• Previous Article
    Supplier financing service decisions for a capital-constrained supply chain: Trade credit vs. combined credit financing
  • JIMO Home
  • This Issue
  • Next Article
    Algorithmic computation of MAP/PH/1 queue with finite system capacity and two-stage vacations
doi: 10.3934/jimo.2018202

An integrated approach based on Fuzzy Inference System for scheduling and process planning through multiple objectives

Department of Management, University of Isfahan, Isfahan, Iran

* Corresponding author: Dariush Mohamadi Zanjirani

Received  October 2017 Revised  June 2018 Published  December 2018

Integrated process planning and scheduling (IPPS) problems are one of the most important flexible planning functions for a job shop manufacturing. In a manufacturing order to produce n jobs (parts) on m machines in a flexible manufacturing environment, an IPPS system intends to generate the process plans for all n parts and the overall job-shop schedule concurrently, with the objective of optimizing a manufacturing objective such as make-span. The optimization of the process planning and scheduling will be applied through an integrated approach based on Fuzzy Inference System (FIS), to provide for flexibilities of the given components and consider the qualitative parameters. The FIS, Constraint Programming (CP) and Simulated Annealing (SA) algorithms are applied in this design. The objectives of the proposed model consist of maximization of processes utility, minimization of make-span and total production costs including costs of flexible tools, machines, process and TADs. The proposed approach indicates that The CP and SA algorithms are able to resolve the IPPS problem with multiple objective functions. The experiments and related results indicate that the CP method outperforms the SA algorithm.

Citation: Dariush Mohamadi Zanjirani, Majid Esmaelian. An integrated approach based on Fuzzy Inference System for scheduling and process planning through multiple objectives. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2018202
References:
[1]

A. AkgunE. A. SezerH. A. NefesliogluC. Gokceoglu and B. Pradhan, An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm, Computers & Geosciences, 38 (2012), 23-34. doi: 10.1016/j.cageo.2011.04.012. Google Scholar

[2]

IBM, IBM ILOG CPLEX Optimization Studio 12.5 User's Manual, 2012.Google Scholar

[3]

S. KirkpatrickC. Gelatt and M. Vecchi, Optimization by simulated annealing, Science, New Series, 220 (1983), 671-680. doi: 10.1126/science.220.4598.671. Google Scholar

[4]

X. LiL. GaoX Shao and C. Wang, Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling, Computers & Operations Research, 37 (2010), 656-667. doi: 10.1016/j.cor.2009.06.008. Google Scholar

[5]

W. Li and C. A. McMahon, A simulated annealing-based optimization approach for integrated process planning and scheduling, International Journal of Computer Integrated Manufacturing, 20 (2007), 80-95. doi: 10.1080/09511920600667366. Google Scholar

[6]

K. Lian, Optimization of process planning with various flexibilities using an imperialist competitive algorithm, The International Journal of Advanced Manufacturing Technology, 59 (2012), 815-828. doi: 10.1007/s00170-011-3527-8. Google Scholar

[7]

J. LinM. LiuJ. Hao and S. Jiang, A multi-objective optimization approach for integrated production planning under interval uncertainties in the steel industry, Computers & Operations Research, 72 (2016), 189-203. doi: 10.1016/j.cor.2016.03.002. Google Scholar

[8]

T. Majozi and X. Zhu, A combined fuzzy set theory and MILP approach in integration of planning and scheduling of batch plants-Personnel evaluation and allocation, Computers & Chemical Engineering, 29 (2005), 2029-2047. doi: 10.1016/j.compchemeng.2004.07.038. Google Scholar

[9]

X. N. Shen and X. Yao, Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems, Information Science, 298 (2015), 198-224. doi: 10.1016/j.ins.2014.11.036. Google Scholar

[10]

J. M. Usher and K. J. Fernandes, Dynamic process planning-The staticphase, Journal of Materials Processing Technology, 61 (1996), 53-58. Google Scholar

[11]

S.-y. Wan, Integrated Process Planning and Scheduling with Setup Time Consideration by Ant Colony Optimization, HKU Theses Online (HKUTO), 2012. doi: 10.5353/th_b4961807. Google Scholar

[12]

Y. Wang, A PSO-based multi-objective optimization approach to the integration of process planning and scheduling, in Control and Automation (ICCA), 8th IEEE International Conference on IEEE, eds. Y. Zhang and J. Y. H. Fuh, 2010. doi: 10.1109/ICCA.2010.5524365. Google Scholar

[13]

L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338-353. doi: 10.1016/S0019-9958(65)90241-X. Google Scholar

[14]

L. Zhang and T. Wong, Solving integrated process planning and scheduling problem with constructive meta-heuristics, Information Sciences, 340/341 (2016), 1-16. doi: 10.1016/j.ins.2016.01.001. Google Scholar

[15]

L. Zhang and T. Wong, An object-coding genetic algorithm for integrated process planning and scheduling, European Journal of Operational Research, 244 (2015), 434-444. doi: 10.1016/j.ejor.2015.01.032. Google Scholar

[16]

W. Zhanjie, The research about integration of process planning and production scheduling based on genetic algorithm, in Computer Science and Software Engineering, International Conference on IEEE, eds.T. Ju, 2008. doi: 10.1109/CSSE.2008.845. Google Scholar

show all references

References:
[1]

A. AkgunE. A. SezerH. A. NefesliogluC. Gokceoglu and B. Pradhan, An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm, Computers & Geosciences, 38 (2012), 23-34. doi: 10.1016/j.cageo.2011.04.012. Google Scholar

[2]

IBM, IBM ILOG CPLEX Optimization Studio 12.5 User's Manual, 2012.Google Scholar

[3]

S. KirkpatrickC. Gelatt and M. Vecchi, Optimization by simulated annealing, Science, New Series, 220 (1983), 671-680. doi: 10.1126/science.220.4598.671. Google Scholar

[4]

X. LiL. GaoX Shao and C. Wang, Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling, Computers & Operations Research, 37 (2010), 656-667. doi: 10.1016/j.cor.2009.06.008. Google Scholar

[5]

