scholarly journals An ALNS algorithm for the static dial-a-ride problem with ride and waiting time minimization

OR Spectrum ◽  
2021 ◽  
Author(s):  
Christian Pfeiffer ◽  
Arne Schulz
Keyword(s):  
Waiting Time ◽  
Dynamic Programming Algorithm ◽  
Computational Effort ◽  
Programming Algorithm ◽  
Ride Sharing ◽  
Time Minimization ◽  
Neighbourhood Search ◽  
Large Neighbourhood ◽  
Adaptive Large Neighbourhood Search ◽  
Problem Setting

AbstractThe paper investigates the static dial-a-ride problem with ride and waiting time minimization. This is a new problem setting of significant practical relevance because several ride-sharing providers launched in recent years in large European cities. In contrast to the standard dial-a-ride problem, these providers focus on the general public. Therefore, they are amongst others in competition with taxis and private cars, which makes a more customer-oriented objective necessary. We present an adaptive large neighbourhood search (ALNS) as well as a dynamic programming algorithm (DP), which are tested in comprehensive computational studies. Although the DP can only be used for a single tour and, due to the computational effort, as a restricted version or for small instances, the ALNS also works efficiently for larger instances. The results indicate that ride-sharing proposals may help to solve the trade-off between individual transport, profitability of the provider, and reduction of traffic and pollution.

scholarly journals Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm

2019 ◽  
Vol 31 (4) ◽  
pp. 1051-1078 ◽  
Author(s):  
Lei He ◽  
Mathijs de Weerdt ◽  
Neil Yorke-Smith
Keyword(s):  
Time Windows ◽  
General Purpose ◽  
Time Sequence ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Start Time ◽  
Neighbourhood Search ◽  
Large Neighbourhood ◽  
Adaptive Large Neighbourhood Search ◽  
Large Neighbourhood Search

AbstractIn intelligent manufacturing, it is important to schedule orders from customers efficiently. Make-to-order companies may have to reject or postpone orders when the production capacity does not meet the demand. Many such real-world scheduling problems are characterised by processing times being dependent on the start time (time dependency) or on the preceding orders (sequence dependency), and typically have an earliest and latest possible start time. We introduce and analyze four algorithmic ideas for this class of time/sequence-dependent over-subscribed scheduling problems with time windows: a novel hybridization of adaptive large neighbourhood search (ALNS) and tabu search (TS), a new randomization strategy for neighbourhood operators, a partial sequence dominance heuristic, and a fast insertion strategy. Through factor analysis, we demonstrate the performance of these new algorithmic features on problem domains with varying properties. Evaluation of the resulting general purpose algorithm on three domains—an order acceptance and scheduling problem, a real-world multi-orbit agile Earth observation satellite scheduling problem, and a time-dependent orienteering problem with time windows—shows that our hybrid algorithm robustly outperforms general algorithms including a mixed integer programming method, a constraint programming method, recent state-of-the-art problem-dependent meta-heuristic methods, and a two-stage hybridization of ALNS and TS.

scholarly journals Modeling Single-Picker Routing Problems in Classical and Modern Warehouses

2021 ◽  
Author(s):  
Dominik Goeke ◽  
Michael Schneider
Keyword(s):  
Dynamic Programming Algorithm ◽  
Practical Interest ◽  
Computational Effort ◽  
Programming Algorithm ◽  
Substantial Part ◽  
Routing Problem ◽  
Solution Approach ◽  
Compact Formulation ◽  
Single Block ◽  
The Cost

The standard single-picker routing problem (SPRP) seeks the cost-minimal tour to collect a set of given articles in a rectangular single-block warehouse with parallel picking aisles and a dedicated storage policy, that is, each stock-keeping unit is only available from one storage location in the warehouse. We present a compact formulation that forgoes classical subtour elimination constraints by directly exploiting two of the properties of an optimal picking tour used in the dynamic programming algorithm published in the seminal paper of Ratliff and Rosenthal. We extend the formulation to three important settings prevalent in modern e-commerce warehouses: scattered storage, decoupling of picker and cart, and multiple end depots. In numerical studies, our formulation outperforms existing standard SPRP formulations from the literature and proves able to solve large instances within short runtimes. Realistically sized instances of the three problem extensions can also be solved with low computational effort. For scattered storage, we note a rough tendency that runtimes increase with longer pick lists or a higher degree of duplication. In addition, we find that decoupling of picker and cart can lead to substantial cost savings depending on the speed and capacity of the picker when traveling alone, whereas additional end depots have rather limited benefits in a single-block warehouse. Summary of Contribution: Efficiently routing order pickers is of great practical interest because picking costs make up a substantial part of operational warehouse costs. For the prevalent case of a rectangular warehouse with parallel picking aisles, we present a highly effective modeling approach that covers—in addition to the standard setting—several important storage and order-picking strategies employed in modern e-commerce warehouses: scattered storage, decoupling of picker and cart, and multiple end depots. In this way, we provide practitioners as well as scientists with an easy and quick way of implementing a high-quality solution approach for routing pickers in the described settings. In addition, we shed some light on the cost benefits of the different storage and picking strategies in numerical experiments.

scholarly journals Elements of Comprehensive Pipeline Optimization

1996 ◽  
Author(s):  
Michael Short ◽  
Steven H. Meller
Keyword(s):  
Real World ◽  
Dynamic Programming Algorithm ◽  
Computational Effort ◽  
Operating Conditions ◽  
Programming Algorithm ◽  
Cost Structures ◽  
Look Ahead ◽  
Full Analysis ◽  
Manageable Level ◽  
Utility Cost

It is well known that algorithms exist for reducing pipeline operating costs. These algorithms are exact for ideal pipelines and need to be modified to provide solutions for the real world. The issues include pipeline configurations, utility cost structures, and quantification of hydraulic safety. Successful modification requires understanding of the pipeline operating environment (on-line operations) and must be linked to pipeline operating conditions. Many of the optimization tools available to the pipeline industry today are based upon a dynamic programming algorithm attributed to Bellman. The costs of unit operations are balanced with the energy absorbed in heat due to frictional and other losses. This is carried out in such a way as to reduce the massive computational effort of an exhaustive solution search to a manageable level. For a pedagogical treatment of the problem, this is adequate. However, there are many significant factors which need to be added into and around this basic calculation. First, an algorithm with electrical cost factors only cannot evaluate penalties associated with poor hydraulics choices. Demand grouping, parallel pipelines, large amplitude pressure cycles, look ahead, and unit cycling also can and should be included in a full analysis. A modification to Bellman’s algorithm for non-linear pipeline configurations and electrical contracts will be developed and discussed in the context of a real-world petroleum pipeline operation.

scholarly journals Parallel-Machine Scheduling with Time-Dependent and Machine Availability Constraints

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Cuixia Miao ◽  
Juan Zou
Keyword(s):  
Dynamic Programming ◽  
Minimization Problem ◽  
Completion Time ◽  
Dynamic Programming Algorithm ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Programming Algorithm ◽  
Availability Constraints ◽  
Time Minimization

We consider the parallel-machine scheduling problem in which the machines have availability constraints and the processing time of each job is simple linear increasing function of its starting times. For the makespan minimization problem, which is NP-hard in the strong sense, we discuss the Longest Deteriorating Rate algorithm and List Scheduling algorithm; we also provide a lower bound of any optimal schedule. For the total completion time minimization problem, we analyze the strong NP-hardness, and we present a dynamic programming algorithm and a fully polynomial time approximation scheme for the two-machine problem. Furthermore, we extended the dynamic programming algorithm to the total weighted completion time minimization problem.

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