Search

In this research, we study a supply chain which consists of a firm and a set of customer groups, where the demand (order quantity) from each customer group depends on the quoted delivery time. The prices of the product are determined and different to each customer group. The firm's objective is to maximize the profit by quoting different delivery times for all customer groups. A mathematical model is developed, which is proved to be NP hard. Both a mixed integer programming (MIP) formulation and the branch-and-bound algorithm are proposed to study the moderate size of the problem. Also, a heuristic algorithm is developed which is able to obtain the optimal solution quickly in most of cases when customer size is less than 20.

Nowadays, it is popular to outsource transportation and distribution of finished products to third-party logistics (3PL) providers in many industries. In order to shorten the response time from order receipt to delivery, and also to improve on-time delivery accuracy, the decision of manufacturing scheduling and transportation scheduling should consider the constraints between manufacturing and transportation. In this paper, we study a coordinated scheduling problem of parallel machine assembly manufacturing and multi-destination transportation in the make-to-order (MTO) consumer electronics supply chain (CESC). By considering the constraints between parallel machine assembly and 3PL transportation, the overall problem is decomposed into a parallel machine scheduling sub-problem and a 3PL transportation sub-problem. The 3PL transportation problem is proved to be NP-complete. Heuristic algorithms are proposed to solve the parallel machine assembly scheduling problem.

In this paper, the problem of minimizing total absolute differences of completion times (TADC) of jobs and mean flowtime is studied. The objective of minimizing TADC ensures the completion times of jobs are close to one another, while the objective of minimizing mean flowtime minimizes the average time spent by the jobs in the system. Early completion of jobs is not desirable in delivery situations when jobs have to be dispatched together for a customized delivery or assembly and, particularly, when the finished jobs incur costs for preserving/holding them till other matching job orders complete. In this work, lower bounds on TADC and mean flowtime subject to minimum TADC for the static jobshop problem are reported. A simulated annealing algorithm using the concept of backward scheduling is proposed for minimizing mean flowtime subject to optimal TADC, and the proposed algorithm is evaluated using 82 jobshop scheduling problems taken from literature, of size varying from 6-jobs 6-machines to 50-jobs 20-machines. Finally, we combine a statistical optimum prediction technique with the proposed simulated annealing algorithm, and evaluate the statistical bounds established for the objective of minimizing mean flowtime subject to minimum TADC on the benchmark problems.