W. Li and C. A. McMahon, A simulated annealing-based optimization approach for integrated process planning and scheduling, International Journal of Computer Integrated Manufacturing, 20 (2007), 80-95. doi: 10.1080/09511920600667366. Google Scholar

[6]

K. Lian, Optimization of process planning with various flexibilities using an imperialist competitive algorithm, The International Journal of Advanced Manufacturing Technology, 59 (2012), 815-828. doi: 10.1007/s00170-011-3527-8. Google Scholar

[7]

J. LinM. LiuJ. Hao and S. Jiang, A multi-objective optimization approach for integrated production planning under interval uncertainties in the steel industry, Computers & Operations Research, 72 (2016), 189-203. doi: 10.1016/j.cor.2016.03.002. Google Scholar

[8]

T. Majozi and X. Zhu, A combined fuzzy set theory and MILP approach in integration of planning and scheduling of batch plants-Personnel evaluation and allocation, Computers & Chemical Engineering, 29 (2005), 2029-2047. doi: 10.1016/j.compchemeng.2004.07.038. Google Scholar

[9]

X. N. Shen and X. Yao, Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems, Information Science, 298 (2015), 198-224. doi: 10.1016/j.ins.2014.11.036. Google Scholar

[10]

J. M. Usher and K. J. Fernandes, Dynamic process planning-The staticphase, Journal of Materials Processing Technology, 61 (1996), 53-58. Google Scholar

[11]

S.-y. Wan, Integrated Process Planning and Scheduling with Setup Time Consideration by Ant Colony Optimization, HKU Theses Online (HKUTO), 2012. doi: 10.5353/th_b4961807. Google Scholar

[12]

Y. Wang, A PSO-based multi-objective optimization approach to the integration of process planning and scheduling, in Control and Automation (ICCA), 8th IEEE International Conference on IEEE, eds. Y. Zhang and J. Y. H. Fuh, 2010. doi: 10.1109/ICCA.2010.5524365. Google Scholar

[13]

L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338-353. doi: 10.1016/S0019-9958(65)90241-X. Google Scholar

[14]

L. Zhang and T. Wong, Solving integrated process planning and scheduling problem with constructive meta-heuristics, Information Sciences, 340/341 (2016), 1-16. doi: 10.1016/j.ins.2016.01.001. Google Scholar

[15]

L. Zhang and T. Wong, An object-coding genetic algorithm for integrated process planning and scheduling, European Journal of Operational Research, 244 (2015), 434-444. doi: 10.1016/j.ejor.2015.01.032. Google Scholar

[16]

W. Zhanjie, The research about integration of process planning and production scheduling based on genetic algorithm, in Computer Science and Software Engineering, International Conference on IEEE, eds.T. Ju, 2008. doi: 10.1109/CSSE.2008.845. Google Scholar

