A mathematical formulation and efficient heuristics for the dynamic container relocation problem

The container relocation problem (CRP) is concerned with emptying a single yard‐bay which contains J containers each following a given pickup order so as to minimize the total number of relocations made during their retrieval process. The CRP can be modeled as a binary integer programming (IP) problem and is known to be NP‐hard. In this work, we focus on an extension of the CRP to the case where containers are both received and retrieved from a single yard‐bay, and call it the dynamic container relocation problem. The arrival (departure) sequences of containers to (from) the yard‐bay is assumed to be known a priori. A binary IP formulation is presented for the problem. Then, we propose three types of heuristic methods: index based heuristics, heuristics using the binary IP formulation, and a beam search heuristic. Computational experiments are performed on an extensive set of randomly generated test instances. Our results show that beam search heuristic is very efficient and performs better than the other heuristic methods.Copyright © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 101–118, 2014     

Optimizing barge utilization in hinterland container transportation

In hinterland container transportation the use of barges is getting more and more important. We propose a real‐life operational planning problem model from an inland terminal operating company, in which the number of containers shipped per barge is maximized and the number of terminals visited per barge is minimized. This problem is solved with an integer linear program (ILP), yielding strong cost reductions, about 20%, compared to the method used currently in practice. Besides, we develop a heuristic that solves the ILP in two stages. First, it decides for each barge which terminals to visit and second it assigns containers to the barges. This heuristic produces almost always optimal solutions and otherwise near‐optimal solutions. Moreover, the heuristic runs much faster than the ILP, especially for large‐sized instances.     

Analysis of a new vehicle scheduling and location problem

We consider a container terminal discharging containers from a ship and locating them in the terminal yard. Each container has a number of potential locations in the yard where it can be stored. Containers are moved from the ship to the yard using a fleet of vehicles, each of which can carry one container at a time. The problem is to assign each container to a yard location and dispatch vehicles to the containers so as to minimize the time it takes to download all the containers from the ship. We show that the problem is NP‐hard and develop a heuristic algorithm based on formulating the problem as an assignment problem. The effectiveness of the heuristic is analyzed from both worst‐case and computational points of view. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 363–385, 2001     

Optimally scheduling interfering and non‐interfering cranes

This article treats the problem of scheduling multiple cranes processing jobs along a line, where cranes are divided into different groups and only cranes in the same group can interfere with each other. Such crane scheduling problems occur, for example, at indented berths or in container yards where double rail‐mounted gantry cranes stack containers such that cranes of the same size can interfere with each other but small cranes can pass underneath larger ones. We propose a novel algorithm based on Benders decomposition to solve this problem to optimality. In a computational study, it is shown that this algorithm solves small and medium‐sized instances and even many large instances within a few seconds or minutes. Moreover, it improves several best known solutions from the literature with regard to the simpler problem version with only one crane group. We also look into whether investment in more complicated crane configurations with multiple crane groups is actually worthwhile.     

Quay crane scheduling at container terminals to minimize the maximum relative tardiness of vessel departures

In this paper, we study the problem of scheduling quay cranes (QCs) at container terminals where incoming vessels have different ready times. The objective is to minimize the maximum relative tardiness of vessel departures. The problem can be formulated as a mixed integer linear programming (MILP) model of large size that is difficult to solve directly. We propose a heuristic decomposition approach to breakdown the problem into two smaller, linked models, the vessel‐level and the berth‐level models. With the same berth‐level model, two heuristic methods are developed using different vessel‐level models. Computational experiments show that the proposed approach is effective and efficient. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

Mathematical formulation and heuristic algorithm for the block relocation and loading problem

This study considers the block relocation and loading problem in container terminals. The optimal loading sequence and relocation location are simultaneously decided on the basis of the desired ship‐bay and initial yard space configuration. An integer linear programming model is developed to minimize the number of relocations in the yard space on the basis of no shifts in the ship bay. The accuracy of the model is tested on small‐scale scenarios by using CPLEX. Considering the problem size in the real world, we present a rule‐based heuristic method that is combined with a mathematical model for the removal, loading, and relocation operations. The influence of rules on algorithm performance is also analyzed, and the heuristic algorithm is compared with different types of algorithms in the literature. The extensive numerical experiments show the efficiency of the proposed heuristic algorithm.     

