Minimizing takeoff and landing risk in helicopter pickup and delivery operations

The problem of minimizing total helicopter passenger risk caused by takeoffs and landings is studied. There are passenger pickup and delivery demands to be satisfied at given points by flights starting and ending in the same heliport and visiting several points. For each point, the delivery demand is the number of passengers to be transported from the heliport to this point and the pickup demand is the number of different passengers to be transported from this point to the heliport. Each pickup and delivery demand must be satisfied in full by one flight. There are an upper bound on the number of flights and an upper bound on the helicopter passenger capacity. The objective function is a linear combination of the numbers of passengers involved in takeoffs and landings at visited points. A solution is characterized by the number of flights, sets of visited points and their sequences for all flights. Properties of optimal solutions are established. Several cases are proved NP-hard. A quadratic boolean programming formulation and two dynamic programming algorithms are suggested for the general case. Computer experiments demonstrated that they are able to solve real-life instances. Polynomial time algorithms are presented for special cases. Implementation of the suggested solutions into the real helicopter operations should decrease the number of fatalities.     

Anticipatory shipment for pickup point supply

Many retailers allow customers to shop online and collect their orders at a pickup point nearby. In this paper, we study the anticipatory shipment of items to such pickup points in order to improve service and operational efficiency. We formulate a stochastic programming model to support the selection of products and associated quantities to ship to the pickup points in anticipation of customers’ demand. The results of our numerical experiments suggest that anticipatory shipments can have substantial benefits both in terms of cost and lead-time. The benefits increase with the storage space at the pickup point. The anticipatory shipment strategy is especially beneficial in a setting which requires short delivery lead-times and when the e-fulfilment warehouse is further away.     

Exact approaches for the pickup and delivery problem with loading cost

In this paper, we propose a branch-and-cut algorithm and a branch-and-price algorithm to solve the pickup and delivery problem with loading cost (PDPLC), which is a new problem derived from the classic pickup and delivery problem (PDP) by considering the loading cost in the objective function. Applications of the PDPLC arise in healthcare transportation where the objective function is customer-centric or service-based. In the branch-and-price algorithm, we devise an ad hoc label-setting algorithm to solve the pricing problem and employ the bounded bidirectional search strategy to accelerate the label-setting algorithm. The proposed algorithms were tested on a set of instances generated by a common data generator in the literature. The computational results showed that the branch-and-price algorithm outperformed the branch-and-cut algorithm by a large margin, and can solve instances with 40 requests to optimality in a reasonable time frame.     

Greedy algorithms for the profit-aware social team formation problem

Motivated by applications in online labor markets, we study the problem of forming multiple teams of experts in a social network to accomplish multiple tasks that require different combinations of skills. Our goal is to maximize the total profit of tasks that are completed by these teams subject to the capacity constraints of the experts. We study both the offline and online settings of the problem. For the offline problem, we present a simple and practical algorithm that improves upon previous results in many situations. For the online problem, we design competitive deterministic and randomized online algorithms. These are complemented by some hardness results in both settings.

     

众包供应链基于On/OffLine混合定制设计生产决策模型分析

互联网平台经济（Platform Economy）正颠覆传统企业商业模式,众包供应链（Crowdsourcing Supply Chain,CSC）作为一种新型的"互联网平台+设计创新"供应链,也正成为人们关注和研究的热点。本文在订单定制设计模式（Engineering to Order,ETO）的基础上,将定制设计和订单生产两环节相结合,并以交货期为驱动,按常规生产时间和加班生产时间来优化生产流程。在此框架下,建立了线下（Offline）自行定制设计的供应链生产模型;接着结合互联网众包平台的特点,以及众包线上（Online）和线下（Offline）的互补性,将众包线上定制设计环节有机嵌入融合到线下自行定制设计供应链中,以此优化和构建出基于众包的线上线下混合定制设计的供应链生产决策模型,设计出众包线上定制设计和线下定制设计动态切换条件;并通过粒子群算法（Particle Swarm Optimization,PSO）对上述模型进行求解;最后通过实例进行分析,发现定制订单数量不多时,线上线下混合定制设计对成本的降低不是很显著,但随着订单数量越多,线上线下混合定制设计优势将显著变化,并且具有一定程度的抗风险性;通过这个转换点也发现,众包定制设计的订单生产最好安排在期初和期末,众包线上定制设计订单应尽可能减少挤占线下自行设计的常规生产时间,而应转向在加班时间生产更为经济;同时,通过增加对众包设计者的设计报酬,发现不仅对整个供应链的成本影响不大,反而对众包设计者形成较大激励作用,进一步证明该模式的可行性和实用性。     

