Canrong Zhang

A hybrid Lagrangian-simulated annealing-based heuristic for the parallel-machine capacitated lot-sizing and scheduling problem with sequence-dependent setup times

This paper examines the parallel-machine capacitated lot-sizing and scheduling problem with sequence-dependent setup times, time windows, machine eligibility and preference constraints. Such problems are quite common in the semiconductor manufacturing industry. In particular, this paper pays special attention to the chipset production in the semiconductor Assembly and Test Manufacturing (ATM) factory and constructs a Mixed Integer Programming (MIP) model for the problem. The primal problem is decomposed into a lot-sizing subproblem and a set of single-machine scheduling subproblems by Lagrangian decomposition. A Lagrangian-based heuristic algorithm, which incorporates the simulated annealing algorithm aimed at searching for a better solution during the feasibility construction stage, is proposed. Computational experiments show that the proposed hybrid algorithm outperforms other heuristic algorithms and meets the practical requirement for the tested ATM factory. The lot-sizing and scheduling problem with multiple constraints is formulated.The primal problem is decomposed into simple subproblems by Lagrangian decomposition.A hybrid Lagrangian-simulated annealing-based heuristic is proposed.It outperforms other heuristic methods and meets practical requirement of the tested firm.

Conservative allocation models for outbound containers in container terminals

This paper examines location assignment for outbound containers in container terminals. It is an extension to the previous modeling work of Kim et al. (2000) and Zhang et al. (2010). The previous model was an “optimistic” handling way and gave a moderate punishment for placing a lighter container onto the top of a stack already loaded with heavier containers. Considering that the original model neglected the stack height and the state-changing magnitude information when interpreting the punishment parameter and hid too much information about the specific configurations for a given stack representation, we propose two new “conservative” allocation models in this paper. One considers the stack height and the state-changing magnitude information by reinterpreting the punishment parameter and the other further considers the specific configurations for a given stack representation. Solution qualities for the “optimistic” and the two “conservative” allocation models are compared on two performance indicators. The numerical experiments indicate that both the first and second “conservative” allocation models outperform the original model in terms of the two performance indicators. In addition, to overcome computational difficulties encountered by the dynamic programming algorithm for large-scale problems, an approximate dynamic programming algorithm is presented as well.

Location assignment for outbound containers with adjusted weight proportion

This paper studies the location assignment for arriving outbound containers during container-receiving stage. For the problem, the literature assumed that the proportion of the remaining containers on a weight group keeps unchanged throughout the container-receiving process. This assumption is inconsistent with the practice that it should be constantly adjusted according to the containers that have already been received. We therefore propose two other handling ways in this paper, leading to two new dynamic programming models. These two models are compared with the existent model on small-scale instances. For large-scale instances, a two-stage heuristic is proposed. In the first stage, a neighborhood searching heuristic is developed to generate the priority sequence of stacking patterns for each weight group of containers; in the second stage, a rollout-based heuristic is proposed to improve the incumbent solution by simulating more stack alternatives for each arriving container. The numerical experiments show that the model with adjusted weight proportion can significantly reduce state size and improve stacking quality, and that the proposed two-stage heuristic is effective and efficient for large-scale instances.

A Lagrangian relaxation-based method and models evaluation for multi-level lot sizing problems with backorders

The capacitated multi-level lot sizing problem with backorders has received a great deal of attention in extant literature on operations and optimization. The facility location model and the classical inventory and lot sizing model with (@?, S) cuts have been proposed to formulate this problem. However, their comparative effectiveness has not yet been explored and is not known. In this paper, we demonstrate that on linear programming relaxation, the facility location formulation yields tighter lower bounds than classical inventory and lot sizing model. It further shows that the facility location formulation is computationally advantageous for deriving both lower and upper bounds. The results are expected to provide guidelines for choosing an effective formulation during the development of solution procedures. We also propose a Lagrangian relaxation-based heuristic along with computational results that indicate its competitiveness with other heuristics and a prominent commercial solver, Cplex 11.2.

A DECISION SUPPORT SYSTEM FOR THE ALLOCATION OF YARD CRANES AND BLOCKS IN CONTAINER TERMINALS

This paper examines the allocation of yard cranes and blocks for yard activities in container terminals. In this paper, the yard cranes are confined to rail mounted gantry cranes (RMGC), which are characterized by the restricted traveling range on a pair of rails. Since RMGCs and yard blocks are tightly bound to each other, when allocating them, we should make sure that the RMGCs allocated for a yard activity are able to together cover the blocks allocated for the corresponding yard activity. In addition, considering that there are four basic activities occurring in the yard which compete with each other for the scarce resources and have different requirements and priorities in the allocation of blocks and yard cranes, we treat them in a single model rather than in multiple independent models as were generally done in literature. A mixed integer programming model is constructed, and an iterative decomposition solution procedure is proposed for the problem. Based on the solution procedure, a decision support system is developed and implemented for a terminal in Tianjin seaport. Using the actual data, the numerical experiments show the effectiveness and efficiency of the decision support system.

