A greedy heuristic and simulated annealing approach for a bicriteria flowshop scheduling problem with precedence constraints—a practical manufacturing case

Abstract

This paper considers a flowshop scheduling problem with two criteria, where the primary (dominant) criterion is the minimization of material waste and the secondary criterion is the minimization of the total tardiness time. The decision maker does not authorize trade-offs between the criteria. In view of the nature of this problem, a hierarchical (lexicographical) optimization approach is followed. An effective greedy heuristic is proposed to minimize the material waste and a simulated annealing (SA) algorithm is developed to minimize the total tardiness time, subjective to the constraint computed for the primary criterion. The solution accuracy is compared with the optimal solution obtained by complete enumeration for randomly generated problem sets. From the results, it is observed that the greedy heuristic produces the optimal solution and the SA solution does not differ significantly from the optimal solution.

Energy and Bursty Packet Loss Tradeoff Over Fading Channels: A System-Level Model

“…In SA algorithm, 100 temperature values are simulated according to FA temperature schedule while 50(M + 1) random configuration of transition probability matrix are generated for a single iteration at temperature T b . The cooling schedule parameters in (27) are computed after extensive experimentation such that muting occurs frequently at high temperature and almost seizes at low temperature. pairs is the same, their effect on QoE may vary considerably and dictates which parameter needs to be employed.…”

“…The parameters of the temperature schedule can be computed via experimentation, e.g., as in [14], [27]. The pseudocode for the optimization of programming problem using SA is presented in Algorithm 1.…”

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

“…As an example, in a multi-stage manufacturing process, each scheduled job needs to go through several operations to become a finished product. In a single machine scenario, any machine breaks down could cause delay of the entire production, as reported in [29], [27].…”

“…Job-shop scheduling comprises a finite set of jobs that needs to be processed either on single or multiple machines [27], [24], subject to a number of performance measures. As an example, in a multi-stage manufacturing process, each scheduled job needs to go through several operations to become a finished product.…”

“…In addition to the GA [52], examples of recent and classical meta-heuristics methods used to solve optimisation problems include Cuckoo search [28] and simulated annealing [29], [27], respectively. In cellular manufacturing systems, the problem of time-tabling in a flowline was investigated in [52].…”