On-line integrated production and outbound distribution scheduling to minimize the maximum delivery completion time

Ng, C. T.; Lu, Lingfa

doi:10.1007/s10951-011-0260-y

Cited by 21 publications

(6 citation statements)

References 18 publications

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“…Recently, there are a few works on IPDS problems with dynamic order arrivals. Ng and Lu (2012) considered an online IPDS problem with single machine, single vehicle, and single customer, to minimize the maximum delivery time. Zhang et al (2018) considered an online order picking and delivery problem with multiple delivery zones and limited vehicle capacity, where orders are grouped into batches and then delivered to their assigned zones.…”

Section: Literature Reviewmentioning

confidence: 99%

An approximate dynamic programming approach for production‐delivery scheduling under non‐stationary demand

Liu

Wang

Lee

et al. 2021

Naval Research Logistics

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We consider an integrated production and delivery scheduling problem with non-stationary demand in a two-stage supply chain, where orders arrive dynamically and the demand is time-varying. Orders should be first processed on identical machines and then delivered to a single next-stage destination by the transporters with fixed departure times. The objective is to minimize the order waiting time via production-delivery scheduling. We formulate the problem into a Markov decision process model and develop an approximate dynamic programming (ADP) method. To shrink action (decision) space, we propose the shorter processing time first and first completion first delivery (SPTm/FCFD) principle to determine order processing sequences and order delivery, and then we establish two constraints to eliminate a fraction of inferior actions. Based on the SPTm/FCFD principle, we propose the SPT/FCFD rule, and show its optimality for two scenarios. In addition, we deploy five basis functions to approximate the value function. The superior performance of ADP policy is validated via numerical experiments, compared with four benchmark policies. We also empirically study the impact of demand features on the waiting time, and results show that these features significantly affect the performances of all polices. In practice, it is suggested to postpone the peak demand, when total demand exceeds the available production capacity.

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Section: Literature Reviewmentioning

confidence: 99%

An approximate dynamic programming approach for production‐delivery scheduling under non‐stationary demand

Liu

Wang

Lee

et al. 2021

Naval Research Logistics

View full text Add to dashboard Cite

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“…A feasible integrated production and distribution schedule is made with different approaches to how a job can be produced and delivered. Ng and Lu [17] considered the on-line integrated production and outbound distribution scheduling problem to minimize the maximum delivery completion time. Chandra et al [18] adopted a mathematical optimization method to minimize the maximum scheduling cost based on a single machine scheduling problem.…”

Section: Related Studiesmentioning

confidence: 99%

A Practical Production-Distribution Rescheduling Model With Conflict and Unexpected Disruptions

2019

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The production-distribution rescheduling problem aims at coordinating separate plans made by producers and distributors to eliminate the effect of unexpected disruptions in practice. In this paper, we propose a rescheduling model that considers unexpected disruptions. Meanwhile, a conflict resolution strategy is also considered in the model since conflicts commonly exist among the respective plans made by producers and distributors. In the model, two aspects are considered. On one hand, we try to minimize the operation cost and penalty cost associated with the conflict resolution strategy. On the other hand, we expect to minimize the difference between the original plans and the rescheduled ones. A genetic algorithm (GA) is developed to solve this problem. A case study based on the largest integrated coal enterprise in China shows that once a certain unexpected disruption occurs, the most effective approach is to adjust the plans that consist of the output, transportation volume, and sales volume, which has strong relationships with the disruption. If the scale of the disruption is not excessive, the suggested model can neutralize the influence of the variation. INDEX TERMS Production and distribution, rescheduling process, model and algorithm, conflict management, unexpected disruption.

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“…Indeed, when = 1 and is known in advance, Ng and Lu [19] provided an optimal online algorithm for the general case in which the processing time of each job is a nonnegative number. When ≥ 2 and is known in advance, they provided an online algorithm which is not best possible.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Online Scheduling on a Single Machine with Grouped Processing Times

Liu

Wan

Wei

2015

Discrete Dynamics in Nature and Society

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We consider the online scheduling problem on a single machine with the assumption that all jobs have their processing times in[p,(1+α)p], wherep>0andα=(5-1)/2. All jobs arrive over time, and each job and its processing time become known at its arrival time. The jobs should be first processed on a single machine and then delivered by a vehicle to some customer. When the capacity of the vehicle is infinite, we provide an online algorithm with the best competitive ratio of(5+1)/2. When the capacity of the vehicle is finite, that is, the vehicle can deliver at mostcjobs at a time, we provide another best possible online algorithm with the competitive ratio of(5+1)/2.

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On-line integrated production and outbound distribution scheduling to minimize the maximum delivery completion time

Cited by 21 publications

References 18 publications

An approximate dynamic programming approach for production‐delivery scheduling under non‐stationary demand

An approximate dynamic programming approach for production‐delivery scheduling under non‐stationary demand

A Practical Production-Distribution Rescheduling Model With Conflict and Unexpected Disruptions

Online Scheduling on a Single Machine with Grouped Processing Times

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