Novel methods for resource allocation in humanitarian logistics considering human suffering

Abstract Deprivation cost is often used as a key economic metric of human suffering associated with emergency logistics. An improved approach is proposed for effective and equitable critical resource allocation within emergency logistics that considers human suffering by using this economic representation. A dynamic programming model is constructed for a multi-period resource allocation dispatch problem extracted to represent the disaster response phase, with special attention paid to the human suffering resulting from the delivery delay. Extensive numerical experiments are conducted to validate the computational performance and solution quality of the dynamic programming method. Based on the best solutions, an optimal delivery pattern with a cyclically sequenced feature is identified and its sufficient condition is provided as well. Furthermore, a piecewise linear method is proposed to accommodate large-scale instances that cannot be applied to the “Cyclic Delivery Approach”.

Behaviour perception-based disruption models for the parallel machine capacitated lot-sizing and scheduling problem

Capacitated lot-sizing and scheduling problem under disruption environment is a frequently encountered problem in manufacturing industry. This paper focuses on dealing with the case that the disruption is caused by machine breakdowns. Such case frequently arises during the process of the execution of a planned schedule. As a result, a reschedule needs to be applied, and then the decision-maker naturally may compare the reschedule results with the original one. Rather than from the conventional cost-saving perspective, this paper makes comparison from the attitude or the human behaviour perception of decision-makers towards the deviation from the original schedule. A non-linear mixed integer programming model is constructed with the objective of minimising the negative deviation based on the Prospect Theory, a psychologically more accurate description of decision-making. The non-linear term introduced by the Prospect Theory is approximately linearised by a series of piecewise linear segments. Then, an MIP-based fix-and-optimise algorithm is proposed to solve the approximated MIP problem. In numerical experiments, the impacts of several key factors of the proposed model and algorithm are explored. Two adjustment policies are compared, and the trade-off between cost saving and minimisation of the human behaviour perception deviation of decision-makers is discussed as well.

Rollout Algorithms for Resource Allocation in Humanitarian Logistics

ABSTRACT Large-scale disasters and catastrophic events typically result in a significant shortage of critical resources, posing a great challenge to allocating limited resources among different affected areas to improve the quality of emergency logistics operations. This article pays attention to the performance of resource allocation, which includes three metrics: efficiency, effectiveness, and equity, respectively corresponding to economic cost, service quality, and fairness. In particular, the effectiveness metric considers human suffering by depicting it as deprivation cost, an economic valuation measurement that has been recently proposed and the equity metric concerns about the service equality at the end of planning horizon. A nonlinear integer model is first proposed and then an equivalent dynamic programming model is developed to avoid the nonlinear terms created by the introduction of the deprivation cost. The dynamic programming method can solve small-scale problems to optimality but meets difficulty when solving medium- and large-scale problems, due to the curse of dimensionality. Therefore, an approximate dynamic programming algorithm, called the rollout algorithm, is proposed to overcome this computational difficulty. The computational complexity of the proposed algorithm is theoretically analyzed. Furthermore, a modified version of the rollout algorithm is presented, with its computational complexity analyzed. Extensive numerical experiments are conducted to test the performance of the proposed algorithms, and the experimental results demonstrate that the initially proposed rollout algorithm yields optimal or near-optimal solutions within a reasonable amount of time. In addition, the impacts of some important parameters are investigated and managerial insights are drawn.

A Combinatorial Benders’ Cuts Algorithm for the Local Container Drayage Problem

This paper examines the local container drayage problem under a special operation mode in which tractors and trailers can be separated; that is, tractors can be assigned to a new task at another location while trailers with containers are waiting for packing or unpacking. Meanwhile, the strategy of sharing empty containers between different customers is also considered to improve the efficiency and lower the operation cost. The problem is formulated as a vehicle routing and scheduling problem with temporal constraints. We adopt combinatorial benders’ cuts algorithm to solve this problem. Numerical experiments are performed on a group of randomly generated instances to test the performance of the proposed algorithm.

The Assignment of Customers in Multi-depot Vehicle Routing Problem with Fleet Size Constraint for Each Depot

This paper addresses a multi-depot vehicle routing problem with partial customers not yet assigned to depots and each depot having fleet size constraint. An integrated mathematical model has been constructed with the objective of minimizing both the total required fleet size and overall traveling distance. To overcome the computation difficulty resulting from high order mathematic model, the problem is decomposed into two sub-problems. Moreover, to keep the consistency between decomposed problems and original problem, partial decision of the second sub-problem is taken into the consideration of the first sub-problem. For larger instances, two heuristics have also been developed. The computation results tested on real-life data of auto spare parts show that taking partial second decision into the consideration of the first decision does improve solution quality and the heuristics could provide encouraging methods for practical use.