Simultaneous dock assignment and sequencing of inbound trucks under a fixed outbound truck schedule in multi-door cross docking operations

This paper studies the simultaneous dock assignment and sequencing of inbound trucks for a multi-door cross docking operation with the objective to minimize total weighted tardiness, under a fixed outbound truck departure schedule. The problem is newly formulated and solved by six different metaheuristic algorithms, which include simulated annealing, tabu search, ant colony optimization, differential evolution, and two hybrid differential-evolution algorithms. To evaluate the total weighted tardiness associated with any given inbound-truck sequence and dock assignment, an operational policy is developed. This policy is employed by every metaheuristic algorithm in searching for the optimal dock assignment and sequence. Each metaheuristic algorithm is tested with 40 problems. The major conclusions are: (1) metaheuristic is generally an effective optimization method for the subject problem; (2) population based metaheuristic algorithms are generally more effective than projection based metaheuristic algorithms; (3) proper selection of algorithmic parameters is important and more critical for projection based metaheuristic algorithms than population based algorithms; (4) the two best algorithms are ant colony optimization and hybrid differential evolution 2; among them, ACO takes less time than hybrid 2 and thus can be declared the best among all the six metaheuristic algorithms tested.     

Managing truck arrivals with time windows to alleviate gate congestion at container terminals

Long truck queues at gates often limit the efficiency of a container terminal and generate serious air pollution. To reduce the gate congestion, this paper proposes a method called ‘vessel dependent time windows (VDTWs)' to control truck arrivals, which involves partitioning truck entries into groups and assigning different time windows to the groups. The proposed VDTWs method includes three steps: (1) predicting truck arrivals based on the time window assignment, (2) estimating the queue length of trucks, and (3) optimizing the arrangement of time windows to minimize the total cost in the system. A conventional Genetic Algorithm (GA), a multi-society GA, and a hybrid algorithm using GA and Simulated Annealing are used to solve the optimization problem. A case study based on a real container terminal in China is performed, which shows the VDTWs method can flatten the truck arrivals and reduce the gate congestion significantly.     

Optimal inventory control of empty containers in inland transportation system

In this paper, we deal with an inventory control problem of empty containers in an inland transportation system. In inland container transportation, freights (containers) are transported between terminal and the customer’s location by trucks, trains and barges. Empty containers are an important logistic resource and shipping companies try to operate and manage empty containers efficiently. Because of the trade imbalance between hub ports, empty containers should be periodically repositioned from surplus areas to shortage areas. However, it is not easy to exactly forecast the demand of empty containers, and we therefore need to build an efficient way to reposition the empty containers. In this paper, we consider a shortage area and propose an efficient inventory policy to control empty containers. We assume that demands per unit time are independent and identically distributed random variables. To satisfy the demand of empty containers, we reposition empty containers from other hubs based on the (s, S) inventory policy, and also consider the lease of empty containers with zero lead time. For the leased containers, we should return the number of empty containers leased to the leaser after the specified period. For a given policy, simulation is used to estimate the expected cost rate and we use the optimization tool, OptQuest^® in Arena to obtain the near optimal (s, S) policy in numerical examples.     

A heuristic algorithm for a multi-product dynamic lot-sizing and shipping problem

This paper analyzes a dynamic lot-sizing problem, in which the order size of multiple products and a single container type are simultaneously considered. In the problem, each order (product) placed in a period is immediately shipped by some containers in the period and the total freight cost is proportional to the number of containers used. It is also assumed that backlogging is not allowed. The objective of this study is to simultaneously determine the lot-sizes and the transportation policy that minimizes the total costs, which consist of production cost, inventory holding cost, and freight cost. Because this problem is NP-hard, a heuristic algorithm with an adjustment mechanism is proposed based on the optimal solution properties. The computational results from a set of simulation experiment are also presented.     

A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times,sequence-dependent setup times and learning effect

This paper addresses multi-objective (MO) optimization of a single-model assembly line balancing problem (ALBP) where the operation times of tasks are unknown variables and the only known information is the lower and upper bounds for operation time of each task. Three objectives are simultaneously considered as follows: (1) minimizing the cycle time, (2) minimizing the total equipment cost, and (3) minimizing the smoothness index. In order to reflect the real industrial settings adequately, it is assumed that the task time is dependent on worker(s) (or machine(s)) learning for the same or similar activity and sequence-dependent setup time exists between tasks. Finding an optimal solution for this complicated problem especially for large-sized problems in reasonable computational time is cumbersome. Therefore, we propose a new solution method based on the combination of particle swarm optimization (PSO) algorithm with variable neighborhood search (VNS) to solve the problem. The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Comparison with an existing multi-objective evolutionary computation method in the literature shows the superior efficiency of our proposed PSO/VNS algorithm.     