Integrated Ant Colony and Tabu Search approach for time dependent vehicle routing problems with simultaneous pickup and delivery

Today manufacturers have become much more concerned with the coordination of both manufacturing (of new products) and recycling (of reusable resources) operations. This requires simultaneous scheduling of both forward and reverse flows of goods over a supply chain network. This paper studies time dependent vehicle routing problems with simultaneous pickup and delivery (TD-VRPSPD). We formulate this problem as a mixed integer programming model, where the time step function is used to calculate the travel time. To efficiently solve this complex problem, we develop a hybrid algorithm that integrates both Ant Colony System (ACS) and Tabu Search (TS) algorithms. Our algorithm uses the pheromones, travel time and vehicle residual loading capacity as a factor structure according to the characteristics of TD-VRPSPD. In our computational experiments, 56 groups of benchmark instances are used to evaluate the performance of our hybrid algorithm. In addition, we compare the performance of our hybrid algorithm with those of individual ACS and TS algorithms. The computational results suggest that our hybrid algorithm outperform stand-alone ACS and the TS algorithms.     

考虑订单类型的两台平行批处理机在线调度模型研究

探讨了两台平行批处理机的调度决策问题,着重考虑了订单具有不同加工类型、同一批次只能加工相同类型的订单以及机器批容量有限的调度情形。针对订单实时到达且需要立即决策是否接受的实际情景,运用在线理论构建了平行机批调度在线模型。证明了该问题的竞争比下界为2Bw/（1+√Bw）,其中B和w分别表示批容量和单个订单的最大完工收益。进而设计给出了收益阈值算法PT并证明其对于订单具有紧交货期限的情形竞争比为2（1+Bw）/（1+√Bw）;对于非紧交货期限的情形,证明了修正的PT算法具有竞争比为1+2（1+Bw）/（1+√Bw）。     

Serve or skip: the power of rejection in online bottleneck matching

We consider the online matching problem, where n server-vertices lie in a metric space and n request-vertices that arrive over time each must immediately be permanently assigned to a server-vertex. We focus on the egalitarian bottleneck objective, where the goal is to minimize the maximum distance between any request and its server. It has been shown that while there are effective algorithms for the utilitarian objective (minimizing total cost) in the resource augmentation setting where the offline adversary has half the resources, these are not effective for the egalitarian objective. Thus, we propose a new Serve-or-Skip (SoS) bicriteria analysis model, where the online algorithm may reject or skip up to a specified number of requests, and propose two greedy algorithms: GriNN(t) and \({{\textsc {Grin}}^*(t)}\). We show that the SoS model of resource augmentation analysis can essentially simulate the doubled-server-capacity model, and then examine the performance of GriNN(t) and \({\textsc {Grin}^*(t)}\).     

Governance Considerations for Seeker–Solver Relationships: A Knowledge‐Based Perspective in Crowdsourcing for Innovation Contests

The need to solve innovation problems and insource knowledge has led to an increasing number of organizations engaging in crowdsourcing activities and subsequently establishing working relationships with winning solution providers. Using a knowledge‐based view and the problem‐solving perspective, we develop a theoretical framework suggesting how specific innovation problem attributes (i.e. the decomposability, formulation and search space of the problem) influence the governance decision (unilateral vs. bilateral) of seekers to manage the relationship with winning solvers. We empirically analyse the framework using 582 challenges broadcast on the NineSigma crowdsourcing platform. Our results indicate that problem attributes – the formulation and search space of the problem – have a positive effect on seekers’ preference towards unilateral governance structures. However, we did not find any empirical confirmation of the effect that the decomposability of the innovation problem has on seekers’ preference towards unilateral governance structures. This study offers several contributions to the crowdsourcing literature, and also has important implications for managers of organizations aiming to insource knowledge through crowdsourcing for innovation contests.     