Figure 1.  The flowchart of the proposed process planningscheduling techniques
Figure 2.  Structure of the Material Flow FIS
Figure 3.  Structure of the Production Ease FIS
Figure 4.  The Flowchart of SA Algorithm
Figure 5.  Pseudo-Code of SA Algorithm
Figure 6.  Parts Operations Sequence in SA solution
Figure 7.  Machines Operations Sequence in SA solution
Figure 9.  Machines operations sequence in CP
Figure 8.  Parts Operations Sequence in CP
Table 1.  Inputs of fuzzy inference systems
Table 2.  Output of fuzzy inference systems
Table 3.  The Goal Programming Formulation of the IPPS Problem
Parameters
$p$: Part indicator, $p=1, \ldots, PNo$
$O^p$: The set indicating operations of $p^{th}$ part
$i$: Operation indicator, $i\in O^p$, $p=1, \ldots, PNo$
d: TAD indicator
t: Tool indicator
m: machine indicator
$Machine_i$ : The set indicating machine candidates of the $i^{th}$ operation
$TAD_i$: The set indicating TAD candidates of the $i^{th}$ operation
$Tool_i$: The set indicating tool candidates of the $i^{th}$ operation
$MC_m$: The cost of using $m^{th}$ machine per operation
$TC_t$: The cost of using $i^{th}$ tool per operation
$MFE_{m_k m_l}$: The rate of Material Flow Ease between machines $m_k$ and $m_l$
$PE_{m_k m_l}$: The rate of Production Ease between machines $m_k$ and $m_l$
Decision Variables
$operation_{imtd}^p = \left\{ \begin{array}{l} 1\;\;{\rm{If the}}\;{i^{th}}\;{\rm{operation}}\;{\rm{of}}\;{\rm{part}}\;p\;{\rm{is}}\;{\rm{done}}\;{\rm{in}}\;{m^{th}}\;{\rm{machine}}\\ \;\;\;{\rm{with}}\;{t^{th}}\;{\rm{tool}}\;{\rm{and}}\;{d^{th}}\;{\rm{TAD}}\\ {\rm{0}}\;\;{\rm{Otherwise}} \end{array} \right.$
$part_{ij}^p = \left\{ \begin{array}{l} 1\;\;{\rm{If}}\;{\rm{the}}\;{j^{th}}\;{\rm{operation}}\;{\rm{of}}\;{\rm{part}}\;p\;{\rm{is}}\;{\rm{done}}\;{\rm{immediately}}\;{\rm{after}}\\ \;\;\;\;{\rm{the}}\;{i^{th}}\;{\rm{operation}}\;{\rm{of}}\;{\rm{part}}\;p\\ 0\;\;{\rm{Otherwise}} \end{array} \right.$
$\begin{array}{l} time_i^p{\rm{ = Cumulative}}\;{\rm{time}}\;{\rm{of}}\;{\rm{operations}}\;{\rm{of}}\;{\rm{part}}\;p\;{\rm{completed}}\;{\rm{until}}\;{\rm{the}}\\ \;\;\;\;\;\;\;\;\;\;{\rm{completion}}\;{\rm{of}}\;{\rm{the}}\;{i^{th}}\;{\rm{operation}} \end{array}$
$O{M_i}{\rm{ = The}}\;{i^{th}}\;{\rm{operation}}\;{\rm{machine, }}\;O{M_i} \in Machin{e_i}$
$O{T_i}{\rm{ = The}}\;{i^{th}}\;{\rm{operation}}\;TAD{\rm{, }}\;O{T_i} \in TA{D_i}$
Proposed Goal Programming Model:
$Min\ D =\displaystyle \left( {{w_1} \times {\rm{ }}\frac{{{d_1}^ - }}{{{G_{TWC}}}}} \right) + \left( {{w_2} \times {\rm{ }}\frac{{{d_2}^ - }}{{{G_{MakeSpan}}}}} \right) + \left( {{w_3} \times {\rm{ }}\frac{{{d_3}^ + }}{{{G_U}}}} \right)~~~~~~~~~~~(1)$
$ Subject$ $to: $
$TWC = \sum\limits_{i \in {O^p}}^{} {\sum\limits_{m \in Machin{e_i}} {\sum\limits_{t \in Too{l_i}} {\sum\limits_{d \in TA{D_i}} {operation_{_{imtd}}^p.(M{C_{O{M_i}}} + T{C_{O{T_i}}}} } )} } ~~~~(2)$
$MakeSpan = \mathop {\max }\limits_{\scriptstyle\, \, \, \, \, \, \, i \in {O^p}\atop \scriptstyle p = 1, ..., PNo} (time_{_i}^p)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(3)$
$U = \sum\limits_{p = 1}^{PNo} {\sum\limits_{i, j \in {O^p}/part_{_{ij}}^p = 1}^{} {(ME{F_{O{M_i}, O{M_j}}} + P{E_{O{M_i}, O{M_j}}})} }~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(4)$
$TWC - d_1^ - \le {G_{TWC}}~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(5)$
$MakeSpan - d_2^ - \le {G_{MakeSpan}}~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(6)$
$U + d_3^ + \ge {G_U}~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(7)$
$d_1^ -, d_2^ -, d_3^ + \ge 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(8)$
Parameters
$p$: Part indicator, $p=1, \ldots, PNo$
$O^p$: The set indicating operations of $p^{th}$ part
$i$: Operation indicator, $i\in O^p$, $p=1, \ldots, PNo$
d: TAD indicator
t: Tool indicator
m: machine indicator
$Machine_i$ : The set indicating machine candidates of the $i^{th}$ operation
$TAD_i$: The set indicating TAD candidates of the $i^{th}$ operation
$Tool_i$: The set indicating tool candidates of the $i^{th}$ operation
$MC_m$: The cost of using $m^{th}$ machine per operation
$TC_t$: The cost of using $i^{th}$ tool per operation
$MFE_{m_k m_l}$: The rate of Material Flow Ease between machines $m_k$ and $m_l$
$PE_{m_k m_l}$: The rate of Production Ease between machines $m_k$ and $m_l$
Decision Variables
$operation_{imtd}^p = \left\{ \begin{array}{l} 1\;\;{\rm{If the}}\;{i^{th}}\;{\rm{operation}}\;{\rm{of}}\;{\rm{part}}\;p\;{\rm{is}}\;{\rm{done}}\;{\rm{in}}\;{m^{th}}\;{\rm{machine}}\\ \;\;\;{\rm{with}}\;{t^{th}}\;{\rm{tool}}\;{\rm{and}}\;{d^{th}}\;{\rm{TAD}}\\ {\rm{0}}\;\;{\rm{Otherwise}} \end{array} \right.$