大数据驱动下的新一代高速公路 智慧诱导技术

为了解决高速公路出行路径选择问题,基于图论模糊算法,提出了大数据下的智慧诱导技术。首先对高速路网在路径诱导的实时性、线路规划的精准度和算法的适用性方面进行优化;其次通过利用贪心算法和整体寻优算法,对经典路径诱导算法进行研究和比选;最后针对高速路网提出基于大数据动态规划的路径诱导技术,采用大数据、内存计算、图计算和AI结合的方式来实现大数据的动态实时路径诱导。研究结果表明,智慧诱导技术可主动为有不同诉求目标的出行者提供实时最优的方案选择,解决复杂路网下动态路径的合理诱导问题。所提出的方法可实现大数据驱动下的智慧诱导,对进一步提升公路智能化和精细化管理水平具有借鉴意义。     

A class of chance constrained multiobjective linear programming with birandom coefficients and its application to vendors selection

In this paper, a class of chance constrained multiobjective linear programming model with birandom coefficients is considered for vendor selection problem. Firstly we present a crisp equivalent model for a special case and give a traditional method for crisp model. Then, the technique of birandom simulation is applied to deal with general birandom objective functions and birandom constraints which are usually difficult to be converted into their crisp equivalents. Furthermore, a genetic algorithm based on birandom simulation is designed for solving a birandom multiobjective vendor selection problem. Finally, a real numbers example is given. The paper makes certain contribution in both theoretical and application research related to multiobjective chance constrained programming, as well as in the study of vendor selection problem under uncertain environment.     

The storage location assignment problem for outbound containers in a maritime terminal

This paper addresses the storage location assignment problem for outbound containers. The problem is decomposed into two stages. The yard bays and the amount of locations in each yard bay, which will be assigned to the containers bounded for different ships, are determined in the first stage. The exact storage location for each container is determined in the second stage. The problem in the first stage is solved by a mixed integer programming model, while a hybrid sequence stacking algorithm is applied to solve the problem in the second stage. Experimental results show that the proposed approach is effective and efficient in solving the storage location assignment problem for outbound containers.     

An LP-based tabu search for batch scheduling in a cutting process with finite buffers

This paper addresses a cutting stock problem under typical resource constraints that arise when working centres with nesting capabilities are associated with automatic feeders/stackers. The critical resource is the number of buffers available to host the batches built up by the centre. To cope with it, pattern and batch sequencing problems must be addressed simultaneously. A tabu-search algorithm exploring batch output sequences is proposed. The algorithm never opens more stacks than buffers, respects batch compatibility/precedence constraints, and keeps the maximum order spread under control. To demonstrate its effectiveness and efficiency, a computational study was set up, solving 920 test problems derived from the literature. The study enabled a proper tuning of the method and offered encouraging results: in 228 cases an optimum was found; in nearly all, the gap from the optimum was below 1%. Computation times range from fractions of seconds to a couple of minutes in the worst cases. Compared to existing methods, the algorithm provides on average the same solution quality, with the advantage of solving a problem which is more general and hence closer to application. The paper includes a discussion on the method extensions required to deal with asynchronous stacking and heterogeneous batches.     

Scheduling a single batch processing machine with arbitrary job sizes and incompatible job families

Motivated by a bottleneck operation in a multi-layer ceramic capacitor production line, we study a scheduling problem of batch processing machine in which a number of jobs are processed simultaneously as a batch. The performance measures considered include makespan, total completion time, and total weighted completion time. We first present a new simple integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is very large, we propose a number of heuristic algorithms and design a hybrid genetic algorithm to solve practical big-size problems in a reasonable computational time. To verify performance of the algorithms, we compare them with lower bounds for the problems. From the results of these computational experiments the heuristic algorithms including the genetic algorithm show different performances for the three problems.     

