of vendor-managed inventory (VMI) over independent decision making with information sharing (IS) under non-stationary stochastic demand with service-level constraints. For this purpose, we utilize mixed-integer linear programming formulations to quantify the benefits that can be accrued by a supplier, multiple retailers and the system as a whole by switching from IS to VMI. More specifically, we investigate the incremental value that VMI provides beyond IS in terms of expected cost savings, inventory reductions, and decrease in shipment sizes from the supplier to the retailers by conducting a large number of computational experiments. Results reveal that the decision transfer component of VMI improves these performance measures significantly when the supplier׳s setup cost is low and order issuing efficiency is high. The benefits offered by VMI are negligible under the problem settings where the supplier׳s order issuing efficiency is low and the production setup serves solely a single replenishment under IS.     

Supplier competition and its impact on firm׳s sourcing strategy

Most research on firms׳ sourcing strategies assumes that wholesale prices and reliability of suppliers are exogenous. It is of our interest to study suppliers׳ competition on both wholesale price and reliability and firms׳ corresponding optimal sourcing strategy under complete information. In particular, we study a problem in which a firm procures a single product from two suppliers, taking into account suppliers׳ price and reliability differences. This motivates the suppliers to compete on these two factors. We investigate the equilibria of this supplier game and the firm׳s corresponding sourcing decisions. Our study shows that suppliers׳ reliability often plays a more important role than wholesale price in supplier competition and that maintaining high reliability and a high wholesale price is the ideal strategy for suppliers if multiple options exist. The conventional wisdom implies that low supply reliability and high demand uncertainty motivate dual-sourcing. We notice that when the suppliers׳ shared market/transportation network is often disrupted and demand uncertainty is high, suppliers׳ competition on both price and reliability may render the sole-sourcing strategy to be optimal in some cases that depend on the format of suppliers׳ cost functions. Moreover, numerical study shows that when the cost or vulnerability (to market disruptions) of one supplier increases, its profit and that of the firm may not necessarily decrease under supplier competition.     

Design-balanced capacitated multicommodity network design with heterogeneous assets

Management of assets plays an essential role in determination of service plans operated by carriers in the transportation and logistics system. In this paper, we introduce certain issues related to management of heterogeneous assets in the well-known design-balanced capacitated multicommodity network design, where design-balanced requirements are explicitly defined based on heterogeneous assets.Taking vehicles as an example of heterogeneous assets, we first present an arc-based formulation for the proposed problem and discuss two associated subproblems. We then propose a tabu search based metaheuristic for this problem. Over a wide range of network design instances, we respectively compare our approach with CPLEX with one-hour and ten-hour time limits. Computational results demonstrate that the proposed approach performs very well in terms of solution quality and computing time, especially for large instances.     

Resolution Search and Dynamic Branch-and-Bound

A novel approach to pure 0-1 integer programming problems called Resolution Search has been proposed by Chvatal (Discrete Applied Mathematics, vol. 73, pp. 81–99, 1997) as an alternative to implicit enumeration, with a demonstration that the method can yield more effective branching strategies. We show that an earlier method called Dynamic Branch-and-Bound (B&B) yields the same branching strategies as Resolution Search, and other strategic alternatives in addition. Moreover, Dynamic B&B is not restricted to pure 0-1 problems, but applies to general mixed integer programs containing both general integer and continuous variables.We provide examples comparing Resolution Search to enhanced variants. We also show the relation of these approaches to Dynamic B&B, suggesting the value of further study of this neglected approach.