$part_{ij}^p = \left\{ \begin{array}{l} 1\;\;{\rm{If}}\;{\rm{the}}\;{j^{th}}\;{\rm{operation}}\;{\rm{of}}\;{\rm{part}}\;p\;{\rm{is}}\;{\rm{done}}\;{\rm{immediately}}\;{\rm{after}}\\ \;\;\;\;{\rm{the}}\;{i^{th}}\;{\rm{operation}}\;{\rm{of}}\;{\rm{part}}\;p\\ 0\;\;{\rm{Otherwise}} \end{array} \right.$
$\begin{array}{l} time_i^p{\rm{ = Cumulative}}\;{\rm{time}}\;{\rm{of}}\;{\rm{operations}}\;{\rm{of}}\;{\rm{part}}\;p\;{\rm{completed}}\;{\rm{until}}\;{\rm{the}}\\ \;\;\;\;\;\;\;\;\;\;{\rm{completion}}\;{\rm{of}}\;{\rm{the}}\;{i^{th}}\;{\rm{operation}} \end{array}$
$O{M_i}{\rm{ = The}}\;{i^{th}}\;{\rm{operation}}\;{\rm{machine, }}\;O{M_i} \in Machin{e_i}$
$O{T_i}{\rm{ = The}}\;{i^{th}}\;{\rm{operation}}\;TAD{\rm{, }}\;O{T_i} \in TA{D_i}$
Proposed Goal Programming Model:
$Min\ D =\displaystyle \left( {{w_1} \times {\rm{ }}\frac{{{d_1}^ - }}{{{G_{TWC}}}}} \right) + \left( {{w_2} \times {\rm{ }}\frac{{{d_2}^ - }}{{{G_{MakeSpan}}}}} \right) + \left( {{w_3} \times {\rm{ }}\frac{{{d_3}^ + }}{{{G_U}}}} \right)~~~~~~~~~~~(1)$
$ Subject$ $to: $
$TWC = \sum\limits_{i \in {O^p}}^{} {\sum\limits_{m \in Machin{e_i}} {\sum\limits_{t \in Too{l_i}} {\sum\limits_{d \in TA{D_i}} {operation_{_{imtd}}^p.(M{C_{O{M_i}}} + T{C_{O{T_i}}}} } )} } ~~~~(2)$
$MakeSpan = \mathop {\max }\limits_{\scriptstyle\, \, \, \, \, \, \, i \in {O^p}\atop \scriptstyle p = 1, ..., PNo} (time_{_i}^p)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(3)$
$U = \sum\limits_{p = 1}^{PNo} {\sum\limits_{i, j \in {O^p}/part_{_{ij}}^p = 1}^{} {(ME{F_{O{M_i}, O{M_j}}} + P{E_{O{M_i}, O{M_j}}})} }~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(4)$
$TWC - d_1^ - \le {G_{TWC}}~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(5)$
$MakeSpan - d_2^ - \le {G_{MakeSpan}}~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(6)$
$U + d_3^ + \ge {G_U}~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(7)$
$d_1^ -, d_2^ -, d_3^ + \ge 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(8)$
Table 4.  Sources Specifications
The cost peroperation(MC) Symbol Source
50 M1 CNC milling machine 1
60 M2 CNC milling machine 2
30 M3 Grinding CNC machine
35 M4 Column drilling equipment
20 M5 Hand drilling equipment
20 M6 Grinding machine
The cost per operation(TC) Symbol Resource
6 T1 Drill 1
5 T2 Drill 2
10 T3 Drill 3
15 T4 Drill 4
13 T5 Drill 5
14 T6 Drill 6
8 T7 Drill 7
10 T8 Drill 8
5 T9 Drill 9
10 T10 Polishers
15 T11 Reamer 1
20 T12 Reamer 2
18 T13 Reamer 3
15 T14 Diamond blades
18 T15 Milling plate
13 T16 Spark
24 T17 Magnetic stone
The cost peroperation(MC) Symbol Source
50 M1 CNC milling machine 1
60 M2 CNC milling machine 2
30 M3 Grinding CNC machine
35 M4 Column drilling equipment
20 M5 Hand drilling equipment
20 M6 Grinding machine
The cost per operation(TC) Symbol Resource
6 T1 Drill 1
5 T2 Drill 2
10 T3 Drill 3
15 T4 Drill 4
13 T5 Drill 5
14 T6 Drill 6
8 T7 Drill 7
10 T8 Drill 8
5 T9 Drill 9
10 T10 Polishers
15 T11 Reamer 1
20 T12 Reamer 2
18 T13 Reamer 3
15 T14 Diamond blades
18 T15 Milling plate
13 T16 Spark
24 T17 Magnetic stone
Table 5.  Technical Specifications of Part 1
Features Index Operation TAD candidate Machine candidate Tool candidate Machining time for each candidate machine (s)
F1 Oper1 Milling -Z M1, M2 T14, T15, T16, T17 40, 38
F2 Oper2 Milling +Z M1, M2 T14, T15, T16, T17 37, 38
F3 Oper3 Milling -Z, -Y M1, M2 T14, T15, T16, T17 41, 43
F4 Oper4 Milling +Y, -Z M1, M2 T14, T15, T16, T17 31, 30
F5 Oper5 Boring +Y, -Y M6 T10 40
F6 Oper6 Drilling -Z M3, M4, M5 T1 40, 50, 30
Oper7 Reaming -Z M3, M4, M5 T11 40, 50, 30
F7 Oper8 Ribbing -Z, -Y M1, M2 T14, T15, T16, T17 54, 52
F8 Oper9 Drilling +X, -X M3, M4, M5 t2 20, 30, 21
F9 Oper10 Drilling +X, -X M3, M4, M5 t3 50, 60, 40
F10 Oper11 Drilling +Y M3, M4, M5 t4 60, 50, 30
Oper12 reaming +Y M3, M4, M5 T12 60, 50, 30
F11 Oper13 Boring A M6 T10 50
Features Index Operation TAD candidate Machine candidate Tool candidate Machining time for each candidate machine (s)
F1 Oper1 Milling -Z M1, M2 T14, T15, T16, T17 40, 38
F2 Oper2 Milling +Z M1, M2 T14, T15, T16, T17 37, 38
F3 Oper3 Milling -Z, -Y M1, M2 T14, T15, T16, T17 41, 43
F4 Oper4 Milling +Y, -Z M1, M2 T14, T15, T16, T17 31, 30
F5 Oper5 Boring +Y, -Y M6 T10 40
F6 Oper6 Drilling -Z M3, M4, M5 T1 40, 50, 30
Oper7 Reaming -Z M3, M4, M5 T11 40, 50, 30
F7 Oper8 Ribbing -Z, -Y M1, M2 T14, T15, T16, T17 54, 52
F8 Oper9 Drilling +X, -X M3, M4, M5 t2 20, 30, 21
F9 Oper10 Drilling +X, -X M3, M4, M5 t3 50, 60, 40
