A dynamic programming algorithm for days-off scheduling with sequence dependent labor costs

Elshafei, Moustafa; Alfares, Hesham K.

doi:10.1007/s10951-007-0040-x

Cited by 34 publications

(34 citation statements)

References 16 publications

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“…The resource allocation of the air purifier in the design process is based on previous design experience. Fixed resource allocation determines that the execution efficiency of the task teams corresponding to the five tasks has the following upper and lower limits: 0<v1≤12, 0<v2≤14, 0<v3≤10, 0<v4≤7, 0<v5≤15, that : V=diag (12,14,10,7,15) In this paper, the dynamic programming method is used to optimize the optimal task distribution schemes in different stages, and the specific process reference Moustafa Elshafei et al [9]. According to the optimal task distribution in each stage, the definition of task distribution matrix Ki in Eq.3 and the calculation method of total time labor in Eq.3 to Eq.5 are combined.…”

Section: Construction Of Multi-stage Iteration Model With Resource Bamentioning

confidence: 99%

Research on multi-stage iterative model solving method with resource optimization configuration

et al. 2020

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Aiming at the problem of unreasonable resource allocation in the existing coupling design iterative model based on efficiency constraint, the resource equilibrium strategy was introduced into the iterative model. In order to reduce the time cost of product development, a multi-stage iterative model with optimal resource allocation was constructed and the optimal resource allocation of each task group was obtained by solving this model. Taking the design and development process of an air purifier as an example, the validity of this model was verified. The research shows that after introducing resource equilibrium strategy into the iterative model based on efficiency constraint, the time cost of iterative mode in different stages decreases, and with the increase of the number of iteration stages, the time cost decreases first and then increases. The research results provide a theoretical basis for designers to reasonably select resource allocation mode and number of iteration stages in actual product development so as to optimize resource allocation and reduce development cost.

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Section: Construction Of Multi-stage Iteration Model With Resource Bamentioning

confidence: 99%

Research on multi-stage iterative model solving method with resource optimization configuration

et al. 2020

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“…Simulation was used to study various assignment strategies in light of the uncertainty. More recently, Elshafei and Alfares solved the days‐off scheduling problem with sequence‐dependent labor costs using DP. Because all possible weekly patterns are created during the course of the algorithm, the approach was limited to instances of moderate size.…”

Section: Literaturementioning

confidence: 99%

Bounded flexibility in days-on and days-off scheduling

Brunner

Bard

Köhler

2013

Naval Research Logistics

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This article presents a flexible days‐on and days‐off scheduling problem and develops an exact branch and price (B&P) algorithm to find solutions. The main objective is to minimize the size of the total workforce required to cover time‐varying demand over a planning horizon that may extend up to 12 weeks. A new aspect of the problem is the general restriction that the number of consecutive days on and the number of consecutive days off must each fall within a predefined range. Moreover, the total assignment of working days in the planning horizon cannot exceed some maximum value. In the B&P framework, the master problem is stated as a set covering‐type problem whose columns are generated iteratively by solving one of three different subproblems. The first is an implicit model, the second is a resource constrained shortest path problem, and the third is a dynamic program. Computational experiments using both real‐word and randomly generated data show that workforce reductions up to 66% are possible with highly flexible days‐on and days‐off patterns. When evaluating the performance of the three subproblems, it was found that each yielded equivalent solutions but the dynamic program proved to be significantly more efficient. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 678–701, 2013

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“…Reported application areas include retail sales [4,5], manufacturing [6], transportation [7,8,9], health care delivery [10,11,12], and the telecommunications industry [13,14]. Solution approaches have incorporated diverse industrial engineering methods such as mathematical programming [15,16,17], simulation [18], dynamic programming [19], genetic algorithms [20], and other heuristic procedures [21,22]. Lesaint et al [23] document development and deployment of a dynamic scheduler which employs a combination of heuristic search and constraint-based reasoning to schedule 40,000 telecommunication service technicians with estimated cost avoidance of $250 million per year.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling for a Service Technician Workforce with Time-varying Work Volume and Technician Availability

Dietz¹

2017

AJETM

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A practical scheduling method is developed and implemented to determine the optimal allocation of technicians to candidate tour types and start times in a field service environment. Historical data is aggregated to determine a weekly work volume distribution and technician availability profile. These and other quantitative factors populate a mixed integer programming model for determining the distribution of technician tours that will minimize queueing delay in completing service, subject to side constraints on tour type quantities. The approach has been successfully implemented to schedule installation and maintenance technicians at a major telecommunication service provider and could easily be adapted to other operational contexts.

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A dynamic programming algorithm for days-off scheduling with sequence dependent labor costs

Cited by 34 publications

References 16 publications

Research on multi-stage iterative model solving method with resource optimization configuration

Research on multi-stage iterative model solving method with resource optimization configuration

Bounded flexibility in days-on and days-off scheduling

Optimal Scheduling for a Service Technician Workforce with Time-varying Work Volume and Technician Availability

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