Constraint-based ACO for a shared resource constrained scheduling problem

We consider a scheduling problem arising in the mining industry. Ore from several mining sites must be transferred to ports to be loaded on ships in a timely manner. In doing so, several constraints must be met which involve transporting the ore and deadlines. These deadlines are two-fold: there is a preferred deadline by which the ships should be loaded and there is a final deadline by which time the ships must be loaded. Corresponding to the two types of deadlines, each task is associated with a soft and hard due time. The objective is to minimize the cumulative tardiness, measured using the soft due times, across all tasks. This problem can be formulated as a resource constrained job scheduling problem where several tasks must be scheduled on multiple machines satisfying precedence and resource constraints and an objective to minimize total weighted tardiness. For this problem we present hybrids of ant colony optimization, Beam search and constraint programming. These algorithms have previously shown to be effective on similar tightly-constrained combinatorial optimization problems. We show that the hybrid involving all three algorithms provides the best solutions, particularly with respect to feasibility. We also investigate alternative estimates for guiding the Beam search component of our algorithms and show that stochastic sampling is the most effective.     

A state space augmentation algorithm for the replenishment cycle inventory policy

In this work we propose an efficient dynamic programming approach for computing replenishment cycle policy parameters under non-stationary stochastic demand and service level constraints. The replenishment cycle policy is a popular inventory control policy typically employed for dampening planning instability. The approach proposed in this work achieves a significant computational efficiency and it can solve any relevant size instance in trivial time. Our method exploits the well known concept of state space relaxation. A filtering procedure and an augmenting procedure for the state space graph are proposed. Starting from a relaxed state space graph our method tries to remove provably suboptimal arcs and states (filtering) and then it tries to efficiently build up (augmenting) a reduced state space graph representing the original problem. Our experimental results show that the filtering procedure and the augmenting procedure often generate a small filtered state space graph, which can be easily processed using dynamic programming in order to produce a solution for the original problem.     

A coevolutionary algorithm for the flexible delivery and pickup problem with time windows

This paper addresses a flexible delivery and pickup problem with time windows (FDPPTW) and formulates the problem into a mixed binary integer programming model in order to minimize the number of vehicles and to minimize the total traveling distance. This problem is shown to be NP-hard. In this study, therefore, a coevolutionary algorithm incorporated with a variant of the cheapest insertion method is developed to speed up the solution procedure. The FDPPTW scheme overcomes the shortcomings of the existing schemes for the delivery and pickup problems. By testing with some revised Solomon's benchmark problems, the computational results have shown the efficiency and the effectiveness of the developed algorithm.     

An integrated distribution routing model in multi-supply center system

A distribution routing problem with time constraint is one of the important problems in distribution and supply center management. This research is concerned with an integrated distribution routing problem for multi-supply centers based on improved genetic algorithm and graphical user interface (GUI)-type programming. In this research, we proposed a method based on a three-step approach: in step 1 a sector clustering model is developed to transfer the multi-supply center problem to single supply center problems which are easier to be solved; in step 2 we developed a vehicle routing model with time constraints and in step 3 we developed a GA-TSP model which can improve the vehicle routing schedules. The objective of the problem is to minimize the logistic cost for a set of customers without being tardy or exceeding the capacity or travel time of the vehicles. For computational purpose, we developed a GUI-type computer program according to the proposed methods and the sample outputs show that the proposed method is very effective on a set of standard test problems, and it could be potentially useful in solving the distribution routing problems.     

A new approach to job shop scheduling problems with due date constraints considering operation subcontracts

We consider a kind of job shop scheduling problems with due-date constraints, where temporal relaxation of machine capacity constraint is possible through subcontracts. In practice, this kind of problem is frequently found in manufacturing industries where outsourcing of manufacturing operation is possible through subcontract. We present a heuristic algorithm that addresses the problem by solving a series of smaller subproblems to optimality. For the sake of efficiency, the algorithm repeatedly executes in two steps—(1) improving the sequence of operations and (2) picking out the operations to be subcontracted—on bottleneck machines. Experiments are conducted for example problems, and the result of the experiment confirms the viability of the suggested algorithm.     

SOLVING LARGE REPLACEMENT PROBLEMS WITH BUDGET CONSTRAINTS

Traditional replacement models assume unlimited capital. In practice, however, firms frequently use budgets to control their expenditures. Budget constraints necessitate that all replacement decisions be considered as a portfolio, creating a difficult combinatorial problem. In previous research, we developed a branch-and-bound algorithm for solving moderately-sized problems optimally. In this paper, we propose a dual heuristic for dealing with large, realistically sized problems. First, we solve the individual replacement problems ignoring the budget constraints. Then, we reduce, or eliminate, if possible, budget violations by solving a Lagrangian dual problem. The computational tests suggest that the effectiveness of the approach increases with problem size.     