F10 Oper11 Drilling +Y M3, M4, M5 t4 60, 50, 30
Oper12 reaming +Y M3, M4, M5 T12 60, 50, 30
F11 Oper13 Boring A M6 T10 50
Table 6.  Technical Specifications of Part 2
Features Index operation TAD candidate Machine candidate Tool candidate Machining time for each candidate machine (s)
F1 oper1 Milling -Y M1, M2 T14, T15, T16, T17 30, 20
F2 oper2 milling +Z M1, M2 T14, T15, T16, T17 35, 29
F3 oper3 milling -Y, -Y M1, M2 T14, T15, T16, T17 29, 24
F4 oper4 drilling +Z, -Z M3, M4, M5 T4 57, 66, 51
F5 oper5 reaming +X M3, M4, M5 T5 41, 59, 38
oper6 drilling +X M3, M4, M5 T13 52, 71, 41
F6 oper7 reaming -Y, +Y M3, M4, M5 t6 30, 41, 28
F7 oper8 ribbing -Z, +Z, -X M1, M2 T14, T15, T16, T17 47, 49
F8 oper9 milling A M1, M2 T14, T15, T16, T17 40, 41
F9 oper10 boring +X, -X, +Y, -Y m6 T10 40
Features Index operation TAD candidate Machine candidate Tool candidate Machining time for each candidate machine (s)
F1 oper1 Milling -Y M1, M2 T14, T15, T16, T17 30, 20
F2 oper2 milling +Z M1, M2 T14, T15, T16, T17 35, 29
F3 oper3 milling -Y, -Y M1, M2 T14, T15, T16, T17 29, 24
F4 oper4 drilling +Z, -Z M3, M4, M5 T4 57, 66, 51
F5 oper5 reaming +X M3, M4, M5 T5 41, 59, 38
oper6 drilling +X M3, M4, M5 T13 52, 71, 41
F6 oper7 reaming -Y, +Y M3, M4, M5 t6 30, 41, 28
F7 oper8 ribbing -Z, +Z, -X M1, M2 T14, T15, T16, T17 47, 49
F8 oper9 milling A M1, M2 T14, T15, T16, T17 40, 41
F9 oper10 boring +X, -X, +Y, -Y m6 T10 40
Table 7.  Technical Specifications of Part 3
Features Index operation TAD candidate Machine candidate Tool candidate Machining time for each candidate machine (s)
F1 oper1 Milling -Y, -X M1, M2 T14, T15, T16, T17 30, 35
F2 oper2 Milling +Z, +Z M1, M2 T14, T15, T16, T17 28, 30
F3 oper3 Milling +Z, +Y M1, M2 T14, T15, T16, T17 33, 30
F4 oper4 Milling +X, -X M1, M2 T14, T15, T16, T17 29, 25
F5 oper5 Milling +X, -X M1, M2 T14, T15, T16, T17 37, 31
F6 oper6 Drilling -Z, +Z M3, M4, M5 T9 50, 60, 40
F7 oper7 Milling -Z M1, M2 T14, T15, T16, T17 40, 39
F8 ooper8 Drilling -Z M3, M4, M5 T8 39, 50, 38
Features Index operation TAD candidate Machine candidate Tool candidate Machining time for each candidate machine (s)
F1 oper1 Milling -Y, -X M1, M2 T14, T15, T16, T17 30, 35
F2 oper2 Milling +Z, +Z M1, M2 T14, T15, T16, T17 28, 30
F3 oper3 Milling +Z, +Y M1, M2 T14, T15, T16, T17 33, 30
F4 oper4 Milling +X, -X M1, M2 T14, T15, T16, T17 29, 25
F5 oper5 Milling +X, -X M1, M2 T14, T15, T16, T17 37, 31
F6 oper6 Drilling -Z, +Z M3, M4, M5 T9 50, 60, 40
F7 oper7 Milling -Z M1, M2 T14, T15, T16, T17 40, 39
F8 ooper8 Drilling -Z M3, M4, M5 T8 39, 50, 38
Table 8.  Precedence Relations of Part 1
Precedence relations
Oper1 is first operation.
Oper2 is prior to Oper4, Oper5, Oper11 and Oper12.
Oper3 is prior to Oper4, Oper5 and Oper10.
Oper6 is prior to Oper7, Oper11 and Oper12.
Oper8 is prior to Oper13.
Oper9 is prior to Oper13.
Oper11 is prior to Oper12.
Precedence relations
Oper1 is first operation.
Oper2 is prior to Oper4, Oper5, Oper11 and Oper12.
Oper3 is prior to Oper4, Oper5 and Oper10.
Oper6 is prior to Oper7, Oper11 and Oper12.
Oper8 is prior to Oper13.
Oper9 is prior to Oper13.
Oper11 is prior to Oper12.
Table 9.  Precedence Relations of Part 2
Precedence relations
Oper1 is first operation
Oper2 is prior to Oper7, Oper9.
Oper3 is prior to Oper5, Oper9.
Oper4 is prior to Oper5, Oper6.
Oper5 is prior to Oper6.
Oper7 is prior to Oper8, Oper10.
Oper8 is prior to Oper10.
Precedence relations
Oper1 is first operation
Oper2 is prior to Oper7, Oper9.
Oper3 is prior to Oper5, Oper9.
Oper4 is prior to Oper5, Oper6.
Oper5 is prior to Oper6.
Oper7 is prior to Oper8, Oper10.
Oper8 is prior to Oper10.
Table 10.  Precedence Relations of Part 3
Precedence relations
Oper1 is first operation.
Oper2 is prior to Oper4, Oper5.
Oper3 is prior to Oper4, Oper5.
Oper4 is prior to Oper5, Oper6.
Oper5 is prior to Oper6.
Oper7 is prior to Oper8.
Precedence relations
Oper1 is first operation.
Oper2 is prior to Oper4, Oper5.
Oper3 is prior to Oper4, Oper5.
Oper4 is prior to Oper5, Oper6.
Oper5 is prior to Oper6.
Oper7 is prior to Oper8.
Table 11.  Rules of Material Flow FIS on Distance and Trans-portation Ease Parameters
Ease in material displacement in different machines
VL L M H VH
Distance between installed machines VH X X X U U
H X U U O O
M U U O I I
L U O I E A
VL U O I A A
Ease in material displacement in different machines
VL L M H VH
Distance between installed machines VH X X X U U
H X U U O O
M U U O I I
L U O I E A
VL U O I A A
Table 12.  Score of Pair Machines Based on Material Flow FIS
