The objective function (
27) minimizes inventory-holding, setup, and penalty costs for additional capacity units assuming deterministic demands. Inventory-holding costs are only taken into account at the end of each
\({\text{sf}}^{\mathrm{Dth}}\) micro-period. Aiming to schedule production as early as possible within the first macro period
t, we renounce holding costs for the first macro-period and offer an incentive for early production by adding small values (multiples of
\(\epsilon\)) for inventory-holding in the first macro period.
$$\begin{aligned} \begin{aligned} \min Z^{\mathrm{B}}&= \sum \limits _{j=1}^{{\bar{j}}} \sum \limits _{s=1}^{{\bar{s}}} sc_{j} \cdot Y^{\mathrm{B}}_{j,s} + \sum \limits _{j=1}^{{\bar{j}}} \underbrace{\sum \limits _{s={\text{sf}}^{\mathrm{T}}}^{{\bar{s}}}}_{{\text{if }}s \equiv {\text{sf}}^{\mathrm{B}} = 0} {\text{hc}}^{\mathrm{B}}_{j} \cdot I^{\mathrm{B}}_{j,s} + \sum \limits _{s=1}^{{\bar{s}}} {\text{pc}}^{\mathrm{B}}_{s} \cdot C^{\mathrm{B}}_{s} \\&\quad - \sum \limits _{j=1}^{{\bar{j}}} \sum \limits _{s= 1}^{{\text{sf}}^{\mathrm{T}}-1} \epsilon \cdot ({\text{sf}}^{\mathrm{T}} - s) \cdot I^{\mathrm{B}}_{j,s} \end{aligned} \end{aligned}$$
(27)
subject to
$$\begin{aligned}&I^{\mathrm{B}}_{j,s-1} - {\text{BL}}^{\mathrm{B}}_{j,s-1} + X^{\mathrm{B}}_{j,s} = d^{\mathrm{B}}_{j,s} + I^{\mathrm{B}}_{j,s} - {\text{BL}}^{\mathrm{B}}_{j,s} \quad \forall j \in J, s \in S\end{aligned}$$
(28)
$$\begin{aligned}&\sum _{j}^{{\bar{j}}} \kappa _{j} \cdot X^{\mathrm{B}}_{j,s} \le b^{\mathrm{B}}_{s} + C^{\mathrm{B}}_s \quad \forall s \in S \end{aligned}$$
(29)
$$\begin{aligned}&X^{\mathrm{B}}_{j,s} \le m^{\mathrm{B}}_{j,s} \cdot (W^{\mathrm{B}}_{j,s} + W^{\mathrm{B}}_{j,s-1})\quad \forall j \in J, s \in S\end{aligned}$$
(30)
$$\begin{aligned}&W^{\mathrm{B}}_{j,s} \le Y^{\mathrm{B}}_{j,s} + W^{\mathrm{B}}_{j,s-1} \quad \forall j \in J, s \in S\end{aligned}$$
(31)
$$\begin{aligned}&\sum _{j}^{{\bar{j}}} W^{\mathrm{B}}_{j,s} = 1 \quad \forall s \in S. \end{aligned}$$
(32)
Constraints (
28)–(
32) are the well-known
PLSP constraints (Haase
1994). Equation (
28) represents inventory balance constraints extended by the authorized backlogs. Authorized backlogs are restricted to the absolute negative safety stocks determined by constraints (
38) and (
48). Constraints (
29) ensure that the sum of the regular capacity and the additional capacity is not exceeded by the capacity requirements of the production volume. Since additional capacity units are linked to high penalty cost coefficient (
\({\text{pc}}^{\mathrm{B}}_s\)), the model only chooses them if the regular capacity (
\(b^{\mathrm{B}}_{s}\)) results in an infeasible solution. Constraints (
30) ensure that product
j can only be produced in period
s if the resource is set up for it. Furthermore, constraints (
31) link the setup activity variables (
\(Y^{\mathrm{B}}_{j,s}\)) to the setup state variables (
\(W^{\mathrm{B}}_{j,s}\)). The special aspect of the PLSP is that precisely one setup state carry-over of product
j from period
s to
\(s+1\) (Eq. (
32)) exists and that, at most, one setup activity is allowed per period
s (Haase
1994). Note that lot sizes can be produced over one period or several consecutive periods. Consequently, the number of products produced in a period
s is limited to two. With two products per period maximum, the production sequence for the shop floor control is set as follows: the product
j with the setup state carry-over from
\(s-1\) to
s (
\(W^{\mathrm{B}}_{j,s-1}=1\)) is scheduled first and the product
j with the setup activity in period
s (
\(Y^{\mathrm{B}}_{j,s}=1\)) is scheduled second.
