3 research outputs found
A unit cost adjusting heuristic algorithm for the integrated planning and scheduling of a two-stage supply chain
Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic
relationship of multi-supplier-multi-customer in current market gradually, and efficient
scheduling techniques are important tools of the dynamic supply chain relationship establishing
process. This paper studies the optimization of the integrated planning and scheduling problem
of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a
minimum supply chain operating cost, whose manufacturers have different production
capacities, holding and producing cost rates, transportation costs to retailers.
Design/methodology/approach: As a complex task allocation and scheduling problem, this
paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA) heuristic
algorithm that adjust the suppliers’ supplying quantity according to their unit costs step by step
to solve the model.
Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for
optimizing many numerical experiments, results show that the INLP model and the UCA
algorithm can obtain its near optimal solution of the two-stage supply chain’s planning and
scheduling problem within very short CPU time.
Research limitations/implications: The proposed UCA heuristic can easily help managers to
optimizing the two-stage supply chain scheduling problems which doesn’t include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual
commercial relationships, so to make some modification and study on the UCA heuristic
should be able to optimize the integrated planning and scheduling problems of a supply chain
with more reality constraints.
Originality/value: This research proposes an innovative UCA heuristic for optimizing the
integrated planning and scheduling problem of two-stage supply chains with the constraints of
suppliers’ production capacity and the orders’ delivering time, and has a great practical
significance to the dynamic relationship establishment of multi-supplier-multi-customer in
current market.Peer Reviewe
A unit cost adjusting heuristic algorithm for the integrated planning and scheduling of a two-stage supply chain
Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic
relationship of multi-supplier-multi-customer in current market gradually, and efficient
scheduling techniques are important tools of the dynamic supply chain relationship establishing
process. This paper studies the optimization of the integrated planning and scheduling problem
of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a
minimum supply chain operating cost, whose manufacturers have different production
capacities, holding and producing cost rates, transportation costs to retailers.
Design/methodology/approach: As a complex task allocation and scheduling problem, this
paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA) heuristic
algorithm that adjust the suppliers’ supplying quantity according to their unit costs step by step
to solve the model.
Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for
optimizing many numerical experiments, results show that the INLP model and the UCA
algorithm can obtain its near optimal solution of the two-stage supply chain’s planning and
scheduling problem within very short CPU time.
Research limitations/implications: The proposed UCA heuristic can easily help managers to
optimizing the two-stage supply chain scheduling problems which doesn’t include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual
commercial relationships, so to make some modification and study on the UCA heuristic
should be able to optimize the integrated planning and scheduling problems of a supply chain
with more reality constraints.
Originality/value: This research proposes an innovative UCA heuristic for optimizing the
integrated planning and scheduling problem of two-stage supply chains with the constraints of
suppliers’ production capacity and the orders’ delivering time, and has a great practical
significance to the dynamic relationship establishment of multi-supplier-multi-customer in
current market.Peer Reviewe
SUPPLY CHAIN SCHEDULING FOR MULTI-MACHINES AND MULTI-CUSTOMERS
Manufacturing today is no longer a single point of production activity but a chain of activities from the acquisition of raw materials to the delivery of products to customers. This chain is called supply chain. In this chain of activities, a generic pattern is: processing of goods (by manufacturers) and delivery of goods (to customers). This thesis concerns the
scheduling operation for this generic supply chain. Two performance measures considered for evaluation of a particular schedule are: time and cost. Time refers to a span of the time that the manufacturer receives the request of goods from the customer to the time
that the delivery tool (e.g. vehicle) is back to the manufacturer. Cost refers to the delivery cost only (as the production cost is considered as fi xed). A good schedule is thus with short time and low cost; yet the two may be in conflict. This thesis studies the algorithm for the supply chain scheduling problem to achieve a balanced short time and low cost.
Three situations of the supply chain scheduling problem are considered in this thesis: (1) a single machine and multiple customers, (2) multiple machines and a single customer and (3) multiple machines and multiple customers. For each situation, di fferent vehicles characteristics
and delivery patterns are considered. Properties of each problem are explored
and algorithms are developed, analysed and tested (via simulation).
Further, the robustness of the scheduling algorithms under uncertainty and the resilience of the scheduling algorithms under disruptions are also studied. At last a case study, about medical resources supply in an emergency situation, is conducted to illustrate how
the developed algorithms can be applied to solve the practical problem.
There are both technical merits and broader impacts with this thesis study. First, the problems studied are all new problems with the particular new attributes such as on-line, multiple-customers and multiple-machines, individual customer oriented, and limited capacity of delivery tools. Second, the notion of robustness and resilience to evaluate a scheduling algorithm are to the best of the author's knowledge new and may be open to a new avenue for the evaluation of any scheduling algorithm. In the domain of manufacturing and service provision in general, this thesis has provided an e ffective and effi cient tool for managing the operation of production and delivery in a situation where the demand
is released without any prior knowledge (i.e., on-line demand). This situation appears in many manufacturing and service applications