Ease in material flowM1 M2 M3 M4 M5 M6
M1 5.69 2.74 5.69 2.00 1.50 1.50
M2 3.74 5.69 2.56 3.00 4.37 1.50
M3 4.50 4.37 5.69 1.20 0.37 1.50
M4 1.50 1.50 1.20 5.69 2.50 1.20
M5 1.20 2.56 1.50 1.50 5.69 2.56
M6 1.50 2.00 1.50 1.20 3.56 5.69
Ease in material flowM1 M2 M3 M4 M5 M6
M1 5.69 2.74 5.69 2.00 1.50 1.50
M2 3.74 5.69 2.56 3.00 4.37 1.50
M3 4.50 4.37 5.69 1.20 0.37 1.50
M4 1.50 1.50 1.20 5.69 2.50 1.20
M5 1.20 2.56 1.50 1.50 5.69 2.56
M6 1.50 2.00 1.50 1.20 3.56 5.69
Table 13.  Rules of Production Ease FIS on Setup Ease and Compatibility Parameters
Compatibility
VL L M/ H VH
Setup Ease VL X X X U U
L X U U O O
M U U O I I
H U O I E A
VH U O I A A
Compatibility
VL L M/ H VH
Setup Ease VL X X X U U
L X U U O O
M U U O I I
H U O I E A
VH U O I A A
Table 14.  Score of Each Pair Machines Based on ease in production FIS
Ease in productionM1 M2 M3 M4 M5 M6
M1 5.69 1.50 2.50 4.00 3.00 1.50
M2 4.23 5.69 3.00 5.69 3.00 1.50
M3 3.00 3.00 5.69 3.00 5.63 2.50
M4 2.50 2.50 1.50 5.69 2.50 1.50
M5 2.50 4.00 1.50 1.50 5.69 3.00
M6 3.00 3.00 3.50 3.50 3.00 5.69
Ease in productionM1 M2 M3 M4 M5 M6
M1 5.69 1.50 2.50 4.00 3.00 1.50
M2 4.23 5.69 3.00 5.69 3.00 1.50
M3 3.00 3.00 5.69 3.00 5.63 2.50
M4 2.50 2.50 1.50 5.69 2.50 1.50
M5 2.50 4.00 1.50 1.50 5.69 3.00
M6 3.00 3.00 3.50 3.50 3.00 5.69
Table 15.  Goals and Weights of Objectives
Objective Function Goal Weight
TWC $1484$ 1
Make Span $468$ 2
Utility $288.24$ 3
Objective Function Goal Weight
TWC $1484$ 1
Make Span $468$ 2
Utility $288.24$ 3
Table 16.  The Parameters of SA Algorithm
Parameters
The number of initial population $= 20$ The iterations time $= 300 s$
The number of neighborhood $= 10$ Alpha $= 0.99$
Parameters
The number of initial population $= 20$ The iterations time $= 300 s$
The number of neighborhood $= 10$ Alpha $= 0.99$
Table 17.  The optional list of Positions of Operations of Parts
Position Parts Operations Position Parts Operations
1 1 1 17 2 4
2 1 2 18 2 5
3 1 3 19 2 6
4 1 4 20 2 7
5 1 5 21 2 8
6 1 6 22 2 9
7 1 7 23 2 10
8 1 8 24 3 1
9 1 9 25 3 2
10 1 10 26 3 3
11 1 11 27 3 4
12 1 12 28 3 5
13 1 13 29 3 6
14 2 1 30 3 7
15 2 2 31 3 8
16 2 3
Position Parts Operations Position Parts Operations
1 1 1 17 2 4
2 1 2 18 2 5
3 1 3 19 2 6
4 1 4 20 2 7
5 1 5 21 2 8
6 1 6 22 2 9
7 1 7 23 2 10
8 1 8 24 3 1
9 1 9 25 3 2
10 1 10 26 3 3
11 1 11 27 3 4
12 1 12 28 3 5
13 1 13 29 3 6
14 2 1 30 3 7
15 2 2 31 3 8
16 2 3
Table 18.  Solution of SA algorithm
Random Position Considering the Precedence relations Feasible Position Parts Operations Machines Tools TAD
17 14 2 1 2 14 -Z
5 17 2 4 1 15 Z
3 16 2 3 2 16 -Y
10 18 2 5 1 16 -Z
2 19 2 6 6 10 Y
31 15 2 2 4 1 -Z
12 20 2 7 4 11 -Z
19 21 2 8 2 16 -Z
9 23 2 10 4 2 X
27 24 3 1 5 3 -X
21 26 3 3 3 4 Y
18 30 3 7 5 12 Y
14 31 3 8 6 10 A
6 25 3 2 2 15 -Y
13 27 3 4 1 17 Z
4 28 3 5 2 15 -Y
26 29 3 6 3 4 Z
30 22 2 9 4 5 X
7 1 1 1 5 13 X
20 3 1 3 4 6 -Y
16 10 1 10 2 17 -Z
28 2 1 2 1 16 A
8 5 1 5 6 10 -X
25 9 1 9 2 15 -Y
23 6 1 6 2 14 X
29 4 1 4 2 17 z
15 7 1 7 2 17 X
24 8 1 8 2 16 X
22 13 1 13 3 9 z
1 11 1 11 2 14 -Z
11 12 1 12 5 8 -Z
Random Position Considering the Precedence relations Feasible Position Parts Operations Machines Tools TAD
17 14 2 1 2 14 -Z
5 17 2 4 1 15 Z
3 16 2 3 2 16 -Y
10 18 2 5 1 16 -Z
2 19 2 6 6 10 Y
31 15 2 2 4 1 -Z
12 20 2 7 4 11 -Z
19 21 2 8 2 16 -Z
9 23 2 10 4 2 X
27 24 3 1 5 3 -X
21 26 3 3 3 4 Y
18 30 3 7 5 12 Y
14 31 3 8 6 10 A
6 25 3 2 2 15 -Y
13 27 3 4 1 17 Z
4 28 3 5 2 15 -Y
26 29 3 6 3 4 Z
30 22 2 9 4 5 X
7 1 1 1 5 13 X
20 3 1 3 4 6 -Y
16 10 1 10 2 17 -Z
28 2 1 2 1 16 A
8 5 1 5 6 10 -X
25 9 1 9 2 15 -Y
23 6 1 6 2 14 X
29 4 1 4 2 17 z
15 7 1 7 2 17 X
24 8 1 8 2 16 X
22 13 1 13 3 9 z
1 11 1 11 2 14 -Z
11 12 1 12 5 8 -Z
Table 19.  Description of the $Tasks$
$ID$ Exclusive number of each member of the tuples
$PartNo$ The number of the parts
$OperationNo$ The number of operations necessary for part processing
$\{TADNo\}$ The set of TAD candidates
$\{ToolNo\}$ The set of tool candidates
$\{MachineNo\}$ The set of machine candidates
$\{pTime\}$ The set of Machining time for each candidate machine (s)
$\{Succs\}$ The set of successor operations
$ID$ Exclusive number of each member of the tuples
$PartNo$ The number of the parts
$OperationNo$ The number of operations necessary for part processing
$\{TADNo\}$ The set of TAD candidates
$\{ToolNo\}$ The set of tool candidates
$\{MachineNo\}$ The set of machine candidates
$\{pTime\}$ The set of Machining time for each candidate machine (s)
$\{Succs\}$ The set of successor operations
Table 20.  Description of the $Modes$