$$\begin{aligned}&\underbrace{\sum _{\tau =1}^{\tau ^{\mathrm{Smax}}_{j}}}_{{\text{if }}s-\tau \in S \cup \{l^{\mathrm{B}}_{j}\}} V^{\mathrm{B}}_{j,s-\tau ,s} = Y^{\mathrm{B}}_{j,s} + W^{\mathrm{B}}_{j,s-1} \quad \forall j \in J, s \in S \end{aligned}$$
(33)
$$\begin{aligned}&\underbrace{\sum _{\tau =1-l^{\mathrm{B}}_{j}}^{\tau ^{\mathrm{Smax}}_{j}}}_{{\text{if }}l^{\mathrm{B}}_{j}+\tau \in S \cup \{{\bar{s}}+1\}} V^{\mathrm{B}}_{j,l^{\mathrm{B}}_{j},l^{\mathrm{B}}_{j}+tau} = 1\quad \forall j \in J \end{aligned}$$
(34)
$$\begin{aligned}&\underbrace{\sum _{s=l^{\mathrm{B}}_{j}}^{{\bar{s}}}}_{{\text{if }}s \in S \cup \{l^{\mathrm{B}}_{j}\}\; \wedge \; s+\tau ^{\mathrm{Smax}}_{j}\ge {\bar{s}}+1} V^{\mathrm{B}}_{j,s,{\bar{s}}+1} = 1 \quad \forall j \in J \end{aligned}$$
(35)
$$\begin{aligned}&\underbrace{\sum _{\tau =1}^{\tau ^{\mathrm{Smax}}_{j}}}_{{\text{if }} s-\tau \in S \cup \{l^{\mathrm{B}}_{j}\}} V^{\mathrm{B}}_{j,s-\tau ,s} = \underbrace{\sum _{\tau =1}^{{\bar{s}}-s+1}}_{{\text{if }} \tau \le \tau ^{\mathrm{max}}_j} V^{\mathrm{B}}_{j,s,s+\tau } \quad \forall j \in J, s \in S \end{aligned}$$
(36)
$$\begin{aligned}&I^{\mathrm{B}}_{j,s-1} \ge \underbrace{{\text{ss}}^{\mathrm{B}}_{j,l^{\mathrm{B}}_{j},s} \cdot V^{\mathrm{B}}_{j,l^{\mathrm{B}}_{j},s}}_{{\text{if }} s \le l^{\mathrm{B}}_{j}+\tau ^{\mathrm{Smax}}_{j} \; \wedge \; {\text{ss}}^{\mathrm{B}}_{j,l^{\mathrm{B}}_{j},s} \ge 0} + \underbrace{\sum _{\tau =1}^{\min \{\tau ^{\mathrm{max}}_j,s-1\}}}_{{\text{if }}{\text{ss}}^{\mathrm{B}}_{j,s-\tau ,s}\ge 0} {\text{ss}}^{\mathrm{B}}_{j,s-\tau ,s} \cdot V^{\mathrm{B}}_{j,s-\tau ,s} \\&\qquad \forall j \in J, s = sf^{\mathrm{T}}+1,\ldots ,{\bar{s}} \end{aligned}$$
(37)
$$\begin{aligned}&{\text{BL}}^{\mathrm{B}}_{j,s-1} \le \underbrace{-{\text{ss}}^{\mathrm{B}}_{j,l^{\mathrm{B}}_{j},s} \cdot V^{\mathrm{B}}_{j,l^{\mathrm{B}}_{j},s}}_{{\text{if }} s \le l^{\mathrm{B}}_{j}+\tau ^{\mathrm{Smax}}_{j} \; \wedge \; {\text{ss}}^{\mathrm{B}}_{j,l^{\mathrm{B}}_{j},s}< 0} + \underbrace{\sum _{\tau =1}^{\min \{\tau ^{\mathrm{max}}_j,s-1\}}}_{{\text{if }}{\text{ss}}^{\mathrm{B}}_{j,s-\tau ,s} < 0}-{\text{ss}}^{\mathrm{B}}_{j,s-\tau ,s} \cdot V^{\mathrm{B}}_{j,s-\tau ,s} \\&\qquad \forall j \in J, s = {\text{sf}}^{\mathrm{T}}+1,\ldots ,{\bar{s}} \end{aligned}$$
(38)
$$\begin{aligned}&I^{\mathrm{B}}_{j,{\bar{s}}} = \max \{0,i^{\mathrm{B}}_{j,{\bar{s}}}\} \quad \forall j \in J \end{aligned}$$
(39)
$$\begin{aligned}&{\text{BL}}^{\mathrm{B}}_{j,{\bar{s}}}= \max \{0,-i^{\mathrm{B}}_{j,{\bar{s}}}\}\quad \forall j \in J. \end{aligned}$$
(40)
Furthermore, we extend the PLSP model formulation by equations (
33)–(
40) to ensure that the PLSP chooses the correct safety stock for every production cycle. The functionality of Eqs. (
