An overview of production planning models: structural classification and empirical assessment

Abstract

This paper surveys and classifies production planning models introduced in the literature according to their orientation (descriptive and normative models) and according to scope (aggregate planning models, functional interface models, and hierarchical models). Each of these categories is then su b-classified, by the type of formulation followed and solution method used, into exact and heuristic methods. For each of these classes of models, its characteristics, usages and limitation are discussed. The relationship between the descriptive and normative models is analysed with illustrative examples from the industrial sector in the U.S.A. and Egypt.The paper concludes by identifying major gaps in current theory of production planning on the one hand, and between theory and practice on the other hand, suggesting positive steps, and schemes of action for closing these gaps. IntroductionThe production planning problem is concerned with specifying the optimal quantities to produce in order to meet demand for a prespecified planning horizon.Many models, each of which has its pros and cons, have been developed in the literature to help solve this problem. Reviewing these models enabled us to delineate basic gaps in production planning theory, and more importantly between theory and practice. The aim ofthis paper is to identify such gaps, and propose steps for bridging them in a way that improves current production planning systems.We start by delineating basic characteristics of actual production planning systems. We then review current production planning models, illustrating the extent to which the above characteristics are reflected in current model designs, and conclude by identifying major gaps in current theory of production planning on the one hand and between theory and practice on the other hand, suggesting positive steps and schemes of action for closing these gaps.

Weighted Additive Fuzzy Goal Programming Approach to Aggregate Production Planning

“…According to Saad [15], al trad itional models of APP problems may be classified into six categories -(1) linear programming (LP) [5,16], (2) linear decision rule (LDR) [10], (3) transportation method [2], (4) management coefficient approach [3], (5) search decision rule (SDR) [18], and (6) simu lation [11]. When using any of the APP models, the goals and model inputs (resources and demand) are generally assumed to be determin istic/crisp and only APP problems with the single objective of minimizing cost over the planning period can be solved.…”

Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach

“…The proposed MOGA targeted three objective functions. First, it selected total costs as objective function, after reviewing the literature and considering practical situations (Masud & Hwang, 1980;Saad, 1982;Wang & Fang, 2001). The total costs are the sum of the production costs and the costs of changes in labor levels over the planning horizon T. Accordingly, the objective function of the proposed model is as follows:…”

Aggregate production planning with multiple fuzzy goals

“…z 3 Costs of changes in labor levels D nt Forecasted demand of nth product in period t (units) a nt Regular time production cost per unit of nth product in period t ($/unit) Q nt Regular time production of nth product in period t (units) i a Escalating factor of regular time production cost (%) b nt Overtime production cost per unit of nth product in period t ($/unit) O nt Overtime production of nth product in period t (units) i b Escalating factor of overtime production cost (%) c nt Subcontracting cost per unit of nth product in period t ($/unit) S nt Subcontracting volume of nth product in period t (units) i c Escalating factor of subcontract cost (%) d nt Inventory carrying cost per unit of nth product in period t ($/unit) I nt Inventory level in period t of nth product (units) i d Escalating factor of inventory carrying cost (%) e nt Backorder cost per unit of nth product in period t ($/unit) B nt Backorder level of nth product in period t (unit) i e Escalating factor of backorder cost (%) k t Cost to hire one worker in period t ($/man-hour) H t Workers hired in period t (man-hour) m t Cost to layoff one worker in period t ($/man-hour) F t Workers laid off in period t (man-hour) i f Escalating factors of hiring and layoff costs (%) n nt Hours of labor per unit of nth product in period t (manhour/unit) r nt Hours of machine usage per unit of nth product in period t (machine-hour/unit) v nt Warehouse spaces per unit of nth product in period t (ft 2 /unit) W t max Maximum labor level available in period t (man-hour) M t max Maximum machine capacity available in period t (machine-hour) V t max Maximum warehouse space available in period t (ft 2 ) S nt max Maximum subcontracted volume available of nth product in period t (units) …”