Abstract
This paper considers an optimal movement routing problem with constraints. One such constraint is due to decomposing the original problem into a preliminary subproblem and a final subproblem; the tasks related to the preliminary problem must be executed before the tasks of the final subproblem begin. In particular, this condition may arise in the tool control problem for thermal cutting machines with computer numerical control (CNC): if there are long parts among workpieces, the cutting process near a narrow material boundary should start with these workpieces since such parts are subject to thermal deformations, which may potentially cause rejects. The problem statement under consideration involves two zones for part processing. The aggregate routing process in the original problem includes a starting point, a route (a permutation of indices), and a particular track consistent with the route and the starting point. Each of the subproblems has specific precedence conditions, and the travel cost functions forming the additive criterion may depend on the list of pending tasks. A special two-stage procedure is introduced to apply dynamic programming as a solution method. The structure of the optimal solution is established and an algorithm based on this structure is developed. The algorithm is implemented on a personal computer and a computational experiment is carried out.
REFERENCES
Gutin, G. and Punnen, A., The Traveling Salesman Problem and Its Variations, Berlin: Springer, 2002.
Cook, W.J., In Pursuit of the Traveling Salesman. Mathematics at the Limits of Computation, Princeton: Princeton University Press, 2012.
Gimadi, E.Kh. and Khachai, M.Yu., Ekstremal’nye zadachi na mnozhestvakh perestanovok (Extremal Problems on Permutation Sets), Yekaterinburg: UPI Training Center, 2016.
Melamed, I.I., Sergeev, S.I., and Sigal, I.Kh., The Traveling Salesman Problem, Autom. Remote Control, 1989, vol. 50, no. 9, pp. 1147–1173; no. 10, pp. 1303–1324; no. 11, pp. 1459–1479.
Little, J.D., Murty, K.G., Sweeney, D.W., and Karel, C., An Algorithm for the Traveling Salesman Problem, Oper. Res., 1963, no. 11(6), pp. 972–989.
Bellman, R., Dynamic Programming Treatment of the Travelling Salesman Problem, J. ACM, 1962, vol. 9, no. 1, pp. 61–63.
Held, M. and Karp, R., A Dynamic Programming Approach to Sequencing Problems, J. SIAM, 1962, vol. 10, no. 1, pp. 196–210.
Chentsov, A.G., Ekstremal’nye zadachi marshrutizatsii i raspredeleniya zadanii: voprosy teorii (Extremal Problems of Routing and Task Distribution: Theoretical Fundamentals), Moscow–Izhevsk: Regulyarnaya i Khaoticheskaya Dinamika, 2008.
Chentsov, A.G., Chentsov, A.A., and Sesekin, A.N., Zadachi marshrutizatsii peremeshchenii s neadditivnym agregirovaniem zatrat (Movement Routing Problems with Non-additive Cost Aggregation), Moscow: Lenand, 2021.
Petunin, A.A., Chentsov, A.G., and Chentsov, P.A., Optimal’naya marshrutizatsiya instrumenta mashin figurnoi listovoi rezki s chislovym programmnym upravleniem. Matematicheskie modeli i algoritmy (Optimal Tool Routing of Shaped Sheet Cutting Machines with Computer Numerical Control. Mathematical Models and Algorithms), Yekaterinburg: Ural Federal University, 2020.
Chentsov, A.G. and Chentsov, P.A., Dynamic Programming in the Routing Problem: Decomposition Variant, Russian Universities Reports. Mathematics, 2022, vol. 27, no. 137, pp. 95–124.
Chentsov, A.G. and Chentsov, P.A., An Extremal Two-Stage Routing Problem and Procedures Based on Dynamic Programming, Trudy Inst. Mat. i Mekh. Ural. Otd. Ross. Akad. Nauk, 2022, vol. 28, no. 2, pp. 215–248.
Chentsov, A.G., Problem of Successive Megalopolis Traversal with the Precedence Conditions, Autom. Remote Control, 2014, vol. 75, no. 4, pp. 728–744.
Chentsov, A.G. and Chentsov, P.A., Routing under Constraints: Problem of Visit to Megalopolises, Autom. Remote Control, 2016, vol. 77, no. 11, pp. 1957–1974.
Petunin, A.A., On Some Strategies for Constructing the Tool’s Route in the Development of Controlling Programs for Thermal Cutting Machines, Vestn. UGATU, Ser. Upravlen., Vychisl. Tekh. Informatika, 2009, vol. 13, no. 2(35), pp. 280–286.
Frolovskii, V.D., Automating the Design of Controlling Programs for Heat Metal Cutting on Equipment with Digital Program Control, Inform. Tekhnol. Proektirovan. Proizvod., 2005, no. 4, pp. 63–66.
