The scheduling of quay cranes (QCs) to minimize the handling time of a berthed vessel is one of the most important operations in container terminals as it impacts the terminal's overall productivity. In this paper...
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The scheduling of quay cranes (QCs) to minimize the handling time of a berthed vessel is one of the most important operations in container terminals as it impacts the terminal's overall productivity. In this paper, we propose two exact methods to solve the quay crane scheduling problem (QCSP) where a task is defined as handling a single container and subject to different technical constraints including QCs' safety margin, non-crossing, initial position, and nonzero traveling time. The first method is based on two versions of a compact mixed-integer programming formulation that can solve large problem instances using a general purpose solver. The second is a combination of some constraints of the proposed mathematical model and the binary search algorithm to reduce the CPU time, and solve more efficiently large-sized problems. Unlike existing studies, the computational study demonstrates that both methods can reach optimal solutions for large-sized instances and validates their dominance compared to an exact model proposed in the literature which finds solutions only for small problems. (C) 2018 Elsevier Ltd. All rights reserved.
In this paper, we address a resource-constrained project scheduling problem involving a single resource. The resource can be applied at varying consumption rates to the activities of the project. The duration of each ...
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In this paper, we address a resource-constrained project scheduling problem involving a single resource. The resource can be applied at varying consumption rates to the activities of the project. The duration of each activity is defined by a convex, non-increasing time-resource trade-off function. In addition, activities are not preemptable (ie, the resource consumption rate of an activity cannot be altered while the activity is being processed). We explicitly consider variation of the rate at which an activity is performed with variation in resource consumption rate. We designate the number of units (amount of an activity) performed per unit time with variation in resource consumption rate as the processing rate function, and assume this function to be concave. We present a tree-search-based method in concert with the solution of a nonlinear program and the use of dominance properties to determine: (i) the sequence in which to perform the activities of the project, and (ii) the resource consumption rate to allocate to each activity so as to minimize the project duration (makespan). We also present results of an experimental investigation that reveal the efficacy of the proposed methodology Finally, we present an application of this methodology to a practical setting.
Due to the increasing amount of goods transported by vessels and the resulting increased size of the vessels, waterway scheduling becomes a challenging task. Waterways can often only be expanded with enormous costs an...
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Due to the increasing amount of goods transported by vessels and the resulting increased size of the vessels, waterway scheduling becomes a challenging task. Waterways can often only be expanded with enormous costs and environmental damage. Therefore, this paper investigates a scheduling problem on a restricted waterway. Wide vessels are only allowed to pass in a passing box and vessels with deep draught can only pass the waterway in a time window around high tide. We present a mixed-integer program (MIP) for the problem setting and develop techniques which allow us to fix variables and reduce the number of variables and constraints of the model. The resulting model formulations are evaluated in a comprehensive computational study on a real-world setting at the river Elbe next to Hamburg (Germany).
In the literature, various discrete-time and continuous-time mixed-integer linear programming (MIP) formulations for project scheduling problems have been proposed. The performance of these formulations has been analy...
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In the literature, various discrete-time and continuous-time mixed-integer linear programming (MIP) formulations for project scheduling problems have been proposed. The performance of these formulations has been analyzed based on generic test instances. The objective of this study is to analyze the performance of discrete-time and continuous-time MIP formulations for a real-life application of project scheduling in human resource management. We consider the problem of scheduling assessment centers. In an assessment center, candidates for job positions perform different tasks while being observed and evaluated by assessors. Because these assessors are highly qualified and expensive personnel, the duration of the assessment center should be minimized. Complex rules for assigning assessors to candidates distinguish this problem from other scheduling problems discussed in the literature. We develop two discrete-time and three continuous-time MIP formulations, and we present problem-specific lower bounds. In a comparative study, we analyze the performance of the five MIP formulations on four real-life instances and a set of 240 instances derived from real-life data. The results indicate that good or optimal solutions are obtained for all instances within short computational time. In particular, one of the real-life instances is solved to optimality. Surprisingly, the continuous-time formulations outperform the discrete-time formulations in terms of solution quality.
We propose a Branch-and-Cut algorithm for the robust influence maximization problem. The influence maximization problem aims to identify, in a social network, a set of given cardinality comprising actors that are able...
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We propose a Branch-and-Cut algorithm for the robust influence maximization problem. The influence maximization problem aims to identify, in a social network, a set of given cardinality comprising actors that are able to influence the maximum number of other actors. We assume that the social network is given in the form of a graph with node thresholds to indicate the resistance of an actor to influence, and arc weights to represent the strength of the influence between two actors. In the robust version of the problem that we study, the node thresholds and arc weights are affected by uncertainty and we optimize over a worst-case scenario within given robustness budgets. We study properties of the robust solution and showthat even computing theworst-case scenario for given robustness budgets is NP-hard. We implement an exact Branch-and-Cut as well as a heuristic Branch-Cut-and-Price. Numerical experiments show that we are able to solve to optimality instances of size comparable to other exact approaches in the literature for the non-robust problem, and we can tackle the robust version with similar performance. On larger instances (= 2000 nodes), our heuristic Branch-Cutand-Price significantly outperforms a 2-opt heuristic. An extended abstract of this paper appeared in the proceedings of IPCO 2019.