考虑道路通行能力的应急避难点选址模型及算法

在k-中心点问题的基础上,考虑道路的通行能力限制,提出了k-避难点问题。在一般树图结构下,重点分析了1-避难点选址问题,并设计了有效的求解算法;在直线图结构下,首先改进了一般图1-避难点的求解算法,其次分析了2-避难点问题的特点,并给出了一个基于"二分思想"的求解算法,在此基础上,为一般的直线图k-避难点问题设计了求解算法,一般算法的时间复杂性为O(nlog^kn)。所提出的模型在理论上扩展了经典的k-中心点选址问题,所设计的求解算法能够为现实的应急管理规划提供良好的理论支持。     

Facility Dispersion Problems Under Capacity and Cost Constraints

The MAX-MIN dispersion problem, which arises in the placement of undesirable facilities, involves selecting a specified number of sites among a set of potential sites so as to maximize the minimum distance between any pair of selected sites. We consider different versions of this dispersion problem where each potential site has an associated storage capacity and a storage cost. A typical problem in this context is to choose a subset of potential sites so that the total capacity of the chosen sites is at least a given value, the total storage cost is within the specified budget and the minimum distance between any pair of chosen sites is maximized. Since these constrained optimization problems are NP-hard in general, we consider whether there are efficient approximation algorithms for them with good performance guarantees. Our results include approximation algorithms for some versions, approximation schemes for some geometric versions and polynomial algorithms for special cases. We also present results that bring out the intrinsic difficulty of obtaining near-optimal solutions to some versions.     

A multi-period ambulance location and allocation problem in the disaster

To achieve quick response in the disaster, this paper addresses the issue of ambulance location and allocation, as well as the location problem of temporary medical centers. Considering budget and capacity limitations, a multi-period mixed integer programming model is proposed and two hybrid heuristic algorithms are designed to solve this complex problem. The proposed model and algorithm are further verified in a real case study, and the numerical experiments demonstrate the effectiveness of our proposed model. Specifically, we obtain several findings based on the computational results: (1) The best locations of ambulance stations should change in each period because the demand rate changes over time. (2) Involving temporary medical centers is necessary to reduce the average waiting time of injured people. (3) It may not be optimal to allocate ambulances from the nearest ambulance stations because of potentially limited station capacity.

     

New Methods for Resolving Conflicting Requests with Examples from Medical Residency Scheduling

In scheduling medical residents, the objective is often to maximize resident satisfaction across the space of feasible schedules, relative to the many hard constraints that ensure appropriate patient coverage, adequate training opportunities, etc. A common metric of resident satisfaction is the number of time‐off requests that are granted. Simply maximizing this total, however, may lead to undesirable schedules since some requests have higher priority than others. For example, it might be better to grant one resident's request for a family member's wedding in place of two residents’ requests to attend a rugby game. Another approach is to assign a weight to each request and maximize the total weight of granted requests, but determining weights that accurately represent residents’ and schedulers’ preferences can be quite challenging. Instead, we propose to identify the exhaustive collection of maximally feasible and minimally infeasible sets of requests which can then be used by schedulers to select their preferred solution. Specifically, we have developed two algorithms, which we call Sequential Request Selection Via Cuts (Sequential RSVC) and Simultaneous Request Selection Via Cuts (Simultaneous RSVC), to identify these sets by solving two sequences of optimization problems. We present these algorithms along with computational results based on a real‐world problem of scheduling residents at the University of Michigan C.S. Mott Pediatric Emergency Department. Although we focus our exposition on the problem of resident scheduling, our approach is applicable to a broad class of problems with soft constraints.     

