In this study, a genetic algorithm (GA) with priority-based representation is proposed for a flexible job shop scheduling problem (FJSP) which is one of the hardest operations research problems. Investigating the effect of the proposed representation schema on FJSP is the main contribution to the literature. The priority of each operation is represented by a gene on the chromosome which is used by a constructive algorithm performed for decoding. All active schedules, which constitute a subset of feasible schedules including the optimal, can be generated by the constructive algorithm. To obtain improved solutions, iterated local search (ILS) is applied to the chromosomes at the end of each reproduction process. The most widely used FJSP data sets generated in the literature are used for benchmarking and evaluating the performance of the proposed GA methodology. The computational results show that the proposed GA performed at the same level or better with respect to the makespan for some data sets when compared to the results from the literature.
We present a continuous relaxation technique for the Concave
Piecewise Linear Network Flow Problem (CPLNFP), which has a
bilinear objective function and network constraints. We show that
a global optimum of the resulting problem is a solution of CPLNFP.
The theoretical results are generalized for a concave minimization
problem with a separable objective function. An efficient and
effective Dynamic Cost Updating Procedure (DCUP) is considered to
find a local minimum of the relaxation problem, which converges in
a finite number of iterations. We show that the CPLNFP is
equivalent to a Network Flow Problem with Flow Dependent Cost
Functions (NFPwFDCF), and we prove that the solution of the
Dynamic Slope Scaling Procedure (DSSP) is an equilibrium solution
of the NFPwFDCF. The numerical experiments show that the proposed
algorithm can provide a better solution than DSSP using less
amount of CPU time and iterations.
The problems inherent in designing and operating supply
chains provide a rich practical context for the development and
application of optimization models. From large scale (nonlinear)
network design and flow problems to operational execution under
uncertainty, the various problems faced in practice by supply chain
managers often lead to interesting and complex optimization
problems. The primary objective of the Journal of Industrial
and Management Optimization is to ''promote collaboration between
optimization specialists, industrial practitioners and management
scientists so that important practical industrial and management
problems can be addressed by the use of appropriate, recent advanced
optimization techniques.'' This journal, therefore, provides an
excellent fit for the analysis of difficult supply chain design,
planning, and operations problems for which optimization models can
significantly impact performance.
To increase productivity, companies are in search of techniques that enable them to make faster and more effective decisions. Data mining and fuzzy clustering algorithms can serve for this purpose. This paper models the decision making process of a ceramics production company using a fuzzy clustering algorithm and data mining. Factors that affect the quality of slurry are measured over time. Using this data, a fuzzy clustering algorithm assigns the degrees of memberships of the slurry for the different quality clusters. An expert can decide on acceptance or rejection of slurry based on calculated degrees of memberships. In addition, by using data mining techniques we generated some rules that provide the optimum conditions for acceptance of the slurry.
This paper investigates a three-stage supply chain scheduling problem in the application area of aluminium production. Particularly, the first and the third stages involve two factories, i.e., the extrusion factory of the supplier and the aging factory of the manufacturer, where serial batching machine and parallel batching machine respectively process jobs in different ways. In the second stage, a single vehicle transports jobs between the two factories. In our research, both setup time and capacity constraints are explicitly considered. For the problem of minimizing the makespan, we formalize it as a mixed integer programming model and prove it to be strongly NP-hard. Considering the computational complexity, we develop two heuristic algorithms applied in two different cases of this problem. Accordingly, two lower bounds are derived, based on which the worst case performance is analyzed. Finally, different scales of random instances are generated to test the performance of the proposed algorithms. The computational results show the effectiveness of the proposed algorithms, especially for large-scale instances.
This paper develops a multi-period product pricing and service investment model to discuss the optimal decisions of the participants in a supplier-dominant supply chain under uncertainty. The supply chain consists of a risk-neutral supplier and two risk-averse manufacturers, of which one manufacturer can provide real-time customer service based on the Internet of Things (IoT). In each period of the Stackelberg game, the supplier decides its wholesale price to maximize the profit while the manufacturers make pricing and service investment decisions to maximize their respective utility. Using the backward induction, we first investigate the effects of risk-averse coefficients and price sensitive coefficients on the optimal decisions of the manufacturers. We find that the decisions of one manufacturer are inversely proportional to both risk-averse coefficients and its own price sensitive coefficient, while proportional to the price sensitive coefficient of its rival. Then, we derive the first-best wholesale price of the supplier and analyze how relevant factors affect the results. A numerical example is conducted to verify our conclusions and demonstrate the advantages of the IoT technology in long-term competition. Finally, we summarize the main contributions of this paper and put forward some advices for further study.