Wang, G.G. and Xie, S.Q., Optimal Process Planning for a Combined Punch-and-Laser Cutting Machine Using Ant Colony Optimization, Int. J. Product. Res., 2005, vol. 43, no. 11, pp. 2195–2216.
Lee, M.-K. and Kwon, K.-B., Cutting Path Optimization in CNC Cutting Processes Using a Two-Step Genetic Algorithm, Int. J. Product. Res., 2006, no. 44, pp. 5307–5326.
Kuratowski, K. and Mostowski, A., Set Theory, Amsterdam: North-Holland, 1967.
Dieudonné, J., Foundations of Modern Analysis, New York: Academic, 1960.
Cormen, T.H., Leiserson, C.E., and Rivest, R.L., Introduction to Algorithms, Cambridge: MIT Press, 1990.
Warga, J., Optimal Control of Differential and Functional Equations, New York: Academic, 1972.
Chentsov, A.G., On Routing Complexes of Jobs, Vestn. UdGU, Mat. Mekh. Komp’yut. Nauki, 2013, no. 1, pp. 58–82.
Lawler, E.L., Efficient Implementation of Dynamic Programming Algorithms for Sequencing Problems, Report BW106, Amsterdam: Mathematisch Centrum, 1979, pp. 1–16.
Chentsov, A.G. and Chentsov, A.A., On Finding the Value of the Routing Problem with Constraints, Probl. Upravlen. Informat., 2016, no. 1, pp. 41–54.
Petunin, A.A., Chentsov, A.G., and Chentsov, P.A., Optimal Routing in Problems of Sequential Traversal of Megapolises in the Presence of Constraints, Chelyabinsk Physical and Mathematical Journal, 2022, vol. 7, no. 2, pp. 209–233.
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APPENDIX
APPENDIX
Proof of Proposition 2. In view of (4.2),
Since \(\xi \,\lozenge\,\eta \) ∈ P, we obtain (4.5), i.e.,
According to (3.26) and (3.21), \(\mathfrak{C}_{\xi }^{\natural }[{{({{y}_{t}})}_{{t \in \overline {0,N} }}}]\) = \({{V}^{\natural }}\)[x0] = \({{\mathbb{V}}^{\natural }}\), and furthermore, pr2(yN) ∈ X00 due to (3.19); \(\tilde {V}\)*[pr2(yN)] ∈ \({{\mathbb{R}}_{ + }}\). In addition,
Note that by (3.9)–(3.11), however, it follows that
Based on the choice of ξ and \({{({{y}_{t}})}_{{t \in \overline {0,N} }}}\), we have
(see (3.21) and (3.22)). Finally, considering (2.16) and (A.1) leads to the equality
Due to (4.1), (A.5), and the definition of (ωt\({{)}_{{t \in \overline {0,{\mathbf{n}}} }}}\), we obtain
This expression involves the chain of equalities pr2(ωN) = pr2(yN) = pr2(\({{\hat {y}}_{0}}\)) (see (3.4), (3.5), and (3.14)). From (A.3), (A.4), and (A.6) it follows that
Hence, considering (A.2), we can write
where \(\mathfrak{C}_{\xi }^{\natural }[{{({{y}_{t}})}_{{t \in \overline {0,N} }}}]\) = \({{V}^{\natural }}\)[x0] = \({{\mathbb{V}}^{\natural }}\) by the choice of (ξ, (yt\({{)}_{{t \in \overline {0,N} }}}\)) and (3.26). Using (4.4) and (A.7), we derive the chain of equalities
according to the definition of (ωt\({{)}_{{t \in \overline {0,{\mathbf{n}}} }}}\), the property (4.6) is the case, where (4.5) holds due to (A.1).
