Nonlinear programming: theory, algorithms, and applications
Nonlinear programming: theory, algorithms, and applications
Jackson's rule for single-machine scheduling: making a good heuristic better
Mathematics of Operations Research
Heuristics for parallel machine scheduling with delivery times
Acta Informatica
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Synchronized Development of Production, Inventory, and Distribution Schedules
Transportation Science
Scheduling with Fixed Delivery Dates
Operations Research
Supply chain scheduling: Batching and delivery
Operations Research
Integrated Scheduling of Production and Distribution Operations
Management Science
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Genetic optimization of order scheduling with multiple uncertainties
Expert Systems with Applications: An International Journal
Machine scheduling with job class setup and delivery considerations
Computers and Operations Research
Capacity Allocation and Scheduling in Supply Chains
Operations Research
An adaptive annealing genetic algorithm for the job-shop planning and scheduling problem
Expert Systems with Applications: An International Journal
Dynamic supply chain scheduling
Journal of Scheduling
Semi-online two-level supply chain scheduling problems
Journal of Scheduling
A hybrid intelligent model for order allocation planning in make-to-order manufacturing
Applied Soft Computing
Modeling and Pareto optimization of multi-objective order scheduling problems in production planning
Computers and Industrial Engineering
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We consider the supply chain of a manufacturer who produces time-sensitive products that have a large variety, a short life cycle, and are sold in a very short selling season. The supply chain consists of multiple overseas plants and a domestic distribution center (DC). Retail orders are first processed at the plants and then shipped from the plants to the DC for distribution to domestic retailers. Due to variations in productivity and labor costs at different plants, the processing time and cost of an order are dependent on the plant to which it is assigned. We study the following static and deterministic order assignment and scheduling problem faced by the manufacturer before every selling season: Given a set of orders, determine which orders are to be assigned to each plant, find a schedule for processing the assigned orders at each plant, and find a schedule for shipping the completed orders from each plant to the DC, such that a certain performance measure is optimized. We consider four different performance measures, all of which take into account both delivery lead time and the total production and distribution cost. A problem corresponding to each performance measure is studied separately. We analyze the computational complexity of various cases of the problems by either proving that a problem is intractable or providing an efficient exact algorithm for the problem. We propose several fast heuristics for the intractable problems. We analyze the worst-case and asymptotic performance of the heuristics and also computationally evaluate their performance using randomly generated test instances. Our results show that the heuristics are capable of generating near-optimal solutions quickly.