$task$ A tuple type data represent the Task which the Mode is derived from
$PartNo$ Indicates the number of the parts which equal to $task.Part.No$
$OperationNo$ Indicates the number of the operations in one part which equals to $task.Operation.No$
$TADNo$ Indicates the number of TADs which is a member of $task.\{TAD.No\}$
$ToolNo$ Indicates the number of tools which are the members of $task.\{Tool.No\}$
$MachineNo$ Indicates the number of machines which are a member of $task.\{Machine.No\}$
$pTime$ Indicates the machining time for $Machine_{Machine.No}$
$task$ A tuple type data represent the Task which the Mode is derived from
$PartNo$ Indicates the number of the parts which equal to $task.Part.No$
$OperationNo$ Indicates the number of the operations in one part which equals to $task.Operation.No$
$TADNo$ Indicates the number of TADs which is a member of $task.\{TAD.No\}$
$ToolNo$ Indicates the number of tools which are the members of $task.\{Tool.No\}$
$MachineNo$ Indicates the number of machines which are a member of $task.\{Machine.No\}$
$pTime$ Indicates the machining time for $Machine_{Machine.No}$
Table 21.  he Best Solutions of SA algorithm
PartOperation Machine TAD Tool Start Time Machining Time Finish Time
1 1 2 -3 16 0 38 38
1 2 1 3 14 100 37 137
1 3 1 -3 14 59 41 100
1 4 1 2 16 323 31 354
1 5 6 -2 10 394 40 434
1 6 5 -3 1 139 30 169
1 7 3 -3 11 169 40 209
1 8 1 -3 16 269 54 323
1 9 5 1 2 38 21 59
1 10 5 1 3 354 40 394
1 11 5 2 4 209 30 239
1 12 5 2 12 239 30 269
1 13 6 4 10 434 50 484
2 1 2 -2 16 38 20 58
2 2 2 3 15 82 29 111
2 3 2 -3 16 58 24 82
2 4 4 -1 4 269 66 335
2 5 4 1 5 335 59 394
2 6 5 1 13 394 41 435
2 7 5 -2 6 111 28 139
2 8 2 3 14 180 49 229
2 9 2 4 16 139 41 180
2 10 6 1 10 229 40 269
3 1 1 -2 14 0 30 30
3 2 2 3 14 298 30 328
3 3 2 3 14 229 30 259
3 4 1 -1 17 354 29 383
3 5 2 1 14 383 31 414
3 6 5 -3 9 453 40 493
3 7 2 -3 15 259 39 298
3 8 3 -3 8 414 39 453
PartOperation Machine TAD Tool Start Time Machining Time Finish Time
1 1 2 -3 16 0 38 38
1 2 1 3 14 100 37 137
1 3 1 -3 14 59 41 100
1 4 1 2 16 323 31 354
1 5 6 -2 10 394 40 434
1 6 5 -3 1 139 30 169
1 7 3 -3 11 169 40 209
1 8 1 -3 16 269 54 323
1 9 5 1 2 38 21 59
1 10 5 1 3 354 40 394
1 11 5 2 4 209 30 239
1 12 5 2 12 239 30 269
1 13 6 4 10 434 50 484
2 1 2 -2 16 38 20 58
2 2 2 3 15 82 29 111
2 3 2 -3 16 58 24 82
2 4 4 -1 4 269 66 335
2 5 4 1 5 335 59 394
2 6 5 1 13 394 41 435
2 7 5 -2 6 111 28 139
2 8 2 3 14 180 49 229
2 9 2 4 16 139 41 180
2 10 6 1 10 229 40 269
3 1 1 -2 14 0 30 30
3 2 2 3 14 298 30 328
3 3 2 3 14 229 30 259
3 4 1 -1 17 354 29 383
3 5 2 1 14 383 31 414
3 6 5 -3 9 453 40 493
3 7 2 -3 15 259 39 298
3 8 3 -3 8 414 39 453
Table 22.  The Best Solution obtained through CP
PartOperation Machine TAD Tool Start Time Machining Time Finish Time
1 1 2 -3 16 0 38 38
1 2 2 3 16 38 37 75
1 3 1 -3 16 75 41 116
1 8 1 -3 16 116 52 168
1 10 3 1 3 168 40 208
1 6 5 -3 1 208 30 238
1 9 5 -1 2 238 20 258
1 11 5 2 4 258 30 288
1 7 5 -3 11 288 30 318
1 13 6 4 10 318 50 368
1 5 6 2 10 368 40 408
1 12 5 2 12 408 30 438
1 4 1 -3 16 438 30 468
2 1 1 -2 16 0 20 20
2 3 1 -3 16 20 24 44
2 2 1 3 16 44 29 73
2 4 5 3 4 73 51 124
2 5 5 1 5 124 38 162
2 6 5 1 13 162 41 203
2 7 5 2 6 318 28 346
2 8 1 -3 16 351 47 398
2 9 1 4 16 398 40 438
2 10 6 1 10 438 40 478
3 1 1 -2 16 168 30 198
3 7 1 -3 16 198 39 237
3 2 1 1 16 237 28 265
3 3 1 3 16 265 30 295
3 4 1 1 16 295 25 320
3 5 1 -1 16 320 31 351
3 6 5 -3 9 351 40 391
3 8 5 -3 8 438 38 476
PartOperation Machine TAD Tool Start Time Machining Time Finish Time
1 1 2 -3 16 0 38 38
1 2 2 3 16 38 37 75
1 3 1 -3 16 75 41 116
1 8 1 -3 16 116 52 168
1 10 3 1 3 168 40 208
1 6 5 -3 1 208 30 238
1 9 5 -1 2 238 20 258
1 11 5 2 4 258 30 288
1 7 5 -3 11 288 30 318
1 13 6 4 10 318 50 368
1 5 6 2 10 368 40 408
1 12 5 2 12 408 30 438
1 4 1 -3 16 438 30 468
2 1 1 -2 16 0 20 20
2 3 1 -3 16 20 24 44
2 2 1 3 16 44 29 73
2 4 5 3 4 73 51 124
2 5 5 1 5 124 38 162
2 6 5 1 13 162 41 203
2 7 5 2 6 318 28 346
2 8 1 -3 16 351 47 398
2 9 1 4 16 398 40 438
2 10 6 1 10 438 40 478
3 1 1 -2 16 168 30 198
3 7 1 -3 16 198 39 237
3 2 1 1 16 237 28 265
3 3 1 3 16 265 30 295
3 4 1 1 16 295 25 320
3 5 1 -1 16 320 31 351
3 6 5 -3 9 351 40 391
3 8 5 -3 8 438 38 476
Table 23.  Results of SA and CP Algorithm
Objective TWC Make Span Utility Time (Sec)
Max(Min) Function Min Min Max
Goal 1484 468 288
CP 1514 478 258.3761 300
SA 1669 493 87.30202 300
Objective TWC Make Span Utility Time (Sec)
Max(Min) Function Min Min Max
Goal 1484 468 288
CP 1514 478 258.3761 300
SA 1669 493 87.30202 300
[1]