33)–(
38) is similar to the functionality of Eqs. (
9), (
10), and (
12)–(
15) of the CLSP-L (see also Meistering and Stadtler
2017). The only difference is that we neglect the
looking beyond the planning horizon approach of Stadtler (
2000) in the PLSP model formulation as final inventories/backlogs are set by Eqs. (
39) and (
40) to externally given parameters provided by the CLSP-L. Equation (
35) ensures that the last production cycle of each product ends in the last period of the planning interval. Due to externally given initial and final inventories as well as the limited capacity and limited setup activities per micro period
s, the PLSP might result in infeasible solutions. To counteract such situations, the minimum inventory and maximum backlog constraints (
37) and (
38) are only considered for micro-periods
s that do not belong to the first macro-period. However, relaxing constraints (
37) and (
38) enable the PLSP to have both positive inventories and backlogs of product
j in micro-periods of the first macro period. In combination with the objective function (
27), which offers an incentive for positive inventories during the first macro-period, the model formulation results in an unbounded solution. To prevent this, we extended the PLSP model formulation by inequalities (
41) and (
42). Due to binary variables
\(N^{\mathrm{B}}_{j,s}\), backlogs are bounded by
\(m^{\mathrm{BL}}_{j,s}\) and either backlogs or a positive inventory will exist at the end of a micro-period
s in the first macro-period.
$$\begin{aligned} {\text{BL}}^{\mathrm{B}}_{j,s}&\le m^{\mathrm{BL}}_{j,s} \cdot N^{\mathrm{B}}_{j,s} \quad \forall j \in J, s=1,\ldots ,{\text{sf}}^{\mathrm{T}} \end{aligned}$$
(41)
$$\begin{aligned} I^{\mathrm{B}}_{j,s}&\le m^{\mathrm{I}}_{j,s} \cdot (1-N^{\mathrm{B}}_{j,s}) \quad \forall j \in J, s=1,\ldots ,{\text{sf}}^{\mathrm{T}} \end{aligned}$$
(42)
with
$$\begin{aligned}&m^{\mathrm{BL}}_{j,s} =\max \left\{ 0,-i^{\mathrm{B}}_{j,0}\right\} + \sum _{k=1}^{s} d^{\mathrm{B}}_{j,k} \quad \forall j \in J, s=1,\ldots ,{\text{sf}}^{\mathrm{T}} \end{aligned}$$
(43)
$$\begin{aligned}&m^{\mathrm{I}}_{j,s} = i^{\mathrm{B}}_{j,{\bar{s}}} + \sum _{k=s}^{{\bar{s}}} d^{\mathrm{B}}_{j,k} \quad \forall j \in J, s=1,\ldots ,{\text{sf}}^{\mathrm{T}}. \end{aligned}$$
(44)
Finally, constraints (
45)–(
47) set initial states of
\({\text{BL}}^{\mathrm{B}}_{j,s}\),
\(I^{\mathrm{B}}_{j,s}\), and
\(W^{\mathrm{B}}_{j,s}\) and constraints (
48)–(
55) define domains of decision variables. Note that additional capacities (
\(C^{\mathrm{B}}_{s}\)) are limited to the externally given integer value
\(z^{\mathrm{B}}_{\mathrm{max}}\), see (
49).
