Single-Machine Due-Window Assignment and Scheduling with Learning Effect and Resource-Dependent Processing Times

Abstract

We consider a single-machine common due-window assignment scheduling problem, in which the processing time of a job is a function of its position in a sequence and its resource allocation. The window location and size, along with the associated job schedule that minimizes a certain cost function, are to be determined. This function is made up of costs associated with the window location, window size, earliness, and tardiness. For two different processing time functions, we provide a polynomial time algorithm to find the optimal job sequence and resource allocation, respectively.

Optimization for Due-Window Assignment Scheduling with Position-Dependent Weights

“…For the weighted sum of earliness, tardiness, and due-window location penalty minimization, they proposed a polynomial-time algorithm to solve the problem. Wang and Wang [10] considered the single-machine resource allocation scheduling problem with learning effect and CON-DW assignment. Slack due-window (SLK-DW) assignment method (Mosheiov and Oron [11]…”

Due-Window Assignment and Resource Allocation Scheduling with Truncated Learning Effect and Position-Dependent Weights

“…ey proved that several problems can be solved in polynomial time. Wang and Wang [9] and Li et al [10] considered common and slack due-window assignment problems with learning effects and resource allocations. Wang and Wang [11] considered single-machine scheduling problems with learning effects and convex resource allocation function.…”

Scheduling jobs with position and sum-of-processing-time based processing times

“…Extensive surveys of scheduling models and problems involving learning effect (learning curve) and/or deteriorating jobs (time-dependent processing times) are provided by Alidaee and Womer [2], Cheng et al [3], Gawiejnowicz [4], and Biskup [5]. More recent papers which have considered scheduling problems and models with learning effect (learning curve) and/or deteriorating jobs (time-dependent processing times) include Wang and Cheng [6], Eren and Guner [7,8], Eren [9], Wang et al [10,11], Yang and Chand [12], Janiak and Rudek [13], Wu and Lee [14], Lee and Wu [15], Wang et al [16], Wang and Guo [17], Wang et al [18][19][20], Wang and Wang [21], Shen et al [22], Jiang et al [23], Lu and Wang [24], Wang and Wang [25], Wang et al [26], Ji et al [27], Lu et al [28], Wang and Wang [29,30], Wang and Liu [31], Yin et al [32], Huang and Wang [33], Wang and Wang [34], Wang et al [35], Yin et al [36,37], Wang and Wang [38], and Bai et al [39].…”