Tien-Fu Liang, Hung-Wen Cheng. Multi-objective aggregate production planning decisions using two-phase fuzzy goal programming method. Journal of Industrial & Management Optimization, 2011, 7 (2) : 365-383. doi: 10.3934/jimo.2011.7.365

[2]

Didem Cinar, José António Oliveira, Y. Ilker Topcu, Panos M. Pardalos. A priority-based genetic algorithm for a flexible job shop scheduling problem. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1391-1415. doi: 10.3934/jimo.2016.12.1391

[3]

Y. K. Lin, C. S. Chong. A tabu search algorithm to minimize total weighted tardiness for the job shop scheduling problem. Journal of Industrial & Management Optimization, 2016, 12 (2) : 703-717. doi: 10.3934/jimo.2016.12.703

[4]

Adel Dabah, Ahcene Bendjoudi, Abdelhakim AitZai. An efficient Tabu Search neighborhood based on reconstruction strategy to solve the blocking job shop scheduling problem. Journal of Industrial & Management Optimization, 2017, 13 (4) : 2015-2031. doi: 10.3934/jimo.2017029

[5]

Behrad Erfani, Sadoullah Ebrahimnejad, Amirhossein Moosavi. An integrated dynamic facility layout and job shop scheduling problem: A hybrid NSGA-II and local search algorithm. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-34. doi: 10.3934/jimo.2019030

[6]

Feyza Gürbüz, Panos M. Pardalos. A decision making process application for the slurry production in ceramics via fuzzy cluster and data mining. Journal of Industrial & Management Optimization, 2012, 8 (2) : 285-297. doi: 10.3934/jimo.2012.8.285

[7]

Jiuping Xu, Pei Wei. Production-distribution planning of construction supply chain management under fuzzy random environment for large-scale construction projects. Journal of Industrial & Management Optimization, 2013, 9 (1) : 31-56. doi: 10.3934/jimo.2013.9.31

[8]

Abdelghani Bouras, Ramdane Hedjar, Lotfi Tadj. Production planning in a three-stock reverse-logistics system with deteriorating items under a periodic review policy. Journal of Industrial & Management Optimization, 2016, 12 (3) : 1075-1089. doi: 10.3934/jimo.2016.12.1075

[9]

Abdelghani Bouras, Lotfi Tadj. Production planning in a three-stock reverse-logistics system with deteriorating items under a continuous review policy. Journal of Industrial & Management Optimization, 2015, 11 (4) : 1041-1058. doi: 10.3934/jimo.2015.11.1041

[10]

T. W. Leung, Chi Kin Chan, Marvin D. Troutt. A mixed simulated annealing-genetic algorithm approach to the multi-buyer multi-item joint replenishment problem: advantages of meta-heuristics. Journal of Industrial & Management Optimization, 2008, 4 (1) : 53-66. doi: 10.3934/jimo.2008.4.53

[11]

Jiani Wang, Liwei Zhang. Statistical inference of semidefinite programming with multiple parameters. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-12. doi: 10.3934/jimo.2019015

[12]

Xiaochen Sun, Fei Hu, Yancong Zhou, Cheng-Chew Lim. Optimal acquisition, inventory and production decisions for a closed-loop manufacturing system with legislation constraint. Journal of Industrial & Management Optimization, 2015, 11 (4) : 1355-1373. doi: 10.3934/jimo.2015.11.1355

[13]

Ethel Mokotoff. Algorithms for bicriteria minimization in the permutation flow shop scheduling problem. Journal of Industrial & Management Optimization, 2011, 7 (1) : 253-282. doi: 10.3934/jimo.2011.7.253

[14]

Ellina Grigorieva, Evgenii Khailov. A nonlinear controlled system of differential equations describing the process of production and sales of a consumer good. Conference Publications, 2003, 2003 (Special) : 359-364. doi: 10.3934/proc.2003.2003.359

[15]

M. Ramasubramaniam, M. Mathirajan. A solution framework for scheduling a BPM with non-identical job dimensions. Journal of Industrial & Management Optimization, 2007, 3 (3) : 445-456. doi: 10.3934/jimo.2007.3.445

[16]

Louis Caccetta, Syarifah Z. Nordin. Mixed integer programming model for scheduling in unrelated parallel processor system with priority consideration. Numerical Algebra, Control & Optimization, 2014, 4 (2) : 115-132. doi: 10.3934/naco.2014.4.115

[17]

Purnima Pandit. Fuzzy system of linear equations. Conference Publications, 2013, 2013 (special) : 619-627. doi: 10.3934/proc.2013.2013.619

[18]

Jun Pei, Panos M. Pardalos, Xinbao Liu, Wenjuan Fan, Shanlin Yang, Ling Wang. Coordination of production and transportation in supply chain scheduling. Journal of Industrial & Management Optimization, 2015, 11 (2) : 399-419. doi: 10.3934/jimo.2015.11.399

[19]

Lou Caccetta, Elham Mardaneh. Joint pricing and production planning for fixed priced multiple products with backorders. Journal of Industrial & Management Optimization, 2010, 6 (1) : 123-147. doi: 10.3934/jimo.2010.6.123

[20]

Jian Xiong, Yingwu Chen, Zhongbao Zhou. Resilience analysis for project scheduling with renewable resource constraint and uncertain activity durations. Journal of Industrial & Management Optimization, 2016, 12 (2) : 719-737. doi: 10.3934/jimo.2016.12.719

2018 Impact Factor: 1.025

Metrics

  • PDF downloads (30)
  • HTML views (447)
  • Cited by (0)

Other articles
by authors

[Back to Top]