$$\begin{aligned}&{\text{BL}}^{\mathrm{B}}_{j,0} = \max \{0,-i^{\mathrm{B}}_{j,0}\}\quad \forall j \in J\end{aligned}$$
(45)
$$\begin{aligned}&I^{{\rm B}}_{j,0} = \max \{0,i^{\mathrm{B}}_{j,0}\} \quad \forall j \in J\end{aligned}$$
(46)
$$\begin{aligned}&W^{\mathrm{B}}_{j,0} = w^{\mathrm{B}}_{j,0}\quad \forall j \in J\end{aligned}$$
(47)
$$\begin{aligned}&{\text{BL}}^{\mathrm{B}}_{j,s} \ge 0 \quad \forall j \in J, s \in S\end{aligned}$$
(48)
$$\begin{aligned}&C^{\mathrm{B}}_{s} \in {\mathbb {N}} \wedge C^{\mathrm{B}}_{s}\le z^{\mathrm{B}}_{\mathrm{max}} \quad \forall s \in S\end{aligned}$$
(49)
$$\begin{aligned}&I^{\mathrm{B}}_{j,s} \ge 0\quad \forall j \in J, s \in S\end{aligned}$$
(50)
$$\begin{aligned}&N^{\mathrm{B}}_{j,s} \in \{0,1\} \quad \forall j \in J, s \in S\end{aligned}$$
(51)
$$\begin{aligned}&V^{\mathrm{B}}_{j,s,s+\tau } \in \{0,1\} \quad \forall j \in J, s \in S \cup \{l^{\mathrm{B}}_{j}\}, \tau = 1,\ldots ,\tau ^{\mathrm{Smax}}_{j}\end{aligned}$$
(52)
$$\begin{aligned}&W^{\mathrm{B}}_{j,s} \in \{0,1\} \quad \forall j \in J, s \in S\end{aligned}$$
(53)
$$\begin{aligned}&X^{\mathrm{B}}_{j,s} \ge 0\quad \forall j \in J, s \in S\end{aligned}$$
(54)
$$\begin{aligned}&Y^{\mathrm{B}}_{j,s} \in \{0,1\} \quad \forall j \in J, s \in S. \end{aligned}$$
(55)
We add valid inventory inequalities (
56) to set lower bounds for end-of-period inventories of each setup period by adding up period demands beyond the setup period and the safety stocks belonging to the production cycle. Here, inventories for product
j are bounded by the safety stocks required, if the preceding lot size is started before the planning interval (first term of right hand side), or the safety stocks plus demand covering
\(\tau ^{S}\) micro periods, if a lot size is started in period
\(s-1\) (second term of right hand side).
$$\begin{aligned} \begin{aligned} I^{\mathrm{B}}_{j,s-1}&\ge {\text{ss}}^{\mathrm{B}}_{j,l^{S}_j,s} \cdot V^{\mathrm{B}}_{j,l^{S}_j,s} \\&\quad +~ \sum _{\tau =0}^{\min \{{\bar{s}}-s+1,\tau ^{\mathrm{B max}}_j-1\}} \left( {\text{ss}}^{\mathrm{B}}_{j,s-1,s+\tau } + \sum _{k=s}^{s+\tau -2} d^{\mathrm{B}}_{j,k}\right) \cdot V^{\mathrm{B}}_{j,s-1,s+\tau } \\&\qquad \forall j \in J, s= {\text{sf}}^{\mathrm{T}}+1,\ldots ,{\bar{s}}. \end{aligned} \end{aligned}$$
(56)