From theory to practice: maximizing revenues for on-line dial-a-ride

We consider the on-line dial-a-ride problem, where a server fulfills requests that arrive over time. Each request has a source, destination, and release time. We study a variation of this problem where each request also has a revenue that the server earns for fulfilling the request. The goal is to serve requests within a time limit while maximizing the total revenue. We first prove that no deterministic online algorithm can be competitive unless the input graph is complete and edge weights are unit. We therefore focus on these graphs and present a 2-competitive algorithm for this problem. We also consider two variations of this problem: (1) the input graph is complete bipartite and (2) there is a single node that is the source for every request, and present a 1-competitive algorithm for the former and an optimal algorithm for the latter. We also provide experimental results for the complete and complete bipartite graphs. Our simulations support our theoretical findings and demonstrate that our algorithms perform well under settings that reflect realistic dial-a-ride systems.     

Integrating commercial and residential pickup and delivery networks: A case study

This paper presents a strategic analysis of the network design problem faced by pickup and delivery companies operating in metropolitan areas and serving two or more classes of customers. The focus is on a division that treats commercial and residential customers separately, a situation motivated by their respective geographic densities and the size and frequency of their demand. In constructing driver work areas, it is necessary to take into account expected demand, vehicle capacity, time on the road, and the aspect ratio of the individual territories. This leads to a capacitated clustering problem with side constraints that has been the subject of intense research over the last decade.     

Coordinated scheduling of production and delivery stages with stage-dependent inventory holding costs

This paper considers a problem of integrated decision-making for job scheduling and delivery batching wherein different inventory holding costs between production and delivery stages are allowed. In the problem, jobs are processed on a facility at a production stage and then delivered at the subsequent delivery stage by a capacitated vehicle. The objective is to find the coordinated schedule of production and delivery that minimizes the total cost of the associated WIP inventory, finished product inventory and delivery, where both the inventory costs are characterized in terms of the weighted flow-time and the delivery cost is proportional to the required number of delivery batches. It is proved that the problem is NP-hard in the strong sense. Thereupon, three heuristic algorithms are derived. Some restricted cases are also characterized as being solvable in polynomial time. Numerical experiments are conducted to evaluate the performance of the derived heuristic algorithms.     

New Approximation Algorithms for the Steiner Tree Problems

The Steiner tree problem asks for the shortest tree connecting a given set of terminal points in a metric space. We design new approximation algorithms for the Steiner tree problems using a novel technique of choosing Steiner points in dependence on the possible deviation from the optimal solutions. We achieve the best up to now approximation ratios of 1.644 in arbitrary metric and 1.267 in rectilinear plane, respectively.     

Models for the single-vehicle preemptive pickup and delivery problem

In this paper, we study a variant of the well-known single-vehicle pickup and delivery problem where the demands can be unloaded/reloaded at any node. By proving new complexity results, we give the minimum information which is necessary to represent feasible solutions. Using this, we present integer linear programs for both the unitary and the general versions. We then show that the associated linear relaxations are polynomial-time solvable and present some computational results.     

Approximation algorithms for two variants of correlation clustering problem

Correlation clustering problem is a clustering problem which has many applications such as protein interaction networks, cross-lingual link detection, communication networks, and social computing. In this paper, we introduce two variants of correlation clustering problem: correlation clustering problem on uncertain graphs and correlation clustering problem with non-uniform hard constrained cluster sizes. Both problems overcome part of the limitations of the existing variants of correlation clustering problem and have practical applications in the real world. We provide a constant approximation algorithm and two approximation algorithms for the former and the latter problem, respectively.

     

Genetic algorithms for optimization of a mine system under uncertainty

The production planning of a mine system associated with mining, processing and refining stages dictates to determine optimal system parameters such as optimal production rates, location of refining facility and the best reconstruction time of production rates. This paper proposes a combination of the chance constrained programming (CCP) and the genetic algorithms (GA) to find the optimal system parameters simultaneously. In generic form the problem is expressed as the maximization of net present value of future cash flows such that the capacity constraint and predefined specifications are satisfied. The blending requirements expressed in the CCP are transformed into deterministic equivalents. A new form of the problem is solved by the GA. The approach was demonstrated on extraction, processing and refining of four iron ore mines with varying reserves, ore qualities, geological and topographic conditions, four mineral processing units and one refining facility. The results showed that the proposed algorithm could be used to determine optimal production rates, the facility location and the best reconstruction time.