An effective kernel search and dynamic programming hybrid heuristic for a multimodal transportation planning problem with order consolidation

When designing the underground logistics system, it is necessary to consider the uncertainty of logistics nodes, high cost, and high risk. This paper employed the theories of uncertain graph and dynamic programming to solve the network planning problem of underground logistics system. Firstly, we proposed the concepts of uncertainty measure matrix and vertices structure uncertainty graph by using uncertainty measure and uncertainty graph. Secondly, vertices structure uncertainty graph of the underground logistics system was constructed based on our proposed vertices structure uncertainty graph and the uncertainty of logistics nodes. Thirdly, the dynamic programming model of the underground logistics system was established, and its solution algorithm was also designed by improving simulated annealing. Finally, the correctness and feasibility of the method was validated by using a numerical example of the underground logistics system in Xianlin district, Nanjing City in China.

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ONLINE CAPACITY PLANNING FOR REHABILITATION TREATMENTS: AN APPROXIMATE DYNAMIC PROGRAMMING APPROACH

Probability in the Engineering and Informational Sciences ◽

10.1017/s0269964818000402 ◽

2018 ◽

Vol 34 (3) ◽

pp. 381-405

Author(s):

Ingeborg A. Bikker ◽

Martijn R.K. Mes ◽

Antoine Sauré ◽

Richard J. Boucherie

Keyword(s):

Dynamic Programming ◽

Capacity Planning ◽

Approximate Dynamic Programming ◽

Capacity Allocation ◽

Access Time ◽

Programming Approach ◽

Planning Problem ◽

Dynamic Programming Approach ◽

Negative Effect ◽

Long Access

AbstractWe study an online capacity planning problem in which arriving patients require a series of appointments at several departments, within a certain access time target.This research is motivated by a study of rehabilitation planning practices at the Sint Maartenskliniek hospital (the Netherlands). In practice, the prescribed treatments and activities are typically booked starting in the first available week, leaving no space for urgent patients who require a series of appointments at a short notice. This leads to the rescheduling of appointments or long access times for urgent patients, which has a negative effect on the quality of care and on patient satisfaction.We propose an approach for allocating capacity to patients at the moment of their arrival, in such a way that the total number of requests booked within their corresponding access time targets is maximized. The model considers online decision making regarding multi-priority, multi-appointment, and multi-resource capacity allocation. We formulate this problem as a Markov decision process (MDP) that takes into account the current patient schedule, and future arrivals. We develop an approximate dynamic programming (ADP) algorithm to obtain approximate optimal capacity allocation policies. We provide insights into the characteristics of the optimal policies and evaluate the performance of the resulting policies using simulation.

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A Method for Transportation Planning and Profit Sharing in Collaborative Multi-Carrier Vehicle Routing

Mathematics ◽

10.3390/math8101788 ◽

2020 ◽

Vol 8 (10) ◽

pp. 1788

Author(s):

Yanjun Shi ◽

Na Lin ◽

Qiaomei Han ◽

Tongliang Zhang ◽

Weiming Shen

Keyword(s):

Vehicle Routing ◽

Shapley Value ◽

Vehicle Routing Problem ◽

Transportation Planning ◽

Hybrid Genetic Algorithm ◽

Profit Sharing ◽

Planning Problem ◽

Routing Problem ◽

Heuristics Algorithm ◽

Carrier Vehicle

This paper addresses a collaborative multi-carrier vehicle routing problem (CMCVRP) where carriers tackle their orders collaboratively to reduce transportation costs. First, a hierarchical heuristics algorithm is proposed to solve the transportation planning problem. This algorithm makes order assignments based on two distance rules and solves the vehicle routing problem with a hybrid genetic algorithm. Second, the profit arising from the coalition is quantified, and an improved Shapley value method is proposed to distribute the profit fairly to individual players. Extensive experiment results showed the effectiveness of the proposed hierarchical heuristics algorithm and confirmed the stability and fairness of the improved Shapley value method.

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