Proof of Proposition 4. Let \({{x}_{*}}\) ∈ \(X_{{{\text{opt}}}}^{\natural }\), i.e., \({{x}_{*}}\) ∈ X0 and \({{V}^{\natural }}\)[\({{x}_{*}}\)] = \(\mathbb{V}\) (see (4.4)). Using (3.27), we choose
obtaining (ξ, \({{({{y}_{i}})}_{{i \in \overline {0,N} }}}\)) ∈ \({{{\mathbf{D}}}^{\natural }}\)[\({{x}_{*}}\)] with the property \(\mathfrak{C}_{\xi }^{\natural }[{{({{y}_{i}})}_{{t \in \overline {0,N} }}}]\) = \({{V}^{\natural }}\)[\({{x}_{*}}\)] (the optimal FS in the (\({{\mathcal{M}}_{1}}\), \({{x}_{*}}\))-problem). Then, see (4.4), \(\mathfrak{C}_{\xi }^{\natural }[{{({{y}_{i}})}_{{t \in \overline {0,N} }}}]\) = \(\mathbb{V}\). In view of (3.15), we choose
obtaining (η, \({{({{\hat {y}}_{i}})}_{{i \in \overline {0,{\mathbf{n}} - N} }}}\)) ∈ D*[pr2(yN)] with the property
By Proposition 2, (\(\xi \,\lozenge\,\eta \), \({{({{y}_{i}})}_{{i \in \overline {0,N} }}}\,\square \,{{({{\hat {y}}_{i}})}_{{i \in \overline {0,{\mathbf{n}} - N} }}}\)) ∈ \({\mathbf{\tilde {D}}}\)[\({{x}_{*}}\)] is such that (4.6) holds. Considering (2.18), we have the inequality
where \(\mathbb{V}\) ≤ \(\tilde {V}\)[\({{x}_{*}}\)] due to (2.21). As a result, \(\tilde {V}\)[\({{x}_{*}}\)]= \(\mathbb{V}\) and consequently, \({{x}_{*}}\) ∈ \(X_{{{\text{opt}}}}^{0}\) (see (2.25)). Thus,
Let x* ∈ \(X_{{{\text{opt}}}}^{0}\), i.e., x* ∈ X0 and \(\tilde {V}\)[x*] = \(\mathbb{V}\). In view of (2.19), we choose the optimal FS
then \({{\mathfrak{C}}_{\alpha }}[{{({{z}_{i}})}_{{i \in \overline {0,{\mathbf{n}}} }}}]\) = \(\tilde {V}\)[x*] = \(\mathbb{V}\). In addition, α ∈ P, which implies α = α1 α2, where α1 ∈ \({{\mathcal{A}}_{1}}\) and α2 ∈ \({{\mathcal{A}}_{2}}\). Therefore, see (2.11), \({{({{z}_{i}})}_{{i \in \overline {0,{\mathbf{n}}} }}}\) ∈ \(\mathcal{Z}_{{{\alpha }_{1}}{{\alpha }_{2}}}^{{}}\)[x*]. Then \({{({{z}_{i}})}_{{i \in \overline {0,N} }}}\) ∈ \(\mathcal{Z}_{{{{\alpha }_{1}}}}^{\natural }\)[x*] and consequently,
(see (3.18)). We introduce a tuple \({{({{\tilde {z}}_{i}})}_{{t \in \overline {0,{\mathbf{n}} - N} }}}\) in X × X by the rule
Obviously \({{({{\tilde {z}}_{t}})}_{{t \in \overline {0,{\mathbf{n}} - N} }}}\) ∈ \(\mathcal{Z}_{{{{\alpha }_{2}}}}^{*}\)[pr2(zN)] (see (4.3)). Therefore, see (3.5), we have
In addition, (zt\({{)}_{{t \in \overline {0,{\mathbf{n}}} }}}\) = \({{({{z}_{t}})}_{{t \in \overline {0,N} }}}\,\square \,{{({{\tilde {z}}_{t}})}_{{t \in \overline {0,{\mathbf{n}} - N} }}}\). Hence, according to Proposition 3 and (4.4),
where \(\tilde {V}{\text{*}}\)[pr2(zN)} ≤ \(\mathfrak{C}_{{{{\alpha }_{2}}}}^{*}[{{({{\tilde {z}}_{t}})}_{{t \in \overline {0,{\mathbf{n}} - N} }}}]\) (see (3.14)). Now, we obtain
Then \({{\mathbb{V}}^{\natural }}\) ≤ \(\mathfrak{C}_{{{{\alpha }_{1}}}}^{\natural }[{{({{z}_{t}})}_{{t \in \overline {0,N} }}}]\) ≤ \({{\mathbb{V}}^{\natural }}\). As a result, \(\mathfrak{C}_{{{{\alpha }_{1}}}}^{\natural }[{{({{z}_{t}})}_{{t \in \overline {0,N} }}}]\) = \({{\mathbb{V}}^{\natural }}\)[x*] = \({{\mathbb{V}}^{\natural }}\) and consequently, see (3.29), x* ∈ \(X_{{{\text{opt}}}}^{\natural }\). This finally verifies the property \(X_{{{\text{opt}}}}^{0}\) ⊂ \(X_{{{\text{opt}}}}^{\natural }\) and, see (A.8), the equality \(X_{{{\text{opt}}}}^{0}\) = \(X_{{{\text{opt}}}}^{\natural }\) as well.
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Chentsov, A.G., Chentsov, P.A. Two-Stage Dynamic Programming in the Routing Problem with Decomposition. Autom Remote Control 84, 543–563 (2023). https://doi.org/10.1134/S0005117923050053
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DOI: https://doi.org/10.1134/S0005117923050053