We develop a technique for refining the unit commitment solution obtained from solving the Lagrangian. Our model is an integer program with nonlinear constraints. It can be solved to optimality using branch-and-bound....
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We develop a technique for refining the unit commitment solution obtained from solving the Lagrangian. Our model is an integer program with nonlinear constraints. It can be solved to optimality using branch-and-bound. Numerical results indicate a significant improvement in the quality of the solution obtained.
The problem of sequencing and scheduling airplanes landing and taking off on a runway is a major challenge for air traffic management. This difficult real-time task is still carried out by human controllers, with litt...
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The problem of sequencing and scheduling airplanes landing and taking off on a runway is a major challenge for air traffic management. This difficult real-time task is still carried out by human controllers, with little help from automatic tools. Several methods have been proposed in the literature, including mixed-integer programming (MlP)-based approaches. However, there is an opinion that MIP is unattractive for real-time applications, since computation times are likely to grow too large. In this paper, we reverse this claim, by developing a MIP approach able to solve to optimality real-life instances from congested airports in the stringent times allowed by the application. To achieve this, it was mandatory to identify new classes of strong valid inequalities, along with developing effective fixing and lifting procedures.
In this work we assess the benefits of an integrated planning approach for the supply of raw material and the subsequent production process. The supply part is concerned with the collection of raw materials from geogr...
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In this work we assess the benefits of an integrated planning approach for the supply of raw material and the subsequent production process. The supply part is concerned with the collection of raw materials from geographically dispersed suppliers, while the production planning part addresses the conversion of those raw materials into final products to satisfy customer demand. The proposed model is an extension of the model introduced by Kuhn and Liske (2011) considering dynamic demand and general structures of the bill of materials. We investigate two scenarios: one including raw material inventories at the production site, and the other supposing just-in-time (JIT) supply. Numerical experiments show that substantial cost savings are possible with an integrated planning approach compared to a classical sequential approach. The JIT scenario and situations with a rather low utilization in the production system benefit most from the integration. The proposed supply vehicle routing and production planning problem has a reverse structure unlike the well-studied production-distribution systems. Surprisingly, a sensitivity analysis on the dependency of the cost savings on different parameters show a quite similar behavior for both types of planning problems. (C) 2016 Elsevier B.V. All rights reserved.
In this paper, we propose a new formulation based on benders decomposition approach to solve the thermal unit commitment (UC) problem. In the proposed approach, the UC problem is decomposed into a master problem, whic...
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In this paper, we propose a new formulation based on benders decomposition approach to solve the thermal unit commitment (UC) problem. In the proposed approach, the UC problem is decomposed into a master problem, which is an integer optimization problem, and a subproblem, which is a nonlinear optimization problem. The proper on/off states of the generating units are found by solving the master problem using the mixed-integer programming method. The subproblem utilizes the solution of the master problem to form appropriate cuts and returns the cuts to the master problem for running the next iteration of the UC problem. In both optimization problems, corresponding constraints are exactly modeled. To demonstrate the effectiveness of the proposed approach, simulation results are compared with the results obtained by other methods. (C) 2009 Elsevier Ltd. All rights reserved.
This article presents a three-phase methodology for scheduling assembly and test operations for semiconductor devices. The facility in which these operations are performed is a re-entrant flow shop consisting of sever...
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This article presents a three-phase methodology for scheduling assembly and test operations for semiconductor devices. The facility in which these operations are performed is a re-entrant flow shop consisting of several dozen to several hundred machines and up to a 1000 specialized tools. The semiconductor devices are contained in lots, and each lot follows a specific route through the facility, perhaps returning to the same machine multiple times. Each step in the route is referred to as a "pass." In the first phase of the methodology an extended assignment model is solved to simultaneously assign tooling and lots to the machines. Four prioritized objectives are considered: minimize the weighted sum of key device shortages, maximize the weighted sum of lots processed, minimize the number of machines used, and minimize the makespan. In the second phase, lots are optimally sequenced on their assigned machines using the same prioritized objectives. Due to the precedent relations induced by the pass requirements, some lots may have to be delayed or removed from the assignment model solution to ensure that no machine runs beyond the planning horizon. In the third phase, machines are reset to allow additional lots to be processed when tooling is available. The methodology was tested using data provided by the Assembly and Test facility of a leading manufacturer. The results indicate that high-quality solutions can be obtained within 1hour when compared with those obtained with a greedy randomized adaptive search procedure. Cost reductions were observed across all objectives and averaged 62% in the aggregate.
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