Flow shop scheduling with earliness,tardiness, and intermediate inventory holding costs

We consider the problem of scheduling customer orders in a flow shop with the objective of minimizing the sum of tardiness, earliness (finished goods inventory holding), and intermediate (work‐in‐process) inventory holding costs. We formulate this problem as an integer program, and based on approximate solutions to two different, but closely related, Dantzig‐Wolfe reformulations, we develop heuristics to minimize the total cost. We exploit the duality between Dantzig‐Wolfe reformulation and Lagrangian relaxation to enhance our heuristics. This combined approach enables us to develop two different lower bounds on the optimal integer solution, together with intuitive approaches for obtaining near‐optimal feasible integer solutions. To the best of our knowledge, this is the first paper that applies column generation to a scheduling problem with different types of strongly ????‐hard pricing problems which are solved heuristically. The computational study demonstrates that our algorithms have a significant speed advantage over alternate methods, yield good lower bounds, and generate near‐optimal feasible integer solutions for problem instances with many machines and a realistically large number of jobs. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

The quay crane scheduling problem with time windows

The quay crane scheduling problem consists of scheduling tasks for loading and unloading containers on cranes that are assigned to a vessel for its service. This article introduces a new approach for quay crane scheduling, where the availability of cranes at a vessel is restricted to certain time windows. The problem is of practical relevance, because container terminal operators frequently redeploy cranes among vessels to speed up the service of high‐priority vessels while serving low‐priority vessels casually. This article provides a mathematical formulation of the problem and a tree‐search‐based heuristic solution method. A computational investigation on a large set of test instances is used to evaluate the performance of the heuristic and to identify the impact of differently structured crane time windows on the achievable vessel handling time. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Determining crane areas for balancing workload among interfering and noninterfering cranes

This article treats the problem of subdividing an area for storing containers such that the workload is evenly shared among the cranes operating the resulting subareas. We consider two crane sets: while noncrossing constraints between cranes of the same set need to be observed, cranes of different sets do not interfere. Such a problem setting is, for instance, relevant for scheduling the (un‐)loading of vessels by parallel quay cranes operating on opposing berths or in container yards with cross‐over cranes. We formalize the resulting optimization problem, prove computational complexity, and present exact and heuristic solution procedures. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

Capacity allocation to sales agents in a decentralized logistics network

Many logistics systems operate in a decentralized way, while most optimization models assume a centralized planner. One example of a decentralized system is in some sea cargo companies: sales agents, who share ship capacity on a network, independently accept cargo from their location and contribute to the revenue of the system. The central headquarters does not directly control the agents' decisions but can influence them through system design and incentives. In this paper, we model the firm's problem to determine the best capacity allocation to the agents such that system revenue is maximized. In the special case of a single‐route, we formulate the problem as a mixed integer program incorporating the optimal agent behavior. For the NP‐hard multiple‐route case, we propose several heuristics for the problem. Computational experiments show that the decentralized system generally performs worse when network capacity is tight and that the heuristics perform reasonably well. We show that the decentralized system may perform arbitrarily worse than the centralized system when the number of locations goes to infinity, although the choice of sales incentive impacts the performance. We develop an upper bound for the decentralized system, where the bound gives insight on the performance of the heuristics in large systems. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Puzzle‐based storage systems

We introduce and develop models for a physical goods storage system based on the 15‐puzzle, a classic children's game in which 15 numbered tiles slide within a 4 × 4 grid. The objective of the game is to arrange the tiles in numerical sequence, starting from a random arrangement. For our purposes, the tiles represent totes, pallets, or even containers that must be stored very densely, and the objective is to maneuver items to an input–output point for retrieval or processing. We develop analytical results for storage configurations having a single empty location (as in the game) and experimental results for configurations with multiple empty locations. Designs with many empty locations can be made to form aisles, allowing us to compare puzzle‐based designs with traditional aisle‐based designs found in warehousing systems. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Optimal job splitting on a multi‐slot machine with applications in the printing industry

In this article, we define a scheduling/packing problem called the Job Splitting Problem, motivated by the practices in the printing industry. There are n types of items to be produced on an m‐slot machine. A particular assignment of the types to the slots is called a “run” configuration and requires a setup cost. Once a run begins, the production continues according to that configuration and the “length” of the run represents the quantity produced in each slot during that run. For each unit of production in excess of demand, there is a waste cost. Our goal is to construct a production plan, i.e., a set of runs, such that the total setup and waste cost is minimized. We show that the problem is strongly NP‐hard and propose two integer programming formulations, several preprocessing steps, and two heuristics. We also provide a worst‐case bound for one of the heuristics. Extensive tests on real‐world and randomly generated instances show that the heuristics are both fast and effective, finding near‐optimal solutions. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

海港口岸犯罪分子利用集装箱偷渡的预防与控制

随着我国集装箱进出口贸易的迅猛增长和各地对偷渡活动严厉打击,不法分子开始尝试利用集装箱组织大规模偷渡,并渐呈上升和蔓延趋势,严重干扰了口岸正常的出入境秩序。从集装箱运输流程主要环节（装货、施封、存放、运输、装船）入手,通过分析当前海港口岸集装箱偷渡活动的特点及在集装箱管理过程中存在的问题,提出预防与控制集装箱偷渡的对策。     

Balancing perfectly periodic service schedules: An application from recycling and waste management

