Integration of Sales and Operations: A Dynamic Mixed-Integer Programming Game

被引:0
|
作者
Telha, Claudio [1 ]
Carvalho, Margarida [2 ,3 ]
机构
[1] Univ Los Andes, Fac Ingn & Ciencias Aplicadas, Santiago 12455, Chile
[2] Univ Montreal, CIRRELT, 2920 chemin Tour, Montreal, PQ H3T 1J4, Canada
[3] Univ Montreal, Dept Informat & Rech Operat, 2920 chemin Tour, Montreal, PQ H3T 1J4, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Game theory; Stackelberg equilibria; Subgame perfect equilibria; Production planning; Sales; LOT-SIZING PROBLEMS; PRODUCTION DECISIONS; COST ALLOCATION; FORMULATIONS; MODEL; ALGORITHM; BEHAVIOR; PRICE;
D O I
10.1007/s13235-024-00582-7
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
We define a framework to investigate and assess the impact of prompt and dynamic reactions to market competition in production planning problems. It depicts two firms that produce and sell substitutable products over a finite time horizon. Each firm optimizes its sales and production costs, and has sufficient market power to affect the sales of the other firm. The framework can capture several production and market competition features. We model production plans using mixed-integer programs and market competition using sequential Stackelberg games. Under certain conditions, we can solve the models in our framework in polynomial-time. We provide examples to illustrate how the framework can match the requirements of production planning and sales. Then, we perform a computational study to analyze a planning problem that features the possibility of technological investments to reduce the variable production costs. We draw insights on the value of our polynomial-time method to compute subgame perfect equilibria by comparing its optimal production plans against a static model, where firms fix the entire production plan at the beginning of the planning horizon, and a myopic model, where firms ignore the impact that current decisions will have in future periods.
引用
收藏
页码:306 / 328
页数:23
相关论文
共 50 条
  • [1] A mixed-integer programming model for tactical forest operations planning
    Oborn, RMR
    PROCEEDINGS OF THE MEETING ON PLANNING AND IMPLEMENTING FOREST OPERATIONS TO ACHIEVE SUSTAINABLE FORESTS, 1996, 186 : 201 - 211
  • [2] Mixed-integer linear programming optimization for the Supply Chain Game
    Shaltayev, Dmitriy
    DECISION SCIENCES-JOURNAL OF INNOVATIVE EDUCATION, 2021, 19 (04) : 250 - 264
  • [3] Mixed-integer programming for control
    Richards, A
    How, J
    ACC: PROCEEDINGS OF THE 2005 AMERICAN CONTROL CONFERENCE, VOLS 1-7, 2005, : 2676 - 2683
  • [4] Optimization of air vehicles operations using mixed-integer linear programming
    Schumacher, C.
    Chandler, P. R.
    Pachter, M.
    Pachter, L. S.
    JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2007, 58 (04) : 516 - 527
  • [5] Optimizing Dynamic Evacuation Using Mixed-Integer Linear Programming
    Obaid, Hamoud Bin
    Trafalis, Theodore B.
    Abushaega, Mastoor M.
    Altherwi, Abdulhadi
    Hamzi, Ahmed
    MATHEMATICS, 2025, 13 (01)
  • [6] Optimization of complex reactions by mixed-integer iterative dynamic programming
    Hartig, F
    Keil, FJ
    Kafarov, VV
    THEORETICAL FOUNDATIONS OF CHEMICAL ENGINEERING, 1996, 30 (01) : 50 - 60
  • [7] Mixed-integer quadratic programming is in NP
    Del Pia, Alberto
    Dey, Santanu S.
    Molinaro, Marco
    MATHEMATICAL PROGRAMMING, 2017, 162 (1-2) : 225 - 240
  • [8] Mixed-integer programming: A progress report
    Bixby, RE
    Fenelon, M
    Gu, ZH
    Rothberg, E
    Wunderling, R
    THE SHARPEST CUT: THE IMPACT OF MANFRED PADBERG AND HIS WORK, 2004, 4 : 309 - 325
  • [9] Mixed-integer nonlinear programming 2018
    Sahinidis, Nikolaos V.
    OPTIMIZATION AND ENGINEERING, 2019, 20 (02) : 301 - 306
  • [10] Lifting for conic mixed-integer programming
    Alper Atamtürk
    Vishnu Narayanan
    Mathematical Programming, 2011, 126 : 351 - 363