基于改进遗传算法的多项目资源均衡配置研究

如何解决建筑企业在多个项目中资源利用率和配置效率低下的问题,是施工方项目管理的一大难题。本文根据多项目工程施工特点,建立了多项目并行施工时的资源均衡优化模型。为了提高遗传算法对多项目资源配置问题求解的性能,本文对遗传算法进行了改进,并以某公司3 个并行施工的工程项目为例,根据工程施工初始网络计划的资源需求,运用改进遗传算法（IGA）和遗传算法（GA）计算多项目的资源配置方差。结果表明,改进遗传算法可使资源配置达到更好的优化状态,并能得到各个项目非关键线路上工序的最佳开工时间,为建筑施工企业提供了具有实践价值的技术手段。     

Making Virtue of Necessity: The Role of Team Climate for Innovation in Resource‐Constrained Innovation Projects

The effect of financial resource constraints on innovation team performance is ambiguous. On the one hand, the majority of scholars have argued that financial resource constraints have an inhibiting effect on innovation, whereas budgetary slack supports creativity and innovation. Consistent with this notion, in most conceptual models on the management of innovation projects, the availability of slack, or at least adequate (rather than constrained) resources represents an important success factor supporting innovation. On the other hand, popular parlance has it that sometimes “necessity is the mother of innovation,” and literature in cognitive psychology suggests that resource constraints stimulate creativity and innovative behavior. Recent innovation literature indeed provides evidence that remarkable innovation outcomes can be achieved with constrained financial resources. Despite the rapidly growing research on success factors of innovation projects, and the high managerial relevance of budget questions, the influence of financial resource constraints has only very recently started to attract interest. The objective of the present study is to contribute to that research by investigating under what conditions financial resource constraints lead to innovation outcomes. Specifically, team climate for innovation is examined as a potentially important contingency variable of the relationship between financial resource constraints and innovation project performance. By explicitly focusing on team climate for innovation, factors of the work environment in innovation projects are addressed as influential boundary conditions for successfully innovating under financial resource constraints. The hypotheses are tested on a sample of 94 innovation project teams from a variety of industries. To ensure content validity and to avoid a possible common source bias, data from different respondents, i.e., team leaders, team members, and team external managers of the innovation projects, are used. Results of regression analyses show that there is no significant relationship between financial resource constraints and innovation project outcomes in terms of product quality and project efficiency. However, results show a significant interaction term of financial resource constraints and team climate for innovation in that team climate for innovation positively moderates the relationship between financial resource constraints and product quality as well as project efficiency. Thus, the findings of the present study contradict the widespread notion in innovation literature that financial resource constraints have a wholesale inhibiting effect on innovation, thereby providing a differentiated perspective on the relationship between financial resource constraints and innovation. On a practical level, the results of this study highlight a specific condition under which product developers can come up with more innovative solutions despite, or even because of, financial resource constraints.     

Dynamic adjustment of container stacking policy in an automated container terminal

The decision for good stacking positions for incoming containers in an automated container terminal is an important operational problem because it affects the productivity not only for stacking but also for later retrieval. This paper proposes an online search algorithm which dynamically adjusts and optimizes a stacking policy by continuously generating variants of stacking policies and evaluating them while they are actually being applied for determining the stacking positions. Simulation experiments show that the proposed algorithm is effective in enhancing the operational productivity, while other offline optimization methods are computationally infeasible to be applied to this problem.     

Heuristics for operational fixed job scheduling problems with working and spread time constraints

Operational fixed job scheduling problems select a set of jobs having fixed ready and processing times and schedule the selected jobs on parallel machines so as to maximize the total weight. In this study, we consider working time and spread time constrained versions of the operational fixed job scheduling problems. The working time constraints limit the total processing load on each machine. The spread time constraints limit the time between the start of the first job and the finish of the last job on each machine. For the working time constrained problem, we present a filtered beam search algorithm that evaluates the promising nodes of the branch and bound tree. For the spread time constrained problem we propose a two phase algorithm that defines the promising sets for the first jobs and finds a solution for each promising set. The results of our computational tests reveal that our heuristic algorithms perform very well in terms of both solution quality and time.