Production and Delivery Batch Scheduling with Multiple Due Dates to Minimize Total Cost

Journal of Engineering and Technological Sciences

Endang Prasetyaningsih¹  T.M.A. Ari Samadhi²  Suprayogi Suprayogi²  Abdul Hakim Halim² 

[1] Department of Industrial Engineering and Management, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung 40123;Department of Industrial Engineering and Management, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung 40132

关键词: actual flow time;    backward scheduling;    batch scheduling;    integer non-linear programming;    integrated production and delivery.;   

DOI  :  10.5614/j.eng.technol.sci.2017.49.1.2

学科分类：工程和技术（综合）

来源: Institute for Research and Community Services ITB

【 摘 要 】

This paper addresses an integrated production and delivery batch scheduling problem for a make-to-order environment over daily time period, where the holding costs of in-process and completed parts at a supplier location and of completed parts at a manufacturer location are distinguished. All orders of parts with different due dates from the manufacturer arrive at the same time. The parts are produced in production batches and subsequently the completed parts are delivered in delivery batches using a capacitated vehicle in order to be received at the respective due dates. This study was aimed at finding an integrated schedule of production and delivery batches so as to meet the due date at minimum total cost consisting of the corresponding holding cost and delivery cost. The holding cost is a derivation of the so-called actual flow time (AFT), while the delivery cost is assumed to be proportional to the number of deliveries. The problems can be formulated as an integer non-linear programming model, and the global optimal solution can be obtained using optimization software. A heuristic algorithm is proposed to cope with the computational time problem using software. The numerical experiences show that the proposed algorithm yields near global optimal solutions.

【 授权许可】

CC BY