A national recycling and waste management company provides periodic services to its customers from over 160 service centers. The services are performed periodically in units of weeks over a planning horizon. The number of truck‐hours allocated to this effort is determined by the maximum weekly workload during the planning horizon. Therefore, minimizing the maximum weekly workload results in minimum operating expenses. The perfectly periodic service scheduling (PPSS) problem is defined based on the practices of the company. It is shown that the PPSS problem is strongly NP‐hard. Attempts to solve large instances by using an integer programming formulation are unsuccessful. Therefore, greedy BestFit heuristics with three different sorting schemes are designed and tested for six real‐world PPSS instances and 80 randomly generated data files. The heuristics provide effective solutions that are within 2% of optimality on average. When the best found BestFit schedules are compared with the existing schedules, it is shown that operational costs are reduced by 18% on average. © 2012 Wiley Periodicals, Inc. Naval Research Logistics 59: 160–171, 2012     

The network redesign problem for access telecommunications networks

The network redesign problem attempts to design an optimal network that serves both existing and new demands. In addition to using spare capacity on existing network facilities and deploying new facilities, the model allows for rearrangement of existing demand units. As rearrangements mean reassigning existing demand units, at a cost, to different facilities, they may lead to disconnecting of uneconomical existing facilities, resulting in significant savings. The model is applied to an access network, where the demands from many sources need to be routed to a single destination, using either low‐capacity or high‐capacity facilities. Demand from any location can be routed to the destination either directly or through one other demand location. Low‐capacity facilities can be used between any pair of locations, whereas high‐capacity facilities are used only between demand locations and the destination. We present a new modeling approach to such problems. The model is described as a network flow problem, where each demand location is represented by multiple nodes associated with demands, low‐capacity and high‐capacity facilities, and rearrangements. Each link has a capacity and a cost per unit flow parameters. Some of the links also have a fixed‐charge cost. The resulting network flow model is formulated as a mixed integer program, and solved by a heuristic and a commercially available software. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 487–506, 1999     

Dual‐ascent and primal heuristics for production‐assembly‐distribution system design

This article proposes two dual‐ascent algorithms and uses each in combination with a primal drop heuristic embedded within a branch and bound framework to solve the uncapacitated production assembly distribution system (i.e., supply chain) design problem, which is formulated as a mixed integer program. Computational results indicate that one approach, which combines primal drop and dual‐ascent heuristics, can solve instances within reasonable time and prescribes solutions with gaps between the primal and dual solution values that are less than 0.15%, an efficacy suiting it for actual large‐scale applications. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

Rank‐Cluster‐and‐Prune: An algorithm for generating clusters in complex set partitioning problems

Clustering problems are often difficult to solve due to nonlinear cost functions and complicating constraints. Set partitioning formulations can help overcome these challenges, but at the cost of a very large number of variables. Therefore, techniques such as delayed column generation must be used to solve these large integer programs. The underlying pricing problem can suffer from the same challenges (non‐linear cost, complicating constraints) as the original problem, however, making a mathematical programming approach intractable. Motivated by a real‐world problem in printed circuit board (PCB) manufacturing, we develop a search‐based algorithm (Rank‐Cluster‐and‐Prune) as an alternative, present computational results for the PCB problem to demonstrate the tractability of our approach, and identify a broader class of clustering problems for which this approach can be used. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

The building evacuation problem with shared information

In this article, the Building Evacuation Problem with Shared Information (BEPSI) is formulated as a mixed integer linear program, where the objective is to determine the set of routes along which to send evacuees (supply) from multiple locations throughout a building (sources) to the exits (sinks) such that the total time until all evacuees reach the exits is minimized. The formulation explicitly incorporates the constraints of shared information in providing online instructions to evacuees, ensuring that evacuees departing from an intermediate or source location at a mutual point in time receive common instructions. Arc travel time and capacity, as well as supply at the nodes, are permitted to vary with time and capacity is assumed to be recaptured over time. The BEPSI is shown to be NP‐hard. An exact technique based on Benders decomposition is proposed for its solution. Computational results from numerical experiments on a real‐world network representing a four‐story building are given. Results of experiments employing Benders cuts generated in solving a given problem instance as initial cuts in addressing an updated problem instance are also provided. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Capacitated lot‐sizing and scheduling with parallel machines,back‐orders,and setup carry‐over

We address the capacitated lot‐sizing and scheduling problem with setup times, setup carry‐over, back‐orders, and parallel machines as it appears in a semiconductor assembly facility. The problem can be formulated as an extension of the capacitated lot‐sizing problem with linked lot‐sizes (CLSPL). We present a mixed integer (MIP) formulation of the problem and a new solution procedure. The solution procedure is based on a novel “aggregate model,” which uses integer instead of binary variables. The model is embedded in a period‐by‐period heuristic and is solved to optimality or near‐optimality in each iteration using standard procedures (CPLEX). A subsequent scheduling routine loads and sequences the products on the parallel machines. Six variants of the heuristic are presented and tested in an extensive computational study. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009